# Analysis of a Kind of Harr Characteristic Big Data Algorithm for College Students’ Safety Education Method

Authors: Li Wei
Journal: Security and Communication Networks
 (2022)

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## Abstract

In order to improve the effectiveness of safety education in colleges and universities and to maintain the long-term effectiveness of college students’ safety awareness, combined with the current safety problems in colleges and universities, by analyzing the relevance of campus safety accidents and safety education, a Harr characteristic big data algorithm for college students is proposed. Safety education methods have constructed a safety education system for college students. The status quo of safety education is analyzed for college students, a safety education system image acquisition module is designed based on Harr characteristic big data algorithm, Harr characteristic big data algorithm and Adaboost algorithm are used to shorten the training time of college students’ safety education image, learning resources are saved, and remote teaching is realized. A set of safety knowledge question banks based on recommendation algorithm has developed a set of practical safety knowledge online learning and testing systems, and targeted solutions to prevent and reduce campus safety accidents are proposed. The experimental results show that the number of student safety incidents in the school within 1 year after the application of this system is significantly reduced to 58, the system’s experimental response time is only 0.05 s on average, and the student’s satisfaction with the system reaches 93%. The application effect of the system is obvious, and it can effectively prevent and reduce the occurrence of campus safety accidents.


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## Body

## 1. Introduction

At present, China’s higher education is gradually popularized, and the scale of college enrollment is also different from the past. University campuses have gradually transformed from closed “ivory towers” to an all-round, multifunctional, and open “small society” [1]. At the same time, most colleges and universities are currently open ended, and foreigners can almost enter and leave the campus at will. Criminals will take the opportunity to enter the campus to engage in fraud, theft, and other illegal and criminal activities, which adds many uncertain factors to the hidden dangers of school safety [2–4]. Faced with the current complex situation, freshmen, who are new to university campuses are not aware of the dangers, have a weak sense of safety and even give criminals an opportunity to take advantage of them. As a result, improper personal storage and belongings have appeared on the university campus. Thefts caused by various bad habits, such as littering and not locking doors and windows in the dormitory, occur from time to time; the lack of necessary vigilance and the ability to distinguish right and wrong when contacting the society outside the school has caused the occurrence of deceived and deceived incidents. The handling of emergencies lacks appropriate handling experience and handling methods [5].Colleges and universities are knowledge highlands for cultivating talents, developing science and technology, passing on culture, and serving the society and are an important part of public safety construction. In recent years, frequent public safety accidents in colleges and universities, severe and complicated international and domestic public safety situations, and frequent occurrences of public security incidents have had a huge impact on the public, including college students. This situation has made safety education more and more important and an indispensable procedure in college education. As one of the important contents of ideological and political work in colleges and universities, safety education in colleges and universities plays a prominent role in constructing safe campuses, realizing the orderly development of schools, and ensuring the growth of universities. As far as the current situation is concerned, under the background of fragmented, abnormal, low-level, and imperfect safety education for the people in China, the overall level of safety education in colleges and universities is significantly higher than that of other sectors, but it has not yet reached normalization. The degree of standardization, scientification, and efficiency require education itself to form internal and external forces to improve and modify it. At present, most domestic colleges and universities have actively carried out safety education work, but the form of organization is not very reasonable or not very effective. If you want to truly achieve efficient and scientific safety education for students, there should be a standardized safety education system. A standardized safety education system should be formulated by specialized safety trainers, and it should only be a single unilateral explanation of the students by the teacher. A complete safety education system should include a standardized safety education system and a standardized working mechanism. Safety education is not only a simple reminder to students but also a subtle change to students through the safety education system from all aspects. Therefore, it is of great significance to standardize the safety education system to smoothly implement safety education. A complete safety education system can not only standardize students’ daily habits and improve students’ awareness of safety precautions but also enable school management to better implement or carry out safety education activities.At present, scholars in related fields have analyzed the safety education methods for college students. Reference [6] proposes the design and research of college students’ safety education system based on recommendation algorithms, in order to improve the effectiveness of college safety education and maintain the long-term effect of college students’ safety awareness. Combining with the current safety problems in domestic colleges and universities, through analyzing the relevance of campus safety accidents and safety education, targeted solutions to prevent and reduce campus safety accidents are proposed. It is proposed to put college students’ safety education in advance, build a set of safety knowledge question banks based on recommendation algorithm, and develop a set of practical safety knowledge online learning and testing systems. After the practice test of two grade students, the effect is obvious; thus, effectively preventing and reducing the occurrence of campus safety accidents. Reference [7] proposes a safety education method for college students based on the preimplanted immune mechanism. College students generally lack the awareness of self-protection and safety precautions. Therefore, colleges and universities should establish the connotation and scope of related theoretical concepts based on the concept of preimplanted immune precautions and focus on the themes of personal safety, property safety, and psychological safety. Two aspects of education and education methods are started to establish a comprehensive and systematic safety education system to ensure the safety and stability of the school and the health and success of students. Reference [8] reviewed the national swimming and water safety education framework according to PW 1927 and proposed drowning prevention strategies. The framework provides a comprehensive overview of the skills that should be taught to children (5–14 years old) according to age and development. The review resulted in a greater consensus in the industry on national standards to improve swimming and water safety, which in turn increased ownership and encouraged the sector to further adapt the framework. The policy meaning review process provides a model for other countries, organizations, or departments to follow when revising and/or developing injury prevention guidelines, especially with the participation of key stakeholders. The study supported by the Royal Australian Life Saving Association is acknowledged. The drowning prevention research of the Royal Life Saving Association of Australia is supported by the Australian government.The above methods are effective, but the efficiency and effectiveness of safety education for college students still need to be improved. Aiming at the problems of the above methods, this study proposes a safety education method for college students based on the Harr characteristic big data algorithm. The innovation of the design method is to analyze the current situation of college students’ safety education and design the image acquisition module of safety education system based on Harr feature big data algorithm; using Harr characteristic big data algorithm and AdaBoost algorithm to shorten the training time of college students’ safety education image can save learning resources and realize distance teaching to a certain extent. At the same time, a set of safety knowledge question banks based on recommendation algorithm is constructed, and a practical online learning and testing system of safety knowledge is developed. Solutions to prevent and reduce campus safety accidents are put forward, and the effectiveness of the design method is proved by research.


