Public security case data processing method and device, electronic equipment and medium

Abstract

The embodiment of the invention provides a public security case data processing method and device, electronic equipment and a medium. The method comprises the steps that multiple pieces of public security case data occurring in a target city are acquired; dividing the target city into a plurality of statistical areas based on case occurrence sites of public security cases in the public security case data; performing clustering processing on the public security case data according to a plurality of selected clustering themes in each statistical region to obtain a data cluster corresponding to each clustering theme; and determining the public security risk level of each statistical area based on the data clusters.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method for processing public security case data, a device for processing public security case data, an electronic device, and a storage medium. Background Art

[0002] With the acceleration of urbanization, urban security issues are becoming increasingly complex and severe. Traditional urban security management mainly relies on manual statistics and empirical judgment, which has at least the following defects:

[0003] On the one hand, manual statistics are inefficient and difficult to process large-scale public security data. In addition, public security data are of various types. It is difficult to intuitively obtain effective key information by relying solely on manual statistics, which is not conducive to a macro view of the overall public security situation distribution in the city. On the other hand, empirical judgments are often subjective and lack scientific basis and accuracy, which reduces the credibility of data analysis results obtained by manual statistics and analysis. Summary of the invention

[0004] In view of the above problems, a method and device for processing public security case data, an electronic device, and a medium are proposed to overcome the above problems or at least partially solve the above problems, including:

[0005] A method for processing public security case data, the method comprising:

[0006] Obtain data on multiple public security cases that occurred in the target city;

[0007] Dividing the target city into a plurality of statistical areas based on the locations where the public security cases in the plurality of public security case data occurred;

[0008] In each statistical area, clustering is performed on the public security case data according to the selected multiple clustering themes to obtain a data cluster corresponding to each clustering theme;

[0009] A public security risk level for each statistical area is determined based on the data clusters.

[0010] Optionally, the target city is divided into a plurality of statistical areas based on the locations where the public security cases in the plurality of public security case data occurred, including:

[0011] Determine a plurality of initial cluster centers based on the locations where the public security cases in the plurality of public security case data occurred;

[0012] Determine the distance information from each case location to each initial cluster center:

[0013] Allocating each case occurrence location to the cluster corresponding to the initial cluster center based on the distance information;

[0014] Update the initial cluster center according to the location of each case in each cluster;

[0015] Perform iterative clustering based on the updated cluster centers;

[0016] When a preset iteration termination condition is met during the iterative clustering process, the target city is divided into a plurality of statistical areas according to the current clustering.

[0017] Optionally, determining the public security risk level of each statistical area based on the data cluster includes:

[0018] Get the weight information corresponding to each cluster topic;

[0019] Determining a public security index value for each clustering topic based on the data cluster;

[0020] The weight information is used to perform weighted summation on the public security index values ​​to obtain the public security risk level of each statistical area.

[0021] Optionally, it also includes:

[0022] When the public security risk level of the target statistical area is higher than a preset risk level, a target public security management measure corresponding to the public security risk level is determined from a plurality of preset public security management measures.

[0023] Optionally, it also includes:

[0024] A visualization chart is generated based on the data cluster and / or the public security risk level, and the visualization chart is displayed.

[0025] Optionally, generating a visual chart based on the data cluster and / or the public security risk level includes:

[0026] generating a heat map based on the public security risk level, wherein different colors are used in the heat map to represent different public security risk levels;

[0027] Or generate a case type pie chart based on data clusters with case type as the clustering theme;

[0028] Or generate a time series graph based on data clusters clustered by the time of case occurrence.

[0029] Optionally, it also includes:

[0030] The plurality of public security case data are preprocessed, and the preprocessing methods include but are not limited to: data cleaning, data denoising, and data standardization.

[0031] A device for processing public security case data, the device comprising:

[0032] The public security case data acquisition module is used to obtain data on multiple public security cases that occurred in the target city;

[0033] A statistical area division module, used for dividing the target city into a plurality of statistical areas based on the locations where the public security cases in the plurality of public security case data occurred;

[0034] A data clustering module is used to perform clustering processing on the public security case data in each statistical area according to a plurality of selected clustering themes to obtain a data cluster corresponding to each clustering theme;

[0035] The public security risk level determination module is used to determine the public security risk level of each statistical area based on the data cluster.

