# Distributed Sound Transmission and Smart City Planning Management Based on Convolutional Neural Network

Authors: Yingchuan Ma; Yue Yu; Zhaoming Ma
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

In this article, we provide a detection method based on a convolutional neural network algorithm. Preamble detection is very important in underwater acoustic communication. Only the preamble signal can trigger the receiving operation to effectively process the acoustic data received underwater. The prerequisite for specifying the joint position of the distributed acoustic multiarray is the mutual calibration calculation of each array. This paper designs and develops a prototype system for microphone array sound source positioning and provides an optimized system to obtain calibration source mapping points for distributed sound transmission mutual calibration, and for mutual distributed acoustic microphone arrays, use epipolar geometry and normalization algorithm for calibration to provide a basis for a mutual calibration calculation model and functional module design. Finally, perform numerical simulation verification. It provides a design plan and implementation method for the Internet-based smart city education system, aiming to achieve professional education and educational needs related to the Internet of Things. First, we will take Internet application technology as an example to analyze the functional requirements of the education system, including the positioning of human resource development, integrated sensors, C/C#/Java application development technology, and other technical means to realize the smart city education system. Based on the theory of business management, this paper studies the business planning issues of smart city construction. The main content is divided into three parts: business composition and management and planning management. This is an important part of business. It plays a decisive role in the overall planning and implementation stage and protects the implementation progress of the project and ensures the quality of the project by including important content.


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## Body

## 1. Introduction

This paper presents Convolutional Neural Networks and Taigman’s Entire Neural Network, combining a local music classification setup with an overall distribution. Obtain the ability of the existing neural folding network layer, uniformly process the data in all frequency bands, bypass the sound-based feature convolution neural frequency band data, divide the characteristics of the music interval according to frequency, and divide the folded iron core into different region, so that different frequency bands have different characteristics. In order to solve the problem of poor localization and spatial mixing persistence of distributed sound cards, a distributed Kalman filter speaker tracking model based on distributed microphone network is designed. This method is a strategy for evaluating noise and noise delay. This strategy may cause the audio source to have wrong values during the tracking process, which may lead to incorrect judgments. The Langevin model is used to describe this strategy.After mastering the motion state of the loudspeaker, the distributed acoustic Kalman filter can be used to accurately estimate the position of the loudspeaker. This method effectively utilizes the signals from the current and previous frames, solves the problems of durability, reduced communication volume and reliability of distributed speakers, and has excellent stability. The overall plan implemented in this article includes four main systems. The short-term implementation plan focuses on the phased implementation of the plan. Create an execution plan: In order to gradually build smart city projects such as basic platforms, infrastructure, data resources, e-government, smart services, smart industries, and city operation management, urban management models based on big data and cloud computing will be added every year. Comprehensive database applications can improve the level of modernization and complex management of the city, thereby promoting the gradual construction of the entire smart city. A smart city is a complex planning and management of data sensing, collection, transmission, storage, and processing sensors. Taking smart cities as the theme, with large-scale application projects such as the Internet of Things (IoT) and embedded systems as the background, construct and train the system’s Internet of Things engineering education system. These systems support wireless sensor networks, RFID technology, computer network technology, java application development and other processes. The Internet of Things can also cultivate students’ comprehensive capabilities in planning management, deployment, debugging, and operation to meet the needs of innovative application development. Therefore, choosing a smart city to engage in a career in IoT engineering education can meet the needs of students and can also train students, which is a practical training teaching.


## 2. Related Work

The literature describes the hardware development of the perception layer [1]. With CC2530 and a variety of sensors as the core, it can meet the hardware requirements of urban lighting monitoring, traffic dispatching, fire monitoring, and other applications. The literature describes the configuration of the CNN framework for driving detection [2–5]. Considering that it is troublesome and expensive to replace the battery in underwater communication equipment, the construction of the CNN structure not only must consider the accuracy of recognition but also must consider the issue of energy consumption to ensure that the algorithm of the structure is not too complicated [6]. It is expected to build a simple 5-layer network model for subsequent experiments. The deep learning framework used in this article is TensorFlow. The literature introduces the subject’s CNN model, which is used to train many pilot signals generated by simulations, and uses pilot signal data and interference data collected in underwater acoustic experiments with low signal-to-noise ratio as experimental data [7–10].We evaluated the data and training results and achieved good detection performance. The literature describes the time-frequency cepstrum coefficient. First, we study the Fourier transform and the short-time Fourier transform, compare and analyze the two, and then study the knowledge related to the Mel frequency cepstrum coefficient based on the short-time Fourier transform and analyze the extraction process of the Mel frequency cepstrum coefficient to study the wavelet definition and characteristics of transform, compare and analyze wavelet transform and Fourier transform, and finally study the Coch filter cepstrum system based on wavelet transform [11–13]. The literature introduces convolutional neural networks based on auditory features. First, we studied the three basic elements of convolutional neural networks and analyzed the definitions, operations, attributes, and functions of these three elements in detail [14]. After that, the role of the two most widely used loss functions in convolutional neural networks is studied in detail [15–17].


## 3. Research on Distributed Acoustic Positioning and Calibration System Based on Convolutional Neural Network

### 3.1. Basic Principles of Distributed Optical Fiber Acoustic Wave Sensing System

#### 3.1.1. The Overall Structure of Distributed Optical Fiber Acoustic Wave Sensing

Distributed optical fiber acoustic wave detection is based on theΦ-OTDR detection technology, which uses ultranarrow linewidth lasers, which makes the pulse impulse have a high correlation. When the fiber is interfered, it is easy to be distorted. It affects the fiber folding index and also affects the information related to the optical phase of the interference point and the change of the back reflection intensity at this time [18–20]. By comparing the back of adjacent moments with the Riley hash curve, we can know the location of the intersection. Distributed optical fiber acoustic wave detection is usually used in the green area along the expressway, or it can be directly used in the hollow core of the communication optical cable that has been laid along the expressway.The laser signal emitted by the pulsed laser is modulated into a highly consistent laser pulse signal through the acousto-optic modulator. After the signal is amplified by the fiber amplifier doped with the amplifier, the pulsed laser signal is transmitted on the detection fiber in the following manner: the backscattered signal of the pulsed laser detection fiber signal is transmitted to the photodetector after passing through. Through the circulator and the3×3 coupler, the phase information of the interference signal modulated by the detection fiber can be obtained [21–23]. The light sensor converts the detected light signal into a corresponding analog signal and quickly collects the synchronization signal to generate a digital signal, which is transmitted to the signal processing program in real time through the network. The signal processing mainframe is a typical computer mainframe (PC) or FPGA/DSP-based mainboard that analyzes and processes the fiber identification signal and uses specific signal processing algorithms to receive transaction data.


