Analytical method, device, computer equipment, storage medium and program product

Abstract

The present application relates to an analysis method, device, computer equipment, storage medium and computer program product. The method comprises: obtaining violation data and violation governance data of a target area; determining a first grid heat map of the target area according to the violation data, and determining a second grid heat map of the target area according to the violation governance data; generating and displaying a target grid heat map according to the first grid heat map and the second grid heat map, and the target grid heat map is used to characterize the violation governance situation of the target area. The use of this method can effectively improve the reliability and accuracy of urban traffic analysis, thereby effectively improving the management efficiency of urban traffic.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an analysis method, apparatus, computer equipment, storage medium and computer program product. Background Art

[0002] With the increase in the number of feedback-type alarms, the emergency alarm channel of the traffic alarm station has been severely squeezed. Although illegal parking, vehicles with alcohol, license plates, and certificates involved in feedback-type alarms have been regulated, the number of reaction-type alarms has not been effectively reduced. Therefore, there is a deviation between the focus of governance and the hot spots of feedback.

[0003] In the prior art, most of the urban traffic is analyzed based on the feedback alarm or the historical experience of law enforcement personnel, so as to manage the urban traffic according to the analysis results.

[0004] However, this method not only has low accuracy but also poor reliability, which leads to low efficiency in urban traffic management. Summary of the invention

[0005] Based on this, it is necessary to provide an analysis method, device, computer equipment, computer-readable storage medium and computer program product that can improve the management efficiency of urban traffic in response to the above-mentioned technical problems.

[0006] In a first aspect, the present application provides an analysis method, comprising:

[0007] Obtain violation data and violation management data of a target area; determine a first grid heat map of the target area based on the violation data, and determine a second grid heat map of the target area based on the violation management data; generate and display a target grid heat map based on the first grid heat map and the second grid heat map, and the target grid heat map is used to characterize the violation management situation of the target area.

[0008] In one of the embodiments, the first grid heat map of the target area is determined based on the violation data, and the second grid heat map of the target area is determined based on the violation governance data, including: gridding the target area to obtain multiple grid sub-areas; determining location information of the violation based on the violation data, and determining location information of the governance behavior based on the violation governance data; generating the first grid heat map and the second grid heat map based on the location information of the violation, the location information of the governance behavior and the multiple grid sub-areas.

[0009] In one of the embodiments, the first grid heat map and the second grid heat map are generated based on the location information of the violation, the location information of the governance behavior and the multiple grid sub-areas, including: determining a first number of violations in each of the grid sub-areas based on the location information of the violation and the location information of the multiple grid sub-areas; determining a second number of governance behaviors in each of the grid sub-areas based on the location information of the governance behavior and the location information of the multiple grid sub-areas; rendering the multiple grid sub-areas based on the first number, the second number and a preset rendering rule to obtain the first grid heat map and the second grid heat map, the rendering rule including a correspondence between quantity and color.

[0010] In one embodiment, the first grid heat map of the target area is determined according to the violation data, and the second grid heat map of the target area is determined according to the violation governance data, including: inputting the violation data and the violation governance data into a pre-trained natural language processing model to obtain a first label corresponding to the violation data and a second label corresponding to the violation governance data output by the natural language processing model; according to preset labels, the first label and the second label are used to screen the violation data and the violation governance data to determine target violation data and target violation governance data; determining the first grid heat map of the target area according to the target violation data, and determining the second grid heat map of the target area according to the target violation governance data.

[0011] In one of the embodiments, the target grid heat map is generated and displayed based on the first grid heat map and the second grid heat map, including: for a first grid sub-area in the first grid heat map, determining a second grid sub-area corresponding to the first grid sub-area in the second grid heat map, the first grid sub-area being any grid sub-area in the first grid heat map; according to a preset fusion rule, the color of the first grid sub-area and the color of the second grid sub-area are rendered on the colors of multiple grid sub-areas in the initial grid heat map to generate the target grid heat map.

[0012] In one of the embodiments, the method further includes: determining a target enforcement strategy according to the target grid heat map, and enforcing the target area based on the target enforcement strategy.

[0013] In a second aspect, the present application also provides an analysis device, comprising:

[0014] An acquisition module, used to acquire violation data and violation governance data of a target area;

[0015] A determination module, used to determine a first grid heat map of the target area according to the violation data, and to determine a second grid heat map of the target area according to the violation management data;

[0016] The execution module is used to generate and display a target grid heat map based on the first grid heat map and the second grid heat map, and the target grid heat map is used to characterize the governance of illegal behaviors in the target area.

[0017] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements any of the methods described in the first aspect when executing the computer program.

[0018] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any of the methods described in the first aspect above.

[0019] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the methods described in the first aspect above.

[0020] The above-mentioned analysis method, device, computer equipment, storage medium and computer program product obtain the violation data and violation management data of the target area; determine the first grid heat map of the target area according to the violation data, and determine the second grid heat map of the target area according to the violation management data; generate and display the target grid heat map according to the first grid heat map and the second grid heat map, and the target grid heat map is used to characterize the violation management situation of the target area. The analysis method provided by the present application determines the first grid heat map and the second grid heat map based on the violation data and the violation management data of the target area, respectively, and then generates the target grid heat map based on the first grid heat map and the second grid heat map. Since the target grid heat map can be used to characterize the violation management situation of the target area, the traffic analysis result of the target area can be determined based on the target grid heat map. The analysis method provided by the present application effectively improves the accuracy and reliability of urban traffic analysis, thereby effectively improving the management efficiency of urban traffic. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 A schematic diagram of a flow chart of an analysis method in an embodiment;

[0023] Figure 2 A schematic diagram of a flow chart of a method for determining a first grid heat map and a second grid heat map in one embodiment;

