Intelligent police system and method based on Internet of Things technology

Abstract

The present invention relates to the field of smart policing technology, and in particular to a smart policing system and method based on the Internet of Things technology. The present invention is to solve the problems of untimely information acquisition, insufficient data utilization, and unreasonable allocation of police resources in the current police work in the prior art. Through the police data acquisition module, police data transmission module, police data processing module, police data application module, and police data security module in the smart policing system based on the Internet of Things technology, as well as the mutual relationship between the various units in the module, hidden crime patterns and public security laws are discovered, and through intelligent analysis functions such as similar case recommendations and crime predictions, powerful assistance is provided for the judgment of cases, helping the police to quickly identify suspects or clues, greatly improving the efficiency and precision of police work.

Description

Technical Field

[0001] The present invention relates to the field of smart policing technology, and in particular to a smart policing system and method based on Internet of Things technology. Background Art

[0002] With the rapid development of society, crime forms are becoming increasingly complex, and criminals are constantly innovating their methods of committing crimes. The traditional police work model mainly relies on manpower to carry out intelligence collection, case investigation, public security management and other tasks, resulting in problems such as untimely information acquisition, insufficient data utilization, and unreasonable allocation of police resources in the current police work. It is difficult to adapt to the current rapidly changing public security situation, which affects the efficiency and quality of police work.

[0003] Therefore, there is an urgent need to develop intelligent policing systems and methods based on Internet of Things technology to overcome the shortcomings of existing technologies. Summary of the invention

[0004] In order to solve the problems of untimely information acquisition, insufficient data utilization, unreasonable allocation of police resources, etc. in the current police work in the prior art, the present invention provides such an intelligent police system based on the Internet of Things technology, including a police data collection module, a police data transmission module, a police data processing module, a police data application module and a police data security module;

[0005] The police data collection module is used to automatically collect various police-related data information;

[0006] The police data transmission module adopts 5G technology and uses edge computing technology to build an Internet of Things covering the police data collection module, realize the transmission of police-related data and perform data preprocessing on the police-related data;

[0007] The police data processing module is used to store, mine and analyze the collected police-related data, extract key police information, and display it visually;

[0008] The police data application module is used to provide personalized smart police applications for different types of police to improve police work efficiency;

[0009] The police data security module is used to ensure the network security, data security and application security of the intelligent police system based on Internet of Things technology.

[0010] Furthermore, the police data processing module includes a police preprocessing unit, a police data storage unit, a police correlation analysis unit, a police assessment and early warning unit, and a police visualization unit;

[0011] The police preprocessing unit is used to perform preprocessing operations on the latest police-related data obtained, so that the latest police-related data meets the needs of subsequent analysis and judgment, and store the preprocessed latest police-related data in the police data storage unit;

[0012] The police data storage unit is used to classify, store, manage and back up the massive police-related data collected by the police data collection unit;

[0013] The police correlation analysis unit is used to perform correlation analysis and deep mining on the massive amount of police-related data collected to discover hidden crime patterns and public security laws;

[0014] The police analysis and early warning unit is used to analyze key personnel and abnormal events in massive police-related data, and to conduct intelligent analysis and early warning of the key personnel and abnormal events;

[0015] The police visualization unit is used to display the analysis results of massive police-related data in a visual manner.

[0016] Furthermore, the police data application module includes a police situation application unit, a police situation analysis and judgment auxiliary unit, a command and dispatch unit, an investigation and evidence collection unit, and a police interaction unit;

[0017] The police situation application unit provides mobile police situation services to front-line police officers through the mobile police APP. The mobile police situation services include mobile office, information query and intelligence reporting;

[0018] The police situation analysis auxiliary unit is used to assist in police situation analysis and provide analysis functions based on existing case information, including similar case recommendations and crime prediction;

[0019] The command and dispatch unit is used to provide actual combat command and dispatch support for the commander, and the actual combat command and dispatch support includes police force deployment, police situation handling and cross-regional coordination;

[0020] The investigation and evidence collection unit is used to analyze and compare video materials related to the case and quickly identify suspects or suspicious clues;

[0021] The police interaction unit is used to conduct online interaction between community police and residents in the area, and provide services including public security publicity and police-civilian communication.

[0022] Furthermore, the police data security module includes a device access authentication unit, a data encryption transmission unit, an authority control unit and a security audit monitoring unit;

[0023] The device access authentication unit is used to perform identity authentication and authority control on IoT devices connected to the intelligent police system to prevent illegal device access;

[0024] The data encryption transmission unit is used to ensure that the police-related data collected by the police data collection module is encrypted before transmission to prevent the data from being stolen or tampered with;

[0025] The authority control unit is used to implement refined authority management for different users and strictly control the access and use authority of police-related data;

[0026] The security audit monitoring unit is used to monitor and audit the operating status of the intelligent policing system and user behavior throughout the process, and to promptly discover and deal with potential safety hazards.