[0036] An electronic device comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein the computer program implements the method for processing public security case data as described above when executed by the processor.

[0037] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for processing public security case data as described above is implemented.

[0038] The embodiments of the present invention have the following advantages:

[0039] In an embodiment of the present invention, multiple public security case data occurring in a target city are obtained; and then based on the locations where the public security cases in the multiple public security case data occurred, the target city can be divided into multiple statistical areas; and in each statistical area, the public security case data can be clustered according to multiple selected clustering topics to obtain a data cluster corresponding to each clustering topic; and then based on the data cluster, the public security risk level of each statistical area is determined, thereby realizing automatic analysis based on the collected public security case data to determine the public security risk level of each statistical area divided in the target city, thereby realizing determination of the overall public security form distribution of the target city, and no manual access is required during the entire process, and objective and accurate data analysis results can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0041] Figure 1 It is a flowchart of a method for processing public security case data provided by one embodiment of the present invention;

[0042] Figure 2 is a flowchart of another method for processing public security case data provided by one embodiment of the present invention;

[0043] Figure 3 is a flowchart of another method for processing public security case data provided by one embodiment of the present invention;

[0044] Figure 4 It is a schematic diagram of the structure of a device for processing public security case data provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all 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.

[0046] Reference Figure 1 , shows a flowchart of a method for processing public security case data provided by an embodiment of the present invention, which may specifically include the following steps:

[0047] Step S101, obtaining data of multiple public security cases occurring in a target city;

[0048] In practical applications, in order to achieve security control of the city, data on multiple public security cases that occurred in the target city can be obtained and analyzed to determine the public security situation of the target city.

[0049] Among them, the city's public security case data may include but is not limited to any one or more of the following: basic case information (case type, time of occurrence, location, persons involved, etc.), alarm records (alarm time, location, type, etc.), patrol data (patrol route, time, problems found, etc.), surveillance video data and other multi-source data.

[0050] In one embodiment of the present invention, after obtaining the public security case data, the plurality of public security case data may be preprocessed so that standardized processing can be performed based on the preprocessed data in subsequent steps, wherein the preprocessing method includes but is not limited to one or more of the following:

[0051] Data cleaning, data denoising, and data standardization.

[0052] Data cleaning can be used to remove duplicate data, erroneous data, and outliers. For example, by comparing the timestamps and location information of the data, duplicate alarm records can be identified and deleted; statistical methods can be used to detect and remove abnormal data that is significantly deviated from the normal range.

[0053] Data denoising can use filtering technology to reduce noise interference in data. For example, for surveillance video data, median filtering, Gaussian filtering and other methods can be used to remove noise in the image and improve data quality.

[0054] Data standardization can standardize data from different sources to make them have a unified format and unit. For example, the time of case occurrence can be uniformly converted into a specific time format, and the location information can be converted into a unified geographic coordinate system.

[0055] Step S102, dividing the target city into a plurality of statistical areas based on the locations where the public security cases in the plurality of public security case data occurred;

[0056] After obtaining the public security case data, the public security case data may at least include the location where each public security case occurred. The location where the case occurred can be represented by longitude and latitude information. Each public security case can be divided into urban areas based on the location where the case occurred as a data point, and the target city can be divided into multiple statistical areas.

[0057] In practical applications, a preset first clustering algorithm can be selected to implement the zoning of the target city and obtain multiple statistical areas. The first clustering algorithm can be set according to the actual scenario, and there is no limitation on this in the embodiment of the present invention. One or more public security case data can be included in each statistical area. The statistical area can be used to centrally manage multiple public security case data with similar locations. In the embodiment of the present invention, data analysis can be performed for each statistical area to determine the public security characteristics of each statistical area, and the overall public security situation of the target city can also be analyzed by analyzing multiple different statistical areas.