[0024] Figure 3 A flow chart of a method for generating the first grid heat map and the second grid heat map according to the location information of the illegal behavior, the location information of the governance behavior and the multiple grid sub-areas in one embodiment;

[0025] Figure 4 A schematic flow chart of a method for determining a first grid heat map and a second grid heat map in another embodiment;

[0026] Figure 5 A schematic diagram of a process for generating and displaying the target grid heat map in one embodiment;

[0027] Figure 6 is a schematic flow chart of an analysis method in another embodiment;

[0028] Figure 7 is a structural block diagram of an analysis device in one embodiment;

[0029] Figure 8 is an internal structure diagram of a computer device in one embodiment;

[0030] Fig. 9 is a schematic diagram of a first grid heat map in one embodiment;

[0031] Fig.10 Schematic diagram of a second grid heat map in one embodiment. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0033] With the increase in the number of feedback-type alarms, the emergency alarm channel of the traffic alarm station has been severely squeezed. Although illegal parking, vehicles with alcohol, license plates, and certificates involved in feedback-type alarms have been regulated, the number of reaction-type alarms has not been effectively reduced. Therefore, there is a deviation between the focus of governance and the hot spots of feedback.

[0034] In the prior art, most of the urban traffic is analyzed based on feedback alarms or the historical experience of law enforcement personnel, and urban traffic is managed based on the analysis results. For example, if the traffic alarm station receives more feedback alarms in area A, it will determine that area A needs to increase management efforts. Or, if law enforcement personnel determine that there is a greater possibility of violations in area B based on historical law enforcement experience, they will increase management efforts in area B.

[0035] However, since this method analyzes urban traffic through reactive alarms received by traffic alarm stations or historical experience of law enforcement personnel, on the one hand, there is a problem of low reliability. On the other hand, due to the relatively single means of analysis, there is also a problem of poor accuracy of the analysis results, which in turn leads to low management efficiency of urban traffic.

[0036] In view of this, the present application provides an analysis method that can effectively improve the reliability and accuracy of urban traffic analysis, thereby effectively improving the management efficiency of urban traffic.

[0037] The analysis method provided in the embodiment of the present application may be executed by a computer device, which may be a server.

[0038] In an exemplary embodiment, Figure 1 As shown, an analysis method is provided, the method comprising the following steps:

[0039] Step 101: Obtain violation data and violation governance data in a target area.

[0040] Optionally, the target area is an area where an analysis of the violation management situation needs to be conducted. The target area may be the entire area of ​​a city, such as City A, or may be a certain area in City A, such as Area B in City A.

[0041] In one embodiment of the present application, in a possible implementation method, the violation data can be obtained based on a traffic alarm platform, and the violation management data can be obtained based on a traffic mobile police system. Specifically, the violation report information received by the alarm receiving point of the traffic alarm platform can be obtained first, and then the required data items can be extracted from the violation report information as needed to generate the violation data. For example, the violation time, violation location (entered into the computer by the staff answering the phone), violation address (a description of the alarm location in the alarm personnel's phone number, which can be used to correct the alarm location), violation type and violation management department corresponding to the violation are extracted from the violation report information to generate the violation data. The violation location is the address where the violation occurred determined by the staff at the alarm receiving point based on the description of the crowd who provided the violation report information. The violation address is the audio of the address where the violation occurred recorded by the recording equipment at the police receiving station based on the description of the people who provided the violation report information. When the violation location is wrong, the violation location can be corrected based on the violation address; the management information in the traffic mobile policing system can also be obtained first, and then the required data items can be extracted from the management information as needed to generate the violation management data, for example, the violation management penalty number, violation management location, violation management address, violation management department, violation type and violation time can be extracted from the management information to generate the violation management data, the violation management location is the violation management address manually entered by the violation management personnel, and the violation management address is the latitude and longitude information of the violation management address generated by the law enforcement instrument used by the management personnel.

[0042] It should be noted that the purpose of the embodiment of the present application is to analyze urban traffic. Therefore, the data obtained are traffic violation data and traffic violation management data. However, in actual applications, the data obtained are different depending on the purpose of analysis. For example, if the purpose is environmental management, the data obtained are environmental violation data and environmental violation management data.

[0043] Step 102: determine a first grid heat map of the target area according to the violation data, and determine a second grid heat map of the target area according to the violation management data.

[0044] Optionally, the grid heat map refers to a method of visualizing grid data through color coding, and the value of each grid in the grid heat map can be represented by the depth of color.

[0045] In one embodiment of the present application, in a possible implementation method, as described above, the violation data includes violation address information, the violation governance data includes violation governance address information, and a violation grid heat map of the target area is generated based on the violation address information and the plane map of the target area, that is, a first grid heat map, and a violation governance grid heat map of the target area is generated based on the violation governance address information and the plane map of the target area, that is, a second grid heat map.

[0046] In one embodiment of the present application, in another possible implementation method, the violation data and the plane map of the target area can be input into a pre-trained grid heat map generation model to obtain a violation grid heat map of the target area output by the grid heat map generation model, that is, a first grid heat map, and the violation management data and the plane map of the target area can be input into a pre-trained grid heat map generation model to obtain a violation management grid heat map of the target area output by the grid heat map generation model, that is, a second grid heat map.

[0047] Step 103: Generate and display a target grid heat map according to the first grid heat map and the second grid heat map.

[0048] Among them, the target grid heat map is used to characterize the governance of illegal behaviors in the target area.

[0049] In one embodiment of the present application, in a possible implementation method, the colors of the corresponding grid sub-areas in the first grid heat map and the second grid heat map can be fused to generate and display the target grid heat map.