[0027] Furthermore, the police correlation analysis unit performs correlation analysis and deep mining on the massive police-related data collected to discover hidden crime patterns and public security laws in the following specific steps:

[0028] S101: Acquire a massive amount of police-related data stored in a police data storage unit, wherein the massive amount of police-related data includes structured data mainly including case information, police situation records and personnel information, and unstructured data mainly including case description texts and on-site investigation photos; extract key information of the case from the massive amount of police-related data, structure the unstructured data, and extract feature values ​​from image and video data to unify data formats from different sources;

[0029] S102: associating data from different sources and of different types according to predefined association rules to form a global crime data association network, wherein the association rules include people, objects, places, and gangs;

[0030] S103: performing statistical analysis based on the global crime data association network, wherein the statistical analysis includes statistically analyzing the distribution patterns of different types of associations, statistically analyzing the centrality of different nodes in the global crime data association network, and statistically analyzing the strength of associations;

[0031] S104: Detecting abnormal associations in the global crime data association network based on known common crime patterns; and performing association reasoning and prediction on persons, time and place based on the global crime data association network;

[0032] S105: Integrate the results of the associated reasoning and prediction to form a structured and visual analysis report; and store the analysis report in the police data storage unit.

[0033] Furthermore, the specific steps of the police assessment and early warning unit for intelligent assessment and real-time early warning of key personnel and abnormal events are as follows:

[0034] S201: Acquire a large amount of police-related data stored in a police data storage unit and an output result of the police association analysis unit, wherein the output result of the police association analysis unit includes multi-dimensional association rules, abnormal events, and key personnel information;

[0035] S202: Based on the deployed abnormal behavior detection model, the behavior data of key personnel are detected in real time, and combined with the abnormal event information provided by the police association analysis unit, the key personnel involved are monitored and analyzed; when the abnormal behavior of the key personnel deviating from the normal mode is detected, an abnormal behavior alarm is generated according to the risk level of the abnormal behavior of the key personnel;

[0036] S203: Analyze the association between the abnormal behavior of the key personnel and the information of elements such as people, objects, places and gangs according to the multi-dimensional association rules, obtain the deep relationship and rules behind the abnormal behavior, and predict high-risk behaviors that may cause abnormal events;

[0037] S204: Correlate the abnormal behavior alarm of the key personnel with the result of the element information correlation analysis, determine whether there is an abnormal event that has occurred or is about to occur, conduct risk assessment on the identified abnormal event, and trigger a corresponding early warning mechanism according to the risk assessment level;

[0038] S205: Based on the analysis and prediction results of the abnormal behaviors and abnormal events of the key personnel, early warning information is generated and pushed to relevant police officers in a timely manner through the police situation application unit to guide the relevant police officers to take targeted prevention and control measures.

[0039] Furthermore, the specific calculation formula of the abnormal behavior detection model is:

[0040]

[0041] Among them, F(loss) is the objective function value of the abnormal behavior detection model; is the complexity of the abnormal behavior detection model, where w represents the weight of the abnormal behavior model on different features, where different features include personal location and behavior model; ∑ j C j ∑ i θ ij is the classification error loss of the abnormal behavior detection model on the training data, where C j is the importance of the jth time-space interval, θ ij is the abnormality degree of the i-th person in the j-th time-space interval; is a constraint, For known abnormal behavior patterns, is the characteristic of abnormal events, γ1 is used to control The relative importance of γ2 is used to control the relative importance of .

[0042] Furthermore, the police situation analysis auxiliary unit provides the following specific steps for the analysis function based on the existing case information:

[0043] S301: The police situation analysis auxiliary unit obtains the existing case information entered by the police; pre-processes the existing case information, extracts key feature elements, and structures the unstructured data in the existing case information; determines whether the key feature element extraction and unstructured data conversion are successful, if yes, proceeds to step S302; if no, continues to obtain the existing case information entered by the police;

[0044] S302: Input the key feature elements into the pre-built case association analysis model, mine the key aspects of the association between the input case and the historical cases stored in the police data storage unit, and calculate the association strength, wherein the key aspects include the time, location, method and personnel of the crime; sort the historical cases from high to low according to the association strength, select the N historical cases with the highest association with the case to be determined, and generate a similar case library;

[0045] S303: Scoring the historical cases in the similar case database to select key difficult cases, wherein the scoring is performed according to the difficulty of solving the cases;

[0046] S304: Analyze the criminal patterns and characteristics of the cases according to the key difficult cases, summarize the key factors for solving the cases in the historical cases, form reference suggestions for solving the cases, and generate a case analysis report;

[0047] S305: The police officer carries out the next investigative action plan based on the case analysis report; after completing the analysis, the analysis of the case is entered into the police situation analysis auxiliary unit, and the police situation analysis auxiliary unit optimizes the analysis process based on the case analysis.