#### 3.1.2. Performance Parameters of Distributed Optical Fiber Acoustic Wave Sensing System

The distance is the maximum distance collected by the fiber channel, which is mainly determined by the modular pulse playback frequency mf. The pi-OTDR technology is based on the Riley degradation measurement and vibrating en fiber. If the fiber channel has multiple light pulses at the same time, Riley spreads on the back of this pulse. Interaction is performed, so if the Riley diffusion is received from the optical fiber via a photographic sensor, the next pulse can be sent. The detection distanceL is as follows:
(1)L=12fm×cng.The spatial accuracy is the shortest distance between two adjacent points.φ-OTDR mode accuracy is determined by the pulse width of the light source.
(2)Δz1=Tw2×cng.In practical applications, the response delay of the Rayleigh light sensor when receiving distributed light affects the spatial accuracy.(3)Δz2=12fd×cng.According to Nyquist’s theorem,(4)Δz3=12fs×cng.Finally, the overall resolution is the maximum resolution among the three modes.(5)Δz=maxΔz1,Δz2,Δz3.


#### 3.1.3. Microphone Sound Source Localization Module Design

The sound localization module is designed and developed based on the open source framework in many years. Figure1 is designed and developed for the microphone array to obtain the sound source position. The beamforming method used in the microphone array is used to determine the location of the sound source. First, set a preset position point on the acoustic camera mapping plane, and determine its coordinates [24]. After determining the coordinates, calculate the position coordinates of the microphone element sensor in the microphone array, calculate the time delay information of a pair of microphone sensors, and accumulate the audio signal delay received by the microphone sensor to obtain the preset position point of the acoustic camera mapping plane. Find the maximum value of the sound energy, and determine the mapping point of the potential sound source. The processing flow is shown in Figure 1.Figure 1
Sound source localization program architecture diagram.Table1 shows the results of the sound source position. The average longitude deviation of the sound source position is 3.22°, and the average size deviation is 1.35° of Robo, so the sound source position can be accurately grasped [25]. From the test results obtained from the sound source position, it can be seen that when the test sound source is close, the longitude deviation determined by the sound source position is lower than that of the far place. In other words, the result of sound source localization is more accurate. When the test source is far away, the error is obviously lower than the error when the source is turned off.Table 1
Location data.
Longitude (deg)Latitude (deg)Longitude deviation (deg)Latitude deviation (deg)Sound source distance from the array 1 m38.5718.30.422.0532.2721.451.331.333.222.42.180.4Sound source distance 2 m from the array25.9522.455.160.336.7525.45.732.635.8724.44.671.55Average value——3.221.35


### 3.2. Convolutional Neural Network Model Based on Auditory Characteristics

#### 3.2.1. Convolution Operation

Convolution operation is an important analytical operation method in analytic mathematics. Convolution operation is defined as an operation that creates another function through functionsx and w. The function w represents the size of the overlapping area, and the definition formula of convolution in the convolutional neural network is as follows:
(6)st=xt∗wt=∑τ=−∞τ=+∞xτwt−τ.When processing two-dimensional grid data, the specific calculation formula of two-dimensional convolution is as follows:(7)si,j=∑m=0M∑n=0Nwm,nxi+m,j+n+wb.When the input of the function changes, the output also changes. The description of the translational equilateral attribute data can be found in formula (8). The translational equivariant property causes certain functions to move from the input position and cause the same movement in the output.
(8)fgx=gfx.Most of the existing neural networks effectively use sigmoid and tanh functions, so that the network model can be better applied in nonlinear expression capabilities. The mathematical representations of sigmoid and tanh functions are given in(9)Sigmoidx=11+e−x,(10)Tanhx=ex−e−xex+e−x.For the research and development of convolutional neural networks, the current ReLU function has good performance in convolutional neural networks. At the same time, the ReLU function also has good nonlinear expression ability and can well solve the back propagation caused by differentiation [26].The network model widely used in convolutional neural networks guarantees the speed of training and convergence, provides the PReLU function based on the ReLU function, and compares it with ImageNe to show the effectiveness and reliability of the PReLU function. The PReLU function can protect the speed of network convergence by making the output average close to 0. The mathematical expressions of the ReLU and PReLU functions are given in formula (11).
(11)ReLUx=x,ifx>0,0,ifx≤0,(12)PReLUx=x,ifx>0,ax,ifx≤0.The mathematical expression of the SoftPlus function appears in(13)SoftPlusx=log1+ex.The definition expression of the Softmax function is(14)σzj=ezj∑i=1Kezi.The Softmax function can map theK-dimensional feature vector to the K-dimensional probability vector to complete multiple classification tasks commonly used in the last layer of the convolutional neural network.The average error loss function represents the Euclidean distance between the predicted value and the actual value. The mathematical expression function of the mean square error loss is(15)C=12n∑xyx−aLx2.The loss function can not only measure the performance of the network model but also minimize the loss function and carry out the work of forming and improving the network model. At present, the basic idea of many mainstream methods for training convolutional neural networks is backpropagation. It completes the neural network training task by adjusting the parameters to reduce the loss function through the reverse radio wave. Formula (16) of the network model can be adjusted with w and b, so that the parameter n=1; then,
(16)∂C∂w=a−yσ′zx,(17)∂C∂b=a−yσ′z.The loss function of cross entropy is based on the concept of cross section. The cross entropy loss function is defined as follows:(18)C=1n∑xylna+1−yln1−a.Formula (18) in the network model can adjust the w and b parameters so that n=1 is convenient for simple calculation; then,
(19)∂C∂w=xσz−y,∂C∂b=σz−y.Calculate the unbiased estimate of the gradient; the formula is as follows:(20)g^=1n∑i∇θCfxi;θ,yi.


#### 3.2.2. Auditory Characteristic Convolutional Neural Network Model

In this article, we designed the auditory feature convolutional neural network based on the core idea of the auditory feature convolutional neural network and the classic AlexNet network model. In 2012, Geoffrey Hinton and his student Alexoff broke the previous record using the AlexNet network model in the ImageNet competition and won by an absolute advantage. The championship makes the convolutional neural network in computer vision occupying the core position [27]. However, the aspect ratio of the Mel frequency or the cepstrum coefficient of the cochlear filter extracted in this article is relatively short, and the cursor input is short, so the AlexNet network model cannot be fully applied. Therefore, this article modifies the first few layers of the AlexNet network model. The first few layers of network convolution processing and pooling operation select the kernel with aspect ratio, which makes it suitable for processing the Mel frequency, cepstrum coefficient, and cochlear filter cepstrum coefficient of the relative aspect ratio extracted from this article [28]. This article also uses the accumulative neural network based on sound capabilities by sharing the frequency characteristics of music. Next, because the convolution in each frequency domain is shared and the convolution in other regions is not shared, the function of learning different music features in different frequency regions is realized and completed, and the convolutional neural network was based on auditory features of network definition.The first layer of the neural network forms the auditory function, through the interaction and accumulation of the three elements of convolution operation, pooling operation and activation function, and extracting higher-level functional tasks from time-frequency feature coefficients. In the latter layers of the auditory trait convolutional neural network model, the importance of each trait is calculated through fully connected layers, so that music can be better classified [29].The auditory feature convolutional neural network designed in this paper inherits the excellent features of general convolutional neural networks and fully integrates auditory features. Therefore, in time domain analysis, when a specific music feature is identified, it will be transformed into another point in time. The auditory convolutional neural network learns, recognizes, and ignores time node information, and the output results are consistent [30]. It meets the demand that people only care about the musical characteristics of the music and not the time when the musical characteristics appear when analyzing music. Displaying the same function in different frequency bands will have different meanings. It divides the music interval characteristics into different areas according to frequency to prevent the overall core from sharing. It will only be shared internally. We can use the characteristics of frequency bands to distinguish data in multiple frequency bands and further verify based on the results of the data [31]. In summary, the auditory feature convolutional neural network designed in this article can better combine the auditory features and the advantages of ordinary convolutional neural networks. This makes the sound characteristics of neural networks superior to standard neural networks used to automatically classify music.