[0050] In one embodiment of the present application, in another possible implementation method, the second grid heat map can also be rendered based on the first grid heat map to generate and display the target grid heat map. Specifically, there are N grids in the first grid heat map, and there are also N grids in the second grid heat map, and the grids in the first grid heat map and the second grid heat map correspond to each other. Taking the A grid in the first grid heat map and the A' grid in the second grid heat map as examples, the first color of the A grid is obtained, and the second color of the A' grid is obtained. According to the first color, the second color and the preset rendering rules, the target color of the A' grid is determined, and the A' grid is rendered based on the target color. The above steps are performed on each grid in the second grid heat map to generate a target grid heat map.

[0051] In one embodiment of the present application, in another possible implementation method, the first grid heat map can also be rendered according to the second grid heat map to generate and display the target grid heat map. Specifically, there are N grids in the first grid heat map, and there are N grids in the second grid heat map, and the grids in the first grid heat map and the second grid heat map correspond to each other. Taking the A grid in the first grid heat map and the A' grid in the second grid heat map as examples, the first color of the A grid is obtained, and the second color of the A' grid is obtained. According to the first color, the second color and the preset rendering rules, the target color of the A grid is determined, and the A grid is rendered based on the target color. The above steps are performed on each grid in the first grid heat map to generate a target grid heat map.

[0052] The above-mentioned analysis method obtains the violation data and violation management data of the target area; determines the first grid heat map of the target area according to the violation data, and determines the second grid heat map of the target area according to the violation management data; generates and displays the target grid heat map according to the first grid heat map and the second grid heat map, and the target grid heat map is used to characterize the violation management situation of the target area. The analysis method provided in the present application determines the first grid heat map and the second grid heat map based on the violation data and the violation management data of the target area, respectively, and then generates the target grid heat map based on the first grid heat map and the second grid heat map. Since the target grid heat map can be used to characterize the violation management situation of the target area, the traffic analysis result of the target area can be determined based on the target grid heat map. The analysis method provided in the present application effectively improves the accuracy and reliability of urban traffic analysis, thereby effectively improving the management efficiency of urban traffic.

[0053] In an exemplary embodiment, Figure 2 As shown, determining a first grid heat map of the target area according to the violation data, and determining a second grid heat map of the target area according to the violation governance data, includes the following steps:

[0054] Step 201: divide the target area into grids to obtain multiple grid sub-areas.

[0055] In one embodiment of the present application, in a possible implementation method, the plane map of the target area can be gridded based on the preset grid unit area to obtain multiple grid sub-areas. For example, the target area is City A, and the preset grid unit area can be 1 square kilometer. Then, based on the preset grid unit area, the plane map of the target area is gridded to obtain N square grid sub-areas of 1 square kilometer. The preset grid unit area can be set by the technician according to actual needs.

[0056] In one embodiment of the present application, in another possible implementation method, the plane map of the target area and the preset grid unit area can also be input into the grid division model to obtain multiple grid sub-areas corresponding to the target area output by the grid division model.

[0057] Step 202: Determine the location information of the illegal behavior based on the illegal behavior data, and determine the location information of the governance behavior based on the illegal behavior governance data.

[0058] In an optional embodiment of the present application, the violation data includes violation address information, and the violation address information is geocoded to obtain violation latitude and longitude information corresponding to the violation address information.

[0059] In one embodiment of the present application, in a possible implementation method, the latitude and longitude information of the violation is determined as the location information of the violation, and the violation management data includes a violation management address, which is the latitude and longitude information of the violation management address generated by the law enforcement instrument used by the management personnel, and the latitude and longitude information of the violation management address is determined as the location information of the management behavior.

[0060] Step 203: Generate the first grid heat map and the second grid heat map according to the location information of the illegal behavior, the location information of the governance behavior and the multiple grid sub-areas.

[0061] In one embodiment of the present application, in a possible implementation method, the longitude and latitude information of the multiple grid sub-areas can be first obtained, and then the location information of the violation and the longitude and latitude information of the multiple grid sub-areas are input into the grid heat map generation model to obtain a first grid heat map output by the grid heat map generation model, and then the location information of the governance behavior and the longitude and latitude information of the multiple grid sub-areas are input into the grid heat map generation model to obtain a second grid heat map output by the grid heat map generation model.

[0062] In one embodiment of the present application, in another possible implementation method, the first number of violations in each grid sub-area can be determined based on the location information of the violation and the location information of the multiple grid sub-areas, and the second number of governance behaviors in each grid sub-area can be determined based on the location information of the governance behavior and the location information of the multiple grid sub-areas. Then, according to the first number, the second number and the preset rendering rules, the multiple grid sub-areas are rendered to obtain the first grid heat map and the second grid heat map.

[0063] The above-mentioned method of gridding the target area to obtain multiple grid sub-areas, determining the location information of the violation according to the violation data, and determining the location information of the governance behavior according to the violation governance data, and generating the first grid heat map and the second grid heat map according to the location information of the violation, the location information of the governance behavior and the multiple grid sub-areas, generates the first grid heat map and the second grid heat map based on the multiple grid sub-areas corresponding to the target area and the location information of the violation and the location information of the governance behavior, and can display the violations and governance behaviors of each grid sub-area of ​​the target area in a visual manner, so that the user can quickly understand the governance situation information of the target area based on the first grid heat map and the second grid heat map, effectively improving the efficiency of urban traffic management.

[0064] In an exemplary embodiment, Figure 3 As shown, the first grid heat map and the second grid heat map are generated according to the location information of the illegal behavior, the location information of the governance behavior and the multiple grid sub-areas, including the following steps:

[0065] Step 301: Determine a first number of violations in each of the grid sub-areas based on the location information of the violation and the location information of the plurality of grid sub-areas.

