# Data Mining Algorithm for Power User Network Based on Multi-Information Fusion

Authors: Shutong Huang
Journal: International Transactions on Electrical Energy Systems
 (2022)

---

## Abstract

In order to solve the problem of accurately mining the power consumption characteristic data of power users, this paper proposes a research on power user network data mining algorithms based on multi-information fusion. In the past, the mining methods based on neural networks and cure algorithms were affected by noise data, resulting in low mining accuracy. To solve this problem, this paper proposes a user-complex power consumption feature mining method based on the K-means clustering algorithm. In the K-means clustering algorithm, the principle of mining complex power consumption characteristics of users is studied, and the data are cleaned, integrated, and preprocessed by protocol transformation to avoid noise interference. The information entropy principle is used to cluster the matrix to regularize the feature points. According to the complex power consumption characteristics, the power consumption feature points are determined through the cluster, the distance between the clusters is calculated, the power consumption feature information gain is obtained, and the user complex power consumption feature mining is completed. The experimental results show that the mining accuracy of this method is up to 99%, providing users with better quality services.Conclusion. The automatic control performance of the system is good, and the acquisition results are accurate and reliable.


---

## Body

## 1. Introduction

With the explosive growth of power distribution data brought about by the large-scale popularization and application of smart meters and the lean management of the power grid, accurate classification and characteristic analysis of power users have become an important task for power grid companies [1, 2]. Mastering the characteristics of user demand response helps to fully tap the potential of user demand response and improve the implementation efficiency of demand response, which is the basis for formulating a demand-side management scheme and electricity price package. With the development of economy and technology, the types and behaviors of power users are complex and diverse [3]. The obtained data of power users show the characteristics of high-dimensional and massive data, which makes it more difficult to classify users and analyze response characteristics. Therefore, efficient processing of complex user data and accurate analysis of power consumption characteristics are very important [4–6].At present, the research on user response characteristics is mainly divided into two categories: machine rational analysis and nonmachine rational analysis. The mechanism analysis is mainly to analyze and model the relationship between electricity price and other incentive signals and user load in a specific environment [7]. For example, analyze the power consumption characteristics and strategic bidding behavior of power users in the market environment and establish a bilateral game model between power producers and power users [8]. Taking the time-of-use price environment as an example, the self-elasticity and mutual elasticity of users with price changes are analyzed, and the user response model is established [9]. These studies can clearly explain the mechanism of electricity price and other incentive signals on user load and are well adapted to small-scale specific users, but the modeling is complex and not suitable for users with large differences in characteristics and a large number of users [10]. Nonmachine rational analysis analyzes the effect of incentive means such as electricity price on user load through user data mining. Its core technology is clustering algorithms and data fitting, which is mainly divided into two steps: ① classify the power consumption characteristics of users through a clustering algorithm and classify the users with the same power consumption characteristics; ② use historical data to model the response characteristics of each type of user requirement.


## 2. Literature Review

With the rapid development of the economy, science, and technology, users’ electricity consumption behavior will be more diversified, and the dimension of electricity consumption data will be higher. Clustering faces not only the problem of increasing data volume but also the problem of the high-dimension of electricity consumption data. In order to solve this problem, the nonmechanism analysis method is more concerned. Shao, Z., and others used the fuzzy c-means (FCM) clustering method to cluster power users. This method is a soft clustering algorithm, which overcomes the classification shortcomings of hard clustering. However, this method is sensitive to the initial clustering center and needs to manually determine the cluster number, so it is easy to fall into the local optimal solution [11]. Geetha, S., and others analyzed the power consumption data by using the agglomerative hierarchical clustering method, which does not need to determine the classification number, but the clustering quality is limited for the consolidation of complex data types [12]. Zhou, Y., and others considered the random characteristics of user load, proposed a user classification and behavior modeling method based on the Fourier transform method but ignored more nonprincipal factor information [13]. A self-organizing map (SOM) clustering algorithm and deep learning method are used to classify power consumption patterns of power users. These two algorithms belong to machine learning clustering methods, which can effectively process high-dimensional data but are difficult to adapt to large-scale data. HaDy, A. K., and others used piecewise linear regression to classify and model user demand responses. Although this method is simple to operate, the relationship between incentive and user power consumption is not simply linear, which makes the model less accurate [14]. Zhou, J., and others constructed an analysis model of user response behavior based on long-term and short-term memory networks and applied the equal gradient iterative learning method to obtain appropriate incentive schemes [15]. The above methods try to cluster the data after dimensionality reduction and can solve the problem of clustering some high-dimensional data, but it is difficult to deal with high-dimensional nonlinear data in the process of dimensionality reduction. Technologies such as the automatic encoder (AE) can effectively solve the problem of “dimension disaster” in the face of high-dimensional data and have been applied in engineering [16, 17].With the increasing demand of power consumption in recent years, there are security problems in power user data [18]. Mining the complex power consumption characteristics of users plays an important role in improving the operation safety of the power system. When the power system is affected by various factors, there are certain power consumption characteristic data [19]. However, these data are mixed with normal power consumption characteristic data, and the mining of normal power consumption characteristic data is subject to a lot of interference, resulting in low mining accuracy. In the past, the mining method based on a neural network was used to generate mining feature data by preprocessing the data. The mining method based on a cure algorithm is adopted to randomly distribute the normal data and power consumption characteristic data in the same proportion and classify the data characteristics by setting different parameters [20]. Both methods can be easily broken down by noise data and have low mining risk. Given the disadvantages of these two methods, we propose a K-means-based and algorithm-based group algorithm to eliminate the complex characteristics of consumer electronics. K-means is the algorithm group that has been taught to learn in detail about the complex characteristics of electronic users.