## 2. Analysis of the Status Quo of Safety Education for College Students

(1)
The safety education system needs to be improved, with poor effectiveness and insufficient safety culture atmosphereAt present, colleges and universities have a single model of safety education for students, and the education knowledge of survivability training, life protection awareness, accidents, and cyberculture safety has not been incorporated into the new safety education system [9]. Safety education courses are not systematic. Most colleges and universities only focus on safety awareness education for freshmen when they first enter the school. They lack later safety emergency technical guidance and some effective practice drills. The effectiveness is not strong, and safety education lacks a long-term mechanism. Many colleges and universities still have an insufficient safety culture atmosphere. The methods and channels of safety education and publicity are relatively single, and the breadth and depth are not enough. Safety education also mainly relies on traditional and backward methods, such as publicity boards, and billboards, completely ignoring the cognitive characteristics and acceptance habits of the current colleges and universities with the post-95 and post-00 students as the main group of students, and it cannot arouse these students at all. Students often turn a blind eye to the attention of students. Safety publicity does not make full use of the current various new media such as exclusive APP, WeChat, and Weibo and does not create a strong campus safety culture atmosphere [10].(2)
The lack of safety teachers and single educational methodsAt present, the safety education for students in colleges and universities is mainly based on counselors and security officials. These persons have not received professional safety training and learning, which makes the school’s safety education and publicity lack of professional teachers; safety education methods also show a single method. Most of them still stay on traditional preaching. Most of them use the method of holding lectures and class meetings, and they are also limited to the enrollment education of new students, military training theory education or “temporary education” after safety accidents. There is a lack of continuity and long-term effectiveness of safety education [11–13].(3)
Unclear responsibilities of safety education, leading to formalization of workSafety education in Chinese colleges and universities is currently not a required course for a degree, nor is it listed as an extracurricular mandatory practice content. The main departments responsible for safety education are mainly instructors and teachers responsible for ideological and political education. Therefore, unclear responsibilities lead to a fluke in the work. Psychologically, the current method of safety education has become “after-the-fact education” or “coping education.” After receiving the relevant safety incident, the counselor will notify and explain the relevant content of safety education to the college students through the form of holding class meetings or mass messaging to communicate the requirements of the superior documents. This kind of education model seems to have completed the tasks assigned by the superiors. Although to a certain extent, it has achieved a certain “propaganda warning” effect, but because of its single format and boring content, it has no effect on the education level and influence of each student. Good feedback, it is impossible to fully realize the role of education guidance, and it is impossible for every college student to participate in safety education [14–16].Aiming at the above-mentioned problems in the safety education of college students, this study employs a kind of Harr characteristic big data algorithm for college students’ safety education method.


## 3. College Student Safety Education System Design

### 3.1. Overall Framework Design of the Safety Education System for College Students

According to the needs of standardized, systematic, scientific, and information construction of college students’ safety education, combined with the current situation of college students’ safety education, this study designs the overall framework of college students’ safety education system. Firstly, the system should systematize the safety knowledge such as safety laws and regulations, traffic safety, fire safety, employment safety, psychological safety, and network security to form a teaching mode information system, so that the learning of college students’ safety knowledge can be carried out step by step. Secondly, the image acquisition module and the safety knowledge database of college students’ safety education are designed to improve the learning initiative, such as online practice knowledge search and selection and safety skill simulation training. Finally, the online learning and testing module of safety education is designed, taking fully into account the interactive relationship between students and students, and students and teachers.In order to improve the safety education level of college students, a kind of Harr characteristic big data algorithm college student safety education system and method are designed. The output end of the image acquisition unit in the system is connected to the input end of the data transmission network, the output end of the data transmission network is connected to the input end of the remote monitoring center, and the output end of the remote monitoring center is respectively connected to the Harr characteristic algorithm model [17–19]. The data sharing module is connected to the input end of the data display unit. The image acquisition module can realize realistic on-site learning observation through remote teaching, saving a lot of learning resources. Teachers can realize remote teaching without visiting the site and can observe the facial expressions of students, which improves the teacher-student interaction ability in the teaching process [20–22]. Through the teaching of a teacher and students in multiple classrooms, the listening information of multiple teachers is obtained, which greatly saves classroom and teacher resources, and improves the technical level of safety education for college students.In summary, the overall framework of the safety education system is designed for college students, as shown in Figure1.Figure 1
The overall framework of the safety education system for college students.It can be seen from Figure1 that the overall framework of the safety education system for college students constructed in this study is composed of three parts: an image acquisition module, a safety knowledge question bank for safety education for college students, and a safety education online learning and testing module.


### 3.2. Design of the Image Acquisition Module of the Safety Education System Based on Harr Characteristic Big Data Algorithm

The Harr-type feature is a rectangular feature, defined as the difference between the sum of the gray values of the black and white rectangles in the corresponding regions of the image subwindow, which reflects the local gray changes of the image [23–25]. Rectangular features are not as refined as other types of filters, such as edge filters with controllable directions. Unlike the direction-controllable filter, the available poses for the rectangular feature are only vertical, horizontal, and oblique. Combined with integral images, the efficiency of the rectangular feature set largely compensates for its limited adaptability. Using Harr characteristic big data algorithm to construct the image acquisition module of the safety education system can effectively shorten the training time of students’ safety education learning images, realize realistic on-site learning observation through remote teaching, save a lot of learning resources, and improve the interaction ability between teachers and students in the teaching process.


#### 3.2.1. Harr-NMF Features

NMF is a matrix decomposition method under the constraint that all elements in the matrix are nonnegative numbers. The Harr-NMF feature calculation is as follows:(1)
LetH be the Harr eigenvector of length L, and its absolute value is taken and transformed into an m × n matrix C, where L = m × n, m > n.(2)
NMF decomposition of rankr is performed on the matrix C obtained after transformation:(1)C=W×HT,whereW and H are the nonnegative base matrix and coefficient matrix of m × r and n × r, respectively, r << m.(3)
Each column vectorvi of W and H is normalized, namely,(2)vi=vivi.(4)
All the column vectors are concatenated into Harr-NMF features. In this study, the normalized training sample is 24 × 24 pixels in size, withL = 162336, m = 5073, n = 32, r = 3, and Harr-NMF feature length of 15315, and NMF has a certain effect on image noise stability. The base matrix and coefficient matrix pairs obtained by decomposition retain the main features of the original matrix. Therefore, on the basis of preserving the Harr feature, harR-NMF feature greatly reduces the Harr feature dimension to reduce the calculation time of subsequent algorithms.