[0066] In one embodiment of the present application, in a possible implementation method, the longitude and latitude information of each grid sub-area can be determined first. Specifically, the longitude and latitude information of each grid sub-area can be determined based on the longitude and latitude information of the grid center position of each grid sub-area and the preset grid unit area. Taking the A grid sub-area among the multiple grid sub-areas as an example, the target violation behavior data is determined from the violation behavior data based on the location information of the violation behavior and the longitude and latitude information of the A grid sub-area. The longitude and latitude corresponding to the location information of the target violation behavior data belong to the longitude and latitude area corresponding to the longitude and latitude information of the A grid sub-area. Assuming that the target violation behavior data is 100, the first number of the A grid sub-area is 100. The above steps are performed on each of the multiple grid sub-areas to obtain the first number of violations in each of the grid sub-areas.

[0067] Step 302: Determine a second number of governance behaviors in each of the grid sub-areas based on the location information of the governance behavior and the location information of the plurality of grid sub-areas.

[0068] In one embodiment of the present application, in a possible implementation method, the longitude and latitude information of each grid sub-area can be determined first. Specifically, the longitude and latitude information of each grid sub-area can be determined based on the longitude and latitude information of the grid center position of each grid sub-area and the preset grid unit area. Taking grid sub-area A among multiple grid sub-areas as an example, target violation behavior governance data is determined from the violation behavior governance data based on the location information of the governance behavior and the longitude and latitude information of the A grid sub-area. The longitude and latitude corresponding to the location information of the target violation behavior governance data belong to the longitude and latitude area corresponding to the longitude and latitude information of the A grid sub-area. Assuming that the target violation behavior governance data is 100, the second number of the A grid sub-area is 100. The above steps are performed on each of the multiple grid sub-areas to obtain the second number of governance behaviors in each of the grid sub-areas.

[0069] Step 303: Render the plurality of grid sub-areas according to the first number, the second number and a preset rendering rule to obtain the first grid heat map and the second grid heat map.

[0070] The rendering rule includes a corresponding relationship between quantity and color.

[0071] In one embodiment of the present application, in a possible implementation method, the first quantity and the second quantity can be sorted respectively to obtain a first quantity list and a second quantity list. After obtaining the first quantity list, the first 5% of the grid sub-areas in the first quantity list can be rendered as a first color, 5%-15% of the grid sub-areas in the first quantity list can be rendered as a second color, 15%-25% of the grid sub-areas in the first quantity list can be rendered as a third color, and the remaining grid sub-areas can be rendered as a fourth color. If the first quantity of a grid sub-area is 0, the grid sub-area is not rendered to generate a first grid heat map, which can be as follows Fig. 9 As shown, after obtaining the second quantity list, the first 5% of the grid sub-areas in the second quantity list can be rendered as the first color, 5%-15% of the grid sub-areas in the second quantity list can be rendered as the second color, 15%-25% of the grid sub-areas in the second quantity list can be rendered as the third color, and the remaining grid sub-areas can be rendered as the fourth color. If the second quantity of a grid sub-area is 0, the grid sub-area is not rendered to generate a second grid heat map, which can be shown as Fig.10As shown, in the first grid heat map, the first color is used to represent that the violation in the area corresponding to the grid sub-area is serious, the second color is used to represent that the violation in the area corresponding to the grid sub-area is relatively serious, the third color is used to represent that the violation in the area corresponding to the grid sub-area is generally serious, and the fourth color is used to represent that the violation in the area corresponding to the grid sub-area is not serious. In the second grid heat map, the first color is used to represent that the violation governance in the area corresponding to the grid sub-area is strong, the second color is used to represent that the violation governance in the area corresponding to the grid sub-area is relatively strong, the third color is used to represent that the violation governance in the area corresponding to the grid sub-area is general, and the fourth color is used to represent that the violation governance in the area corresponding to the grid sub-area is weak.

[0072] In one embodiment of the present application, in another possible implementation method, the preset rendering rule may also include a preset threshold value. According to the first number, the second number and the preset rendering rule, the multiple grid sub-areas are rendered to obtain the first grid heat map and the second grid heat map. For example, the grid sub-areas whose first number is greater than A will be rendered as the first color, the grid sub-areas whose first number is less than A and greater than B will be rendered as the second color, the grid sub-areas whose first number is less than B and greater than C will be rendered as the third color, and the grid sub-areas whose first number is less than C will be rendered as the fourth color. If the second number of a grid sub-area is 0, the grid sub-area will not be rendered to generate the first grid heat map, which can be as follows Fig. 9 As shown, the grid sub-areas whose second number is greater than A will be rendered as the first color, the grid sub-areas whose second number is less than A and greater than B will be rendered as the second color, the grid sub-areas whose second number is less than B and greater than C will be rendered as the third color, and the grid sub-areas whose second number is less than C will be rendered as the fourth color. If the second number of a grid sub-area is 0, the grid sub-area will not be rendered to generate the second grid heat map, which can be shown as Fig.10 shown.

[0073] The method determines the first number of violations in each grid sub-area based on the location information of the violation and the location information of the multiple grid sub-areas, determines the second number of governance behaviors in each grid sub-area based on the location information of the governance behavior and the location information of the multiple grid sub-areas, and renders the multiple grid sub-areas based on the first number, the second number and the preset rendering rules to obtain the first grid heat map and the second grid heat map, wherein the rendering rules include a method of corresponding relationship between quantity and color, and generates the first grid heat map and the second grid heat map based on the first number of violations and the second number of governance behaviors in each grid sub-area, respectively, so that the violations and governance behaviors in each grid sub-area of ​​the target area can be displayed in a visual manner, so that the user can quickly understand the governance situation information of the target area based on the first grid heat map and the second grid heat map, effectively improving the efficiency of urban traffic management.