### 3.3. Module Design of Sound Source Array Calibration System

#### 3.3.1. Stochastic Gradient Descent Algorithm

Stochastic Gradient Reduction (SGD) and its derived algorithms are the most commonly used optimization algorithms in machine learning, especially in convolutional neural networks; the stochastic steepest descent algorithm is simple in principle, easy to implement, and relatively stable and reliable in performance.Update speed parameterv; the formula is as follows:
(21)v=αv−εg^.Update the parameters; the formula is as follows:(22)θ=θ+v.Update timet, step length s, and subvariable r; the formula is as follows:
(23)t=t+1,s=ρ1s+1−ρ1g^,r=ρ2r+1−ρ2g^⊙g^.Use the formula to correct the first and second deviations:(24)s^=s1−ρ1t,r^=r1−ρ2t.


### 3.4. Function Analysis of Calibration System

The purpose of the distributed microphone array calibration system designed in this paper is to obtain the mutual calibration result of the distributed microphone array and design these two modules to reduce the mutual calibration error. When a distributed microphone array runs a cross-calibration solution, the array elements in a single microphone array are biased, and there is a resolution limit to the preset position in the mapping plane [32]. At this time, it is necessary to design a self-calibration and repeat mutual calibration module in these two cases, the purpose of which is to improve the accuracy of the coordinate mapping point and use the coordinate mapping point as an input parameter. The mutual calibration solution shows the business process of a complete calibration system, which can provide a high-quality mutual calibration solution, as shown in Figure 2.Figure 2
Functional structure of the calibration system.The functions required to complete the calibration system include three main parts:(1)
The correction system can reduce the deviation of the elements of the microphone array(2)
The calibration system can obtain more accurate calibration element mapping points(3)
The calibration system can provide solutions for mutual calibration of distributed microphone arrays


### 3.5. System Simulation Experiment Design and Result Analysis

After x1, x2, x3, and XN, compared with the entire neural network, the neural network based on sound capabilities no longer widely distributes the core of the convolution but is distributed in the Kexi area, so that the convolution core of different frequency bands can learn each frequency [33]. The characteristics of the domain overcome the uniform processing of all frequency characteristics in the standard convolutional neural network, and there is no difference in frequency band data. Divide the basis of the characteristics of high, medium, and low intervals and different frequency bands, so that other music characteristics can be learned in different frequency bands.The TensorFlow framework of Python language is used to complete the definition of the network, the cross-entropy loss function is selected as the loss function, and the momentum algorithm is selected as the network optimization algorithm. The input data matches the input data of the convolutional neural network [34]. Through the experiment, we can start the network training work through the above related preparations, and use the TensorBoard tool to visualize the learning process. We will combine the CFCC + auditory convolutional neural network to analyze the training course.As the number of training increases, the loss function of the network model gradually decreases, which shows that the optimization algorithm continuously and effectively reduces the loss. It plays a role in adjusting network parameters, activating functions and training network models. In the later stage of training, the loss function of the network model is reduced until it is relatively stable. This shows that the loss function is gradually converging, and finally, the network teaching is completed.As the number of training increases, the accuracy of the training set and validation set in the early and midtraining period will also increase. The network model is continuously optimized, and the performance of the network model is continuously improved. In the later stages of learning, the accuracy of the learning set is still increasing, but the accuracy of the verification set is not increasing, but decreasing. This means that the web template must complete learning to prevent the analysis of the validation set from deteriorating. The turning point when the validation set drops is the best point of the model, and the network model is the best model in this mode.After obtaining the trained model, the validation set will be tested. The size of the validation set is 10000, and the results are shown in Table2.Table 2
The prediction result of the validation set.
Correct markPredictive valueNightclubMovementLearnCoffee shopAccuracySumNightclub2042356475181.81%2496Movement26120276612981.63%2483Learn55122216718885.58%2532Coffee shop43126198212285.25%2489Sum240126312478249083.58%10000Analyzing the results in Table2, songs suitable for nightclubs are more easily classified as songs suitable for sports, and songs suitable for sports are also more easily classified as songs suitable for nightclubs. This is because club songs and sports songs have a faster rhythm. Both types of music are closer to songs suitable for learning, while sports and cafes are more suitable for quiet music, while some are slightly more rhythmic [35]. Music suitable for cafes can be easily classified as sports and learning, and music cafes have a variety of quiet and rhythmic music styles. The results in Table 2 show that music with similar styles is more prone to misclassification.In this experiment, a set of MFCC + auditory convolutional neural network and CFCC + auditory convolutional neural network were tested. The experimental results are shown in Table3.Table 3
Experimental results of convolutional neural network based on auditory characteristics.
Experiment numberFeatureNetwork modelAccuracyStandard deviationTime cost (s)1MFCCAuditory characteristic convolutional neural network78.55%8.0583%0.0652CFCCAuditory characteristic convolutional neural network83.56%6.2606%0.065Summarizing the results of all the above experiments, you can summarize and analyze to obtain the comparative results of the entire experiment, as shown in Table4.Table 4
Comparison of overall experimental results.
Experiment numberFeatureNetwork modelAccuracyStandard deviationTime cost (s)1MFCCAlexNet72.67%8.62%0.0332MFCCResNet76.33%8.37%0.3543CFCCAlexNet74.53%6.87%0.0334CFCCResNet79.74%5.66%0.3535MFCCAuditory characteristic convolutional neural network78.55%8.04%0.0686CFCCAuditory characteristic convolutional neural network83.56%6.24%0.065


## 3.1. Basic Principles of Distributed Optical Fiber Acoustic Wave Sensing System

### 3.1.1. The Overall Structure of Distributed Optical Fiber Acoustic Wave Sensing

Distributed optical fiber acoustic wave detection is based on theΦ-OTDR detection technology, which uses ultranarrow linewidth lasers, which makes the pulse impulse have a high correlation. When the fiber is interfered, it is easy to be distorted. It affects the fiber folding index and also affects the information related to the optical phase of the interference point and the change of the back reflection intensity at this time [18–20]. By comparing the back of adjacent moments with the Riley hash curve, we can know the location of the intersection. Distributed optical fiber acoustic wave detection is usually used in the green area along the expressway, or it can be directly used in the hollow core of the communication optical cable that has been laid along the expressway.The laser signal emitted by the pulsed laser is modulated into a highly consistent laser pulse signal through the acousto-optic modulator. After the signal is amplified by the fiber amplifier doped with the amplifier, the pulsed laser signal is transmitted on the detection fiber in the following manner: the backscattered signal of the pulsed laser detection fiber signal is transmitted to the photodetector after passing through. Through the circulator and the3×3 coupler, the phase information of the interference signal modulated by the detection fiber can be obtained [21–23]. The light sensor converts the detected light signal into a corresponding analog signal and quickly collects the synchronization signal to generate a digital signal, which is transmitted to the signal processing program in real time through the network. The signal processing mainframe is a typical computer mainframe (PC) or FPGA/DSP-based mainboard that analyzes and processes the fiber identification signal and uses specific signal processing algorithms to receive transaction data.