#### 3.2.2. Adaboost Algorithm

After obtaining the Harr-NMF features of the training samples, a large number of positive and negative samples are learned by using the Adaboost algorithm [26–28]. The Adaboost algorithm uses the weak classifier to classify the samples according to the obtained Harr-NMF features. In each iteration, the weak classifier with the smallest classification error rate is selected as the optimal classifier for this iteration, and it is the optimal weak classifier. The device is weighted. The weight of each sample is updated according to the previous classification result, the weight of the wrong sample is increased, and the weight of the correct training sample is reduced [29]. After multiple iterations, a series of weak classifiers are cascaded to generate a decision-making classifier. The method of Adaboost algorithm training classifier is as follows.The sample set is set asai1,ai2,…aim,bi, where a is the planning variable, and b represents the results of two different types of division. The initial weights between different samples are as follows:(3)wio=1m.A planning model is generated based on the sample weight, and a new sample is planned. The weight of the sample of the misjudgment is increased, and it is used for the next round of testing. Letn be the number of iterations, and a parameter λt is randomly selected in the AdaBoost algorithm for intuitive evaluation of the real number of weights. At first, λt is defined as follows:(4)λt=12ln1−φtφt.Here, φt represents the real number of the weight of the weak classifier of the planning result. There are misjudgments among the classifiers, resulting in a relatively low detection rate of the entire classifier after cascading. Therefore, a method to increase the secondary auxiliary classifier is proposed, so that the cascade classifier can obtain a better false detection rate and detection rate [30–32]. Figure 2 shows the improved structure.Figure 2
AdaBoost algorithm cascade diagram.The most time-consuming part of the Adaboost algorithm is to update the weights of all training samples, that is, the probability distribution of the training samples has been completely changed, and all weak classifiers need to be retrained for the next round of training.In order to ensure the processing efficiency of the Adaboost algorithm, the weight distribution momentum parameter is introduced [33]. Momentum parameters are assigned through weights to speed up the learning and generation direction of Adaboost algorithm subclassifiers in a certain direction, thereby reducing algorithm iterations in incorrect directions and improving algorithm efficiency. The initial weight distribution of the converted training data set is optimized by the momentum method to satisfy the following relationship:(5)Wt+1=Wt+Vt+1.Here, Vt+1 is the iterative weight added by the momentum method to assign the momentum parameter. The calculation formula of this parameter is as follows:(6)Vt+1=−γ∇WLWt+μVt.Here, γ is the learning rate of the Adaboost algorithm, μ is the coefficient of the weight distribution momentum parameter, and LWt is the distribution gradient value of the weight of the training data set.


#### 3.2.3. Design of the Image Acquisition Module of the Safety Education System

A safety education image information detection model is constructed, Harr-NMF feature extraction method is used to carry out fuzzy correlation analysis of safety education image information [34], an output autocorrelation feature matching model of safety education image information is constructed, and statistical analysis of safety education image information is conducted using ant colony algorithm; the feature distribution set of hybrid genetic ant colony i at time t is represented as w1,j,w2,j,…,wtj, where t is the number of safety education image information, and wtj is the weighting coefficient of safety education image information mining. Semantic feature analysis method establishes the fuzzy semantic feature rule set of safety education image information mining and obtains the self-adaptive weighting coefficient of safety education image information as follows:(7)STDfi,j=Freqi,jmaxlFreqi,j.Here,maxlFreqi,j is the ant colony constraint feature quantity for the optimization of safety education image information mining between dj. ki is the pheromone concentration of ant colony search:(8)Idfi=logNni,where(9)wi,j=tfi,j×Idfi.Here,di and dj are the feature quantities of fuzzy rules for safety education image information mining. The fuzzy clustering method is used to carry out the shortest path of ant colony search for safety education image information mining. The calculation formula is as follows:(10)Ddi,dj=di.djdi×dj.Using statistical information analysis method, the fuzzy feature distribution set of safety education image information mining is established:(11)PK=T|R=1=PK=TPK=1|K=TPR=1,where(12)PK=T=Cs,PR=1|K=1=NBC,PR=1=NSS.Here, NB is the number of the mixed genetic ant colony, and NS is the pheromone concentration of the ant colony in S. The fuzzy information feature analysis method is adopted to realize the output autocorrelation feature matching of safety education image information.


### 3.3. Construction of Safety Knowledge Question Bank for College Students’ Safety Education

The construction of safety education online learning and testing system must first build a safety knowledge question bank, which is a basic work. The question bank of a college safety education online learning and testing system is divided into two categories: comprehensive knowledge question bank and sample question bank; the types mainly include three types of single-choice questions, multiple-choice questions, and true or false questions. Comprehensive knowledge mainly includes public security knowledge, fire protection knowledge, fraud, traffic safety, food safety, laboratory safety, national security, etc., accounting for about 60% of the entire question bank type; at the same time, the distribution ratio of these knowledge points is related to the occurrence of a safety incident in a university in Qingdao The ratio types are basically the same. The case question bank mainly includes related cases that have occurred in colleges and universities since 2010, especially the incidents that occurred in a certain college in Qingdao. The way of compiling a case question is to educate freshmen on safety knowledge, which is more targeted and operable, and is more targeted and operable for students. The educational significance is greater.In order to make the knowledge question bank more pertinent, a content-based recommendation system is designed to match different question types according to different professional categories and different characteristics of users.The feature vectors that define students and question types aref and g, respectively. The components of the student feature vector describe the major, educational background, grade, knowledge level, etc.; the feature vector of the question type includes the knowledge base professional category, safety knowledge level, knowledge point coverage degree, etc.Assuming that thei types of questions that students have done on the m day are a total of roads, and ami and bmj represent the score and total score of the j question, respectively, then the total score Mi and total score Ni of the i types of questions of the user on that day are as follows:(13)Mi=∑j−1liθ×ami,Ni=∑j−1liθ×bmj.Here,θ is the tilt, the level of difficulty and easy, which has three values, and represents the weights of difficulty, medium, and easy, respectively.Calculate the students’ scores for the question types according to the students’ answering conditions:(14)Xi=∑m−1t1/log8m×Mi∑m−1t1/log8m×Ni.The student feature vector is calculated by the above formula, and the initial vector is 0.The value range of each component of the feature vector of the question type is to extract the feature vector based on the features of the previously established security knowledge base, including the knowledge base professional category, security knowledge level, and knowledge point coverage.The similarity between the student and the question type is calculated by calculating the two feature vectors of the student and the question type:(15)df,g=∑fi−gi2.According to the similarity matrix, the corresponding question is selected and recommended to the user.


