Traffic police law enforcement daily supervision system

Abstract

The invention relates to the technical field of internet and supervision, in particular to a traffic police law enforcement daily supervision system, which comprises the following modules: a data acquisition module, which mainly aims at acquiring various data in a traffic police law enforcement process in real time; according to the scheme, by integrating and analyzing information from a plurality of data sources and utilizing advanced data analysis technologies such as abnormal behavior recognition and trend prediction, potential law enforcement risks can be predicted and warned, law enforcement officers are helped to make preparations in advance, errors and omissions in the law enforcement process are reduced, and meanwhile, the law enforcement efficiency is improved. Through powerful data management and report generation functions, a law enforcement report can be automatically generated, detailed data support is provided for law enforcement supervision and management, the implementation of the functions not only improves the law enforcement standardization and efficiency, but also enhances the trust and satisfaction of the public to law enforcement work, and the law enforcement management and management efficiency is improved. And a powerful support is provided for constructing a fair and efficient law enforcement environment.

Description

Technical Field

[0001] The present invention relates to the field of Internet and supervision technology, and in particular to a daily supervision system for traffic police law enforcement. Background Art

[0002] As urban traffic becomes increasingly busy, the requirements for police enforcement are also increasing. The behavior of on-duty police officers in the process of performing their duties is increasingly attracting public attention. However, the current supervision of on-duty police enforcement behavior mainly relies on manual spot checks and post-event reviews. This method may be inefficient and difficult to fully cover. It is impossible to detect and correct irregular law enforcement behavior in real time, which may also have a certain impact on improving the level of police enforcement and public satisfaction.

[0003] Therefore, inventors in this field have proposed a daily supervision system for traffic police law enforcement. The system integrates and analyzes the surveillance video data of each intersection in combination with the video data of law enforcement recorders, and uses advanced data analysis technologies, such as abnormal behavior recognition and trend prediction, to predict and warn of potential law enforcement risks, help law enforcement personnel prepare in advance, and reduce errors and omissions in the law enforcement process. Summary of the invention

[0004] Technical problems to be solved: The supervision method of law enforcement behavior of on-duty police officers mainly relies on manual spot checks and post-event reviews, which cannot detect and correct irregular law enforcement behaviors in real time, and may also have a certain impact on improving the law enforcement level of police officers and public satisfaction.

[0005] In view of the deficiencies in the prior art, the present invention provides a daily supervision system for traffic police law enforcement, thereby solving the technical problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A traffic police law enforcement daily supervision system, the system includes the following modules: Data collection module: The main goal of data collection is to obtain various data in the process of traffic police enforcement in real time, including video, audio, text and sensor data, so as to carry out subsequent data processing, analysis and real-time monitoring; Data processing module: The data processing module is the core of the supervision system and is responsible for extracting, cleaning, labeling, storing and analyzing information from the collected raw data. This module ensures the quality and availability of data and provides accurate data support for intelligent analysis and real-time monitoring. Intelligent analysis module, which is the core part of the system, uses abnormal behavior recognition algorithms to analyze processed data to identify patterns, predict trends, and provide support for decision-making. This module includes abnormal behavior recognition, trend prediction, and pattern recognition functions; Abnormal behavior identification includes abnormal behavior intervals by establishing behavior intervals. When a behavior is in the abnormal behavior interval, it is determined to be abnormal. The overall behavior pattern of law enforcement personnel is analyzed through machine learning models. If significant deviations from normal behavior patterns are found, it may be determined to be abnormal. The time series of law enforcement behavior is analyzed. If certain behaviors are found to be abnormally frequent or rare in a specific time period, they may be determined to be abnormal. Application module: The application module is the interface for direct interaction between the system and the user. It provides real-time data analysis, synchronous monitoring, and real-time early warning functions, and realizes the closure of the supervision process, including pre-event prediction, in-event processing, and post-event analysis. The display module is the user interface of the system, which is responsible for presenting the processed data to the user in an intuitive and easy-to-understand way. It includes a large screen terminal, a PC terminal and an APP terminal, and provides real-time data analysis, synchronous monitoring and real-time warning functions. In a possible implementation, the abnormal behavior identification algorithm formula is as follows: Compute statistical features: Mean ; variance ; Standard Deviation ; Skewness ; Setting the threshold: Set thresholds for mean, variance, and skewness to distinguish normal from abnormal behavior. These thresholds can be determined through expert experience or historical data. Compare eigenvalues: ; For each statistical feature, if ,in is the set threshold (usually greater than 1), then the feature value is considered abnormal. Beneficial effects compared with the prior art: In this solution, comprehensive monitoring of law enforcement activities is achieved by integrating and analyzing information from multiple data sources, such as law enforcement recorders, road monitoring, PDT radios, etc. Advanced data analysis technologies, such as abnormal behavior recognition and trend prediction, can predict and warn of potential law enforcement risks, help law enforcement personnel prepare in advance, and reduce errors and omissions in the law enforcement process. Through in-depth analysis of a large amount of law enforcement data, patterns and trends in law enforcement can be identified, providing scientific decision-making support for traffic management departments. At the same time, through powerful data management and report generation functions, law enforcement reports can be automatically generated to provide detailed data support for law enforcement supervision and management. The realization of these functions not only improves the standardization and efficiency of law enforcement, but also enhances the public's trust and satisfaction with law enforcement, and provides strong support for building a fair and efficient law enforcement environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings.