### 3.1.2. Performance Parameters of Distributed Optical Fiber Acoustic Wave Sensing System

The distance is the maximum distance collected by the fiber channel, which is mainly determined by the modular pulse playback frequency mf. The pi-OTDR technology is based on the Riley degradation measurement and vibrating en fiber. If the fiber channel has multiple light pulses at the same time, Riley spreads on the back of this pulse. Interaction is performed, so if the Riley diffusion is received from the optical fiber via a photographic sensor, the next pulse can be sent. The detection distanceL is as follows:
(1)L=12fm×cng.The spatial accuracy is the shortest distance between two adjacent points.φ-OTDR mode accuracy is determined by the pulse width of the light source.
(2)Δz1=Tw2×cng.In practical applications, the response delay of the Rayleigh light sensor when receiving distributed light affects the spatial accuracy.(3)Δz2=12fd×cng.According to Nyquist’s theorem,(4)Δz3=12fs×cng.Finally, the overall resolution is the maximum resolution among the three modes.(5)Δz=maxΔz1,Δz2,Δz3.


### 3.1.3. Microphone Sound Source Localization Module Design

The sound localization module is designed and developed based on the open source framework in many years. Figure1 is designed and developed for the microphone array to obtain the sound source position. The beamforming method used in the microphone array is used to determine the location of the sound source. First, set a preset position point on the acoustic camera mapping plane, and determine its coordinates [24]. After determining the coordinates, calculate the position coordinates of the microphone element sensor in the microphone array, calculate the time delay information of a pair of microphone sensors, and accumulate the audio signal delay received by the microphone sensor to obtain the preset position point of the acoustic camera mapping plane. Find the maximum value of the sound energy, and determine the mapping point of the potential sound source. The processing flow is shown in Figure 1.Figure 1
Sound source localization program architecture diagram.Table1 shows the results of the sound source position. The average longitude deviation of the sound source position is 3.22°, and the average size deviation is 1.35° of Robo, so the sound source position can be accurately grasped [25]. From the test results obtained from the sound source position, it can be seen that when the test sound source is close, the longitude deviation determined by the sound source position is lower than that of the far place. In other words, the result of sound source localization is more accurate. When the test source is far away, the error is obviously lower than the error when the source is turned off.Table 1
Location data.
Longitude (deg)Latitude (deg)Longitude deviation (deg)Latitude deviation (deg)Sound source distance from the array 1 m38.5718.30.422.0532.2721.451.331.333.222.42.180.4Sound source distance 2 m from the array25.9522.455.160.336.7525.45.732.635.8724.44.671.55Average value——3.221.35


## 3.1.1. The Overall Structure of Distributed Optical Fiber Acoustic Wave Sensing

Distributed optical fiber acoustic wave detection is based on theΦ-OTDR detection technology, which uses ultranarrow linewidth lasers, which makes the pulse impulse have a high correlation. When the fiber is interfered, it is easy to be distorted. It affects the fiber folding index and also affects the information related to the optical phase of the interference point and the change of the back reflection intensity at this time [18–20]. By comparing the back of adjacent moments with the Riley hash curve, we can know the location of the intersection. Distributed optical fiber acoustic wave detection is usually used in the green area along the expressway, or it can be directly used in the hollow core of the communication optical cable that has been laid along the expressway.The laser signal emitted by the pulsed laser is modulated into a highly consistent laser pulse signal through the acousto-optic modulator. After the signal is amplified by the fiber amplifier doped with the amplifier, the pulsed laser signal is transmitted on the detection fiber in the following manner: the backscattered signal of the pulsed laser detection fiber signal is transmitted to the photodetector after passing through. Through the circulator and the3×3 coupler, the phase information of the interference signal modulated by the detection fiber can be obtained [21–23]. The light sensor converts the detected light signal into a corresponding analog signal and quickly collects the synchronization signal to generate a digital signal, which is transmitted to the signal processing program in real time through the network. The signal processing mainframe is a typical computer mainframe (PC) or FPGA/DSP-based mainboard that analyzes and processes the fiber identification signal and uses specific signal processing algorithms to receive transaction data.


## 3.1.2. Performance Parameters of Distributed Optical Fiber Acoustic Wave Sensing System

The distance is the maximum distance collected by the fiber channel, which is mainly determined by the modular pulse playback frequency mf. The pi-OTDR technology is based on the Riley degradation measurement and vibrating en fiber. If the fiber channel has multiple light pulses at the same time, Riley spreads on the back of this pulse. Interaction is performed, so if the Riley diffusion is received from the optical fiber via a photographic sensor, the next pulse can be sent. The detection distanceL is as follows:
(1)L=12fm×cng.The spatial accuracy is the shortest distance between two adjacent points.φ-OTDR mode accuracy is determined by the pulse width of the light source.
(2)Δz1=Tw2×cng.In practical applications, the response delay of the Rayleigh light sensor when receiving distributed light affects the spatial accuracy.(3)Δz2=12fd×cng.According to Nyquist’s theorem,(4)Δz3=12fs×cng.Finally, the overall resolution is the maximum resolution among the three modes.(5)Δz=maxΔz1,Δz2,Δz3.


## 3.1.3. Microphone Sound Source Localization Module Design

The sound localization module is designed and developed based on the open source framework in many years. Figure1 is designed and developed for the microphone array to obtain the sound source position. The beamforming method used in the microphone array is used to determine the location of the sound source. First, set a preset position point on the acoustic camera mapping plane, and determine its coordinates [24]. After determining the coordinates, calculate the position coordinates of the microphone element sensor in the microphone array, calculate the time delay information of a pair of microphone sensors, and accumulate the audio signal delay received by the microphone sensor to obtain the preset position point of the acoustic camera mapping plane. Find the maximum value of the sound energy, and determine the mapping point of the potential sound source. The processing flow is shown in Figure 1.Figure 1
Sound source localization program architecture diagram.Table1 shows the results of the sound source position. The average longitude deviation of the sound source position is 3.22°, and the average size deviation is 1.35° of Robo, so the sound source position can be accurately grasped [25]. From the test results obtained from the sound source position, it can be seen that when the test sound source is close, the longitude deviation determined by the sound source position is lower than that of the far place. In other words, the result of sound source localization is more accurate. When the test source is far away, the error is obviously lower than the error when the source is turned off.Table 1
Location data.
Longitude (deg)Latitude (deg)Longitude deviation (deg)Latitude deviation (deg)Sound source distance from the array 1 m38.5718.30.422.0532.2721.451.331.333.222.42.180.4Sound source distance 2 m from the array25.9522.455.160.336.7525.45.732.635.8724.44.671.55Average value——3.221.35


