Big data alarm intelligent analysis system

Abstract

The invention discloses a big data alarm condition intelligent analysis system, and the system comprises a central processing unit which is used for coordinating the work of all modules; the data acquisition module is used for collecting structured and unstructured alarm data of each channel; the data storage module is used for storing the collected alarm data; the data processing module is used for cleaning and preprocessing the data and quickly processing and responding to the real-time alarm data; and the data analysis module is used for analyzing the data, and analyzing repeated alarm conditions, crime rates, crime time distribution and crime conditions of all regions. According to the invention, the intelligent early warning module carries out early warning on possible alarm conditions based on comprehensive analysis of historical data and real-time monitored data, so that police officers are prompted to quickly intervene; and rapid capture and fixed-point strike of criminals are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent analysis of police situations, and in particular to a big data intelligent analysis system for police situations. Background Art

[0002] At present, there are many cases happening in society, and judicial personnel will analyze and compare various case information through some data systems to obtain useful information for the cases and further analyze and judge the cases.

[0003] An intelligent police situation analysis system disclosed in the existing publication number CN115577087A relates to the technical field of police situation analysis. A police situation collection module is set to collect archival files of historical cases stored in a police situation archive; a police situation information storage module is set to save the collected police situation archive data, and the police situation files are stored in order in chronological order; a key information extraction module is set to obtain key information factors of each police situation file by using regular matching, text matching and NLP technology; a police situation relevance analysis module is set to calculate the similarity of key information factors between each police situation file according to the key information factors extracted from different police situation files, and calculate the comprehensive similarity; a police situation information integration module is set to judge whether the case files are similar according to the comprehensive similarity; the problem of automatically identifying the relevance of cases is solved.

[0004] The existing technology can identify the relevance of cases to a certain extent, but it cannot make reasonable predictions about upcoming police incidents, nor can it intuitively display the police situation, and thus cannot reasonably dispatch police force and materials. For this reason, we propose a big data police situation intelligent analysis system. Summary of the invention

[0005] The purpose of the present invention is to provide a big data police intelligence analysis system to solve the problem that the prior art cannot reasonably predict the impending police situation, cannot intuitively display the police situation, and thus cannot reasonably dispatch police force and materials.

[0006] To achieve the above purpose, the present invention provides the following technical solution: a big data police intelligence analysis system, comprising:

[0007] A central processing unit, which is used to coordinate the work of each module;

[0008] A data collection module, which is used to collect structured and unstructured police data from various channels;

[0009] A data storage module, wherein the data storage module is used to store the collected alarm data;

[0010] A data processing module, which is used to clean and pre-process data, and quickly process and respond to real-time alarm data;

[0011] A data analysis module, which is used to analyze the data, including repeated police reports, crime rates, crime time distribution, and crime in various regions;

[0012] An intelligent early warning module, the intelligent early warning module is used to issue a crime early warning;

[0013] A correlation analysis module, which is used to analyze the correlation between similar cases;

[0014] A dispatch module, which is used to dispatch police forces and materials according to the police situation analyzed by the data analysis module;

[0015] A visualization module is used to display police data and analysis results.

[0016] Preferably, the channels through which the data acquisition module acquires data include alarm telephone, network media, and video surveillance.

[0017] Preferably, the data cleaning and preprocessing in the data processing module is to remove redundant information, fill in missing values, and standardize the data format.

[0018] Preferably, the data analysis module uses statistical methods to analyze crime rates and crime time distribution; uses a learning training model to predict future criminal activities; and uses a geographic information system to analyze the spatial distribution of crime, thereby determining the crime situation in each region.

[0019] Preferably, the intelligent early warning module is based on a comprehensive analysis of historical data and real-time monitoring data to warn of possible police situations, prompting police officers to intervene quickly.

[0020] Preferably, the visualization module intuitively displays the police situation in the form of maps and charts.