[0008] Figure 1 The framework diagram of a daily supervision system for traffic police law enforcement. DETAILED DESCRIPTION

[0009] Preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the present invention can also be implemented in various forms, so the present invention is not limited to the embodiments described below. The technical solution in the embodiment of the present application is to solve the problems of the above-mentioned background technology, and the overall idea is as follows: Example:

[0010] Please refer to Figure 1 As shown, this embodiment introduces a traffic police law enforcement daily supervision system, including the following modules: 1. Data acquisition module. The data acquisition module is the core component of the law enforcement supervision system. Its main goal is to obtain various types of data in the process of traffic police law enforcement in real time, including video, audio, text and sensor data, etc., so as to carry out subsequent data processing, analysis and real-time monitoring. In order to achieve this goal, through the joint public security network database, multiple different data sources are collected, such as the six-in-one platform, duty system, law enforcement recorder, alcohol tester, PDT handheld device, etc.

[0011] 1.1 Law enforcement recorder collection: Law enforcement recorders are key equipment for recording the law enforcement process. We need to ensure that law enforcement recorders can clearly record the words and deeds of law enforcement officers in real time, including their behavior, language, and interactions with the public. These high-definition video materials will provide important visual information for subsequent behavior analysis; 1.2 Video surveillance system acquisition: Through high-definition cameras covering law enforcement areas, such as traffic intersections, accident scenes, law enforcement stations, etc., video data is compressed to a minimum through video encoding, while ensuring the real-time and clarity of transmission. The real-time transmitted video data will be sent to the monitoring center for real-time monitoring and analysis by law enforcement personnel and supervisors; 1.3 Alcohol tester collection: The alcohol tester is an important device for judging whether law enforcement officers are suitable for law enforcement tasks. Through Bluetooth or wireless modules, the test results of the alcohol tester are transmitted to the data collection system in real time. In this way, we can ensure that all law enforcement officers have undergone alcohol tests before performing tasks to ensure the legality and rationality of law enforcement actions; 1.4 Duty system collection: The duty system records the law enforcement plans, task allocation and execution status of law enforcement officers. These data are crucial for monitoring the law enforcement behavior of law enforcement officers. I extract this information from the duty system and integrate it into the data collection module to conduct a comprehensive law enforcement behavior analysis; 1.5 Six-in-one platform collection: The six-in-one platform is an important data source in the public security network. It integrates a variety of law enforcement-related information. By connecting to the six-in-one platform in the public security network, we can obtain comprehensive law enforcement-related data, such as the identity information of law enforcement personnel, the driving trajectory of law enforcement vehicles, and detailed records of law enforcement events. These data will provide comprehensive background information for our law enforcement supervision system; 1.6 Other sensors and equipment collection: In addition to the above equipment, we can also use other sensors and equipment to collect the location and status information of law enforcement personnel. For example, PDT handheld radios can provide the location information of law enforcement personnel, and electronic tags can record the status information of law enforcement personnel, such as whether they are on duty or performing tasks. These data will help us monitor the law enforcement behavior of law enforcement personnel more accurately.