## 3. Method

### 3.1. K-Means Clustering Algorithm

The K-means clustering algorithm takes minimizing the objective function as the optimization objective, iterates continuously, and selects K-means initial condensing points to meet the local optimal clustering effect. The combination is selected according to different initial condensing points. In the process of clustering using a k-means algorithm, different initial condensing points need to be selected many times, and then a clustering operation is carried out. Before clustering with a k-means algorithm, data preprocessing is required. Assume that the data first used as a K-cluster algorithm consists ofm data examples and k-data properties. At this point, k > 0 is an integer. The whole K-key set includes changing the m data examples for the numbers of squares of the middle group. The formula is(1)F=∑i=1KFi=∑i=1K∑j=1mαijAj−Bi2,In formula (1), F represents the objective function of the sum of squares of the distance between cluster centers, Aj represents J data characteristic attributes, Bi represents i feature clustering centers, and αij represents the weight coefficient.During clustering, it is necessary to obtain the actual value of the target function by the number of squares at a distance from the center of the group. Figure1 shows the application process when using the K-mean algorithm group to exclude the complex characteristics of electronic users.Figure 1
Application process of the K-means clustering algorithm.As the application process is shown in Figure1, the dataset is first determined, then the average values are randomly selected, and the K-mean group algorithm is used to calculate the value of F. If the value of F changes continuously during iteration, it indicates that the agglomeration does not reach the observed mass. At this point, the subgroup needs to be modified, and the process needs to be repeated.The calculation formula of cluster center (2) is as follows:(2)Oij=Aj−Bi−1,The formula of the equation (2) mainly calculates the distance from the sample data to the feature cluster center.Based on the above given model, the steps to use the K-mean grouping algorithm are as follows:(1)
take point K at the first condensation point first;(2)
calculate the distance from the data structure to the location of the special groups;(3)
K gives all the points to the group structure of K objects according to the principle of their agglomeration;(4)
count the centroids of K-specific sites in the group;(5)
repeat steps (2), (3), and (4) until the centroid needs to be repaired;(6)
we use K-agglomeration.


### 3.2. Mining of Complex Power Consumption Characteristics of Users

The data of the characteristics of the power supply are combined with the total power consumption of the customer, and the samples are sampled, completed, and sent to the office for data collection to measure the energy consumption of consumers.Figure2 shows the mining principles that illustrate the combined power consumption of consumers.Figure 2
Principle of user complex power consumption feature mining.According to the above-mentioned mining principles, the complex power consumption characteristics of users are mined with the support of the K-means clustering algorithm.