[0048] Furthermore, the specific steps of the command and dispatch unit providing actual combat command and dispatch support to the commander are:

[0049] S401: The command and dispatch unit integrates the acquired police situation-related data, and performs real-time analysis on the integrated data to identify the characteristics of the police situation, determine the nature, severity and evolution trend of the police situation, and predict the required police resources;

[0050] S402: According to the analysis results in S401, dynamically adjust police resources and provide an optimal police deployment plan;

[0051] S403: The command and dispatch unit searches the police data storage unit for similar historical police cases, analyzes the handling experience and effects of the historical police cases, summarizes and extracts effective police handling processes and key points, and generates auxiliary decision-making suggestions;

[0052] S402: The command and dispatch unit displays the police force dispatch information on the police visualization interface, and the command personnel implement the police force dispatch command through the displayed content;

[0053] S403: The command and dispatch unit monitors the dynamic changes of the police situation and evaluates the handling effect; and records the entire process of police situation handling to form a complete police situation log and summary report.

[0054] Furthermore, the intelligent policing method based on the Internet of Things technology includes the following steps:

[0055] S1: Automatically collect various police-related data information through the police data collection module;

[0056] S2: The police data transmission module pre-processes the police-related data collected by the police data collection module through edge computing technology, and transmits the pre-processed police-related data to the police data processing module through 5G technology;

[0057] S3: The police data processing module stores, mines and analyzes the police-related data collected by the police data collection module, extracts key police information, and displays it visually;

[0058] S4: Provide personalized smart police applications for different police types through the police data application module to improve police work efficiency;

[0059] S5: Ensure the network security, data security and application security of the smart police system based on IoT technology through the police data security module.

[0060] Beneficial effects:

[0061] 1: The present invention adopts the Internet of Things technology, uses various sensors and smart devices to realize the automatic collection of police-related data, integrates 5G communication and edge computing technology, builds an Internet of Things covering various data collection nodes, and realizes efficient, real-time transmission and preprocessing of massive police-related data, laying the foundation for subsequent data analysis and application;

[0062] 2: The present invention conducts real-time monitoring, analysis and assessment of key personnel and abnormal events, issues timely warnings for abnormal behaviors and high-risk events discovered, and formulates targeted prevention and control measures. At the same time, through in-depth mining and analysis of historical data, hidden crime patterns and public security laws are discovered, which is conducive to the early formulation of prevention and control strategies and the realization of preventive public security management and control based on big data and artificial intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 is a system architecture diagram of the present invention;

[0064] Figure 2 This is an operation flow chart of the police-related analysis unit of the present invention;

[0065] Figure 3 This is an operation flow chart of the police assessment and early warning unit of the present invention;

[0066] Figure 4 This is an operation flow chart of the alarm situation analysis auxiliary unit of the present invention;

[0067] Figure 5 It is an operation flow chart of the command and dispatch unit of the present invention. DETAILED DESCRIPTION

[0068] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0069] As attached Figure 1-Figure 5 As shown, the intelligent police system based on the Internet of Things technology includes a police data collection module, a police data transmission module, a police data processing module, a police data application module and a police data security module.

[0070] In this embodiment, the police data collection module is composed of various sensors, video surveillance and police equipment, etc., and is used to automatically collect various police-related data information. Among them, the police data collection module includes a police equipment unit, a police video surveillance unit, and a police environment perception unit.

[0071] The police equipment unit includes law enforcement recorders, walkie-talkies and other equipment, which can automatically collect data such as the police officer's location, audio and video, and upload them through the police data transmission module.

[0072] The police video surveillance unit obtains monitoring data of key areas by sharing and connecting with social video surveillance resources.

[0073] The police environment perception unit uses environmental monitoring sensors to collect real-time environmental data such as temperature, humidity, and noise within the jurisdiction.

[0074] In this embodiment, the police data transmission module adopts 5G technology and uses edge computing technology to build an Internet of Things covering the police data collection module, realize the transmission of police-related data and perform data preprocessing on the police-related data.

[0075] The police data transmission module sets up computing and storage nodes at the edge of the network, builds an Internet of Things covering the police data transmission module, and performs preliminary processing and analysis on various police-related data collected by the police data collection module, thereby reducing the pressure on the police data processing module.

[0076] In this embodiment, the police data processing module gathers and stores various police-related data transmitted from the police data transmission module, stores, mines and analyzes the collected police-related data, extracts key police information, and displays it visually. Among them, the police data processing module includes a police preprocessing unit, a police data storage unit, a police correlation analysis unit, a police assessment and early warning unit, and a police visualization unit.

[0077] The police preprocessing unit is responsible for preprocessing the latest police-related data obtained by the police data collection module. Although the police data transmission module performs preliminary processing and analysis on the acquired police-related data, data loss and damage may occur during the transmission process, and the police-related data after preliminary processing does not meet the needs of subsequent analysis and processing. Therefore, the police preprocessing unit is required to perform a series of preprocessing operations such as data cleaning, data integration, and data transformation on the latest acquired police-related data, and convert the preliminary processed police-related data into a structured, standardized, high-quality data set to meet the needs of subsequent analysis and application. The police preprocessing unit stores the preprocessed data in the police data storage unit for other module units to call.

