Police force layout method and device based on multi-source data fusion

Abstract

The invention relates to the technical field of urban planning, and discloses a police force layout method and device based on multi-source data fusion, and the method comprises the steps: obtaining the road network data of a target region; analyzing the road network data to determine a candidate police station set; the method comprises the steps of determining a demand point set and a demand weight corresponding to each demand point by analyzing region POI data corresponding to a target region, and determining police demands under each demand level based on the demand weights; based on the geographic positions of the candidate local police station set and the demand point set, calculating the passing time of each local police station in the candidate local police station set to each demand point in the demand point set in different time periods, and generating a passing time set; based on the passing time set, analyzing the service efficiency of each local police station to each demand point in different scenes, and generating a service efficiency set; by applying the method, the police dispatching time of the urban police station can be shortened, and the public security prevention and control efficiency of the police station can be improved.

Description

Technical Field

[0001] The present application relates to the field of urban planning technology, and in particular to a method and device for deploying police force based on multi-source data fusion. Background Art

[0002] At present, my country's cities are facing problems such as large population concentration, increasingly complex road networks and shortage of police resources. According to the standardized standards for alarm service work, it is required that urban areas must "arrive at the scene within 5 minutes" and suburban areas must "arrive at the scene within 10 minutes" to avoid economic losses and casualties caused by delays in police dispatch. On the one hand, this problem requires the authorities to make overall plans for the layout of regional police stations, and on the other hand, to deploy corresponding police resources to each police station based on the police force needs in the jurisdiction.

[0003] However, although there is a wealth of literature on the spatial layout of urban police stations and the allocation of police resources, there is a lack of research on the joint optimization of the two.

[0004] In view of this, how to provide a systematic optimization method for the location spatial layout of police stations and the allocation of police resources, so as to achieve rapid police response and scientific allocation of police resources, has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The embodiments of the present application provide a police force deployment method based on multi-source data fusion, a police force deployment device based on multi-source data fusion, an electronic device and a computer storage medium, which are used to solve the current problems of slow police response and uneven distribution of police resources.

[0006] In a first aspect of an embodiment of the present application, a police force deployment method based on multi-source data fusion is provided, comprising:

[0007] Obtain road network data for the target area;

[0008] By analyzing the road network data, a candidate police station set is determined, wherein the candidate police station set carries the locations of existing police stations in the target area;

[0009] By analyzing the regional POI data corresponding to the target area, a set of demand points and the demand weights corresponding to each demand point are determined, and based on the demand weights, the police force requirements under each demand level are determined;

[0010] Based on the geographical locations of the candidate police station set and the demand point set, the travel time from each police station in the candidate police station set to each demand point in the demand point set is calculated in different time periods to generate a travel time set, wherein the travel time is the travel time corresponding to the shortest path from each police station to each demand point;

[0011] Based on the travel time set, the service efficiency of each police station for each demand point in different scenarios is analyzed to generate a service efficiency set;

[0012] Based on the service effectiveness set, the police force layout is determined, wherein the police force layout includes the police station layout and the police force allocation corresponding to each police station.

[0013] In a second aspect of an embodiment of the present application, a police force deployment device based on multi-source data fusion is provided, comprising:

[0014] An acquisition module is configured to acquire road network data of a target area;

[0015] A first determination module is configured to determine a candidate police station set by analyzing the road network data, wherein the candidate police station set carries existing police station locations in a target area;

[0016] The second determination module is configured to determine a set of demand points and demand weights corresponding to each demand point by analyzing regional POI data corresponding to the target area, and determine the police force requirements under each demand level based on the demand weights;

[0017] The first generation module is configured to calculate the travel time from each police station in the candidate police station set to each demand point in the demand point set in different time periods based on the geographical locations of the candidate police station set and the demand point set, and generate a travel time set, wherein the travel time is the travel time corresponding to the shortest path from each police station to each demand point;

[0018] The second generation module is configured to analyze the service efficiency of each police station for each demand point in different scenarios based on the travel time set to generate a service efficiency set;

[0019] The third determination module is configured to determine the police force layout based on the service effectiveness set, wherein the police force layout includes the police station layout and the police force allocation corresponding to each police station.