[0058] Step S103, in each statistical area, clustering the public security case data according to the selected multiple clustering themes to obtain a data cluster corresponding to each clustering theme;

[0059] After obtaining multiple statistical areas of the target city, each statistical area can be analyzed separately. Specifically, multiple clustering topics can be set according to the indicators used to evaluate urban safety and public security. Then, each statistical area can be clustered in turn according to the selected clustering topics to obtain data clusters corresponding to different clustering topics. The clustering topics may include but are not limited to the time of case occurrence, case type, etc.

[0060] In the embodiment of the present invention, within the statistical area, a preset second clustering algorithm can be used to implement clustering processing according to clustering themes, and each statistical area obtains a corresponding data cluster after classification by clustering theme, and the data cluster can reflect the distribution of the corresponding clustering theme within the statistical area. Among them, the second clustering algorithm can be set according to the actual scenario, and there is no restriction on this in the embodiment of the present invention, and the second clustering algorithm can be the same as the first clustering algorithm, or different from the first clustering algorithm.

[0061] For example, taking the time of case occurrence as the clustering theme, after clustering analysis of different types of public security data in a statistical area (i.e., different types of public security data contained in a data cluster) based on the K-Means algorithm (a clustering algorithm) and the time of case occurrence as the clustering theme, multiple data clusters corresponding to different time periods can be obtained. Each data cluster corresponds to a time period when the case occurred, and the number of cases contained in a data cluster can reflect the frequency of case occurrence in the time period when the case occurred corresponding to the data cluster (the more cases, the higher the frequency of case occurrence corresponding to the time period when the case occurred), so as to determine the frequency of case occurrence in each time period when the case occurred in a statistical area.

[0062] Step S104: determining the public security risk level of each statistical area based on the data cluster.

[0063] After clustering to obtain the data clusters of each statistical area, the public security risk level of each statistical area can be determined from multiple perspectives by combining the data clusters obtained from different clustering themes.

[0064] In one embodiment of the present invention, when the public security risk level of the target statistical area is higher than a preset risk level, a target public security management measure corresponding to the public security risk level is determined from a plurality of preset public security management measures.

[0065] In actual applications, public security management of the target city has not been realized. A variety of public security management measures can be pre-set, and different public security management measures can deal with different degrees of public security risks. Therefore, after analyzing and determining the public security risk level of each statistical area, it can be determined whether the public security risk level is higher than the preset risk level. The preset risk level is a critical level for distinguishing whether the current risk needs to be managed by management measures. If the public security risk level is higher than the preset risk level, it is necessary to determine the public security management measures. Therefore, the target public security management measures corresponding to the public security risk level can be determined from multiple preset public security management measures, and then the target public security management measures can be applied; and if the public security risk level is lower than or equal to the preset risk level, it means that the public security status of the current statistical area is good and there is no need to access public security management measures.

[0066] Among them, public security management measures may include but are not limited to: dispatching and arranging more police forces to conduct key security patrols in the statistical area during the time period of high incidence of cases in the statistical area; adding monitoring equipment to key areas where cases frequently occur in the statistical area to carry out key monitoring, etc.

[0067] In actual application, if the public security risk level is higher than the preset risk level, corresponding public security management measures can be selected according to different levels.

[0068] In one embodiment of the present invention, a visualization chart is generated based on the data cluster and / or the public security risk level, and the visualization chart is displayed. The visualization chart enables public security management personnel to intuitively determine the public security situation in the target city.

[0069] Specifically, a heat map can be generated based on the public security risk level, in which different colors are used to represent different public security risk levels. A case type pie chart can be generated based on a data cluster with case type as the cluster theme to show the distribution of different case types in each cluster. A time series chart can be generated based on a data cluster with case occurrence time as the cluster theme to show the trend of crime rate over time, etc.

[0070] In one embodiment of the present invention, by comparing the same cluster analysis results in different statistical areas, it is also possible to clarify the overall distribution of public security indicators corresponding to the cluster analysis results in different statistical areas within the city; for example, taking the cluster analysis results with "case occurrence time" as the clustering theme as an example, based on the cluster analysis results, the high-incidence time period of case occurrence frequency in each statistical area (that is, the public security indicators corresponding to the above cluster analysis results) can be determined, so that on the map of the target city, the statistical areas with the same high-incidence time period can be marked with the same color, and different colors can be used for different high-incidence time periods, so that public security management personnel can very intuitively see the distribution of the overall high-incidence time period of cases in the target city from the map.