#### 3.2.1. Data Preprocessing

The mining data of complex power consumption characteristics of users are affected by external noise interference in the process of collection and transmission, and the mining quality is low. Therefore, it is necessary to preprocess them first.(1) Data cleaning. Fill in gaps in consumer electronics, make data noise, eliminate cluster differences, and avoid unreliable data. In the missing data, the mean and intermediate data can be used for interpolation, and the value of the behavior close to the missing pattern can be found in the interpolation log. If you use constants to fill in missing values, you need to analyze the data attributes in advance to clean the data based on them.(2) Data integration. During data integration, data quality should be improved first to reduce redundant data. The metadata method is used to avoid possible identification problems in the data set. In the case of data redundancies, the correlation analysis method is used to ensure the presence of redundant data and stored in different units of measurement.(3) Document protocol. Consider the difference between it all, show the old file in a small space, and decide whether it is possible to create the old file after compressing the file to show the file large.(4) File transfer. The files are divided into separate sections to remove clutter in the file, convert the next batch into subdivisions, and group the files.


#### 3.2.2. Power Consumption Feature Mining Based on the K-Means Clustering Algorithm

According to the periodicity of users’ complex power consumption characteristics, real-time analysis will be carried out based on the users’ power consumption data attributes to prevent the users’ complex power consumption data from changing and affecting the mining results. Thex targets contained in the user’s complex power consumption characteristics are randomly sampled and divided into n numbers to ensure that the user’s complex power consumption characteristics are randomly sampled and can be identified with the data in each divided cluster. The regions in any segmented cluster are clustered into x/hn cluster blocks, where h represents the complex power consumption characteristics of users after segmentation for the first time.According to the complex power consumption characteristics of each user, the power consumption characteristic points are determined through the change of information in the process of clustering. According to the principle of the minimum example method, the cluster formed by the user’s complex power consumption characteristic data set is normalized to obtain the first clustering structure, which is merged with the information entropy decision. The characterization results of the distance between the two clusters are(3)dminx1,x2=minh∈xv,h2∈x2h−hk,In formula (3), x1, and x2, respectively, represent two cluster sets; h and hk, represent the complex power consumption characteristics of users in the cluster set.If we are looking for hk data points for the complex characteristics of consumer electronics and the combination of x objectives, it is contained in the characteristics of the consumer devices. If we are looking for the strength, we count the data content and select the lowest data based on the hk data. . The combination of x targets the difficult characteristics of the effort of the consumer. At this time, the amount of information carried by the point of convergence is the largest, and a new cluster of user characteristic data is formed through this process. The user’s complex power consumption characteristic information gain z of the two cluster sets is(4)zxhΔ=z1xh+xhh−z1xh,In formula (4), z1 represents the feature information gain of a cluster set. The clustering result represented by the maximum target point of the feature information gain z of the two clustering sets is taken as the clustering result after normalization.After iterating the clustering feature points in any segmentation, the original clustering data in each subregion are reclustered. After calculation, a new cluster centroid is obtained, and the cluster number is marked in turn. Finally, the required number of regulatory clusters is achieved.In the process of clustering, the processing of user characteristic data should be measured according to the clustering growth rate of user complex power consumption characteristic data. Using this method, the error data can be eliminated on time, and the result of feature data mining is more accurate.


### 3.3. Experiment

Using the Linux operating system, 200 power consumption data points of a city are selected as experimental objects to verify the effectiveness of the user-complex power consumption feature mining method based on the K-means clustering algorithm.


#### 3.3.1. Mining of Electricity Consumption Characteristics of Daytime Users

For the situation of high-power consumption by users in the daytime, it is necessary to analyze the load characteristics of users, and the results are shown in Figure3.Figure 3
Power load characteristics of users in daytime.It can be seen from Figure3 that when the time is 6 : 00/12 : 00/18 : 00, it is the peak period of user power consumption, and the highest charge is 244 c. When the time is 8 : 00–10 : 00/13 : 00–17 : 00, the user uses less power, and the load is low. According to this feature, the mining accuracy of the neural network mining method, the mining method based on the CURE algorithm, and the proposed method are compared and analyzed. The results are shown in Table 1.Table 1
Accuracy Analysis of daytime feature mining by three methods.
Time/hNeural network mining method (%)Mining method based on the CURE algorithm (%)Proposed method (%)06.0050829808.0042749710.0038689712.0035609814.0035659716.0031609518.00306096