## 3.2. Convolutional Neural Network Model Based on Auditory Characteristics

### 3.2.1. Convolution Operation

Convolution operation is an important analytical operation method in analytic mathematics. Convolution operation is defined as an operation that creates another function through functionsx and w. The function w represents the size of the overlapping area, and the definition formula of convolution in the convolutional neural network is as follows:
(6)st=xt∗wt=∑τ=−∞τ=+∞xτwt−τ.When processing two-dimensional grid data, the specific calculation formula of two-dimensional convolution is as follows:(7)si,j=∑m=0M∑n=0Nwm,nxi+m,j+n+wb.When the input of the function changes, the output also changes. The description of the translational equilateral attribute data can be found in formula (8). The translational equivariant property causes certain functions to move from the input position and cause the same movement in the output.
(8)fgx=gfx.Most of the existing neural networks effectively use sigmoid and tanh functions, so that the network model can be better applied in nonlinear expression capabilities. The mathematical representations of sigmoid and tanh functions are given in(9)Sigmoidx=11+e−x,(10)Tanhx=ex−e−xex+e−x.For the research and development of convolutional neural networks, the current ReLU function has good performance in convolutional neural networks. At the same time, the ReLU function also has good nonlinear expression ability and can well solve the back propagation caused by differentiation [26].The network model widely used in convolutional neural networks guarantees the speed of training and convergence, provides the PReLU function based on the ReLU function, and compares it with ImageNe to show the effectiveness and reliability of the PReLU function. The PReLU function can protect the speed of network convergence by making the output average close to 0. The mathematical expressions of the ReLU and PReLU functions are given in formula (11).
(11)ReLUx=x,ifx>0,0,ifx≤0,(12)PReLUx=x,ifx>0,ax,ifx≤0.The mathematical expression of the SoftPlus function appears in(13)SoftPlusx=log1+ex.The definition expression of the Softmax function is(14)σzj=ezj∑i=1Kezi.The Softmax function can map theK-dimensional feature vector to the K-dimensional probability vector to complete multiple classification tasks commonly used in the last layer of the convolutional neural network.The average error loss function represents the Euclidean distance between the predicted value and the actual value. The mathematical expression function of the mean square error loss is(15)C=12n∑xyx−aLx2.The loss function can not only measure the performance of the network model but also minimize the loss function and carry out the work of forming and improving the network model. At present, the basic idea of many mainstream methods for training convolutional neural networks is backpropagation. It completes the neural network training task by adjusting the parameters to reduce the loss function through the reverse radio wave. Formula (16) of the network model can be adjusted with w and b, so that the parameter n=1; then,
(16)∂C∂w=a−yσ′zx,(17)∂C∂b=a−yσ′z.The loss function of cross entropy is based on the concept of cross section. The cross entropy loss function is defined as follows:(18)C=1n∑xylna+1−yln1−a.Formula (18) in the network model can adjust the w and b parameters so that n=1 is convenient for simple calculation; then,
(19)∂C∂w=xσz−y,∂C∂b=σz−y.Calculate the unbiased estimate of the gradient; the formula is as follows:(20)g^=1n∑i∇θCfxi;θ,yi.


### 3.2.2. Auditory Characteristic Convolutional Neural Network Model

In this article, we designed the auditory feature convolutional neural network based on the core idea of the auditory feature convolutional neural network and the classic AlexNet network model. In 2012, Geoffrey Hinton and his student Alexoff broke the previous record using the AlexNet network model in the ImageNet competition and won by an absolute advantage. The championship makes the convolutional neural network in computer vision occupying the core position [27]. However, the aspect ratio of the Mel frequency or the cepstrum coefficient of the cochlear filter extracted in this article is relatively short, and the cursor input is short, so the AlexNet network model cannot be fully applied. Therefore, this article modifies the first few layers of the AlexNet network model. The first few layers of network convolution processing and pooling operation select the kernel with aspect ratio, which makes it suitable for processing the Mel frequency, cepstrum coefficient, and cochlear filter cepstrum coefficient of the relative aspect ratio extracted from this article [28]. This article also uses the accumulative neural network based on sound capabilities by sharing the frequency characteristics of music. Next, because the convolution in each frequency domain is shared and the convolution in other regions is not shared, the function of learning different music features in different frequency regions is realized and completed, and the convolutional neural network was based on auditory features of network definition.The first layer of the neural network forms the auditory function, through the interaction and accumulation of the three elements of convolution operation, pooling operation and activation function, and extracting higher-level functional tasks from time-frequency feature coefficients. In the latter layers of the auditory trait convolutional neural network model, the importance of each trait is calculated through fully connected layers, so that music can be better classified [29].The auditory feature convolutional neural network designed in this paper inherits the excellent features of general convolutional neural networks and fully integrates auditory features. Therefore, in time domain analysis, when a specific music feature is identified, it will be transformed into another point in time. The auditory convolutional neural network learns, recognizes, and ignores time node information, and the output results are consistent [30]. It meets the demand that people only care about the musical characteristics of the music and not the time when the musical characteristics appear when analyzing music. Displaying the same function in different frequency bands will have different meanings. It divides the music interval characteristics into different areas according to frequency to prevent the overall core from sharing. It will only be shared internally. We can use the characteristics of frequency bands to distinguish data in multiple frequency bands and further verify based on the results of the data [31]. In summary, the auditory feature convolutional neural network designed in this article can better combine the auditory features and the advantages of ordinary convolutional neural networks. This makes the sound characteristics of neural networks superior to standard neural networks used to automatically classify music.