### 3.4. Construction of Safety Education Online Learning and Testing Modules

The safety knowledge online learning and testing system is written by Hypertext Preprocessor, and the dynamic web page runs very efficiently. The system database is deployed on MySQL, and the data are stored in different tables, which greatly increases the speed of operation and query, and improves flexibility. The SQL language used by MySQL is the most commonly used standardized language for accessing databases. It has the characteristics of small size and fast speed, which provides protection for simultaneous access by multiple users [35].The safety education online learning and testing system supports the combination of automatic scoring and teacher scoring. It can realize the three learning and examination functions such as knowledge-intensive training, mock examination, and formal examination, completely paperless operation. Candidate information can be imported with one key, and candidates’ scores can be exported as excel with one key. All online operations, without time and space constraints, save a lot of resources such as manpower and material resources. The system is deployed on the school server and provides external network access services, so that candidates can log in to the system in time for online learning and testing across the country [36].The examination time of the examination room can be set, and the examination will be automatically stopped when it expires. The official test time can be set in the background, and the test questions are automatically extracted according to the rules of the administrator’s setting of the question type and the question type. Each log-in candidate will extract different test questions from the question bank according to the set rules, at the set test time. The questions are answered internally, and the paper is handed in after answering the test questions or handed in automatically and compulsorily when the time is up. The operation interface is shown in Figure3.Figure 3
Test interface for candidates of a university’s safety knowledge test system.In summary, a safety education method for college students based on Harr characteristic big data algorithm is designed. The safety education system image acquisition module is constructed through the Harr characteristic big data algorithm, which shortens the training time of students’ safety education learning images, realizes realistic on-site learning, observation, and remote teaching, and improves the teacher-student interaction ability in the teaching process. Through the construction of safety education safety knowledge question bank for college students, the students’ independent learning and testing of safety education are realized, and finally, the safety education online learning and testing module is constructed to verify the application effect of students using the college student safety education system.


## 3.1. Overall Framework Design of the Safety Education System for College Students

According to the needs of standardized, systematic, scientific, and information construction of college students’ safety education, combined with the current situation of college students’ safety education, this study designs the overall framework of college students’ safety education system. Firstly, the system should systematize the safety knowledge such as safety laws and regulations, traffic safety, fire safety, employment safety, psychological safety, and network security to form a teaching mode information system, so that the learning of college students’ safety knowledge can be carried out step by step. Secondly, the image acquisition module and the safety knowledge database of college students’ safety education are designed to improve the learning initiative, such as online practice knowledge search and selection and safety skill simulation training. Finally, the online learning and testing module of safety education is designed, taking fully into account the interactive relationship between students and students, and students and teachers.In order to improve the safety education level of college students, a kind of Harr characteristic big data algorithm college student safety education system and method are designed. The output end of the image acquisition unit in the system is connected to the input end of the data transmission network, the output end of the data transmission network is connected to the input end of the remote monitoring center, and the output end of the remote monitoring center is respectively connected to the Harr characteristic algorithm model [17–19]. The data sharing module is connected to the input end of the data display unit. The image acquisition module can realize realistic on-site learning observation through remote teaching, saving a lot of learning resources. Teachers can realize remote teaching without visiting the site and can observe the facial expressions of students, which improves the teacher-student interaction ability in the teaching process [20–22]. Through the teaching of a teacher and students in multiple classrooms, the listening information of multiple teachers is obtained, which greatly saves classroom and teacher resources, and improves the technical level of safety education for college students.In summary, the overall framework of the safety education system is designed for college students, as shown in Figure1.Figure 1
The overall framework of the safety education system for college students.It can be seen from Figure1 that the overall framework of the safety education system for college students constructed in this study is composed of three parts: an image acquisition module, a safety knowledge question bank for safety education for college students, and a safety education online learning and testing module.


## 3.2. Design of the Image Acquisition Module of the Safety Education System Based on Harr Characteristic Big Data Algorithm

The Harr-type feature is a rectangular feature, defined as the difference between the sum of the gray values of the black and white rectangles in the corresponding regions of the image subwindow, which reflects the local gray changes of the image [23–25]. Rectangular features are not as refined as other types of filters, such as edge filters with controllable directions. Unlike the direction-controllable filter, the available poses for the rectangular feature are only vertical, horizontal, and oblique. Combined with integral images, the efficiency of the rectangular feature set largely compensates for its limited adaptability. Using Harr characteristic big data algorithm to construct the image acquisition module of the safety education system can effectively shorten the training time of students’ safety education learning images, realize realistic on-site learning observation through remote teaching, save a lot of learning resources, and improve the interaction ability between teachers and students in the teaching process.


### 3.2.1. Harr-NMF Features

NMF is a matrix decomposition method under the constraint that all elements in the matrix are nonnegative numbers. The Harr-NMF feature calculation is as follows:(1)
LetH be the Harr eigenvector of length L, and its absolute value is taken and transformed into an m × n matrix C, where L = m × n, m > n.(2)
NMF decomposition of rankr is performed on the matrix C obtained after transformation:(1)C=W×HT,whereW and H are the nonnegative base matrix and coefficient matrix of m × r and n × r, respectively, r << m.(3)
Each column vectorvi of W and H is normalized, namely,(2)vi=vivi.(4)
All the column vectors are concatenated into Harr-NMF features. In this study, the normalized training sample is 24 × 24 pixels in size, withL = 162336, m = 5073, n = 32, r = 3, and Harr-NMF feature length of 15315, and NMF has a certain effect on image noise stability. The base matrix and coefficient matrix pairs obtained by decomposition retain the main features of the original matrix. Therefore, on the basis of preserving the Harr feature, harR-NMF feature greatly reduces the Harr feature dimension to reduce the calculation time of subsequent algorithms.