[0071] In one embodiment of the present invention, after dividing the target city into multiple statistical areas, the regional public security characteristics corresponding to each statistical area can be clarified by comparing the cluster analysis results of public security data of different cluster themes in the same statistical area.

[0072] Furthermore, by comparing the same cluster analysis results in different statistical areas, the overall distribution of public security indicators corresponding to the cluster analysis results in different statistical areas within the city can be clarified.

[0073] In practical applications, cluster analysis results of public security data with different cluster themes in the same statistical area and the same cluster analysis results in different statistical areas can also be presented to public security management personnel in the form of visual charts, so that public security case managers can easily view the target visual charts corresponding to specific comparison results through keyword searches, thereby effectively improving the data analysis efficiency of urban public security data and the accuracy of data analysis results.

[0074] In an embodiment of the present invention, multiple public security case data occurring in a target city are obtained; and then based on the locations where the public security cases in the multiple public security case data occurred, the target city can be divided into multiple statistical areas; and in each statistical area, the public security case data can be clustered according to multiple selected clustering topics to obtain a data cluster corresponding to each clustering topic; and then based on the data cluster, the public security risk level of each statistical area is determined, thereby realizing automatic analysis based on the collected public security case data to determine the public security risk level of each statistical area divided in the target city, thereby realizing determination of the overall public security form distribution of the target city, and no manual access is required during the entire process, and objective and accurate data analysis results can be obtained.

[0075] Reference Figure 2 , shows a flowchart of another method for processing public security case data provided by an embodiment of the present invention, which may specifically include the following steps:

[0076] Step S201, obtaining data of multiple public security cases occurring in the target city;

[0077] In practical applications, in order to achieve security control of the city, data on multiple public security cases that occurred in the target city can be obtained and analyzed to determine the public security situation of the target city.

[0078] Among them, the city's public security case data may include but is not limited to any one or more of the following: basic case information (case type, time of occurrence, location, persons involved, etc.), alarm records (alarm time, location, type, etc.), patrol data (patrol route, time, problems found, etc.), surveillance video data and other multi-source data.

[0079] Step S202, determining a plurality of initial cluster centers based on the locations where the public security cases in the plurality of public security case data occurred;

[0080] In practical applications, the number of clusters K can be determined first. Specifically, the elbow rule, silhouette coefficient method, etc. can be used to determine the appropriate number of clusters. For example, by calculating the sum of squared errors (SSE) within clusters under different K values, a curve of SSE changing with K value is plotted. The K value corresponding to the inflection point of the curve is the more appropriate number of clusters.

[0081] Furthermore, the initial cluster center can be determined according to the number of clusters K based on the locations where the public security cases in the plurality of public security case data occurred.

[0082] Specifically, the initial cluster centers can be determined by random initialization or initialization methods based on data distribution. For example, K data points are randomly selected from the data set as the initial cluster centers, or points in areas with high data density are selected as the initial cluster centers based on the distribution characteristics of the data.

[0083] Step S203, determining the distance information from each case occurrence location to each initial cluster center:

[0084] After determining the initial cluster center, the distance information from each case location to each initial cluster can be calculated based on the coordinate information of each case location, so as to achieve data aggregation based on the distance information. The distance calculation can be performed using methods such as Euclidean distance and Manhattan distance.

[0085] Step S204, assigning each case occurrence location to the cluster corresponding to the initial cluster center based on the distance information;

[0086] After calculating the distance information, each case location can be assigned with the initial cluster center as the core position. Specifically, each case location can be assigned to the cluster to which the nearest initial cluster center belongs.

[0087] Step S205, updating the initial cluster center according to the location of each case in each cluster;

[0088] After the initial clustering, the mean of all data points in each cluster can be recalculated to determine the new cluster center in the cluster.