[0012] 2. Data processing module: The data processing module is the core of the supervision system. It is responsible for extracting, cleaning, labeling, storing and analyzing information from the collected raw data. This module ensures the quality and availability of data and provides accurate data support for intelligent analysis and real-time monitoring. 2.1 Data cleaning unit: Identify and remove incomplete, duplicate or erroneous data records, remove useless information such as blank fields, outliers or illogical data, convert data from different sources and formats into a unified format for easy processing and analysis, and apply filtering algorithms to remove noise from the data to improve data quality; 2.2 Data annotation unit: annotate key frames in law enforcement recorders and video streams, mark the behavior, location and other important information of law enforcement personnel; perform speech recognition and annotation on audio data during law enforcement, extract key conversations and instructions, and perform keyword extraction and classification on law enforcement records, reports and other text data; 2.3 Data Storage Unit: Use relational database management system (RDBMS) or NoSQL database, and select the appropriate database according to the data type and query requirements; implement regular backup strategies to ensure data security and recoverability; design disaster recovery plans to deal with possible data loss events; implement access control to ensure that only authorized users can access sensitive data, and use encryption technology to protect stored data and prevent unauthorized access; 3. Intelligent analysis module: The intelligent analysis module is the core part of the system. It uses big data analysis and artificial intelligence technology to analyze the processed data to identify patterns, predict trends, and provide support for decision-making. This module includes functions such as abnormal behavior identification, trend prediction, and pattern recognition; 3.1 Model development: Using machine learning and deep learning technologies, develop algorithmic models for identifying irregular law enforcement behaviors, including image recognition models for analyzing video data, speech recognition models for analyzing audio data, and natural language processing models for analyzing text data; 3.2 Data fusion: Fusion of data from different data sources (e.g., video, audio, text, etc.) to provide a comprehensive analytical perspective; 3.3 Feature extraction: Extract key features from the fused data, which are crucial for identifying irregular law enforcement behaviors; 3.4 Model training and optimization: Use labeled training data sets to train the model, improve the accuracy of the model through iterative optimization, and apply cross-validation and hyperparameter adjustment techniques to optimize model performance; 3.5 Anomaly detection: Establishing a behavior interval includes an abnormal behavior interval. When a behavior is in the abnormal behavior interval, it is determined to be abnormal. The details are as follows: 3.5.1 Law enforcement camera data Normal behavior range: The video from the body camera shows that the law enforcement officer always wears a police hat when performing his duties and uses the body camera correctly when necessary; Abnormal behavior range: The video of the body camera shows that the law enforcement officer is not wearing a police hat when performing duties, or does not turn on or wear the body camera when required; 3.5.2 Video surveillance system data Normal behavior range: The surveillance video shows that the law enforcement officers are dressed properly when performing their duties, such as wearing reflective vests and shoulder lights, and follow the prescribed law enforcement procedures; Abnormal behavior range: surveillance video shows that law enforcement officers are not dressed in accordance with regulations when performing their duties, or fail to follow the prescribed law enforcement procedures; 3.5.3 Alcohol Tester Data Normal behavior range: The test result of the breathalyser is lower than the legal standard, for example, for a driver, the alcohol concentration is less than 0.08% BAC; Abnormal behavior range: The test result of the breathalyser exceeds the legal standard, for example, for a driver, the alcohol concentration exceeds 0.08% BAC; 3.5.4 Service System Data Normal behavior range: The task allocation and execution records in the duty system show that law enforcement officers perform tasks according to plans and regulations; Abnormal behavior range: The task allocation and execution records in the duty system show that law enforcement officers did not perform tasks in accordance with regulations, or there were obvious omissions in the execution of tasks; 3.5.5 Six-in-one platform data Normal behavior range: The data in the six-in-one platform is consistent with other data sources and has no abnormalities, such as law enforcement records, case handling records, etc. Abnormal behavior range: The data in the six-in-one platform is obviously inconsistent with other data sources, such as missing law enforcement records, errors in case handling records, etc. 3.5.6 Sensor and device data Normal behavior range: The location information of law enforcement personnel displayed by the PDT hand-held radio or other positioning equipment is stable and consistent with the actual path of law enforcement activities; Abnormal behavior interval: The PDT handheld radio or other positioning equipment shows abnormal location information of law enforcement officers, such as standing still for a long time, or appearing in a place where they should not be; 3.5.7 Comprehensive judgment: The overall behavior pattern of law enforcement personnel is analyzed through machine learning models. If significant deviations from normal behavior patterns are found, they may be judged as abnormal. The time series of law enforcement behavior is analyzed. If certain behaviors are found to be abnormally frequent or rare in a specific time period, they may be judged as abnormal. In summary, a statistical feature anomaly detection algorithm is used to detect abnormal behavior. The algorithm calculates the statistical features (such as mean, variance, skewness, etc.) of individual behavior sequences and compares them with known normal behavior features to determine whether the behavior is abnormal; enter: : Behavioral feature sequence, where It is The behavior characteristic value at a moment, Refers to the law enforcement process within a period of time. Refers to a law enforcement segment during this period of time, which is presented through images by intercepting videos. That is Images of law enforcement at that moment.