[0074] In an exemplary embodiment, Figure 4 As shown, determining a first grid heat map of the target area according to the violation data, and determining a second grid heat map of the target area according to the violation governance data, includes the following steps:

[0075] Step 401: input the violation data and the violation governance data into a pre-trained natural language processing model to obtain a first label corresponding to the violation data and a second label corresponding to the violation governance data output by the natural language processing model.

[0076] Optionally, the natural language processing model can be a textcnn model.

[0077] In an optional embodiment of the present application, the violation type in the violation data and the violation governance data is a description information of the violation type in a paragraph. A large amount of violation data and violation governance data can be first obtained as a training set, and the violation data and the violation governance data can be labeled to obtain the corresponding violation type. The violation type is used as a verification set, and an initial natural language processing model is trained based on the training set and the verification set to obtain the trained natural language processing model. The violation data and the violation governance data are input into the trained natural language processing model to obtain a first label corresponding to the violation data and a second label corresponding to the violation governance data.

[0078] Step 402: According to the preset label, the first label and the second label screen the violation data and the violation management data to determine the target violation data and the target violation management data.

[0079] Optionally, the preset label can be pre-set by a technician, and the preset label is one of the violation types, including illegal parking, large trucks, motorcycles, zombie cars, buses, hazardous chemical trucks, tricycles, four-wheelers, alcohol-related vehicles, license-related vehicles and certificate-related vehicles.

[0080] In one embodiment of the present application, in a possible implementation method, the violation data and the violation governance data both include the violation type information, and the violation data and the violation governance data can be screened based on the preset label to determine the target violation data and the target violation governance data. For example, if the preset label is a zombie car, the target violation data obtained by screening is violation data of the zombie car type, and the target violation governance data is violation governance data of the zombie car type.

[0081] Step 403: determine a first grid heat map of the target area according to the target violation behavior data, and determine a second grid heat map of the target area according to the target violation behavior governance data.

[0082] In one embodiment of the present application, in a possible implementation method, as described above, if the preset label is a zombie car, the target violation data obtained by screening are violation data of the zombie car type, and the target violation governance data are violation governance data of the zombie car type, then a first grid heat map of the target area is determined based on the target violation data, which is a violation heat map for zombie cars in the target area, and based on the violation data and the violation governance data both including the violation type information, a second grid heat map of the target area is determined based on the target violation governance data, which is a violation governance heat map for zombie cars in the target area.

[0083] The above-mentioned method inputs the violation data and the violation governance data into a pre-trained natural language processing model to obtain a first label corresponding to the violation data and a second label corresponding to the violation governance data output by the natural language processing model. According to the preset label, the first label and the second label are used to screen the violation data and the violation governance data to determine the target violation data and the target violation governance data, and the first grid heat map of the target area is determined according to the target violation data, and the second grid heat map of the target area is determined according to the target violation governance data. The violation data and the violation governance data are screened based on the preset label to determine the target violation data and the target violation governance data, so as to generate a first grid heat map and a second grid heat map for the preset label type according to the target violation data and the target violation governance data, that is, a heat map of a certain type of violation in the target area and a corresponding violation governance heat map can be obtained, which effectively improves flexibility.

[0084] In an exemplary embodiment, Figure 5 As shown, the target grid heat map is generated and displayed according to the first grid heat map and the second grid heat map, including the following steps:

[0085] Step 501: For a first grid sub-region in the first grid heat map, determine a second grid sub-region corresponding to the first grid sub-region in the second grid heat map.

[0086] The first grid sub-region is any grid sub-region in the first grid heat map.

[0087] In one embodiment of the present application, in a possible implementation method, there are N grid sub-areas in the first grid heat map, and there are N grid sub-areas in the second grid heat map, and the grid sub-areas in the first grid heat map and the second grid heat map correspond to each other. Taking the first grid sub-area as the A grid sub-area as an example, the A' grid sub-area corresponding to the A grid sub-area in the second grid heat map is determined to be the second grid sub-area.

[0088] Step 502: According to a preset fusion rule, the color of the first grid sub-region and the color of the second grid sub-region are used to render the colors of multiple grid sub-regions in the initial grid heat map to generate the target grid heat map.

[0089] Optionally, the initial grid heat map is composed of a plurality of grid sub-regions that have not been rendered, and can also be understood as a first grid heat map or a second grid heat map that has not been rendered.

[0090] In one embodiment of the present application, in a possible implementation, if the color of the A grid sub-region is the same as the color of the A' grid sub-region, the A1 grid sub-region corresponding to the A grid sub-region in the initial grid heat map will be rendered as a fourth color.

[0091] In one embodiment of the present application, in another possible implementation method, if the color of the A grid sub-area is different from the color of the A' grid sub-area, for example, one is the first color and the other is the fourth color, then the A1 grid sub-area in the initial grid heat map will be rendered as the first color.

[0092] In one embodiment of the present application, in another possible implementation method, if the color of the A grid sub-area is different from the color of the A' grid sub-area, for example, one is the first color and the other is the second color, then the A1 grid sub-area in the initial grid heat map will be rendered as a third color.

[0093] In one embodiment of the present application, in another possible implementation method, if the color of the A grid sub-area is different from the color of the A' grid sub-area, for example, one is the first color and the other is the third color, then the A1 grid sub-area in the initial grid heat map will be rendered as the second color.

[0094] In one embodiment of the present application, in another possible implementation method, if the color of the A grid sub-area is different from the color of the A' grid sub-area, for example, one is the second color and the other is the third color, then the A1 grid sub-area in the initial grid heat map will be rendered as the third color.

[0095] In one embodiment of the present application, in another possible implementation method, if the color of the A grid sub-area is different from the color of the A' grid sub-area, for example, one is the second color and the other is the third color, then the A1 grid sub-area in the initial grid heat map will be rendered as the third color.