## 3.1. K-Means Clustering Algorithm

The K-means clustering algorithm takes minimizing the objective function as the optimization objective, iterates continuously, and selects K-means initial condensing points to meet the local optimal clustering effect. The combination is selected according to different initial condensing points. In the process of clustering using a k-means algorithm, different initial condensing points need to be selected many times, and then a clustering operation is carried out. Before clustering with a k-means algorithm, data preprocessing is required. Assume that the data first used as a K-cluster algorithm consists ofm data examples and k-data properties. At this point, k > 0 is an integer. The whole K-key set includes changing the m data examples for the numbers of squares of the middle group. The formula is(1)F=∑i=1KFi=∑i=1K∑j=1mαijAj−Bi2,In formula (1), F represents the objective function of the sum of squares of the distance between cluster centers, Aj represents J data characteristic attributes, Bi represents i feature clustering centers, and αij represents the weight coefficient.During clustering, it is necessary to obtain the actual value of the target function by the number of squares at a distance from the center of the group. Figure1 shows the application process when using the K-mean algorithm group to exclude the complex characteristics of electronic users.Figure 1
Application process of the K-means clustering algorithm.As the application process is shown in Figure1, the dataset is first determined, then the average values are randomly selected, and the K-mean group algorithm is used to calculate the value of F. If the value of F changes continuously during iteration, it indicates that the agglomeration does not reach the observed mass. At this point, the subgroup needs to be modified, and the process needs to be repeated.The calculation formula of cluster center (2) is as follows:(2)Oij=Aj−Bi−1,The formula of the equation (2) mainly calculates the distance from the sample data to the feature cluster center.Based on the above given model, the steps to use the K-mean grouping algorithm are as follows:(1)
take point K at the first condensation point first;(2)
calculate the distance from the data structure to the location of the special groups;(3)
K gives all the points to the group structure of K objects according to the principle of their agglomeration;(4)
count the centroids of K-specific sites in the group;(5)
repeat steps (2), (3), and (4) until the centroid needs to be repaired;(6)
we use K-agglomeration.


## 3.2. Mining of Complex Power Consumption Characteristics of Users

The data of the characteristics of the power supply are combined with the total power consumption of the customer, and the samples are sampled, completed, and sent to the office for data collection to measure the energy consumption of consumers.Figure2 shows the mining principles that illustrate the combined power consumption of consumers.Figure 2
Principle of user complex power consumption feature mining.According to the above-mentioned mining principles, the complex power consumption characteristics of users are mined with the support of the K-means clustering algorithm.


### 3.2.1. Data Preprocessing

The mining data of complex power consumption characteristics of users are affected by external noise interference in the process of collection and transmission, and the mining quality is low. Therefore, it is necessary to preprocess them first.(1) Data cleaning. Fill in gaps in consumer electronics, make data noise, eliminate cluster differences, and avoid unreliable data. In the missing data, the mean and intermediate data can be used for interpolation, and the value of the behavior close to the missing pattern can be found in the interpolation log. If you use constants to fill in missing values, you need to analyze the data attributes in advance to clean the data based on them.(2) Data integration. During data integration, data quality should be improved first to reduce redundant data. The metadata method is used to avoid possible identification problems in the data set. In the case of data redundancies, the correlation analysis method is used to ensure the presence of redundant data and stored in different units of measurement.(3) Document protocol. Consider the difference between it all, show the old file in a small space, and decide whether it is possible to create the old file after compressing the file to show the file large.(4) File transfer. The files are divided into separate sections to remove clutter in the file, convert the next batch into subdivisions, and group the files.