[0021] Preferably, the analytical method comprises the following steps:

[0022] S1. Data collection: Collecting alarm data from alarm calls, online media and video surveillance;

[0023] S2, data storage: storage of collected data and processed data;

[0024] S3, Data processing: Remove redundant information from the collected data, fill in missing values, standardize the data format; and quickly process and respond to real-time alarm data;

[0025] S4. Data analysis: Use statistical methods to analyze crime rates and crime time distribution; use learning training models to predict future criminal activities; use geographic information systems to analyze the spatial distribution of crime, thereby determining the crime situation in each region;

[0026] S5. Intelligent early warning: Comprehensive analysis of historical data and real-time monitoring data to warn of possible alarm situations;

[0027] S6. Relevance analysis: Analyze the relevance between cases of the same type, whether the same person is involved, and whether different people involved have similar methods of committing crimes;

[0028] S7. Personnel and material dispatch: Reasonably dispatch police forces and materials according to the predicted police situation trends;

[0029] S8. Display: Police data and analysis results are intuitively displayed in the form of maps and charts.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. Through the analysis of repeated police reports, crime rates, crime time distribution, and crime situations in various regions, on the one hand, we can clearly understand the police data in all aspects; on the other hand, we can provide a basis for subsequent police forecasts.

[0032] 2. The intelligent early warning module is based on a comprehensive analysis of historical data and real-time monitoring data, and warns of possible police situations, prompting police officers to intervene quickly; it achieves the rapid capture and targeted strikes on criminals.

[0033] 3. The dispatch module rationally dispatches police forces and materials according to the predicted police situation trends to improve response efficiency. The rational use of police forces can avoid arranging too many police forces in unnecessary places, avoiding waste of police forces, and also avoiding the problem of insufficient police forces in places where police forces are needed. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0035] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention.

[0037] See also Figure 1 In an embodiment of the present invention, a big data police intelligence analysis system includes:

[0038] A central processing unit, which is used to coordinate the work of each module;

[0039] A data collection module, which is used to collect structured and unstructured police data from various channels. The channels through which the data collection module obtains data include alarm calls, online media, and video surveillance;

[0040] A data storage module, wherein the data storage module is used to store the collected alarm data;

[0041] A data processing module, which is used to clean and pre-process data and quickly process and respond to real-time alarm data. The cleaning and pre-processing of data in the data processing module is to remove redundant information, fill in missing values, and standardize data formats;

[0042] Data analysis module, which is used to analyze data, including repeated police reports, crime rates, crime time distribution, and crime situations in various regions. On the one hand, it can clearly understand the police data in various aspects; on the other hand, it provides a basis for subsequent police forecasts. The data analysis module uses statistical methods to analyze crime rates and crime time distribution; uses learning and training models to predict future criminal activities; and uses geographic information systems to analyze the spatial distribution of crimes, thereby determining the crime situation in various regions;

[0043] Intelligent early warning module, which is used to issue crime warnings. The intelligent early warning module is based on a comprehensive analysis of historical data and real-time monitoring data to warn of possible police situations, prompting police officers to intervene quickly, and achieve rapid capture and targeted strikes on criminals;

[0044] A correlation analysis module, which is used to analyze the correlation between similar cases;

[0045] A dispatching module, which is used to dispatch police force and materials according to the police situation analyzed by the data analysis module; the rational use of police force can avoid arranging too many police forces in unnecessary places, avoiding the waste of police force; and also avoiding the problem of insufficient police force in places where police force is needed;

[0046] The visualization module is used to display the police situation data and analysis results. The visualization module intuitively displays the police situation in the form of maps and charts.

[0047] The analytical method includes the following steps:

[0048] S1. Data collection: Collecting alarm data from alarm calls, online media and video surveillance;

[0049] S2, data storage: storage of collected data and processed data;

[0050] S3, Data processing: Remove redundant information from the collected data, fill in missing values, standardize the data format; and quickly process and respond to real-time alarm data;

[0051] S4. Data analysis: Use statistical methods to analyze crime rates and crime time distribution; use learning training models to predict future criminal activities; use geographic information systems to analyze the spatial distribution of crime, thereby determining the crime situation in each region;

[0052] S5. Intelligent early warning: Comprehensive analysis of historical data and real-time monitoring data to warn of possible alarm situations;

[0053] S6. Relevance analysis: Analyze the relevance between cases of the same type, whether the same person is involved, and whether different people involved have similar methods of committing crimes; discover the connection between similar cases and assist in solving a series of cases or multiple related crimes;

[0054] S7. Personnel and material dispatch: According to the predicted trend of police situation, police force and materials are reasonably dispatched to improve response efficiency;

[0055] S8. Display: Police data and analysis results are displayed visually in the form of maps and charts, making it easier for police officers to understand them quickly and intuitively.