## 3.2.1. Convolution Operation

Convolution operation is an important analytical operation method in analytic mathematics. Convolution operation is defined as an operation that creates another function through functionsx and w. The function w represents the size of the overlapping area, and the definition formula of convolution in the convolutional neural network is as follows:
(6)st=xt∗wt=∑τ=−∞τ=+∞xτwt−τ.When processing two-dimensional grid data, the specific calculation formula of two-dimensional convolution is as follows:(7)si,j=∑m=0M∑n=0Nwm,nxi+m,j+n+wb.When the input of the function changes, the output also changes. The description of the translational equilateral attribute data can be found in formula (8). The translational equivariant property causes certain functions to move from the input position and cause the same movement in the output.
(8)fgx=gfx.Most of the existing neural networks effectively use sigmoid and tanh functions, so that the network model can be better applied in nonlinear expression capabilities. The mathematical representations of sigmoid and tanh functions are given in(9)Sigmoidx=11+e−x,(10)Tanhx=ex−e−xex+e−x.For the research and development of convolutional neural networks, the current ReLU function has good performance in convolutional neural networks. At the same time, the ReLU function also has good nonlinear expression ability and can well solve the back propagation caused by differentiation [26].The network model widely used in convolutional neural networks guarantees the speed of training and convergence, provides the PReLU function based on the ReLU function, and compares it with ImageNe to show the effectiveness and reliability of the PReLU function. The PReLU function can protect the speed of network convergence by making the output average close to 0. The mathematical expressions of the ReLU and PReLU functions are given in formula (11).
(11)ReLUx=x,ifx>0,0,ifx≤0,(12)PReLUx=x,ifx>0,ax,ifx≤0.The mathematical expression of the SoftPlus function appears in(13)SoftPlusx=log1+ex.The definition expression of the Softmax function is(14)σzj=ezj∑i=1Kezi.The Softmax function can map theK-dimensional feature vector to the K-dimensional probability vector to complete multiple classification tasks commonly used in the last layer of the convolutional neural network.The average error loss function represents the Euclidean distance between the predicted value and the actual value. The mathematical expression function of the mean square error loss is(15)C=12n∑xyx−aLx2.The loss function can not only measure the performance of the network model but also minimize the loss function and carry out the work of forming and improving the network model. At present, the basic idea of many mainstream methods for training convolutional neural networks is backpropagation. It completes the neural network training task by adjusting the parameters to reduce the loss function through the reverse radio wave. Formula (16) of the network model can be adjusted with w and b, so that the parameter n=1; then,
(16)∂C∂w=a−yσ′zx,(17)∂C∂b=a−yσ′z.The loss function of cross entropy is based on the concept of cross section. The cross entropy loss function is defined as follows:(18)C=1n∑xylna+1−yln1−a.Formula (18) in the network model can adjust the w and b parameters so that n=1 is convenient for simple calculation; then,
(19)∂C∂w=xσz−y,∂C∂b=σz−y.Calculate the unbiased estimate of the gradient; the formula is as follows:(20)g^=1n∑i∇θCfxi;θ,yi.


## 3.2.2. Auditory Characteristic Convolutional Neural Network Model

In this article, we designed the auditory feature convolutional neural network based on the core idea of the auditory feature convolutional neural network and the classic AlexNet network model. In 2012, Geoffrey Hinton and his student Alexoff broke the previous record using the AlexNet network model in the ImageNet competition and won by an absolute advantage. The championship makes the convolutional neural network in computer vision occupying the core position [27]. However, the aspect ratio of the Mel frequency or the cepstrum coefficient of the cochlear filter extracted in this article is relatively short, and the cursor input is short, so the AlexNet network model cannot be fully applied. Therefore, this article modifies the first few layers of the AlexNet network model. The first few layers of network convolution processing and pooling operation select the kernel with aspect ratio, which makes it suitable for processing the Mel frequency, cepstrum coefficient, and cochlear filter cepstrum coefficient of the relative aspect ratio extracted from this article [28]. This article also uses the accumulative neural network based on sound capabilities by sharing the frequency characteristics of music. Next, because the convolution in each frequency domain is shared and the convolution in other regions is not shared, the function of learning different music features in different frequency regions is realized and completed, and the convolutional neural network was based on auditory features of network definition.The first layer of the neural network forms the auditory function, through the interaction and accumulation of the three elements of convolution operation, pooling operation and activation function, and extracting higher-level functional tasks from time-frequency feature coefficients. In the latter layers of the auditory trait convolutional neural network model, the importance of each trait is calculated through fully connected layers, so that music can be better classified [29].The auditory feature convolutional neural network designed in this paper inherits the excellent features of general convolutional neural networks and fully integrates auditory features. Therefore, in time domain analysis, when a specific music feature is identified, it will be transformed into another point in time. The auditory convolutional neural network learns, recognizes, and ignores time node information, and the output results are consistent [30]. It meets the demand that people only care about the musical characteristics of the music and not the time when the musical characteristics appear when analyzing music. Displaying the same function in different frequency bands will have different meanings. It divides the music interval characteristics into different areas according to frequency to prevent the overall core from sharing. It will only be shared internally. We can use the characteristics of frequency bands to distinguish data in multiple frequency bands and further verify based on the results of the data [31]. In summary, the auditory feature convolutional neural network designed in this article can better combine the auditory features and the advantages of ordinary convolutional neural networks. This makes the sound characteristics of neural networks superior to standard neural networks used to automatically classify music.


## 3.3. Module Design of Sound Source Array Calibration System

### 3.3.1. Stochastic Gradient Descent Algorithm

Stochastic Gradient Reduction (SGD) and its derived algorithms are the most commonly used optimization algorithms in machine learning, especially in convolutional neural networks; the stochastic steepest descent algorithm is simple in principle, easy to implement, and relatively stable and reliable in performance.Update speed parameterv; the formula is as follows:
(21)v=αv−εg^.Update the parameters; the formula is as follows:(22)θ=θ+v.Update timet, step length s, and subvariable r; the formula is as follows:
(23)t=t+1,s=ρ1s+1−ρ1g^,r=ρ2r+1−ρ2g^⊙g^.Use the formula to correct the first and second deviations:(24)s^=s1−ρ1t,r^=r1−ρ2t.


## 3.3.1. Stochastic Gradient Descent Algorithm

Stochastic Gradient Reduction (SGD) and its derived algorithms are the most commonly used optimization algorithms in machine learning, especially in convolutional neural networks; the stochastic steepest descent algorithm is simple in principle, easy to implement, and relatively stable and reliable in performance.Update speed parameterv; the formula is as follows:
(21)v=αv−εg^.Update the parameters; the formula is as follows:(22)θ=θ+v.Update timet, step length s, and subvariable r; the formula is as follows:
(23)t=t+1,s=ρ1s+1−ρ1g^,r=ρ2r+1−ρ2g^⊙g^.Use the formula to correct the first and second deviations:(24)s^=s1−ρ1t,r^=r1−ρ2t.


## 3.4. Function Analysis of Calibration System

The purpose of the distributed microphone array calibration system designed in this paper is to obtain the mutual calibration result of the distributed microphone array and design these two modules to reduce the mutual calibration error. When a distributed microphone array runs a cross-calibration solution, the array elements in a single microphone array are biased, and there is a resolution limit to the preset position in the mapping plane [32]. At this time, it is necessary to design a self-calibration and repeat mutual calibration module in these two cases, the purpose of which is to improve the accuracy of the coordinate mapping point and use the coordinate mapping point as an input parameter. The mutual calibration solution shows the business process of a complete calibration system, which can provide a high-quality mutual calibration solution, as shown in Figure 2.Figure 2
Functional structure of the calibration system.The functions required to complete the calibration system include three main parts:(1)
The correction system can reduce the deviation of the elements of the microphone array(2)
The calibration system can obtain more accurate calibration element mapping points(3)
The calibration system can provide solutions for mutual calibration of distributed microphone arrays