[0089] Step S206, performing iterative clustering according to the updated cluster centers;

[0090] After the cluster center is updated, iterative clustering can be continued, that is, the iterative process is performed according to the aforementioned steps S203, S204, and S205.

[0091] Step S207, when a preset iteration termination condition is met during the iterative clustering process, the target city is divided into a plurality of statistical areas according to the current clustering.

[0092] The preset iteration termination condition may be that the iteration is terminated until the cluster center no longer changes or the preset number of iterations is reached, the corresponding cluster is output, and the statistical area is divided according to the cluster.

[0093] Step S208, in each statistical area, clustering the public security case data according to the selected multiple clustering themes to obtain a data cluster corresponding to each clustering theme;

[0094] Step S209: determining the public security risk level of each statistical area based on the data cluster.

[0095] In an embodiment of the present invention, the first clustering algorithm used to divide the statistical area can be a K-Means algorithm. The statistical area can be clustered based on the data source as the clustering theme, and the above-mentioned urban public security related data are clustered based on the K-Means algorithm to obtain multiple data clusters; wherein the urban public security related data of each data cluster belongs to data with a similar data source, and the clustering area composed of the data source of these urban public security related data contained in a data cluster can be used as a statistical area corresponding to the data cluster.

[0096] In the implementation of the present invention, multiple initial clustering centers can be determined based on the locations of public security cases in the multiple public security case data; the distance information from each case location to each initial clustering center can be determined: each case location is assigned to the cluster corresponding to the initial clustering center based on the distance information; the initial clustering centers are updated according to the locations of each case in each cluster; iterative clustering is performed based on the updated clustering centers; when a preset iteration termination condition is met during the iterative clustering process, the target city is divided into multiple statistical areas according to the current clustering, thereby realizing zoning processing of the target city and avoiding data dispersion and inaccurate analysis results.

[0097] Reference Figure 3 , shows a flowchart of another method for processing public security case data provided by an embodiment of the present invention, which may specifically include the following steps:

[0098] Step S301, obtaining data of multiple public security cases occurring in the target city;

[0099] In practical applications, in order to achieve security control of the city, data on multiple public security cases that occurred in the target city can be obtained and analyzed to determine the public security situation of the target city.

[0100] Among them, the city's public security case data may include but is not limited to any one or more of the following: basic case information (case type, time of occurrence, location, persons involved, etc.), alarm records (alarm time, location, type, etc.), patrol data (patrol route, time, problems found, etc.), surveillance video data and other multi-source data.

[0101] Step S302, dividing the target city into a plurality of statistical areas based on the locations where the public security cases in the plurality of public security case data occurred;

[0102] Step S303, in each statistical area, clustering the public security case data according to the selected multiple clustering themes to obtain a data cluster corresponding to each clustering theme;

[0103] Step S304, obtaining weight information corresponding to each cluster topic;

[0104] In practical applications, different clustering topics can be set with different weight information, which is used to feedback the impact of the clustering topic on urban security. The greater the impact, the higher the weight value.

[0105] The specific weight value can be set according to the actual application scenario, and there is no need to impose too many restrictions on this in the embodiments of the present invention.

[0106] Step S305, determining the public security index value of each clustering topic based on the data cluster;

[0107] The public security index value may be the number of cases distributed in the clustering theme in the data cluster.

[0108] Step S306: Using the weight information to perform weighted summation on the public security index values ​​to obtain the public security risk level of each statistical area.

[0109] For each statistical area, in the statistical area, each cluster analysis result can reflect a public security indicator, such as the "frequency of case occurrence in each case occurrence time period" can be used to determine the "high-incidence period of cases" in the public security indicators.

[0110] Therefore, according to the specific index value of each public security index (determined by the above cluster analysis results) and the index weight corresponding to each public security index, a weighted summation result that can comprehensively represent the public security risk level of a statistical area can be obtained through weighted summation. The larger the value of the weighted summation result, the higher the public security risk level of the statistical area, and the more it needs to focus on strengthening public security management.