[0020] According to a third aspect of an embodiment of the present application, a computing device is provided, including:

[0021] Memory and processor;

[0022] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the above-mentioned police force deployment method based on multi-source data fusion are implemented.

[0023] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, which stores computer-executable instructions, and when the instructions are executed by a processor, the steps of the above-mentioned police force deployment method based on multi-source data fusion are implemented.

[0024] The present application provides a police force deployment method based on multi-source data fusion, including: acquiring road network data of a target area; determining a candidate police station set by analyzing the road network data, wherein the candidate police station set carries existing police station locations in the target area; determining a demand point set and a demand weight corresponding to each demand point by analyzing regional POI data corresponding to the target area, and determining the police force demand at each demand level based on the demand weight; calculating the travel time for each police station in the candidate police station set to reach each demand point in the demand point set in different time periods based on the geographical locations of the candidate police station set and the demand point set, and generating a travel time set, wherein the travel time is the travel time corresponding to the shortest path from each police station to each demand point; based on the travel time set, analyzing the service efficiency of each police station to each demand point in different scenarios, and generating a service efficiency set; determining the police force deployment based on the service efficiency set, wherein the police force deployment includes the police station layout and the police force allocation corresponding to each police station.

[0025] The police force deployment method based on multi-source data fusion provided in the embodiment of the present application, on the one hand, improves the scalability and compatibility of the method by adopting ArcGIS to classify multi-source data such as POI point data, regional administrative planning maps, travel time data, traffic conditions, road network data, etc. into geographic raster data; on the other hand, based on the geographic raster data formed after the fusion of multi-source data, and combined with the relevant requirements of the urban police station’s police rescue site, the site selection layout and police force deployment of the urban police station are optimized in stages, which has significant advantages over the traditional method based on the area division of the jurisdiction, thereby shortening the police response time of the urban police station and improving the efficiency of the police station’s public security control.

[0026] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0028] Figure 1 A flowchart of a police force deployment method based on multi-source data fusion provided in an embodiment of the present application;

[0029] Figure 2 A service time box-line diagram of a police force deployment method based on multi-source data fusion in a reconstruction scenario provided in an embodiment of the present application;

[0030] Figure 3 A service box line diagram of a police force deployment method based on multi-source data fusion in a relocation scenario provided in an embodiment of the present application;

[0031] Figure 4 A schematic diagram of the structure of a police force deployment device based on multi-source data fusion provided in an embodiment of the present application;

[0032] Figure 5 A structural block diagram of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0034] See also Figure 1 , Figure 1 The following is a flow chart of a police force deployment method based on multi-source data fusion provided in an embodiment of the present application. Figure 1 As shown, the specific steps include the following steps.

[0035] Step S102: Acquire the road network data of the target area. It should be noted that the road network data here is the geographic raster data generated after processing the road network data.

[0036] Step S104: determining a candidate police station set by analyzing the road network data, wherein the candidate police station set carries the locations of existing police stations in the target area.

[0037] Step S106: By analyzing the regional POI data corresponding to the target area, a demand point set and a demand weight corresponding to each demand point are determined, and based on the demand weight, the police force demand at each demand level is determined.

[0038] Step S108: Based on the geographical locations of the candidate police station set and the demand point set, calculate the travel time from each police station in the candidate police station set to each demand point in the demand point set in different time periods, and generate a travel time set, wherein the travel time is the travel time corresponding to the shortest path from each police station to each demand point.

[0039] Step S110: Based on the travel time set, the service performance of each police station for each demand point in different scenarios is analyzed to generate a service performance set.

[0040] Step S112: Based on the service effectiveness set, determine the police force layout, wherein the police force layout includes the police station layout and the police force allocation corresponding to each police station.

[0041] The embodiment of the present application adopts the concept of set coverage and maximum coverage modeling, and integrates multi-source data such as POI point data, travel time data, traffic conditions, road network data, etc., to effectively combine the spatial layout planning of the police station and the optimization of police resource allocation, thereby achieving rapid police response and providing decision-making support for the authorities.