[0096] In one embodiment of the present application, in another possible implementation method, if the color of the A grid sub-area is different from the color of the A' grid sub-area, for example, one is the second color and the other is the fourth color, then the A1 grid sub-area in the initial grid heat map will be rendered as the second color.

[0097] In one embodiment of the present application, in another possible implementation method, if the color of the A grid sub-area is different from the color of the A' grid sub-area, for example, one is the third color and the other is the fourth color, then the A1 grid sub-area in the initial grid heat map will be rendered as the third color.

[0098] As described above, the target grid heat map is used to characterize the violation governance situation in the target area. Therefore, the violation governance situation in the target area can be determined based on the color of the target grid heat map.

[0099] In one embodiment of the present application, in a possible implementation method, taking any grid sub-area in the target grid heat map as an example, if the color of the grid sub-area is the fourth color, it indicates that the violation management situation in the corresponding area of ​​the grid sub-area is good; if the color of the grid sub-area is the third color, it indicates that the violation management situation in the corresponding area of ​​the grid sub-area is relatively good; if the color of the grid sub-area is the second color, it indicates that the violation management situation in the corresponding area of ​​the grid sub-area is relatively poor; if the color of the grid sub-area is the first color, it indicates that the violation management situation in the corresponding area of ​​the grid sub-area is poor.

[0100] The above-mentioned method of determining a second grid sub-region corresponding to the first grid sub-region in the second grid heat map for the first grid sub-region in the first grid heat map, wherein the first grid sub-region is any grid sub-region in the first grid heat map, and according to a preset fusion rule, the color of the first grid sub-region and the color of the second grid sub-region are used to render the colors of multiple grid sub-regions in the initial grid heat map to generate the target grid heat map can show the governance of violations in the target area in the form of a grid heat map, so that users can see the traffic analysis results of the target area more intuitively, and effectively improve the management efficiency of urban traffic.

[0101] In an exemplary embodiment, the method further includes: determining a target enforcement strategy according to the target grid heat map, and enforcing the target area based on the target enforcement strategy.

[0102] In one embodiment of the present application, in a possible implementation method, the violation management situation information of each grid sub-area in the target area can be determined according to the target grid heat map, and the target law enforcement strategy can be determined according to the violation management situation information, and the target area can be enforced based on the target law enforcement strategy. For example, if the color of a grid sub-area in the target grid heat map is the first color, it proves that the area corresponding to the grid sub-area has a large number of violations and a small number of management behaviors, or a large number of management behaviors and a small number of violations, resulting in a high degree of asymmetry between violations and management behaviors. Based on this, a target law enforcement strategy is generated for the area, and law enforcement is performed on the area based on the target law enforcement strategy.

[0103] The above-mentioned method of determining the target enforcement strategy according to the target grid heat map and enforcing the target area based on the target enforcement strategy can accurately determine the violation management situation information of each grid sub-area based on the target grid heat map, and generate a target enforcement strategy based on the violation management situation information to improve the efficiency of traffic management.

[0104] In an exemplary embodiment, Figure 6 Another analysis method is provided, which comprises the following steps:

[0105] Step 601, obtaining violation data and violation governance data of a target area; dividing the target area into grids to obtain a plurality of grid sub-areas; determining location information of the violation according to the violation data, and determining location information of the governance behavior according to the violation governance data; determining a first number of violations in each of the grid sub-areas according to the location information of the violation and the location information of the plurality of grid sub-areas; determining a second number of governance behaviors in each of the grid sub-areas according to the location information of the governance behavior and the location information of the plurality of grid sub-areas;

[0106] Step 602: Render the multiple grid sub-areas according to the first quantity, the second quantity and a preset rendering rule to obtain the first grid heat map and the second grid heat map, wherein the rendering rule includes a corresponding relationship between quantity and color.

[0107] Step 603: for a first grid sub-region in the first grid heat map, determine a second grid sub-region corresponding to the first grid sub-region in the second grid heat map, where the first grid sub-region is any grid sub-region in the first grid heat map;

[0108] Step 604: According to a preset fusion rule, the color of the first grid sub-region and the color of the second grid sub-region are used to render the colors of multiple grid sub-regions in the initial grid heat map to generate the target grid heat map, which is used to characterize the violation management situation in the target area.

[0109] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0110] Based on the same inventive concept, the embodiment of the present application also provides an analysis device for implementing the above-mentioned analysis method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more analysis device embodiments provided below can refer to the limitations on the analysis method above, and will not be repeated here.

[0111] In an exemplary embodiment, Figure 7 As shown, an analysis device 700 is provided, comprising: an acquisition module 701, a determination module 702 and an execution module 703, wherein:

[0112] An acquisition module 701 is used to acquire the violation data and violation management data of the target area;

[0113] A determination module 702 is used to determine a first grid heat map of the target area according to the violation data, and to determine a second grid heat map of the target area according to the violation management data;

[0114] The execution module 703 is used to generate and display a target grid heat map based on the first grid heat map and the second grid heat map, and the target grid heat map is used to characterize the governance of illegal behaviors in the target area.

[0115] In one embodiment, the determination module 702 is specifically used to divide the target area into grids to obtain multiple grid sub-areas; determine the location information of the violation based on the violation data, and determine the location information of the governance behavior based on the violation governance data; generate the first grid heat map and the second grid heat map based on the location information of the violation, the location information of the governance behavior and the multiple grid sub-areas.

[0116] In one embodiment, the determination module 702 is specifically used to determine a first number of violations in each of the grid sub-areas based on the location information of the violations and the location information of the multiple grid sub-areas; determine a second number of governance behaviors in each of the grid sub-areas based on the location information of the governance behaviors and the location information of the multiple grid sub-areas; and render the multiple grid sub-areas based on the first number, the second number and a preset rendering rule to obtain the first grid heat map and the second grid heat map, wherein the rendering rule includes a correspondence between quantity and color.