## 3.5. System Simulation Experiment Design and Result Analysis

After x1, x2, x3, and XN, compared with the entire neural network, the neural network based on sound capabilities no longer widely distributes the core of the convolution but is distributed in the Kexi area, so that the convolution core of different frequency bands can learn each frequency [33]. The characteristics of the domain overcome the uniform processing of all frequency characteristics in the standard convolutional neural network, and there is no difference in frequency band data. Divide the basis of the characteristics of high, medium, and low intervals and different frequency bands, so that other music characteristics can be learned in different frequency bands.The TensorFlow framework of Python language is used to complete the definition of the network, the cross-entropy loss function is selected as the loss function, and the momentum algorithm is selected as the network optimization algorithm. The input data matches the input data of the convolutional neural network [34]. Through the experiment, we can start the network training work through the above related preparations, and use the TensorBoard tool to visualize the learning process. We will combine the CFCC + auditory convolutional neural network to analyze the training course.As the number of training increases, the loss function of the network model gradually decreases, which shows that the optimization algorithm continuously and effectively reduces the loss. It plays a role in adjusting network parameters, activating functions and training network models. In the later stage of training, the loss function of the network model is reduced until it is relatively stable. This shows that the loss function is gradually converging, and finally, the network teaching is completed.As the number of training increases, the accuracy of the training set and validation set in the early and midtraining period will also increase. The network model is continuously optimized, and the performance of the network model is continuously improved. In the later stages of learning, the accuracy of the learning set is still increasing, but the accuracy of the verification set is not increasing, but decreasing. This means that the web template must complete learning to prevent the analysis of the validation set from deteriorating. The turning point when the validation set drops is the best point of the model, and the network model is the best model in this mode.After obtaining the trained model, the validation set will be tested. The size of the validation set is 10000, and the results are shown in Table2.Table 2
The prediction result of the validation set.
Correct markPredictive valueNightclubMovementLearnCoffee shopAccuracySumNightclub2042356475181.81%2496Movement26120276612981.63%2483Learn55122216718885.58%2532Coffee shop43126198212285.25%2489Sum240126312478249083.58%10000Analyzing the results in Table2, songs suitable for nightclubs are more easily classified as songs suitable for sports, and songs suitable for sports are also more easily classified as songs suitable for nightclubs. This is because club songs and sports songs have a faster rhythm. Both types of music are closer to songs suitable for learning, while sports and cafes are more suitable for quiet music, while some are slightly more rhythmic [35]. Music suitable for cafes can be easily classified as sports and learning, and music cafes have a variety of quiet and rhythmic music styles. The results in Table 2 show that music with similar styles is more prone to misclassification.In this experiment, a set of MFCC + auditory convolutional neural network and CFCC + auditory convolutional neural network were tested. The experimental results are shown in Table3.Table 3
Experimental results of convolutional neural network based on auditory characteristics.
Experiment numberFeatureNetwork modelAccuracyStandard deviationTime cost (s)1MFCCAuditory characteristic convolutional neural network78.55%8.0583%0.0652CFCCAuditory characteristic convolutional neural network83.56%6.2606%0.065Summarizing the results of all the above experiments, you can summarize and analyze to obtain the comparative results of the entire experiment, as shown in Table4.Table 4
Comparison of overall experimental results.
Experiment numberFeatureNetwork modelAccuracyStandard deviationTime cost (s)1MFCCAlexNet72.67%8.62%0.0332MFCCResNet76.33%8.37%0.3543CFCCAlexNet74.53%6.87%0.0334CFCCResNet79.74%5.66%0.3535MFCCAuditory characteristic convolutional neural network78.55%8.04%0.0686CFCCAuditory characteristic convolutional neural network83.56%6.24%0.065


## 4. Design and Implementation of Data Collection System in Smart City Perception Network

### 4.1. Design and Implementation of Data Collection Terminal

#### 4.1.1. Hardware Design and Implementation

The data collection terminal is the front end of the smart city data collection system and the network node of the basic data collection network. The network node type is usually set on the terminal node, and it enters a sleep state to reduce power consumption when there is no data collection work. Terminal can collect data on the data server in real time through the LowePAN network, gateway, and Internet response server process [36].In urban systems, there are many types of data to be collected, and the types of sensors and signals required are also different. In order to maximize the reuse speed of the data acquisition terminal, the data acquisition terminal hardware is modular.


#### 4.1.2. Design and Implementation of Data Service Software Application Side

The data service platform includes end user management of data applications, data model management, and data service management. To ensure privacy and privacy, authorized data can only be used by registered legitimate applications of the data service platform and their related access rights.As the number of users of application data increases, integrated management of user information and data permissions is required [37]. Therefore, the data service platform must provide user management services in data applications, create databases and user databases in the applications, and manage user access to basic data and data. When users on the data application side request data services on the data service platform, all users must log in to view user information, and the corresponding data is accessed and assigned according to the permissions of the assigned data. The data cannot be accessed without granted privileges [38]. When a data or application user needs a specific information service of a specific information service platform, register the data service platform, and request access to the atmospheric environment road after the data platform administrator verifies the data and sets the data authority, and the user can obtain information through the information service platform service [39]. In this way, the integrated management of user information and data access on the data application side can improve the data security and privacy of the data service platform.


### 4.2. Design of Smart City Data Service Module

The database of the data acquisition management software adopts the open source database MySQL. Among them, MySQL is a relational database. It is welcomed by many system developers because it is free and open source. Each database has one or more different APIs for creating, accessing, managing, searching, and copying stored data. MySQL also has multiple different APIs. The two main tables in the data collection management software database are the data collection rule table and equipment information. The field definitions in the state table are shown in Table5.Table 5
Data collection rule table.
FieldType of dataDescriptionIDINTData rule numberRULENAMEVARCHAR (32)Rule nameRULEDESCVARCHAR (512)Additional description of the ruleACQTIMESTARTDATETIMEAcquisition start timeACQTIMEENDDATETIMEAcquisition end timeACQTIMEREVERSALTINYINTAcquisition time reversal flagACQCYCLEINTCollection cycleRULERUNSTOPTINYINTRules start and stop signsRULEATTRIBUTEIDINTRule attribute numberDATASTRUCTUREIDINTSubitem data structure numberThe equipment information status is shown in Table6.Table 6
Device information status table.
FieldType of dataDescriptionIDINTDevice IDDEVTYPEVARCHAR (16)Equipment typeNUMPROTOCOLVARCHAR (4)Numbering agreementIPV6INF0VARCHAR (32)IPv6 addressAPPPORTINTApplication port numberLONGITUDEDECIMALEquipment installation longitudeLATITUDEDECIMALEquipment installation latitudeDEVCTRLPROIDINTDevice control protocol number


### 4.3. Strategies for Smart City Planning and Management in the Digital Environment

A smart city integrates cloud computing, the Internet of Things, big data, and spatial geographic information and integrates a new information technology. Focus on mobile, security, and high-speed infrastructure construction; accelerate the promotion of information infrastructure carrying and network service functions; improve network security and emergency assistance service systems; and open up the “arteries” [40] of global economic and social development information. It promotes the wide application of local high-speed transmission, packet forwarding, and capacity routing and switching technologies in the wide area network; expands the service area of the wide area network bandwidth; and improves the delivery level of local traffic. Promote the intelligent conversion and upgrade of the local network, and enhance the service delivery capability of the local network.Based on the local economic and social application needs in many regions, build data centers with regional characteristics, concentrate local core computing and supporting equipment, and strengthen the application and promotion of green, energy-saving, low-carbon, and recycling technologies. New energy-saving technologies such as local natural air cooling will promote green development. We will develop a new service model based on a data center in a certain place and strive to form a regional data processing and backup center in the fields of food safety, environmental protection, and cultural creativity.