[0042] Specifically, the urban police station site selection-allocation model constructed in the embodiment of the present application is divided into two stages. The first stage uses the set coverage method to determine the minimum number of police stations to be built from the perspective of spatial accessibility and the principle of "full spatial coverage"; the second stage designs a maximum coverage modeling method to optimize the spatial location and police force allocation of the proposed number of police stations from two aspects: the "gradual coverage decay" of public security services with the response time of police dispatch and the number of police dispatches meeting the "coverage requirements".

[0043] In an embodiment of the present application, the determining of a set of candidate police stations by analyzing the road network data includes: determining road nodes and existing police station locations by analyzing the road network data; analyzing the road nodes and the existing police station locations based on a target algorithm to determine the minimum number of candidate police stations, and generating a set of candidate police stations, wherein the minimum number of police stations is not less than the total number of demand points.

[0044] The method of analyzing the regional POI data corresponding to the target area to determine the demand point set and the demand weight corresponding to each demand point includes: obtaining the regional POI data corresponding to the target area, and gridding the POI data to obtain grid center point data; determining the demand point set based on the grid center point data, wherein the demand point set carries a daytime demand point set and a nighttime demand point set; and spatially analyzing the POI data based on the demand point set to obtain a first demand weight corresponding to each demand point in the daytime demand point set, and a second demand weight corresponding to each demand point in the nighttime demand point set.

[0045] The method of determining the police force demand at each demand level based on the demand weight includes: obtaining the minimum number of police stations required for each demand point from the demand weight, inputting the minimum number into the multi-objective of the asymptotic maximum coverage model, and outputting the police station layout and police force allocation, wherein the police force allocation is the minimum number of police officers corresponding to each police station.

[0046] Specifically, the minimum number of police stations required for each demand point is obtained from the demand weight, the minimum number is input into the multi-objective of the asymptotic maximum coverage model, and the police station layout and police force allocation are output, including: when the police force demand of each demand point is covered in a set time period, the satisfaction of the police dispatch time coverage of each police station for each demand point in the set time period is calculated; the number of configured police officers of each police station in the set time period is calculated; the police force demand weight of each demand point in the set time period is calculated; based on the satisfaction of the police dispatch time coverage, the number of configured police officers and the police force demand weight, the maximum performance of the public security management service of each police station to each demand point in each time period is determined; when the public security management service is provided to the demand point, the number of dispatched police officers that each police station is capable of dispatching to meet the demand of the demand point in each time period is calculated, wherein the number of dispatched police officers is greater than the number of police personnel required for each demand point in the target time period; based on the maximum performance of the public security management service of each police station to each demand point in each time period and the number of dispatched police officers, the police station layout and police force allocation are determined.

[0047] In the embodiment of the present application, based on the geographical locations of the candidate police station set and the demand point set, the travel time from each police station in the candidate police station set to each demand point in the demand point set is calculated in different time periods to generate a travel time set, including: calculating the shortest path from each police station to each demand point based on the police force allocation and road traffic status information, wherein the road traffic status information includes first road traffic status information and second road traffic status information; analyzing the congestion situation of the shortest path under the first road traffic status, and determining the travel time of each police station based on the congestion situation of each road section. The first travel sub-time from the police station to each daytime demand point is used to generate a first travel sub-time set, and based on the first travel sub-time set, the first travel time set corresponding to the daytime demand points is determined; the congestion situation of the shortest path under the second road traffic state is analyzed, and based on the congestion situation of each road section, the second travel sub-time from each police station to each nighttime demand point is determined to generate a second travel sub-time set, and based on the second travel sub-time set, the second travel time set corresponding to the nighttime demand points is determined; based on the first travel time set and the second travel time set, a travel time set is generated.