[0056] The working principle of the present invention is: collecting alarm data obtained from alarm calls, network media and video surveillance; storing the collected data and processed data; removing redundant information from the collected data, filling in missing values, and standardizing the data format; and quickly processing and responding to real-time alarm data; using statistical methods to analyze crime rates and crime time distribution; using learning and training models to predict future criminal activities; using geographic information systems to analyze the spatial distribution of crimes, so as to determine the crime situation in various regions; comprehensive analysis of historical data and real-time monitoring data to warn of possible alarms; analyzing the correlation between cases of the same type, whether the same person is involved, and whether different people involved have similar methods of committing crimes; reasonably dispatching police forces and materials according to predicted alarm trends; and intuitively displaying alarm data and analysis results in the form of maps and charts.

[0057] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A big data police intelligence analysis system, characterized in that: include: A central processing unit, which is used to coordinate the work of each module; A data collection module, which is used to collect structured and unstructured police data from various channels; A data storage module, wherein the data storage module is used to store the collected alarm data; A data processing module, which is used to clean and pre-process data, and quickly process and respond to real-time alarm data; A data analysis module, which is used to analyze the data, including repeated police reports, crime rates, crime time distribution, and crime in various regions; An intelligent early warning module, the intelligent early warning module is used to issue a crime early warning; A correlation analysis module, which is used to analyze the correlation between similar cases; A dispatch module, which is used to dispatch police forces and materials according to the police situation analyzed by the data analysis module; A visualization module is used to display police data and analysis results.

2. According to the big data police intelligence analysis system of claim 1, it is characterized in that: The data acquisition module obtains data through channels including alarm calls, network media, and video surveillance.

3. According to claim 1, a big data police intelligence analysis system is characterized in that: The data processing module cleans and preprocesses the data to remove redundant information, fill in missing values, and standardize the data format.

4. According to claim 1, a big data police intelligence analysis system is characterized in that: The data analysis module uses statistical methods to analyze the crime rate and crime time distribution; Learning training models are used to predict future criminal activities; geographic information systems are used to analyze the spatial distribution of crime, thereby determining the crime situation in each region.

5. According to claim 1, a big data police intelligence analysis system is characterized in that: The intelligent early warning module is based on a comprehensive analysis of historical data and real-time monitoring data to warn of possible police situations, prompting police officers to intervene quickly.

6. The big data police intelligence analysis system according to claim 1 is characterized in that: The visualization module is used to intuitively display the police situation in the form of maps and charts.

7. The big data police intelligence analysis system according to claim 1 is characterized in that: The analysis method includes the following steps: S1. Data collection: Collecting alarm data from alarm calls, online media and video surveillance; S2, data storage: storage of collected data and processed data; S3, Data processing: Remove redundant information from the collected data, fill in missing values, standardize the data format; and quickly process and respond to real-time alarm data; S4. Data analysis: Use statistical methods to analyze crime rates and crime time distribution; use learning training models to predict future criminal activities; use geographic information systems to analyze the spatial distribution of crime, thereby determining the crime situation in each region; S5. Intelligent early warning: Comprehensive analysis of historical data and real-time monitoring data to warn of possible alarm situations; S6. Relevance analysis: Analyze the relevance between cases of the same type, whether the same person is involved, and whether different people involved have similar methods of committing crimes; S7. Personnel and material dispatch: Reasonably dispatch police forces and materials according to the predicted police situation trends; S8. Display: Police data and analysis results are intuitively displayed in the form of maps and charts.

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