### 3.2.2. Adaboost Algorithm

After obtaining the Harr-NMF features of the training samples, a large number of positive and negative samples are learned by using the Adaboost algorithm [26–28]. The Adaboost algorithm uses the weak classifier to classify the samples according to the obtained Harr-NMF features. In each iteration, the weak classifier with the smallest classification error rate is selected as the optimal classifier for this iteration, and it is the optimal weak classifier. The device is weighted. The weight of each sample is updated according to the previous classification result, the weight of the wrong sample is increased, and the weight of the correct training sample is reduced [29]. After multiple iterations, a series of weak classifiers are cascaded to generate a decision-making classifier. The method of Adaboost algorithm training classifier is as follows.The sample set is set asai1,ai2,…aim,bi, where a is the planning variable, and b represents the results of two different types of division. The initial weights between different samples are as follows:(3)wio=1m.A planning model is generated based on the sample weight, and a new sample is planned. The weight of the sample of the misjudgment is increased, and it is used for the next round of testing. Letn be the number of iterations, and a parameter λt is randomly selected in the AdaBoost algorithm for intuitive evaluation of the real number of weights. At first, λt is defined as follows:(4)λt=12ln1−φtφt.Here, φt represents the real number of the weight of the weak classifier of the planning result. There are misjudgments among the classifiers, resulting in a relatively low detection rate of the entire classifier after cascading. Therefore, a method to increase the secondary auxiliary classifier is proposed, so that the cascade classifier can obtain a better false detection rate and detection rate [30–32]. Figure 2 shows the improved structure.Figure 2
AdaBoost algorithm cascade diagram.The most time-consuming part of the Adaboost algorithm is to update the weights of all training samples, that is, the probability distribution of the training samples has been completely changed, and all weak classifiers need to be retrained for the next round of training.In order to ensure the processing efficiency of the Adaboost algorithm, the weight distribution momentum parameter is introduced [33]. Momentum parameters are assigned through weights to speed up the learning and generation direction of Adaboost algorithm subclassifiers in a certain direction, thereby reducing algorithm iterations in incorrect directions and improving algorithm efficiency. The initial weight distribution of the converted training data set is optimized by the momentum method to satisfy the following relationship:(5)Wt+1=Wt+Vt+1.Here, Vt+1 is the iterative weight added by the momentum method to assign the momentum parameter. The calculation formula of this parameter is as follows:(6)Vt+1=−γ∇WLWt+μVt.Here, γ is the learning rate of the Adaboost algorithm, μ is the coefficient of the weight distribution momentum parameter, and LWt is the distribution gradient value of the weight of the training data set.


### 3.2.3. Design of the Image Acquisition Module of the Safety Education System

A safety education image information detection model is constructed, Harr-NMF feature extraction method is used to carry out fuzzy correlation analysis of safety education image information [34], an output autocorrelation feature matching model of safety education image information is constructed, and statistical analysis of safety education image information is conducted using ant colony algorithm; the feature distribution set of hybrid genetic ant colony i at time t is represented as w1,j,w2,j,…,wtj, where t is the number of safety education image information, and wtj is the weighting coefficient of safety education image information mining. Semantic feature analysis method establishes the fuzzy semantic feature rule set of safety education image information mining and obtains the self-adaptive weighting coefficient of safety education image information as follows:(7)STDfi,j=Freqi,jmaxlFreqi,j.Here,maxlFreqi,j is the ant colony constraint feature quantity for the optimization of safety education image information mining between dj. ki is the pheromone concentration of ant colony search:(8)Idfi=logNni,where(9)wi,j=tfi,j×Idfi.Here,di and dj are the feature quantities of fuzzy rules for safety education image information mining. The fuzzy clustering method is used to carry out the shortest path of ant colony search for safety education image information mining. The calculation formula is as follows:(10)Ddi,dj=di.djdi×dj.Using statistical information analysis method, the fuzzy feature distribution set of safety education image information mining is established:(11)PK=T|R=1=PK=TPK=1|K=TPR=1,where(12)PK=T=Cs,PR=1|K=1=NBC,PR=1=NSS.Here, NB is the number of the mixed genetic ant colony, and NS is the pheromone concentration of the ant colony in S. The fuzzy information feature analysis method is adopted to realize the output autocorrelation feature matching of safety education image information.


## 3.2.1. Harr-NMF Features

NMF is a matrix decomposition method under the constraint that all elements in the matrix are nonnegative numbers. The Harr-NMF feature calculation is as follows:(1)
LetH be the Harr eigenvector of length L, and its absolute value is taken and transformed into an m × n matrix C, where L = m × n, m > n.(2)
NMF decomposition of rankr is performed on the matrix C obtained after transformation:(1)C=W×HT,whereW and H are the nonnegative base matrix and coefficient matrix of m × r and n × r, respectively, r << m.(3)
Each column vectorvi of W and H is normalized, namely,(2)vi=vivi.(4)
All the column vectors are concatenated into Harr-NMF features. In this study, the normalized training sample is 24 × 24 pixels in size, withL = 162336, m = 5073, n = 32, r = 3, and Harr-NMF feature length of 15315, and NMF has a certain effect on image noise stability. The base matrix and coefficient matrix pairs obtained by decomposition retain the main features of the original matrix. Therefore, on the basis of preserving the Harr feature, harR-NMF feature greatly reduces the Harr feature dimension to reduce the calculation time of subsequent algorithms.