[0013] Each image Convert to a numerical vector. If the image is in color, each pixel has three color channels (red, green, and blue), then each pixel can be represented as a three-dimensional vector. If the image is in grayscale, each pixel is a scalar value. : The mean of normal behavior characteristics, : standard deviation of normal behavior characteristics, : Whether the behavior is abnormal (True or False); Algorithm steps: 1. Mean: For each image vector, calculate the average value of all elements. If the image is in color, you can calculate the mean of each color channel separately, or you can calculate the total mean of all color channels; ; The mean is the average value of all data points and is used to indicate the center of a data set. In the process of traffic police enforcement, if we focus on a certain feature of the traffic police's uniform (such as the brightness of a reflective vest), the mean can help us understand the average state of the feature within a certain time range. For example, if the average brightness of the reflective vest is lower than the standard value, it may indicate that the uniforms of some traffic police do not meet the requirements. 2. Variance: Variance measures the degree of deviation of data points from the mean. For each image vector, the average of the square of the difference between each element and the mean is calculated; ; Variance measures the degree of deviation of data points from the mean, that is, the fluctuation of data. In the case of non-standard traffic police uniforms, variance can be used to measure the fluctuation of different traffic police uniform feature values ​​(such as color, brightness, etc.). If the variance is large, it means that reflective vests may be taken off during law enforcement, which may be non-standard. 3. Standard Deviation: Variance is the average of the squares of the deviations of each data from the mean, which is used to measure the fluctuation of the data. If the values ​​in the data set are close to the mean, the variance is small; if the values ​​in the data set deviate far from the mean, the variance is large. ; The standard deviation is the square root of the variance. It also measures the degree of fluctuation of the data, but has the same unit as the original data, which is easier to understand intuitively. In the process of traffic police enforcement, if the standard deviation is large, it may mean that the reflective vest may be taken off during the enforcement process, and further inspection is needed to see whether there is any irregular dressing behavior; 4. Skewness: A statistic that describes the morphological characteristics of data distribution. It is used to measure the asymmetry of data distribution. Specifically, skewness describes the degree of deviation of data distribution from its mean, reflecting the direction and degree of skewness of data distribution. ; Skewness is a statistic that measures the direction and degree of skewness of data distribution. It is used to describe the symmetry of data distribution. In the process of traffic police enforcement, if we pay attention to the characteristics of traffic police clothing (such as the brightness or color saturation of reflective vests), skewness can help us identify whether the distribution of these characteristic values ​​is symmetrical. 4.1 Positive skewness (right skewness): If the distribution skewness of the brightness or color saturation of the reflective vest is greater than 0, it means that most values ​​are concentrated in lower brightness or saturation, while a few values ​​are abnormally high. This may mean that most traffic police uniforms meet the standards, but a few traffic police uniforms do not meet the standards. 4.2 Negative skewness (left skewness): If the skewness is less than 0, it means that most values ​​are concentrated in higher brightness or saturation, while a few values ​​are abnormally low, which may mean that most traffic police uniforms are too bright or too saturated and do not meet the standards; 5. Setting thresholds: Setting thresholds for mean, variance, and skewness are key parameters for distinguishing normal behavior from abnormal behavior. In the case of non-standard dress of traffic police, thresholds can be used to determine which dress feature values ​​are considered normal and which are abnormal. These thresholds can be determined through expert experience or historical data; Compare eigenvalues: ; For each statistical feature, if ,in If it is a set threshold (usually greater than 1), the feature value is considered abnormal.