### 3.2.2. Power Consumption Feature Mining Based on the K-Means Clustering Algorithm

According to the periodicity of users’ complex power consumption characteristics, real-time analysis will be carried out based on the users’ power consumption data attributes to prevent the users’ complex power consumption data from changing and affecting the mining results. Thex targets contained in the user’s complex power consumption characteristics are randomly sampled and divided into n numbers to ensure that the user’s complex power consumption characteristics are randomly sampled and can be identified with the data in each divided cluster. The regions in any segmented cluster are clustered into x/hn cluster blocks, where h represents the complex power consumption characteristics of users after segmentation for the first time.According to the complex power consumption characteristics of each user, the power consumption characteristic points are determined through the change of information in the process of clustering. According to the principle of the minimum example method, the cluster formed by the user’s complex power consumption characteristic data set is normalized to obtain the first clustering structure, which is merged with the information entropy decision. The characterization results of the distance between the two clusters are(3)dminx1,x2=minh∈xv,h2∈x2h−hk,In formula (3), x1, and x2, respectively, represent two cluster sets; h and hk, represent the complex power consumption characteristics of users in the cluster set.If we are looking for hk data points for the complex characteristics of consumer electronics and the combination of x objectives, it is contained in the characteristics of the consumer devices. If we are looking for the strength, we count the data content and select the lowest data based on the hk data. . The combination of x targets the difficult characteristics of the effort of the consumer. At this time, the amount of information carried by the point of convergence is the largest, and a new cluster of user characteristic data is formed through this process. The user’s complex power consumption characteristic information gain z of the two cluster sets is(4)zxhΔ=z1xh+xhh−z1xh,In formula (4), z1 represents the feature information gain of a cluster set. The clustering result represented by the maximum target point of the feature information gain z of the two clustering sets is taken as the clustering result after normalization.After iterating the clustering feature points in any segmentation, the original clustering data in each subregion are reclustered. After calculation, a new cluster centroid is obtained, and the cluster number is marked in turn. Finally, the required number of regulatory clusters is achieved.In the process of clustering, the processing of user characteristic data should be measured according to the clustering growth rate of user complex power consumption characteristic data. Using this method, the error data can be eliminated on time, and the result of feature data mining is more accurate.


## 3.2.1. Data Preprocessing

The mining data of complex power consumption characteristics of users are affected by external noise interference in the process of collection and transmission, and the mining quality is low. Therefore, it is necessary to preprocess them first.(1) Data cleaning. Fill in gaps in consumer electronics, make data noise, eliminate cluster differences, and avoid unreliable data. In the missing data, the mean and intermediate data can be used for interpolation, and the value of the behavior close to the missing pattern can be found in the interpolation log. If you use constants to fill in missing values, you need to analyze the data attributes in advance to clean the data based on them.(2) Data integration. During data integration, data quality should be improved first to reduce redundant data. The metadata method is used to avoid possible identification problems in the data set. In the case of data redundancies, the correlation analysis method is used to ensure the presence of redundant data and stored in different units of measurement.(3) Document protocol. Consider the difference between it all, show the old file in a small space, and decide whether it is possible to create the old file after compressing the file to show the file large.(4) File transfer. The files are divided into separate sections to remove clutter in the file, convert the next batch into subdivisions, and group the files.


## 3.2.2. Power Consumption Feature Mining Based on the K-Means Clustering Algorithm

According to the periodicity of users’ complex power consumption characteristics, real-time analysis will be carried out based on the users’ power consumption data attributes to prevent the users’ complex power consumption data from changing and affecting the mining results. Thex targets contained in the user’s complex power consumption characteristics are randomly sampled and divided into n numbers to ensure that the user’s complex power consumption characteristics are randomly sampled and can be identified with the data in each divided cluster. The regions in any segmented cluster are clustered into x/hn cluster blocks, where h represents the complex power consumption characteristics of users after segmentation for the first time.According to the complex power consumption characteristics of each user, the power consumption characteristic points are determined through the change of information in the process of clustering. According to the principle of the minimum example method, the cluster formed by the user’s complex power consumption characteristic data set is normalized to obtain the first clustering structure, which is merged with the information entropy decision. The characterization results of the distance between the two clusters are(3)dminx1,x2=minh∈xv,h2∈x2h−hk,In formula (3), x1, and x2, respectively, represent two cluster sets; h and hk, represent the complex power consumption characteristics of users in the cluster set.If we are looking for hk data points for the complex characteristics of consumer electronics and the combination of x objectives, it is contained in the characteristics of the consumer devices. If we are looking for the strength, we count the data content and select the lowest data based on the hk data. . The combination of x targets the difficult characteristics of the effort of the consumer. At this time, the amount of information carried by the point of convergence is the largest, and a new cluster of user characteristic data is formed through this process. The user’s complex power consumption characteristic information gain z of the two cluster sets is(4)zxhΔ=z1xh+xhh−z1xh,In formula (4), z1 represents the feature information gain of a cluster set. The clustering result represented by the maximum target point of the feature information gain z of the two clustering sets is taken as the clustering result after normalization.After iterating the clustering feature points in any segmentation, the original clustering data in each subregion are reclustered. After calculation, a new cluster centroid is obtained, and the cluster number is marked in turn. Finally, the required number of regulatory clusters is achieved.In the process of clustering, the processing of user characteristic data should be measured according to the clustering growth rate of user complex power consumption characteristic data. Using this method, the error data can be eliminated on time, and the result of feature data mining is more accurate.