[0117] In one embodiment, the determination module 702 is specifically used to input the violation data and the violation governance data into a pre-trained natural language processing model to obtain a first label corresponding to the violation data and a second label corresponding to the violation governance data output by the natural language processing model; according to the preset labels, the first label and the second label are used to screen the violation data and the violation governance data to determine the target violation data and the target violation governance data; according to the target violation data, a first grid heat map of the target area is determined, and according to the target violation data, a second grid heat map of the target area is determined.

[0118] In one embodiment, the execution module 703 is specifically used to determine, for a first grid sub-region in the first grid heat map, a second grid sub-region corresponding to the first grid sub-region in the second grid heat map, where the first grid sub-region is any grid sub-region in the first grid heat map; according to a preset fusion rule, the color of the first grid sub-region and the color of the second grid sub-region are used to render the colors of multiple grid sub-regions in the initial grid heat map to generate the target grid heat map.

[0119] In one embodiment, the execution module 703 is further used to determine a target enforcement strategy according to the target grid heat map, and enforce the target area based on the target enforcement strategy.

[0120] Each module in the above analysis device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0121] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 8As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an analysis method is implemented.

[0122] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0123] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0124] Obtain violation data and violation management data of a target area; determine a first grid heat map of the target area based on the violation data, and determine a second grid heat map of the target area based on the violation management data; generate and display a target grid heat map based on the first grid heat map and the second grid heat map, and the target grid heat map is used to characterize the violation management situation of the target area.

[0125] In one embodiment, when the processor executes the computer program, the following steps are also implemented: gridding the target area to obtain multiple grid sub-areas; determining the location information of the violation based on the violation data, and determining the location information of the governance behavior based on the violation governance data; generating the first grid heat map and the second grid heat map based on the location information of the violation, the location information of the governance behavior and the multiple grid sub-areas.

[0126] In one embodiment, when the processor executes the computer program, the following steps are also implemented: determining a first number of violations in each of the grid sub-areas based on the location information of the violation and the location information of the multiple grid sub-areas; determining a second number of governance behaviors in each of the grid sub-areas based on the location information of the governance behavior and the location information of the multiple grid sub-areas; rendering the multiple grid sub-areas based on the first number, the second number and a preset rendering rule to obtain the first grid heat map and the second grid heat map, wherein the rendering rule includes a correspondence between quantity and color.

[0127] In one embodiment, when the processor executes the computer program, the following steps are also implemented: the violation data and the violation governance data are input into a pre-trained natural language processing model to obtain a first label corresponding to the violation data and a second label corresponding to the violation governance data output by the natural language processing model; according to the preset labels, the first label and the second label are used to screen the violation data and the violation governance data to determine the target violation data and the target violation governance data; a first grid heat map of the target area is determined according to the target violation data, and a second grid heat map of the target area is determined according to the target violation governance data.

[0128] In one embodiment, when the processor executes the computer program, the following steps are also implemented: for the first grid sub-area in the first grid heat map, a second grid sub-area corresponding to the first grid sub-area is determined in the second grid heat map, and the first grid sub-area is any grid sub-area in the first grid heat map; according to a preset fusion rule, the color of the first grid sub-area and the color of the second grid sub-area are used to render the colors of multiple grid sub-areas in the initial grid heat map to generate the target grid heat map.

[0129] In one embodiment, when the processor executes the computer program, the following steps are also implemented: determining a target enforcement strategy based on the target grid heat map, and enforcing the target area based on the target enforcement strategy.

[0130] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0131] Obtain violation data and violation management data of a target area; determine a first grid heat map of the target area based on the violation data, and determine a second grid heat map of the target area based on the violation management data; generate and display a target grid heat map based on the first grid heat map and the second grid heat map, and the target grid heat map is used to characterize the violation management situation of the target area.

[0132] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: gridding the target area to obtain multiple grid sub-areas; determining the location information of the violation based on the violation data, and determining the location information of the governance behavior based on the violation governance data; generating the first grid heat map and the second grid heat map based on the location information of the violation, the location information of the governance behavior and the multiple grid sub-areas.

[0133] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: determining a first number of violations in each of the grid sub-areas based on the location information of the violation and the location information of the multiple grid sub-areas; determining a second number of governance behaviors in each of the grid sub-areas based on the location information of the governance behavior and the location information of the multiple grid sub-areas; rendering the multiple grid sub-areas based on the first number, the second number and a preset rendering rule to obtain the first grid heat map and the second grid heat map, wherein the rendering rule includes a correspondence between quantity and color.

[0134] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: the violation data and the violation governance data are input into a pre-trained natural language processing model to obtain a first label corresponding to the violation data and a second label corresponding to the violation governance data output by the natural language processing model; according to the preset labels, the first label and the second label are used to screen the violation data and the violation governance data to determine the target violation data and the target violation governance data; a first grid heat map of the target area is determined according to the target violation data, and a second grid heat map of the target area is determined according to the target violation governance data.

[0135] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: for the first grid sub-area in the first grid heat map, a second grid sub-area corresponding to the first grid sub-area is determined in the second grid heat map, and the first grid sub-area is any grid sub-area in the first grid heat map; according to a preset fusion rule, the color of the first grid sub-area and the color of the second grid sub-area are used to render the colors of multiple grid sub-areas in the initial grid heat map to generate the target grid heat map.

[0136] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: determining a target enforcement strategy based on the target grid heat map, and enforcing the target area based on the target enforcement strategy.

[0137] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0138] Obtain violation data and violation management data of a target area; determine a first grid heat map of the target area based on the violation data, and determine a second grid heat map of the target area based on the violation management data; generate and display a target grid heat map based on the first grid heat map and the second grid heat map, and the target grid heat map is used to characterize the violation management situation of the target area.