[0111] In an embodiment of the present invention, multiple public security case data occurring in a target city are obtained; based on the locations of the public security cases in the multiple public security case data, the target city is divided into multiple statistical areas; in each statistical area, the public security case data is clustered according to multiple selected clustering topics to obtain a data cluster corresponding to each clustering topic; weight information corresponding to each clustering topic is obtained; the public security index value of each clustering topic is determined based on the data cluster; the public security index value is weighted and summed using the weight information to obtain the public security risk level of each statistical area. Thus, the public security risk level of each statistical area can be intuitively and accurately calculated based on the analysis of the public security case data. The efficiency of public security management is improved.

[0112] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0113] Reference Figure 4 , shows a schematic diagram of the structure of a device for processing public security case data provided by an embodiment of the present invention, which may specifically include the following modules:

[0114] The public security case data acquisition module 401 is used to acquire data of multiple public security cases occurring in the target city;

[0115] A statistical area division module 402 is used to divide the target city into a plurality of statistical areas based on the locations where the public security cases in the plurality of public security case data occurred;

[0116] The data clustering module 403 is used to perform clustering processing on the public security case data in each statistical area according to the selected multiple clustering themes to obtain a data cluster corresponding to each clustering theme;

[0117] The public security risk level determination module 404 is used to determine the public security risk level of each statistical area based on the data cluster.

[0118] In one embodiment of the present invention, the statistical area division module 402 may include the following submodules:

[0119] A submodule for determining the initial distance, used for determining a plurality of initial cluster centers based on the locations where the public security cases in the plurality of public security case data occurred;

[0120] The distance information determination submodule is used to determine the distance information from the location of each case to each initial cluster center:

[0121] A data clustering submodule, for assigning each case occurrence location to a cluster corresponding to the initial cluster center based on the distance information;

[0122] The cluster center update submodule is used to update the initial cluster center according to the location of each case in each cluster;

[0123] Iterative clustering submodule, used for iterative clustering according to the updated cluster centers;

[0124] The statistical area division submodule is used to divide the target city into multiple statistical areas according to the current clustering when a preset iteration termination condition is met during the iterative clustering process.

[0125] In one embodiment of the present invention, the public security risk level determination module 404 may include the following submodules:

[0126] The weight information acquisition submodule is used to obtain the weight information corresponding to each cluster topic;

[0127] A public security index value determination submodule, used to determine the public security index value of each clustering topic based on the data cluster;

[0128] The public security risk level determination submodule is used to use the weight information to perform weighted summation on the public security index values ​​to obtain the public security risk level of each statistical area.

[0129] In one embodiment of the present invention, the device further includes the following modules:

[0130] The public security management measure determination module is used to determine a target public security management measure corresponding to the public security risk level from a plurality of preset public security management measures when the public security risk level of the target statistical area is higher than the preset risk level.

[0131] In one embodiment of the present invention, the device may further include:

[0132] A visualization chart display module is used to generate a visualization chart based on the data cluster and / or the public security risk level, and display the visualization chart.

[0133] In one embodiment of the present invention, the visual chart display module may include the following submodules:

[0134] A heat map generation submodule, used to generate a heat map based on the public security risk level, wherein different colors are used to represent different public security risk levels in the heat map;

[0135] or a case type pie chart generation submodule, for generating a case type pie chart based on a data cluster with case type as a clustering theme;

[0136] Or the time series graph generation submodule is used to generate a time series graph based on data clusters with the case occurrence time as the clustering theme.

[0137] In an embodiment of the present invention, the device may further include the following submodules:

[0138] The preprocessing module is used to preprocess the multiple public security case data, and the preprocessing methods include but are not limited to: data cleaning, data denoising, and data standardization.

[0139] In an embodiment of the present invention, multiple public security case data occurring in a target city are obtained; and then based on the locations where the public security cases in the multiple public security case data occurred, the target city can be divided into multiple statistical areas; and in each statistical area, the public security case data can be clustered according to multiple selected clustering topics to obtain a data cluster corresponding to each clustering topic; and then based on the data cluster, the public security risk level of each statistical area is determined, thereby realizing automatic analysis based on the collected public security case data to determine the public security risk level of each statistical area divided in the target city, thereby realizing determination of the overall public security form distribution of the target city, and no manual access is required during the entire process, and objective and accurate data analysis results can be obtained.