[0078] The police data storage unit adopts a distributed storage architecture to classify, store and manage the massive police-related data received by the police data collection unit. The police data storage unit supports the storage of structured data, semi-structured data and unstructured data, and provides efficient data reading, writing, query and retrieval structures to meet the call requirements of other module units. At the same time, the police data storage unit is also responsible for data backup and recovery to ensure data security and reliability.

[0079] The police correlation analysis unit conducts correlation analysis and in-depth mining on the massive police-related data stored in the police data storage unit. It discovers valuable information such as crime patterns, public security rules, and personnel relationship networks hidden in the police-related data, helping the police to control the public security situation in the jurisdiction from a macro perspective and provide intelligence support for police decision-making.

[0080] In this embodiment, the police correlation analysis unit performs correlation analysis and deep mining on the collected massive police-related data to discover the specific steps of hidden crime patterns and public security laws as shown in the attached figure. Figure 2 Shown are:

[0081] S101: Acquire massive police-related data stored in a police data storage unit, wherein the massive police-related data includes structured data mainly including case information, police records and personnel information, and unstructured data mainly including case description texts and on-site investigation photos; extract key information of the case from the massive police-related data, structure the unstructured data, and extract feature values ​​from image and video data to unify the data formats from different sources.

[0082] S102: According to the predefined association rules, data from different sources and different types are associated to form a global criminal data association network. The association rules include people, objects, locations and gangs. Specifically, by person association, the information of the same person in different cases; by object association, the same object involved in different cases; by location association, different cases at the same crime location; by gang association, different cases involving members of a criminal gang.

[0083] S103: Conduct statistical analysis based on the global crime data association network. The statistical analysis includes statistically analyzing the distribution patterns of different types of associations to discover common crime patterns, statistically analyzing the centrality of different nodes in the global crime data association network to discover key people, objects, and locations; and statistically analyzing the strength of associations to discover the correlation between different crime elements.

[0084] S104: Based on known common crime patterns, detect abnormal associations in the global crime data association network; and simultaneously perform association reasoning and prediction on people, time and place based on the global crime data association network.

[0085] S105: Integrate the results of association reasoning and prediction to form a structured and visual analysis report, and store the analysis report in the police data storage unit. The content of the analysis report includes but is not limited to the global crime data association network, common crime pattern statistics, abnormal association event list, and crime prediction information.

[0086] The police analysis and early warning unit focuses on key personnel and abnormal events in the analysis results. For key personnel, the police analysis and early warning unit continuously tracks their activity trajectories and promptly detects abnormal behaviors; for abnormal time periods, the police analysis and early warning unit explores the criminal patterns and assesses the risk level. When major risks are identified, the police analysis and early warning unit will generate early warning information and push it to relevant personnel through the police visualization unit and the police situation application unit to guide them to take preventive measures.

[0087] In this embodiment, the specific steps of the police assessment and early warning unit for intelligent assessment and real-time early warning of key personnel and abnormal events are as follows: Figure 3 Shown are:

[0088] S201: Acquire a large amount of police-related data stored in a police data storage unit and an output result of a police correlation analysis unit. The output result of the police correlation analysis unit includes multi-dimensional correlation rules, abnormal events, and key personnel information.

[0089] S202: Based on the deployed abnormal behavior detection model, the behavior data of key personnel are detected in real time, and combined with the abnormal event information provided by the police association analysis unit, the key personnel involved are monitored and analyzed; when abnormal behavior that deviates from the normal pattern is detected in key personnel, an abnormal behavior alarm is generated according to the risk level of the abnormal behavior of the key personnel.

[0090] Specifically, the specific calculation formula of the abnormal behavior detection model is:

[0091]

[0092] Among them, F(loss) is the objective function value of the abnormal behavior detection model, that is, the loss function of the abnormal behavior detection model on the training data; is the complexity of the abnormal behavior detection model, which is used to control the generalization ability of the abnormal behavior detection model and avoid overfitting. w represents the weight of the abnormal behavior model on different features, which determines the importance of the abnormal behavior detection model on different features, including personal location and behavior model; ∑ j C j ∑ i θ ij is the classification error loss of the abnormal behavior detection model on the training data, where C j is the importance of the jth spatiotemporal interval (such as a specific location and time period), θ ij is the abnormality degree of the i-th person in the j-th time-space interval, by minimizing ∑ j C j ∑ i θ ij It can improve the accuracy of abnormal behavior models in detecting abnormal behavior in key areas and time periods; is the knowledge constraint item for abnormal behavior detection in the police field, For known abnormal behavior patterns (known suspicious behavior, etc.), is the characteristic of abnormal events (including multi-person gatherings, abnormal vehicle stops, etc.), and γ1 is used to control The relative importance of γ2 is used to control The relative importance of w is the domain knowledge constraint weight.