## 4.1. Design and Implementation of Data Collection Terminal

### 4.1.1. Hardware Design and Implementation

The data collection terminal is the front end of the smart city data collection system and the network node of the basic data collection network. The network node type is usually set on the terminal node, and it enters a sleep state to reduce power consumption when there is no data collection work. Terminal can collect data on the data server in real time through the LowePAN network, gateway, and Internet response server process [36].In urban systems, there are many types of data to be collected, and the types of sensors and signals required are also different. In order to maximize the reuse speed of the data acquisition terminal, the data acquisition terminal hardware is modular.


### 4.1.2. Design and Implementation of Data Service Software Application Side

The data service platform includes end user management of data applications, data model management, and data service management. To ensure privacy and privacy, authorized data can only be used by registered legitimate applications of the data service platform and their related access rights.As the number of users of application data increases, integrated management of user information and data permissions is required [37]. Therefore, the data service platform must provide user management services in data applications, create databases and user databases in the applications, and manage user access to basic data and data. When users on the data application side request data services on the data service platform, all users must log in to view user information, and the corresponding data is accessed and assigned according to the permissions of the assigned data. The data cannot be accessed without granted privileges [38]. When a data or application user needs a specific information service of a specific information service platform, register the data service platform, and request access to the atmospheric environment road after the data platform administrator verifies the data and sets the data authority, and the user can obtain information through the information service platform service [39]. In this way, the integrated management of user information and data access on the data application side can improve the data security and privacy of the data service platform.


## 4.1.1. Hardware Design and Implementation

The data collection terminal is the front end of the smart city data collection system and the network node of the basic data collection network. The network node type is usually set on the terminal node, and it enters a sleep state to reduce power consumption when there is no data collection work. Terminal can collect data on the data server in real time through the LowePAN network, gateway, and Internet response server process [36].In urban systems, there are many types of data to be collected, and the types of sensors and signals required are also different. In order to maximize the reuse speed of the data acquisition terminal, the data acquisition terminal hardware is modular.


## 4.1.2. Design and Implementation of Data Service Software Application Side

The data service platform includes end user management of data applications, data model management, and data service management. To ensure privacy and privacy, authorized data can only be used by registered legitimate applications of the data service platform and their related access rights.As the number of users of application data increases, integrated management of user information and data permissions is required [37]. Therefore, the data service platform must provide user management services in data applications, create databases and user databases in the applications, and manage user access to basic data and data. When users on the data application side request data services on the data service platform, all users must log in to view user information, and the corresponding data is accessed and assigned according to the permissions of the assigned data. The data cannot be accessed without granted privileges [38]. When a data or application user needs a specific information service of a specific information service platform, register the data service platform, and request access to the atmospheric environment road after the data platform administrator verifies the data and sets the data authority, and the user can obtain information through the information service platform service [39]. In this way, the integrated management of user information and data access on the data application side can improve the data security and privacy of the data service platform.


## 4.2. Design of Smart City Data Service Module

The database of the data acquisition management software adopts the open source database MySQL. Among them, MySQL is a relational database. It is welcomed by many system developers because it is free and open source. Each database has one or more different APIs for creating, accessing, managing, searching, and copying stored data. MySQL also has multiple different APIs. The two main tables in the data collection management software database are the data collection rule table and equipment information. The field definitions in the state table are shown in Table5.Table 5
Data collection rule table.
FieldType of dataDescriptionIDINTData rule numberRULENAMEVARCHAR (32)Rule nameRULEDESCVARCHAR (512)Additional description of the ruleACQTIMESTARTDATETIMEAcquisition start timeACQTIMEENDDATETIMEAcquisition end timeACQTIMEREVERSALTINYINTAcquisition time reversal flagACQCYCLEINTCollection cycleRULERUNSTOPTINYINTRules start and stop signsRULEATTRIBUTEIDINTRule attribute numberDATASTRUCTUREIDINTSubitem data structure numberThe equipment information status is shown in Table6.Table 6
Device information status table.
FieldType of dataDescriptionIDINTDevice IDDEVTYPEVARCHAR (16)Equipment typeNUMPROTOCOLVARCHAR (4)Numbering agreementIPV6INF0VARCHAR (32)IPv6 addressAPPPORTINTApplication port numberLONGITUDEDECIMALEquipment installation longitudeLATITUDEDECIMALEquipment installation latitudeDEVCTRLPROIDINTDevice control protocol number


## 4.3. Strategies for Smart City Planning and Management in the Digital Environment

A smart city integrates cloud computing, the Internet of Things, big data, and spatial geographic information and integrates a new information technology. Focus on mobile, security, and high-speed infrastructure construction; accelerate the promotion of information infrastructure carrying and network service functions; improve network security and emergency assistance service systems; and open up the “arteries” [40] of global economic and social development information. It promotes the wide application of local high-speed transmission, packet forwarding, and capacity routing and switching technologies in the wide area network; expands the service area of the wide area network bandwidth; and improves the delivery level of local traffic. Promote the intelligent conversion and upgrade of the local network, and enhance the service delivery capability of the local network.Based on the local economic and social application needs in many regions, build data centers with regional characteristics, concentrate local core computing and supporting equipment, and strengthen the application and promotion of green, energy-saving, low-carbon, and recycling technologies. New energy-saving technologies such as local natural air cooling will promote green development. We will develop a new service model based on a data center in a certain place and strive to form a regional data processing and backup center in the fields of food safety, environmental protection, and cultural creativity.


## 5. Conclusion

With the rapid development of the Internet, multimedia, and information technology, the amount of City has also reached a rapid growth, and people can choose more city. It classifies a large number of music resources well and establishes an effective city search system, which can accurately and quickly search for the required music according to people’s different hobbies and needs. Traditional music classification methods rely on manual tags to classify city. In the era of big data where the number of city is explosively increasing, it is obviously inefficient and impractical to use manual tags to complete large-scale city classification tasks. Automatic city classification methods are gradually becoming a research hotspot and are widely used. Research related to automatic city classification has important research value, because it is the basis for fast and effective retrieval of city resources and has high requirements for potential applications. The construction project is planned and managed strictly in accordance with the relevant content of the project management, and the time is well controlled. It can not only complete the tasks and objectives on time but also enable the project to be completed within the specified time limit. This paper is based on the theory of enterprise management, combined with artificial neural network technology to study the problem of enterprise planning in the construction of smart city. Future smart city planning can be formulated in combination with the latest research progress in Internet of Things technology and can also be combined with the traceability characteristics of blockchain technology to design a decentralized smart city.



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*Source: 5731473-2022-02-21.xml*