[0014] For example, we can set mean thresholds for brightness and color saturation, as well as thresholds for skewness, and compare the actually observed feature values ​​with the set thresholds. If the feature values ​​exceed the normal range (for example, the mean of brightness or color saturation deviates from the normal mean by more than a certain standard deviation, or the skewness exceeds the normal range), it is considered that the traffic policeman's dress is not standard. By comparison, if it is found that the values ​​of certain statistical features are abnormal (for example, the mean of brightness or color saturation is far from the normal mean, or the skewness indicates that the distribution is seriously asymmetric), it can be judged as abnormal, that is, the dress is not standard.

[0015] Comprehensive judgment: If the abnormal condition of any statistical feature is met, then A=True, otherwise A=False.

[0016] The system will obtain video and audio data from the traffic police's law enforcement recorders, on-site surveillance cameras, etc., extract the police's clothing features (such as whether they wear police hats, reflective vests, etc.) through image recognition technology, and extract the language features of the law enforcement process (such as whether they contain necessary greetings, notifications and listening, etc.) through audio recognition technology, calculate the statistical features of the police's behavioral characteristics during duty, such as the mean, variance and skewness of clothing features, and the similarity and speech speed of language features, etc., set the threshold of normal behavioral features based on historical data or expert experience, monitor the police's behavioral characteristics in real time, and compare them with the threshold to determine whether there are any abnormalities. Once the behavioral characteristics are detected to be beyond the normal range, the system will immediately issue an alarm to notify the command center or on-site supervisors. The command center can view the video of the law enforcement recorder in real time, confirm the situation and take corresponding measures; Suppose that during the enforcement of the law, the system detects that a traffic policeman is not wearing a police hat, and the mean deviation of this behavior characteristic from the normal behavior characteristic exceeds the set threshold, the system will automatically trigger an alarm, and the command center can immediately view the video of the law enforcement recorder, confirm the situation, and notify the policeman through wireless communication equipment to make timely corrections. At the same time, this abnormal behavior will be recorded and used for future training and guidance to avoid similar situations from happening again.