[0048] In an embodiment of the present application, the scenario includes a first scenario and a second scenario, wherein the service efficiency of each police station to each demand point in different scenarios is analyzed based on the travel time set to generate a service efficiency set, including: obtaining a first layout corresponding to each police station in the first scenario, and a second layout corresponding to each police station in the second scenario; calculating a first police service time from each police station to each demand point under the first layout, and a second police service time from each police station to each demand point under the second layout; generating a service efficiency set based on the first police service time and the second police service time.

[0049] In the embodiment of the present application, the collective coverage model based on "travel time", that is, the travel time in different time periods, is used instead of the coverage standard based on distance. It should be noted that the purpose of the collective coverage model based on "travel time" adopted in the present application is, on the one hand, to determine the minimum number of police stations required to cover the entire area without considering the existing police station locations; on the other hand, it is to analyze the minimum number of police stations to be rebuilt based on the service coverage ratio while considering the distribution of existing police station locations.

[0050] First, the parameters involved in the model are explained, where the parameters involved include model parameters and decision parameters. The model parameters can be found in Table 1, and the decision parameters can be found in Table 2.

[0051] Table 1 Model parameters

[0052] name meaning I Demand point collection I’ The set of demand points covered by the existing police station layout J Candidate facility point collection (road nodes, existing police station locations) T Time period collection (day and night) N(t,i) The set of facilities that can cover demand point i in time period t w(t,i) The police force demand weight of demand point i in time period t dem(t,i) Indicates the number of police officers required at demand point i in time period t c(t,i,j) represents the coverage service satisfaction in time period t [0,1] T(t,i,j) The travel time from facility point j to demand point i in time period t T0 Maximum full coverage time 5 minutes T1 Maximum coverage time 10 minutes P Current number of police stations N Maximum police force capacity of a police station (100)

[0053] Table 2 Decision variables

[0054] name meaning x(t,i) Binary variable indicating whether the police force demand of demand point i is covered in time period t y(t,j) The number of police officers deployed at facility point j during time period t z(j) A binary variable indicating whether a police station is built at facility point j.

[0055] Secondly, the construction method of the set coverage model based on "travel time" is as follows:

[0056] Based on the objective function (1), it is used to calculate the total number of facilities required to minimize the coverage of the current police station, where the objective function (1) is:

[0057] min∑ j∈J z j (1)

[0058] In addition, the objective function (1) is subject to conditions (2), (3) and (4), where constraint (2) means that any demand point i∈I is covered by at least one police station, and its formula is:

[0059]

[0060] Constraint (3) represents the set of facilities that can cover the demand point i∈I' in any period of time, and its formula is:

[0061] Constraint (4) indicates that the decision variable is a binary variable, and its expression is: j ∈{0, 1},

[0062] Secondly, the construction method of the multi-objective maximum coverage model based on "progressive coverage" is as follows:

[0063] The minimum number of police stations to be built determined by the set coverage model in the first stage is substituted as a parameter into the multi-objective of the asymptotic maximum coverage model in this stage. At the same time, the distribution of police demand in the study area during the day and at night is incorporated into the model parameters to optimize the layout of the proposed police stations and their police force allocation, and on this basis, minimize the number of police officers to be deployed.

[0064] The calculation method of the progressive coverage service satisfaction in the model is shown in formula (5), where c(t,i,j) represents the satisfaction of the alarm time coverage of facility point j to demand point i in time period t, and the value is between [0,1].

[0065]

[0066] Specifically, the asymptotic maximum coverage model is designed based on the objective function (6), where the objective function (6) represents maximizing the total performance of the police station's public security management services for each demand point in each time period, including the objective function (6.1) and the objective function (6.2):

[0067]

[0068]

[0069] In addition, the objective function (6.1) and the objective function (6.2) are subject to conditions (7), (8), (9), (10), (11) and (12), where constraint (7) indicates that only sites where police stations are built can provide public security management services to demand points in each time period. The formula is:

[0070] Constraint (8) represents the number of police stations to be built, and its formula is ∑ j∈J z j =P.