[0139] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: gridding the target area to obtain multiple grid sub-areas; determining the location information of the violation based on the violation data, and determining the location information of the governance behavior based on the violation governance data; generating the first grid heat map and the second grid heat map based on the location information of the violation, the location information of the governance behavior and the multiple grid sub-areas.

[0140] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: determining a first number of violations in each of the grid sub-areas based on the location information of the violation and the location information of the multiple grid sub-areas; determining a second number of governance behaviors in each of the grid sub-areas based on the location information of the governance behavior and the location information of the multiple grid sub-areas; rendering the multiple grid sub-areas based on the first number, the second number and a preset rendering rule to obtain the first grid heat map and the second grid heat map, wherein the rendering rule includes a correspondence between quantity and color.

[0141] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: the violation data and the violation governance data are input into a pre-trained natural language processing model to obtain a first label corresponding to the violation data and a second label corresponding to the violation governance data output by the natural language processing model; according to the preset labels, the first label and the second label are used to screen the violation data and the violation governance data to determine the target violation data and the target violation governance data; a first grid heat map of the target area is determined according to the target violation data, and a second grid heat map of the target area is determined according to the target violation governance data.

[0142] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: for the first grid sub-area in the first grid heat map, a second grid sub-area corresponding to the first grid sub-area is determined in the second grid heat map, and the first grid sub-area is any grid sub-area in the first grid heat map; according to a preset fusion rule, the color of the first grid sub-area and the color of the second grid sub-area are used to render the colors of multiple grid sub-areas in the initial grid heat map to generate the target grid heat map.

[0143] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: determining a target enforcement strategy based on the target grid heat map, and enforcing the target area based on the target enforcement strategy.

[0144] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0145] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0146] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0147] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. An analysis method, characterized in that The method comprises: Obtaining violation data and violation management data of the target area, wherein the violation data is generated based on the violation time, violation location, violation address, violation type and violation management department corresponding to the violation extracted from the violation reporting information; the violation management data is generated based on the violation management penalty order number, violation management location, violation management address, violation management department, violation type and violation time extracted from the management information; Determine a first grid heat map of the target area according to the violation data, and determine a second grid heat map of the target area according to the violation management data; For a first grid sub-area in the first grid heat map, a second grid sub-area corresponding to the first grid sub-area is determined in the second grid heat map, and the first grid sub-area is any grid sub-area in the first grid heat map; according to a preset fusion rule, the color of the first grid sub-area and the color of the second grid sub-area are rendered to the colors of multiple grid sub-areas in the initial grid heat map to generate and display a target grid heat map, and the target grid heat map is used to characterize the violation governance situation in the target area.

2. The method according to claim 1, characterized in that: The step of determining a first grid heat map of the target area according to the violation data, and determining a second grid heat map of the target area according to the violation management data, comprises: Dividing the target area into grids to obtain a plurality of grid sub-areas; Determine the location information of the violation according to the violation data, and determine the location information of the governance behavior according to the violation governance data; The first grid heat map and the second grid heat map are generated according to the location information of the violation, the location information of the governance behavior and the multiple grid sub-areas.

3. The method according to claim 2, characterized in that The step of generating the first grid heat map and the second grid heat map according to the location information of the illegal behavior, the location information of the governance behavior, and the plurality of grid sub-areas includes: Determine a first number of violations in each of the grid sub-areas according to the location information of the violation and the location information of the plurality of grid sub-areas; Determining a second number of governance behaviors in each of the grid sub-areas according to the location information of the governance behavior and the location information of the plurality of grid sub-areas; The plurality of grid sub-areas are rendered according to the first quantity, the second quantity and a preset rendering rule to obtain the first grid heat map and the second grid heat map, wherein the rendering rule includes a corresponding relationship between quantity and color.

4. The method according to claim 1, characterized in that: The step of determining a first grid heat map of the target area according to the violation data, and determining a second grid heat map of the target area according to the violation management data, comprises: Inputting the violation data and the violation governance data into a pre-trained natural language processing model to obtain a first label corresponding to the violation data and a second label corresponding to the violation governance data output by the natural language processing model; According to the preset label, the first label and the second label screen the violation behavior data and the violation behavior governance data to determine the target violation behavior data and the target violation behavior governance data; A first grid heat map of the target area is determined according to the target violation data, and a second grid heat map of the target area is determined according to the target violation governance data.

5. The method according to claim 1, characterized in that The method further comprises: A target enforcement strategy is determined according to the target grid heat map, and enforcement is performed on the target area based on the target enforcement strategy.

6. The method according to claim 1, characterized in that The step of determining the first grid heat map of the target area according to the violation data includes: The violation data and the plane map of the target area are input into a pre-trained grid heat map generation model to obtain a first grid heat map of the target area output by the grid heat map generation model.

7. An analysis device, characterized in that: The device comprises: An acquisition module is used to acquire the violation data and violation governance data of the target area, wherein the violation data is generated based on the violation time, violation location, violation address, violation type and violation governance department corresponding to the violation extracted from the violation reporting information; the violation governance data is generated based on the violation governance penalty order number, violation governance location, violation governance address, violation governance department, violation type and violation time extracted from the governance information; A determination module, configured to determine a first grid heat map of the target area according to the violation data, and determine a second grid heat map of the target area according to the violation management data; An execution module is used to determine, for a first grid sub-area in the first grid heat map, a second grid sub-area corresponding to the first grid sub-area in the second grid heat map, wherein the first grid sub-area is any grid sub-area in the first grid heat map; according to a preset fusion rule, the color of the first grid sub-area and the color of the second grid sub-area are used to render the colors of multiple grid sub-areas in the initial grid heat map to generate and display a target grid heat map, wherein the target grid heat map is used to characterize the violation governance situation in the target area.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images