[0093] S203: Analyze the association between the abnormal behavior of key personnel and the information of elements such as people, objects, places and gangs according to the multi-dimensional association rules in the police association analysis unit, obtain the deep relationship and rules behind the abnormal behavior, and predict high-risk behaviors that may cause abnormal events.

[0094] S204: Combine the abnormal behavior alarm of key personnel with the result of the element information correlation analysis to determine whether there are abnormal events that have occurred or are about to occur, conduct risk assessment on the identified abnormal events, and trigger the corresponding early warning mechanism according to the risk assessment level.

[0095] S205: Based on the analysis and prediction results of abnormal behaviors and abnormal events of key personnel, early warning information is generated and pushed to relevant police officers in a timely manner through the police situation application unit to guide relevant police officers to take targeted prevention and control measures.

[0096] The police visualization unit uses data visualization technology to convert the analysis results of massive police-related data into an intuitive and easy-to-understand visualization interface. Through the visualization interface, users can intuitively understand information within the jurisdiction, including but not limited to crime trends, police situation distribution, and police force deployment.

[0097] In this embodiment, the police data application module provides personalized and intelligent police application services for different police types based on the processing of massive police data to improve the efficiency of police work. Specifically, the police data application module includes a police situation application unit, a police situation analysis and judgment auxiliary unit, a command and dispatch unit, an investigation and evidence collection unit, and a police interaction unit.

[0098] The police situation application unit provides mobile police situation services through the mobile police APP for front-line police officers. The mobile police APP provides functions including but not limited to mobile office, information query and police situation reporting. Police officers can use the mobile police APP to handle police affairs at any time. The mobile office function enables police officers to write case records, review documents, etc. on mobile terminals; the information query function can quickly retrieve various police situation-related data in the intelligent police system; the police situation reporting function enables police officers to upload case clues, suspicious persons and other police situation information to the intelligent police subsystem in real time.

[0099] The police situation analysis auxiliary unit is used to assist police officers in case analysis and decision analysis. When facing a new case, the police situation analysis auxiliary unit automatically searches for similar cases in the intelligent police system based on the existing information for reference by police officers. At the same time, the police situation analysis auxiliary unit can also use the case association analysis model to analyze factors including but not limited to the time, location and means of committing the crime, predict crime trends and patterns, and help the police quickly identify the suspects.

[0100] In this embodiment, the police situation analysis auxiliary unit provides specific steps for the analysis function based on the existing case information as shown in the attached figure. Figure 4 Shown are:

[0101] S301: The police situation analysis auxiliary unit obtains the existing case information of the case entered by the police, and the existing case information includes but is not limited to structured data such as the time, location, means of committing the crime, characteristics of the persons involved, and unstructured data such as case description and on-site investigation; pre-processes the existing case information, extracts key feature elements, and structures the unstructured data in the existing case information; determines whether the extraction of key feature elements and the conversion of unstructured data are successful, and if so, proceeds to step S302; if not, prompts the police to supplement and complete the information and continue to obtain the existing case information entered by the police.

[0102] S302: Input key feature elements into the pre-built case association analysis model, explore the key aspects of the correlation between the input case and the historical cases stored in the police data storage unit, and calculate the correlation strength. The key aspects include but are not limited to the time, location, method and personnel of the crime; sort the historical cases from high to low according to the correlation strength, screen out the top N most similar historical cases with the highest correlation with the case to be judged, and generate a similar case library.

[0103] Specifically, the specific calculation formula of the case association analysis model is:

[0104]

[0105] Among them, A is the feature vector of the newly entered case, which contains the key feature elements of the case, including but not limited to the time, location, and means of committing the crime; B is the feature vector of the historical case, which contains the key feature elements of the historical case; cos(A,B) is the cosine similarity between the newly entered case and the historical case; A i is the i-th component of the feature vector A of the newly entered case, B i is the i-th component of the historical case feature vector B, where each component corresponds to a feature element;

[0106] The case association analysis model is used to calculate the pre-similarity between the newly entered case A and each historical case B, and a set of association strength values ​​scores are obtained:

[0107] score=[cos(A,B1),cos(A,B2)…cos(A,B m )]

[0108] Where m is the number of historical cases. By sorting the historical cases from high to low according to the correlation strength value score, the top N most similar historical cases with the highest correlation with the case to be judged can be screened out.

[0109] S303: Score historical cases in the similar case database according to the degree of difficulty in solving them, and screen out key difficult cases that are difficult to solve and have not yet been solved.

[0110] S304: Analyze the criminal patterns and characteristics of key difficult cases, summarize the key factors for solving cases in historical cases, form reference suggestions for solving cases, and generate case analysis reports.