## 3.2.2. Adaboost Algorithm

After obtaining the Harr-NMF features of the training samples, a large number of positive and negative samples are learned by using the Adaboost algorithm [26–28]. The Adaboost algorithm uses the weak classifier to classify the samples according to the obtained Harr-NMF features. In each iteration, the weak classifier with the smallest classification error rate is selected as the optimal classifier for this iteration, and it is the optimal weak classifier. The device is weighted. The weight of each sample is updated according to the previous classification result, the weight of the wrong sample is increased, and the weight of the correct training sample is reduced [29]. After multiple iterations, a series of weak classifiers are cascaded to generate a decision-making classifier. The method of Adaboost algorithm training classifier is as follows.The sample set is set asai1,ai2,…aim,bi, where a is the planning variable, and b represents the results of two different types of division. The initial weights between different samples are as follows:(3)wio=1m.A planning model is generated based on the sample weight, and a new sample is planned. The weight of the sample of the misjudgment is increased, and it is used for the next round of testing. Letn be the number of iterations, and a parameter λt is randomly selected in the AdaBoost algorithm for intuitive evaluation of the real number of weights. At first, λt is defined as follows:(4)λt=12ln1−φtφt.Here, φt represents the real number of the weight of the weak classifier of the planning result. There are misjudgments among the classifiers, resulting in a relatively low detection rate of the entire classifier after cascading. Therefore, a method to increase the secondary auxiliary classifier is proposed, so that the cascade classifier can obtain a better false detection rate and detection rate [30–32]. Figure 2 shows the improved structure.Figure 2
AdaBoost algorithm cascade diagram.The most time-consuming part of the Adaboost algorithm is to update the weights of all training samples, that is, the probability distribution of the training samples has been completely changed, and all weak classifiers need to be retrained for the next round of training.In order to ensure the processing efficiency of the Adaboost algorithm, the weight distribution momentum parameter is introduced [33]. Momentum parameters are assigned through weights to speed up the learning and generation direction of Adaboost algorithm subclassifiers in a certain direction, thereby reducing algorithm iterations in incorrect directions and improving algorithm efficiency. The initial weight distribution of the converted training data set is optimized by the momentum method to satisfy the following relationship:(5)Wt+1=Wt+Vt+1.Here, Vt+1 is the iterative weight added by the momentum method to assign the momentum parameter. The calculation formula of this parameter is as follows:(6)Vt+1=−γ∇WLWt+μVt.Here, γ is the learning rate of the Adaboost algorithm, μ is the coefficient of the weight distribution momentum parameter, and LWt is the distribution gradient value of the weight of the training data set.


## 3.2.3. Design of the Image Acquisition Module of the Safety Education System

A safety education image information detection model is constructed, Harr-NMF feature extraction method is used to carry out fuzzy correlation analysis of safety education image information [34], an output autocorrelation feature matching model of safety education image information is constructed, and statistical analysis of safety education image information is conducted using ant colony algorithm; the feature distribution set of hybrid genetic ant colony i at time t is represented as w1,j,w2,j,…,wtj, where t is the number of safety education image information, and wtj is the weighting coefficient of safety education image information mining. Semantic feature analysis method establishes the fuzzy semantic feature rule set of safety education image information mining and obtains the self-adaptive weighting coefficient of safety education image information as follows:(7)STDfi,j=Freqi,jmaxlFreqi,j.Here,maxlFreqi,j is the ant colony constraint feature quantity for the optimization of safety education image information mining between dj. ki is the pheromone concentration of ant colony search:(8)Idfi=logNni,where(9)wi,j=tfi,j×Idfi.Here,di and dj are the feature quantities of fuzzy rules for safety education image information mining. The fuzzy clustering method is used to carry out the shortest path of ant colony search for safety education image information mining. The calculation formula is as follows:(10)Ddi,dj=di.djdi×dj.Using statistical information analysis method, the fuzzy feature distribution set of safety education image information mining is established:(11)PK=T|R=1=PK=TPK=1|K=TPR=1,where(12)PK=T=Cs,PR=1|K=1=NBC,PR=1=NSS.Here, NB is the number of the mixed genetic ant colony, and NS is the pheromone concentration of the ant colony in S. The fuzzy information feature analysis method is adopted to realize the output autocorrelation feature matching of safety education image information.


## 3.3. Construction of Safety Knowledge Question Bank for College Students’ Safety Education

The construction of safety education online learning and testing system must first build a safety knowledge question bank, which is a basic work. The question bank of a college safety education online learning and testing system is divided into two categories: comprehensive knowledge question bank and sample question bank; the types mainly include three types of single-choice questions, multiple-choice questions, and true or false questions. Comprehensive knowledge mainly includes public security knowledge, fire protection knowledge, fraud, traffic safety, food safety, laboratory safety, national security, etc., accounting for about 60% of the entire question bank type; at the same time, the distribution ratio of these knowledge points is related to the occurrence of a safety incident in a university in Qingdao The ratio types are basically the same. The case question bank mainly includes related cases that have occurred in colleges and universities since 2010, especially the incidents that occurred in a certain college in Qingdao. The way of compiling a case question is to educate freshmen on safety knowledge, which is more targeted and operable, and is more targeted and operable for students. The educational significance is greater.In order to make the knowledge question bank more pertinent, a content-based recommendation system is designed to match different question types according to different professional categories and different characteristics of users.The feature vectors that define students and question types aref and g, respectively. The components of the student feature vector describe the major, educational background, grade, knowledge level, etc.; the feature vector of the question type includes the knowledge base professional category, safety knowledge level, knowledge point coverage degree, etc.Assuming that thei types of questions that students have done on the m day are a total of roads, and ami and bmj represent the score and total score of the j question, respectively, then the total score Mi and total score Ni of the i types of questions of the user on that day are as follows:(13)Mi=∑j−1liθ×ami,Ni=∑j−1liθ×bmj.Here,θ is the tilt, the level of difficulty and easy, which has three values, and represents the weights of difficulty, medium, and easy, respectively.Calculate the students’ scores for the question types according to the students’ answering conditions:(14)Xi=∑m−1t1/log8m×Mi∑m−1t1/log8m×Ni.The student feature vector is calculated by the above formula, and the initial vector is 0.The value range of each component of the feature vector of the question type is to extract the feature vector based on the features of the previously established security knowledge base, including the knowledge base professional category, security knowledge level, and knowledge point coverage.The similarity between the student and the question type is calculated by calculating the two feature vectors of the student and the question type:(15)df,g=∑fi−gi2.According to the similarity matrix, the corresponding question is selected and recommended to the user.


## 3.4. Construction of Safety Education Online Learning and Testing Modules

The safety knowledge online learning and testing system is written by Hypertext Preprocessor, and the dynamic web page runs very efficiently. The system database is deployed on MySQL, and the data are stored in different tables, which greatly increases the speed of operation and query, and improves flexibility. The SQL language used by MySQL is the most commonly used standardized language for accessing databases. It has the characteristics of small size and fast speed, which provides protection for simultaneous access by multiple users [35].The safety education online learning and testing system supports the combination of automatic scoring and teacher scoring. It can realize the three learning and examination functions such as knowledge-intensive training, mock examination, and formal examination, completely paperless operation. Candidate information can be imported with one key, and candidates’ scores can be exported as excel with one key. All online operations, without time and space constraints, save a lot of resources such as manpower and material resources. The system is deployed on the school server and provides external network access services, so that candidates can log in to the system in time for online learning and testing across the country [36].The examination time of the examination room can be set, and the examination will be automatically stopped when it expires. The official test time can be set in the background, and the test questions are automatically extracted according to the rules of the administrator’s setting of the question type and the question type. Each log-in candidate will extract different test questions from the question bank according to the set rules, at the set test time. The questions are answered internally, and the paper is handed in after answering the test questions or handed in automatically and compulsorily when the time is up. The operation interface is shown in Figure3.Figure 3
Test interface for candidates of a university’s safety knowledge test system.In summary, a safety education method for college students based on Harr characteristic big data algorithm is designed. The safety education system image acquisition module is constructed through the Harr characteristic big data algorithm, which shortens the training time of students’ safety education learning images, realizes realistic on-site learning, observation, and remote teaching, and improves the teacher-student interaction ability in the teaching process. Through the construction of safety education safety knowledge question bank for college students, the students’ independent learning and testing of safety education are realized, and finally, the safety education online learning and testing module is constructed to verify the application effect of students using the college student safety education system.