[0140] In an embodiment of the present invention, multiple public security case data occurring in a target city are obtained; and then based on the locations where the public security cases in the multiple public security case data occurred, the target city can be divided into multiple statistical areas; and in each statistical area, the public security case data can be clustered according to multiple selected clustering topics to obtain a data cluster corresponding to each clustering topic; and then based on the data cluster, the public security risk level of each statistical area is determined, thereby realizing automatic analysis based on the collected public security case data to determine the public security risk level of each statistical area divided in the target city, thereby realizing determination of the overall public security form distribution of the target city, and no manual access is required during the entire process, and objective and accurate data analysis results can be obtained.

[0141] An embodiment of the present invention further provides an electronic device, which may include a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the computer program is executed by the processor, the above-mentioned method for processing public security case data is implemented.

[0142] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for processing public security case data as described above is implemented.

[0143] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0144] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0145] Those skilled in the art will appreciate that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0146] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0147] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0149] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0150] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0151] The above is a detailed introduction to a method and device for processing public security case data, an electronic device, and a medium. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for a person skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for processing public security case data, characterized in that: The method comprises: Obtain data on multiple public security cases that occurred in the target city; Dividing the target city into a plurality of statistical areas based on the locations where the public security cases in the plurality of public security case data occurred; In each statistical area, clustering is performed on the public security case data according to the selected multiple clustering themes to obtain a data cluster corresponding to each clustering theme; A public security risk level for each statistical area is determined based on the data clusters.

2. The method according to claim 1, characterized in that The target city is divided into a plurality of statistical areas based on the locations of the public security cases in the plurality of public security case data, including: Determine a plurality of initial cluster centers based on the locations where the public security cases in the plurality of public security case data occurred; Determine the distance information from each case location to each initial cluster center: Allocating each case occurrence location to the cluster corresponding to the initial cluster center based on the distance information; Update the initial cluster center according to the location of each case in each cluster; Perform iterative clustering based on the updated cluster centers; When a preset iteration termination condition is met during the iterative clustering process, the target city is divided into a plurality of statistical areas according to the current clustering.

3. The method according to claim 1 or 2, characterized in that: Determining the public security risk level of each statistical area based on the data cluster includes: Get the weight information corresponding to each cluster topic; Determining a public security index value for each clustering topic based on the data cluster; The weight information is used to perform weighted summation on the public security index values ​​to obtain the public security risk level of each statistical area.

4. The method according to claim 1 or 2, characterized in that: Also includes: When the public security risk level of the target statistical area is higher than a preset risk level, a target public security management measure corresponding to the public security risk level is determined from a plurality of preset public security management measures.

5. The method according to claim 1, characterized in that Also includes: A visualization chart is generated based on the data cluster and / or the public security risk level, and the visualization chart is displayed.

6. The method according to claim 5, characterized in that The generating of a visualization chart based on the data cluster and / or the public security risk level includes: generating a heat map based on the public security risk level, wherein different colors are used in the heat map to represent different public security risk levels; Or generate a case type pie chart based on data clusters with case type as the clustering theme; Or generate a time series graph based on data clusters clustered by the time of case occurrence.

7. The method according to claim 1, characterized in that Also includes: The plurality of public security case data are preprocessed, and the preprocessing methods include but are not limited to: data cleaning, data denoising, and data standardization.

8. A device for processing public security case data, characterized in that: The device comprises: The public security case data acquisition module is used to obtain data on multiple public security cases that occurred in the target city; A statistical area division module, used for dividing the target city into a plurality of statistical areas based on the locations where the public security cases in the plurality of public security case data occurred; A data clustering module is used to perform clustering processing on the public security case data in each statistical area according to a plurality of selected clustering themes to obtain a data cluster corresponding to each clustering theme; The public security risk level determination module is used to determine the public security risk level of each statistical area based on the data cluster.

9. An electronic device, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the method for processing public security case data as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for processing public security case data as described in any one of claims 1 to 7 is implemented.

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