[0111] S305: The police will carry out the next investigative action plan based on the case analysis report; after completing the analysis, the case analysis results will be entered into the police situation analysis auxiliary unit, and the police situation analysis auxiliary unit will optimize the analysis process based on the case analysis results to accumulate clues for solving the case in the future.

[0112] The command and dispatch unit provides actual command and dispatch support for command personnel. When dealing with complex police situations such as major cases, disasters, mass incidents, etc., the command and dispatch unit can intuitively display information such as police force deployment and resource distribution, and provide process guidance and solution recommendations for police situation handling. At the same time, the command and dispatch unit also supports regional police coordination, and realizes police situation sharing and joint actions by connecting with the police systems of adjacent jurisdictions.

[0113] In this embodiment, the specific steps of the command and dispatch unit providing actual combat command and dispatch support to the commander are as follows: Figure 5 Shown are:

[0114] S401: The command and dispatch unit integrates the acquired police situation-related data, including police situation data, police force deployment data, video surveillance data, etc., and analyzes the integrated data in real time to identify the characteristics of the police situation, determine the nature, severity and evolution trend of the police situation, and predict the required police force resources.

[0115] S402: Based on the analysis results in S401, the police resources are dynamically adjusted through the optimization algorithm to provide the optimal police deployment plan.

[0116] S403: The command and dispatch unit searches the police data storage unit for similar historical police cases, analyzes the handling experience and results of historical police cases, summarizes and extracts effective police handling processes and key points, and generates auxiliary decision-making suggestions.

[0117] S404: The command and dispatch unit displays the police force dispatch information on the police visualization interface, and the command personnel realize the police force dispatch command through the displayed content.

[0118] S405: The command and dispatch unit monitors the dynamic changes of police situations and evaluates the effectiveness of handling them; it also records the entire process of handling police situations to form a complete police situation log and summary report.

[0119] The investigation and evidence collection unit is used to analyze and identify video materials related to the case, and associate the suspected people and vehicles in the video materials with the massive police-related data in the intelligent police system to find suspicious clues. At the same time, the investigation and evidence collection unit can also analyze videos from different sources and at different times to restore the full picture of the case. The investigation and evidence collection unit greatly reduces the workload of investigators and improves the efficiency and accuracy of video investigation.

[0120] Specifically, the police interaction unit is used to conduct online interactions between community police officers and residents in the jurisdiction, providing services including public security publicity and police-civilian communication.

[0121] The police interaction unit is mainly for community police officers, providing them with an online platform for interaction with the people in their jurisdiction. Through the police interaction unit, police officers can release public security information and popularize safety precautions; at the same time, the people in the jurisdiction can report to the police and consult online, achieving zero-distance communication between the police and the people.

[0122] In this embodiment, the police data security module is responsible for facing security risks and challenges such as illegal access, data leakage, unauthorized access, system attacks, etc., to ensure the network security, data security and application security of the intelligent police system. Specifically, the police data security module includes a device access authentication unit, a data encryption transmission unit, a permission control unit and a security audit monitoring unit.

[0123] The device authentication unit is responsible for identity authentication and permission control of various IoT devices connected to the intelligent laser dance system. In order to prevent illegal devices from being mixed in, only authenticated legal devices can access the system, and unauthorized devices will be denied access. At the same time, the device authentication unit also grants different access permissions to different IoT devices, strictly controls the behavior of the devices, and prevents the devices from being maliciously manipulated or implanted with Trojan viruses.

[0124] The data encryption transmission unit is used to ensure the security of the collected police data during transmission. The encryption algorithm is used to encrypt the police-related data before transmission. This prevents the data from being stolen or tampered with. At the same time, after the encrypted police-related data is transmitted to the police data processing module, an alarm will be immediately issued if the data is tampered with, ensuring the confidentiality and integrity of the data.

[0125] The authority control unit is responsible for fine-grained management of the data access rights of users of the intelligent system. In order to prevent users from unauthorized access to police-related data, the authority control unit sets clear data access boundaries for each user based on the access level of the role, so that users with different access levels can only query and operate data within their own authority. Once the access exceeds the boundary, it will be rejected by the intelligent system. At the same time, the authority control unit supports dynamic adjustment of user permissions, and can retract and release access rights in real time according to the needs of police work.

[0126] The security audit monitoring unit is used to continuously monitor and audit the status and user behavior of the intelligent police system. The security audit monitoring records every operation and every security incident of the system around the clock to form a complete audit log. And through big data analysis technology, it continuously predicts abnormal behaviors and suspicious operations, and promptly discovers and handles security risks.

[0127] In this embodiment, there is also a smart policing method based on the Internet of Things technology, specifically:

[0128] S1: Automatically collect various police-related data information through the police data collection module.

[0129] S2: The police data transmission module pre-processes the police-related data collected by the police data collection module through edge computing technology, and transmits the pre-processed police-related data to the police data processing module through 5G technology.

[0130] S3: The police data processing module stores, mines and analyzes the police-related data collected by the police data collection module, extracts key police information, and displays it visually.