## 4. Experimental Analysis

### 4.1. Subjects

In order to verify the effectiveness of the safety education method for college students based on the Harr characteristic big data algorithm designed in this study, a comparative experiment is designed. Taking a university as an example, the security incidents against students that occurred from 2018 to 2020 are counted. As shown in Table1 and Figure 4, it can be seen that among the security incidents against students in the school, the probability of the incident is the highest, accounting for 70% of the annual security incidents. In the case of theft, analyzing the location of the incident, the probability is the highest in the classroom and dormitory. After the classroom, there are relatively few people in the dormitory, which allows thieves to take advantage. When studying in the classroom, it is related to the loss of valuables because they are not carried with them. From the point of time analysis, it occurs in June and September each year. The highest probability is that in June, it was mainly because students entered the exam season and graduation season. The students ignored the safekeeping of their belongings because they concentrated on preparing for the exam. In September, freshmen just entered the school and left the closed-managed high school campus. They still do not have much perceptual understanding of the open university. At this stage, the safety awareness of college students did not keep up with the changes in the rhythm of the society and fell behind, the school did not follow up on safety education in time, and the safety education work was just a formality. Harr feature is a rectangular feature, which is defined as the difference between the sum of gray values of the corresponding areas of black rectangle and white rectangle in the image subwindow, reflecting the local gray change of the image. Rectangular features are not as fine as other types of filters, such as directional edge filters. Different from the directional controllable filter, the available pose of rectangular feature is only vertical, horizontal, and tilt. Combined with integral image, the efficiency of rectangular feature set makes up for its limited adaptability to a great extent. Based on the Harr characteristic big data algorithm, a university security incident from 2018 to 2020 is summarized, as listed in Table 1; a summary of the number of security incidents in a university from 2018 to 2020 is shown in Figure 4.Table 1
Statistical summary of security incidents in a university from 2018 to 2020.
YearsTheftFight caseDeceived caseTraffic caseFire accidentTotalDormitoryClassroomDining roomLibraryStadiumWaterhouseBathhouseOtherSubtotal20182541569231610713184214420193147976421412011235115920203551984431713123471178Figure 4
Summary of the number of security incidents in a university from 2018 to 2020.During the experiment, the above college student’s safety events were taken as the experimental objects using the method of Reference [6], the method of Reference [7], and the method of this article to conduct safety education, analyzing the number of security incidents in colleges and universities after applying the above methods, and deriving the design based on Harr characteristics. The effectiveness of the college students' safety education system based on the Harr feature big data algorithm is obtained.


### 4.2. Analysis of Experimental Results

The college student safety education system based on Harr characteristic big data algorithm designed in this study is applied to this university to test this system, and the number of security incidents is investigated after the school uses this system.In order to test the actual application effect of the system, at the end of the period, a questionnaire survey was started based on the voting function of the system, and surveys were conducted on interaction enhancement, classroom activity, concentration, interest enhancement, efficiency enhancement, etc., setting “very effective, effective, general, and temporary.” There are four voting options for “none,” and 300 valid questionnaires are collected for investigation. The results of the investigation of the application effects of different methods are shown in Figure5.Figure 5
Application effect survey.It can be seen from Figure5 that college students think that the interactive ability is “very effective” and “effective” in total, up to 89%, and only 5% think that there is no effect temporarily. The other indicators are both “very effective” and “effective” in total, more than 80%. Generally speaking, the ideological and political distance education system for college students can effectively improve the interaction ability, classroom activity, concentration, and interest between teachers and students, so that college students can effectively increase their learning enthusiasm in the course.Figure6 shows the number of security incidents in colleges and universities after applying different methods.Figure 6
Number of security incidents in colleges and universities after applying different methods.It can be seen from Figure6 that after the application of this system, the number of student safety incidents in the school has been effectively reduced. There are 58 student safety incidents in 1 year, but the application of the reference method has no obvious effect. The experimental results prove that the safety education of the system has achieved obvious results, and it has obvious effects on the popularization of safety knowledge and the improvement of safety skills of college students.In order to further verify the response performance of the system in this article, the response time of different systems is tested, and the experimental results are shown in Figure7.Figure 7
Response time chart of different system requests.It can be seen from the experimental results in Figure7 that the response times of different systems are different. The experimental response time of the method in Reference [6] is 0.15 s on average, and the experimental response time of the method in Reference [7] is 0.35 s on average, but the average response time of the system in this paper is only 0.05s. The average time is only 0.05 s, and the response time of this system’s startup, catalog, course display, device connection, and experiment is lower than the other two systems, so this system has a lower response time than other systems.The satisfaction of the system modules was investigated, and the following 100 college students were used to score the satisfaction of the system after use, and the satisfaction of the safety knowledge question bank module, safety education online learning module, and safety education online test module in the minds of college students was tested. The test result is shown in Figure8.Figure 8
Satisfaction of college students’ system module.It can be seen from Figure8 that college students are satisfied with the system function module. The results show that 10 college students are satisfied with the system learning and discussion module, teaching plan module, course directory module, video data module, and video playback module, which shows that college students are highly satisfied with the system.To sum up, a safety education method for college students based on Harr feature big data algorithm designed in this study can improve the interaction ability between teachers and students, classroom activity, attention, and interest and effectively improve the learning enthusiasm of college students in the course. It has achieved remarkable results in popularizing safety knowledge and improving safety skills of college students and has a lower response time than other systems; college students are satisfied with the system designed in this study.