## 3.3. Experiment

Using the Linux operating system, 200 power consumption data points of a city are selected as experimental objects to verify the effectiveness of the user-complex power consumption feature mining method based on the K-means clustering algorithm.


### 3.3.1. Mining of Electricity Consumption Characteristics of Daytime Users

For the situation of high-power consumption by users in the daytime, it is necessary to analyze the load characteristics of users, and the results are shown in Figure3.Figure 3
Power load characteristics of users in daytime.It can be seen from Figure3 that when the time is 6 : 00/12 : 00/18 : 00, it is the peak period of user power consumption, and the highest charge is 244 c. When the time is 8 : 00–10 : 00/13 : 00–17 : 00, the user uses less power, and the load is low. According to this feature, the mining accuracy of the neural network mining method, the mining method based on the CURE algorithm, and the proposed method are compared and analyzed. The results are shown in Table 1.Table 1
Accuracy Analysis of daytime feature mining by three methods.
Time/hNeural network mining method (%)Mining method based on the CURE algorithm (%)Proposed method (%)06.0050829808.0042749710.0038689712.0035609814.0035659716.0031609518.00306096


## 3.3.1. Mining of Electricity Consumption Characteristics of Daytime Users

For the situation of high-power consumption by users in the daytime, it is necessary to analyze the load characteristics of users, and the results are shown in Figure3.Figure 3
Power load characteristics of users in daytime.It can be seen from Figure3 that when the time is 6 : 00/12 : 00/18 : 00, it is the peak period of user power consumption, and the highest charge is 244 c. When the time is 8 : 00–10 : 00/13 : 00–17 : 00, the user uses less power, and the load is low. According to this feature, the mining accuracy of the neural network mining method, the mining method based on the CURE algorithm, and the proposed method are compared and analyzed. The results are shown in Table 1.Table 1
Accuracy Analysis of daytime feature mining by three methods.
Time/hNeural network mining method (%)Mining method based on the CURE algorithm (%)Proposed method (%)06.0050829808.0042749710.0038689712.0035609814.0035659716.0031609518.00306096


## 4. Results and Discussion

For the case of high-power consumption by users at night, it is necessary to analyze the load characteristics of users, and the results are shown in Figure4.Figure 4
Power load characteristics of users at night.It can be seen from Figure4 that when the time is 18 : 00/06 : 00, it is the peak period of users' power consumption, while when the time is 20.00/22.00, users' power consumption is relatively large. According to this feature, the mining accuracy of the neural network mining method, the mining method based on the CURE algorithm, and the proposed method are compared and analyzed. The results are shown in Table 1 and Table 2.Table 2
Accuracy analysis of night feature mining by three methods.
Time/hNeural network mining method (%)Mining method based on the CURE algorithm (%)Methods proposed in this paper (%)18.0050809920.0052759822.0058719824.0055709802.0056759804.0051709706.00527097It can be seen from Table1 that the mining accuracy of the proposed K-means clustering method is high, up to 98%, while the mining accuracy of the mining method based on the CURE algorithm remains above 60%, and the mining accuracy of the neural network mining method is always lower than 50%. It can be seen that the mining method based on the K-means clustering algorithm has high accuracy for mining the complex electricity consumption characteristics of daytime users.It can be seen from Table2 that the accuracy of the three methods for feature mining at night is higher than that during the day. The mining method based on the K-means clustering algorithm has a mining accuracy of up to 99%. Therefore, the mining method based on the K-means clustering algorithm has high accuracy in mining the complex power consumption characteristics of night users.


## 5. Conclusion

This article presents an algorithm for deleting data from electronic devices based on multiple data connections. K-meansthat a group algorithm is used for real-time data analysis and then for impact research to study the characteristics of electronic users. The characteristics of consumers are extracted effectively using the difference of all the different power consumption of different consumers. Selecting the K value and the initialization of the K-means that the group algorithm will provide a new way to eliminate the complex characteristics of consumer electronics and increase energy efficiency. The example shows that the subtraction function using k-means that the algorithm group to group the complex characteristics of the consumer can be strengthened and can eliminate the characteristics difficult on consumer electronics with high risk.



---

*Source: 2337460-2022-09-06.xml*