[0131] S4: Provide personalized smart policing applications for different types of police through the police data application module to improve police work efficiency.

[0132] S5: Ensure the network security, data security and application security of the smart police system based on IoT technology through the police data security module.

[0133] The above-mentioned embodiments only express the preferred implementation modes of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person skilled in the art, several modifications, improvements and substitutions can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the attached claims.

Claims

1. The intelligent police system based on Internet of Things technology is characterized by: It includes police data collection module, police data transmission module, police data processing module, police data application module and police data security module; The police data collection module is used to automatically collect various police-related data information; The police data transmission module adopts 5G technology and uses edge computing technology to build an Internet of Things covering the police data collection module, realize the transmission of police-related data and perform data preprocessing on the police-related data; The police data processing module is used to store, mine and analyze the collected police-related data, extract key police information, and display it visually; The police data application module is used to provide personalized smart police applications for different types of police to improve police work efficiency; The police data security module is used to ensure the network security, data security and application security of the intelligent police system based on the Internet of Things technology; The police data processing module includes a police preprocessing unit, a police data storage unit, a police correlation analysis unit, a police judgment and early warning unit, and a police visualization unit; The police preprocessing unit is used to perform preprocessing operations on the latest police-related data obtained, so that the latest police-related data meets the needs of subsequent analysis and judgment, and store the preprocessed latest police-related data in the police data storage unit; The police data storage unit is used to classify, store, manage and back up the massive police-related data collected by the police data collection unit; The police correlation analysis unit is used to perform correlation analysis and deep mining on the massive amount of police-related data collected to discover hidden crime patterns and public security laws; The police analysis and early warning unit is used to analyze key personnel and abnormal events in massive police-related data, and to conduct intelligent analysis and early warning of the key personnel and abnormal events; The police visualization unit is used to display the analysis results of massive police-related data in a visual manner; The police correlation analysis unit performs correlation analysis and deep mining on the massive police-related data collected to discover hidden crime patterns and public security laws in the following specific steps: S101: Acquire a massive amount of police-related data stored in a police data storage unit, wherein the massive amount of police-related data includes structured data mainly including case information, police situation records and personnel information, and unstructured data mainly including case description texts and on-site investigation photos; extract key information of the case from the massive amount of police-related data, structure the unstructured data, and extract feature values ​​from image and video data to unify data formats from different sources; S102: associating data from different sources and of different types according to predefined association rules to form a global crime data association network, wherein the association rules include people, objects, places, and gangs; S103: performing statistical analysis based on the global crime data association network, wherein the statistical analysis includes statistically analyzing the distribution patterns of different types of associations, statistically analyzing the centrality of different nodes in the global crime data association network, and statistically analyzing the strength of associations; S104: Detecting abnormal associations in the global crime data association network based on known common crime patterns; and performing association reasoning and prediction on persons, time and place based on the global crime data association network; S105: Integrate the results of the association reasoning and prediction to form a structured and visual analysis report; and store the analysis report in the police data storage unit; The specific steps of the police assessment and early warning unit for intelligent assessment and real-time early warning of key personnel and abnormal events are as follows: S201: Acquire a large amount of police-related data stored in a police data storage unit and an output result of the police association analysis unit, wherein the output result of the police association analysis unit includes multi-dimensional association rules, abnormal events, and key personnel information; S202: Based on the deployed abnormal behavior detection model, the behavior data of key personnel are detected in real time, and combined with the abnormal event information provided by the police association analysis unit, the key personnel involved are monitored and analyzed; when the abnormal behavior of the key personnel deviating from the normal mode is detected, an abnormal behavior alarm is generated according to the risk level of the abnormal behavior of the key personnel; S203: Analyze the association between the abnormal behavior of the key personnel and the information of elements such as people, objects, places and gangs according to the multi-dimensional association rules, obtain the deep relationship and rules behind the abnormal behavior, and predict high-risk behaviors that may cause abnormal events; S204: Correlate the abnormal behavior alarm of the key personnel with the result of the element information correlation analysis, determine whether there is an abnormal event that has occurred or is about to occur, conduct risk assessment on the identified abnormal event, and trigger a corresponding early warning mechanism according to the risk assessment level; S205: Generate early warning information based on the analysis and prediction results of the abnormal behaviors and abnormal events of the key personnel, and push it to the relevant police officers in a timely manner through the police situation application unit to guide the relevant police officers to take targeted prevention and control measures; The specific calculation formula of the abnormal behavior detection model is: Among them, F(loss) is the objective function value of the abnormal behavior detection model; is the complexity of the abnormal behavior detection model, where w represents the weight of the abnormal behavior model on different features, where different features include personal location and behavior model; ∑ j C1∑ I θ Ij is the classification error loss of the abnormal behavior detection model on the training data, where C j is the importance of the jth time-space interval, θ ij is the abnormality degree of the i-th person in the j-th time-space interval; is a constraint item, For known abnormal behavior patterns, is the characteristic of abnormal events, γ1 is used to control The relative importance of γ2 is used to control the relative importance of .