[0017] 3.6 Behavior analysis: Analyze the specific behavior of law enforcement personnel, including law enforcement language, actions, law enforcement procedures, etc., to ensure that they comply with regulations; 3.7 Trend prediction: Use time series analysis or machine learning models to predict the trend of irregular law enforcement behavior; 3.8 Real-time analysis: Real-time data analysis is achieved to instantly identify and warn of irregular behaviors during law enforcement; 3.9 Result verification: Verify the results of intelligent analysis to ensure the accuracy and reliability of early warning; 3.10 Feedback loop: Feedback analysis results and warnings to law enforcement personnel and management to improve law enforcement processes and enhance system performance; 4. Application module: The application module is the interface for direct interaction between the system and the user. It provides functions such as real-time data analysis, synchronous monitoring, and real-time early warning, and realizes the closure of the supervision process, including pre-event prediction, in-event processing, and post-event analysis; 4.1 Real-time data analysis: Develop a real-time data stream processing module to perform real-time analysis on the received data, provide a dynamic monitoring view, and use data visualization technology to intuitively display the analysis results in the form of charts, maps, etc.; 4.2 Synchronous monitoring: realize synchronous monitoring with law enforcement recorders, video surveillance and other equipment, receive and display data in real time, provide equipment status monitoring, and ensure the normal operation of all equipment; 4.3 Real-time warning: Based on the output of the intelligent analysis layer, a real-time warning system is implemented to provide immediate reminders of irregular law enforcement behaviors, and a warning notification mechanism is designed to notify relevant personnel through APP, SMS, email, etc. 4.4 Closure of regulatory process: Achieve foresight: predict potential law enforcement risks through data analysis and notify law enforcement officers in advance; Realize in-progress processing: Real-time monitoring of ongoing law enforcement activities, timely detection and handling of problems; Implement post-event analysis: review and analyze law enforcement activities, summarize lessons learned, and optimize law enforcement processes; 4.5 Data query and display: Provide a user-friendly query interface, allowing users to query law enforcement data based on time, location, event and other conditions, and realize multi-dimensional data display, including maps, charts, lists and other forms; 4.6 Construction of law enforcement portraits: Using machine learning algorithms, build personal law enforcement portraits based on the law enforcement data of law enforcement officers, display the law enforcement behavior patterns of law enforcement officers, and provide a basis for performance evaluation and training; 4.7 Report generation and export: Provides a one-click report generation tool that supports custom report content and format, allowing users to export reports to PDF, Word and other formats for easy sharing and archiving; 5. Display module: The display module is the user interface of the system, responsible for presenting the processed data to the user in an intuitive and easy-to-understand way. It includes large-screen terminals, PC terminals and APP terminals, providing real-time data analysis, synchronous monitoring, real-time early warning and other functions; 5.1 Large-screen display: Design a large-screen display interface for the command center or monitoring center to display global law enforcement data and warning information, realize data visualization, including maps, charts, lists, etc., to provide a global perspective; 5.2 PC application: Through the PC application, it provides functions such as data collection, data management, data query, and data display, realizes multi-window operation, and supports users to view data from different data sources at the same time; 5.3 APP functions: Through mobile applications, it provides functions such as data reception, data query, data feedback, and data reporting, realizes data visualization on mobile devices, and supports users to monitor law enforcement activities in real time when they are out; 5.4 Real-time data display: By processing the received real-time data and displaying it on the interface in the form of charts, maps, etc., a dynamic view that is updated in real time is provided, so that users can instantly understand the status of law enforcement activities; 5.5 Early warning information push: Through the early warning information push mechanism, when the system detects irregular law enforcement behavior, it notifies the user in real time, and implements multiple notification methods, including APP push, SMS, email, etc.; 5.6 User interaction design: Through an intuitive user interaction interface, ensure that users can easily query and monitor data, provide user operation guides and help documents, and reduce users' learning costs; 5.7 Data query and screening: Through the data query function, users are allowed to screen data according to time, location, event and other conditions, provide screening condition combinations, and support users to customize the query conditions; 5.8 Report and Export: Through the report generation tool, users are supported to generate customized reports as needed, and users are allowed to export query results or reports into PDF, Word and other formats; To sum up, the implementation of the specific law enforcement and supervision system is as follows: 1. System deployment and data docking: Deploying a law enforcement supervision system within the public security network is the first step to achieve efficient law enforcement supervision. This system needs to be designed to run stably and be able to process large amounts of data. In order to ensure that the system can access the required data sources, we need to conduct detailed demand analysis and system design. Data docking is the key to whether the system can work properly, because it involves multiple different data sources, such as the six-in-one platform, duty system, law enforcement recorder, alcohol tester, PDT handheld radio, etc. These systems may use different data formats and protocols. Therefore, an efficient data docking mechanism is used to ensure that data can be transmitted seamlessly. In addition, data encryption technology is used to ensure data security and privacy protection to ensure that all data transmission complies with relevant laws and regulations; 2. Data collection and preprocessing: Data collection is the basis for system operation. We need to ensure that the system can automatically and accurately collect law enforcement-related data from various data sources, including video, audio, text records, etc. These data are the basis for subsequent analysis and model training. Data cleaning is a key step to ensure data quality. We need to remove invalid or erroneous data, such as blurred videos, audio with excessive background noise, etc. Data labeling is an important part of providing accurate samples for model training. We need to label the data in detail, such as identifying whether the police are wearing reflective vests, shoulder lights, police hats, and whether the law enforcement recorder is turned on or worn when necessary. These labeling information will be used to train the deep learning model so that it can identify irregular law enforcement behavior; 3. Model training and optimization: Model training is the core part of the system. It uses labeled data to train deep learning models, including image recognition, target detection, speech recognition and other models. These models need to be able to accurately identify irregular law enforcement behaviors, such as not wearing reflective vests or shoulder lights. Model optimization is an ongoing process. We need to iteratively tune the results of the model output to improve the accuracy and efficiency of the model. This may involve adjusting model parameters, increasing the diversity of training data, improving algorithms, etc. 4. Real-time monitoring and early warning: Real-time monitoring is the key to ensuring that law enforcement behavior is standardized. We need to use video data from road monitoring and law enforcement recorders to monitor the law enforcement behavior of police officers in real time. Through the trained model, the abnormal behavior recognition algorithm is used to analyze the monitoring video and law enforcement recorder video in real time to identify irregular law enforcement behavior. Once irregular behavior is found, the system automatically generates early warning information and pushes it to relevant personnel and management departments through the system. This can promptly remind the police to correct irregular behavior and avoid the occurrence of irregular law enforcement behavior; 5. Standardized guidance for law enforcement behavior: Standardized push is the process of pushing warning information and guidance information on standardized law enforcement behavior to on-duty police officers. This can be achieved through the notification function within the system to ensure that police officers can receive feedback in a timely manner. Law enforcement training is an important means to improve the level of law enforcement. We need to regularly train and guide police officers on law enforcement standards based on the results of model analysis. This can not only improve the level of police officers' law enforcement, but also deepen their understanding of law enforcement standards. 6. Law enforcement data management and analysis: Data management is the key to ensuring the integrity and security of law enforcement data. We need to store and manage the collected law enforcement data to ensure that the data will not be lost or illegally accessed. Data analysis is the key to generating law enforcement reports. We need to analyze law enforcement data and generate law enforcement reports to provide decision support for traffic management departments. These reports can help traffic management departments understand the law enforcement situation and develop more effective law enforcement strategies. 7. Law enforcement supervision and feedback: Through the system, we supervise law enforcement behavior to ensure the standardization and legality of law enforcement activities. By establishing a law enforcement feedback mechanism, we collect opinions and suggestions from police officers and the public on the law enforcement supervision system. These feedbacks will help us continuously optimize system functions and improve the practicality and effectiveness of the system.