## 4.1. Subjects

In order to verify the effectiveness of the safety education method for college students based on the Harr characteristic big data algorithm designed in this study, a comparative experiment is designed. Taking a university as an example, the security incidents against students that occurred from 2018 to 2020 are counted. As shown in Table1 and Figure 4, it can be seen that among the security incidents against students in the school, the probability of the incident is the highest, accounting for 70% of the annual security incidents. In the case of theft, analyzing the location of the incident, the probability is the highest in the classroom and dormitory. After the classroom, there are relatively few people in the dormitory, which allows thieves to take advantage. When studying in the classroom, it is related to the loss of valuables because they are not carried with them. From the point of time analysis, it occurs in June and September each year. The highest probability is that in June, it was mainly because students entered the exam season and graduation season. The students ignored the safekeeping of their belongings because they concentrated on preparing for the exam. In September, freshmen just entered the school and left the closed-managed high school campus. They still do not have much perceptual understanding of the open university. At this stage, the safety awareness of college students did not keep up with the changes in the rhythm of the society and fell behind, the school did not follow up on safety education in time, and the safety education work was just a formality. Harr feature is a rectangular feature, which is defined as the difference between the sum of gray values of the corresponding areas of black rectangle and white rectangle in the image subwindow, reflecting the local gray change of the image. Rectangular features are not as fine as other types of filters, such as directional edge filters. Different from the directional controllable filter, the available pose of rectangular feature is only vertical, horizontal, and tilt. Combined with integral image, the efficiency of rectangular feature set makes up for its limited adaptability to a great extent. Based on the Harr characteristic big data algorithm, a university security incident from 2018 to 2020 is summarized, as listed in Table 1; a summary of the number of security incidents in a university from 2018 to 2020 is shown in Figure 4.Table 1
Statistical summary of security incidents in a university from 2018 to 2020.
YearsTheftFight caseDeceived caseTraffic caseFire accidentTotalDormitoryClassroomDining roomLibraryStadiumWaterhouseBathhouseOtherSubtotal20182541569231610713184214420193147976421412011235115920203551984431713123471178Figure 4
Summary of the number of security incidents in a university from 2018 to 2020.During the experiment, the above college student’s safety events were taken as the experimental objects using the method of Reference [6], the method of Reference [7], and the method of this article to conduct safety education, analyzing the number of security incidents in colleges and universities after applying the above methods, and deriving the design based on Harr characteristics. The effectiveness of the college students' safety education system based on the Harr feature big data algorithm is obtained.


## 4.2. Analysis of Experimental Results

The college student safety education system based on Harr characteristic big data algorithm designed in this study is applied to this university to test this system, and the number of security incidents is investigated after the school uses this system.In order to test the actual application effect of the system, at the end of the period, a questionnaire survey was started based on the voting function of the system, and surveys were conducted on interaction enhancement, classroom activity, concentration, interest enhancement, efficiency enhancement, etc., setting “very effective, effective, general, and temporary.” There are four voting options for “none,” and 300 valid questionnaires are collected for investigation. The results of the investigation of the application effects of different methods are shown in Figure5.Figure 5
Application effect survey.It can be seen from Figure5 that college students think that the interactive ability is “very effective” and “effective” in total, up to 89%, and only 5% think that there is no effect temporarily. The other indicators are both “very effective” and “effective” in total, more than 80%. Generally speaking, the ideological and political distance education system for college students can effectively improve the interaction ability, classroom activity, concentration, and interest between teachers and students, so that college students can effectively increase their learning enthusiasm in the course.Figure6 shows the number of security incidents in colleges and universities after applying different methods.Figure 6
Number of security incidents in colleges and universities after applying different methods.It can be seen from Figure6 that after the application of this system, the number of student safety incidents in the school has been effectively reduced. There are 58 student safety incidents in 1 year, but the application of the reference method has no obvious effect. The experimental results prove that the safety education of the system has achieved obvious results, and it has obvious effects on the popularization of safety knowledge and the improvement of safety skills of college students.In order to further verify the response performance of the system in this article, the response time of different systems is tested, and the experimental results are shown in Figure7.Figure 7
Response time chart of different system requests.It can be seen from the experimental results in Figure7 that the response times of different systems are different. The experimental response time of the method in Reference [6] is 0.15 s on average, and the experimental response time of the method in Reference [7] is 0.35 s on average, but the average response time of the system in this paper is only 0.05s. The average time is only 0.05 s, and the response time of this system’s startup, catalog, course display, device connection, and experiment is lower than the other two systems, so this system has a lower response time than other systems.The satisfaction of the system modules was investigated, and the following 100 college students were used to score the satisfaction of the system after use, and the satisfaction of the safety knowledge question bank module, safety education online learning module, and safety education online test module in the minds of college students was tested. The test result is shown in Figure8.Figure 8
Satisfaction of college students’ system module.It can be seen from Figure8 that college students are satisfied with the system function module. The results show that 10 college students are satisfied with the system learning and discussion module, teaching plan module, course directory module, video data module, and video playback module, which shows that college students are highly satisfied with the system.To sum up, a safety education method for college students based on Harr feature big data algorithm designed in this study can improve the interaction ability between teachers and students, classroom activity, attention, and interest and effectively improve the learning enthusiasm of college students in the course. It has achieved remarkable results in popularizing safety knowledge and improving safety skills of college students and has a lower response time than other systems; college students are satisfied with the system designed in this study.


## 5. Conclusion

In this study, a method of college students’ safety education based on Harr characteristic big data algorithm is proposed. The image acquisition module of safety education system is constructed by using Harr characteristic big data algorithm, and the college students’ safety education system is designed. The following conclusions are drawn through empirical experiments:(1)
This study designs a safety education method for college students based on Harr characteristic big data algorithm, which can improve the interaction ability between teachers and students, classroom activity, attention, and interest(2)
The method designed in this study can effectively reduce the occurrence of security incidents in colleges and universities(3)
The request-response time of this method is short(4)
College students’ satisfaction with the system module is high, which proves the effectiveness of this methodFor future research, on the basis of good education, we should strengthen the emphasis on safety education. Different safety education is carried out for college students, and better safety education programs and systems are chosen. This is a routine but extremely important work in colleges and universities. For university managers, we should not only improve the good safety education system and add diversified safety education courses but also make more innovative attempts on the basis of the original safety education system, effectively carrying out safety education for students and further improving students’ safety awareness.



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*Source: 5766802-2022-03-16.xml*