2. According to claim 1, the intelligent police system based on Internet of Things technology is characterized in that: The police data application module includes a police situation application unit, a police situation analysis and judgment auxiliary unit, a command and dispatch unit, an investigation and evidence collection unit, and a police interaction unit; The police situation application unit provides mobile police situation services to front-line police officers through the mobile police APP. The mobile police situation services include mobile office, information query and intelligence reporting; The police situation analysis auxiliary unit is used to assist in police situation analysis and provide analysis functions based on existing case information, including similar case recommendations and crime prediction; The command and dispatch unit is used to provide actual combat command and dispatch support for the commander, and the actual combat command and dispatch support includes police force deployment, police situation handling and cross-regional coordination; The investigation and evidence collection unit is used to analyze and compare video materials related to the case and quickly identify suspects or suspicious clues; The police interaction unit is used to conduct online interaction between community police and residents in the area, and provide services including public security publicity and police-civilian communication.

3. According to claim 1, the intelligent police system based on Internet of Things technology is characterized in that: The police data security module includes a device access authentication unit, a data encryption transmission unit, an authority control unit and a security audit monitoring unit; The device access authentication unit is used to perform identity authentication and authority control on IoT devices connected to the intelligent police system to prevent illegal device access; The data encryption transmission unit is used to ensure that the police-related data collected by the police data collection module is encrypted before transmission to prevent the data from being stolen or tampered with; The authority control unit is used to implement refined authority management for different users and strictly control the access and use authority of police-related data; The security audit monitoring unit is used to monitor and audit the operating status of the intelligent policing system and user behavior throughout the process, and to promptly discover and deal with potential safety hazards.

4. According to claim 3, the intelligent police system based on Internet of Things technology is characterized in that: The specific steps of the police situation analysis auxiliary unit providing analysis functions based on the existing case information are as follows: S301: The police situation analysis auxiliary unit obtains the existing case information entered by the police; pre-processes the existing case information, extracts key feature elements, and structures the unstructured data in the existing case information; determines whether the key feature element extraction and unstructured data conversion are successful, if yes, proceeds to step S302; if no, continues to obtain the existing case information entered by the police; S302: Input the key feature elements into the pre-built case association analysis model, mine the key aspects of the association between the input case and the historical cases stored in the police data storage unit, and calculate the association strength, wherein the key aspects include the time, location, method and personnel of the crime; sort the historical cases from high to low according to the association strength, select the N historical cases with the highest association with the case to be determined, and generate a similar case library; S303: Scoring the historical cases in the similar case database to select key difficult cases, wherein the scoring is performed according to the difficulty of solving the cases; S304: Analyze the criminal patterns and characteristics of the cases according to the key difficult cases, summarize the key factors for solving the cases in the historical cases, form reference suggestions for solving the cases, and generate a case analysis report; S305: The police will carry out the next step of the investigation action plan based on the case analysis report; After the analysis and judgment is completed, the analysis and judgment situation of the input case is entered into the police situation analysis and judgment auxiliary unit, and the police situation analysis and judgment auxiliary unit optimizes the analysis and judgment process according to the analysis and judgment situation of the case.

5. The intelligent police system based on Internet of Things technology according to claim 2 is characterized in that: The specific steps of the command and dispatch unit providing actual combat command and dispatch support to the commander are: S401: The command and dispatch unit integrates the acquired police situation-related data, and performs real-time analysis on the integrated data to identify the characteristics of the police situation, determine the nature, severity and evolution trend of the police situation, and predict the required police resources; S402: According to the analysis results in S401, dynamically adjust police resources and provide an optimal police deployment plan; S403: The command and dispatch unit searches the police data storage unit for similar historical police cases, analyzes the handling experience and effects of the historical police cases, summarizes and extracts effective police handling processes and key points, and generates auxiliary decision-making suggestions; S402: The command and dispatch unit displays the police force dispatch information on the police visualization interface, and the command personnel implement the police force dispatch command through the displayed content; S403: The command and dispatch unit monitors the dynamic changes of the police situation and evaluates the handling effect; and records the entire process of police situation handling to form a complete police situation log and summary report.

6. The method of the intelligent police system based on the Internet of Things technology according to claim 1 is characterized in that: The steps include: S1: Automatically collect various police-related data information through the police data collection module; S2: The police data transmission module pre-processes the police-related data collected by the police data collection module through edge computing technology, and transmits the pre-processed police-related data to the police data processing module through 5G technology; S3: The police data processing module stores, mines and analyzes the police-related data collected by the police data collection module, extracts key police information, and displays it visually; S4: Provide personalized smart police applications for different police types through the police data application module to improve police work efficiency; S5: Ensure the network security, data security and application security of the smart police system based on IoT technology through the police data security module.

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