[0018] 8. Improve the image of law enforcement: standardize law enforcement behavior, improve the image of police officers, and enhance the public's trust in police law enforcement. Through regular public satisfaction surveys, we can understand the public's satisfaction with law enforcement. Based on the feedback, we can make corresponding improvements to improve the public's satisfaction with law enforcement; 9. Continuous improvement and upgrading: Based on new demands in law enforcement practice and technological advances, the law enforcement and supervision system will be continuously upgraded and optimized. Through the actual effects of law enforcement and supervision and public feedback, the law enforcement and supervision process will be continuously improved to improve efficiency and effectiveness. This may involve the introduction of new technologies, such as artificial intelligence, big data analysis, etc., to improve the performance and accuracy of the system.

[0019] Finally, it should be noted that: Obviously, the above embodiments are only examples for clearly explaining the present invention, and are not intended to limit the implementation methods. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from this are still within the scope of protection of the present invention.

Claims

1. A daily traffic police law enforcement supervision system, characterized in that: The system includes the following modules: Data collection module: The main goal of data collection is to obtain various data in the process of traffic police enforcement in real time, including video, audio, text and sensor data, so as to carry out subsequent data processing, analysis and real-time monitoring; Data processing module: The data processing module is the core of the supervision system and is responsible for extracting, cleaning, labeling, storing and analyzing information from the collected raw data. This module ensures the quality and availability of data and provides accurate data support for intelligent analysis and real-time monitoring. Intelligent analysis module, which is the core part of the system, uses abnormal behavior recognition algorithms to analyze processed data to identify patterns, predict trends, and provide support for decision-making. This module includes abnormal behavior recognition, trend prediction, and pattern recognition functions; Abnormal behavior identification is achieved by establishing a behavior interval, which contains an abnormal behavior interval. When a behavior is determined to be within the abnormal behavior interval, it is determined that the behavior is abnormal. The abnormal behavior determination uses a machine learning model to analyze the overall behavior pattern of law enforcement personnel. If a significant deviation from the normal behavior pattern is found, it may be determined to be abnormal. The time series of law enforcement behavior is analyzed. If certain behaviors are found to be abnormally frequent or rare in a specific time period, they may be determined to be abnormal. Application module: The application module is the interface for direct interaction between the system and the user. It provides real-time data analysis, synchronous monitoring, and real-time early warning functions, and realizes the closure of the supervision process, including pre-event prediction, in-event processing, and post-event analysis. The display module is the user interface of the system, responsible for presenting the processed data to the user in an intuitive and easy-to-understand manner. It includes large-screen terminals, PC terminals and APP terminals, providing real-time data analysis, synchronous monitoring and real-time warning functions.

2. A traffic police law enforcement daily supervision system as claimed in claim 1, characterized in that: The abnormal behavior recognition algorithm formula is as follows: Compute statistical features: Mean ; variance ; Standard Deviation ; Skewness ; Setting the threshold: Set thresholds for mean, variance, and skewness to distinguish normal from abnormal behavior. These thresholds can be determined through expert experience or historical data. Compare eigenvalues: ； For each statistical feature, if ,in If the set threshold is usually greater than 1, the feature value is considered abnormal.

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