[0071] Constraint (9) indicates that in each time period, when providing public security management services to demand points, the police station is required to dispatch the number of police officers that can meet the needs of the demand points. The formula is:

[0072]

[0073] Constraint (10) indicates that the number of police officers deployed in a police station cannot exceed its maximum number limit, and its formula is:

[0074] Constraint (11) indicates that the decision variable is a binary variable, and its formula is:

[0075]

[0076] Constraint (12) indicates that the decision variable is an integer variable, and its formula is:

[0077]

[0078] Taking District B in City A as an example, the police force deployment method based on multi-source data fusion provided in this application is explained, and its specific implementation method is as follows.

[0079] First, the distribution of candidate police stations was determined. The study area was set as District B in City A. The road network data in the study area was used to obtain road node data through spatial analysis. Then, a clustering algorithm was used to obtain 300 central nodes as the candidate police station set. In addition, a crawler program was designed using Python to obtain the locations of 59 existing police stations in the area and added to the candidate police station set. The geographic coordinate system was WGS1984, and subsequent data sources all used this coordinate system.

[0080] Then, the distribution of demand points is determined, ArcGIS is used to grid the area, and the grid center point data (700m*700m) is obtained, which is used as the demand point set. Due to the lack of population distribution data in the study area, this paper crawls the POI data of the study area, and uses government buildings, companies, schools and other points of interest as the basis for calculating the daytime demand weight of the study area, and residential houses, apartments and other points of interest as the basis for calculating the nighttime demand weight. The calculation method is to first screen the POI data, then convert the Gaode coordinate system of the POI data to the WGS1984 coordinate system, and then use ArcGIS to perform spatial analysis operations such as cropping, kernel density analysis, and value extraction to points on the data, and finally obtain the demand weight distribution of the demand points during the day and night, as shown in the figure. Then, based on the demand weight division, this paper gives the number of police personnel required when each level of demand point is alarmed.

[0081] Secondly, obtain other data related to the scheme. Since this application uses travel time as the police station's response efficiency, it is necessary to provide a method for calculating the travel time from candidate police stations (359) to demand points (950). Specifically, this application uses a simplified method to calculate the travel time matrix, that is, the driving route planning interface in the Gaode API is used to obtain the shortest path from each candidate police station to each demand point during the morning and evening rush hours on weekdays, and then the vehicle speed of each section of the shortest path is set according to the traffic conditions (congestion: 10km / h, slow traffic: 30km / h, smooth traffic: 80km / h), and then the cumulative sum is obtained to obtain the travel time of each shortest path. It should be noted that since the model designed in this application involves two time periods, daytime and nighttime, and the police station's rescue is a risk-averse decision, the daytime travel time matrix is ​​calculated using the morning rush hour period, and the nighttime travel time matrix is ​​calculated using the evening rush hour period.

[0082] Finally, the coverage service efficiency under reconstruction scenarios and relocation scenarios was analyzed. The first scenario was analyzed based on the reconstruction scenario, that is, the existing police station layout in the area was analyzed, and 0 (that is, the existing police station layout was kept unchanged), 1, 2, 3, 5, 10, and 20 police stations were added; the second scenario was analyzed based on the relocation scenario, that is, the layout of the completely relocated police stations was analyzed, and the model was used to optimize 10, 20, 30, 40, 50, and 59 police stations.

[0083] After obtaining the optimal layout of the police station in each scenario, the travel time data during the evening peak period is used as simulation data to calculate the police service time of all demand points under the optimal layout of each scenario. Figure 2 and Figure 3 The service time box plots for each scenario are shown, where Figure 2 A service time box-line diagram of a police force deployment method based on multi-source data fusion in a reconstruction scenario provided in an embodiment of the present application; Figure 3 A service box diagram of a police force deployment method based on multi-source data fusion in a relocation scenario provided in an embodiment of the present application.

[0084] Depend on Figure 2 and Figure 3It can be seen that the dispatch time of the police stations in all areas under the reconstruction scenario and the relocation scenario is within 12 minutes. In the reconstruction scenario, as the number of rebuilt police stations increases, the quartiles of the dispatch time gradually decrease, but the rate of decline slows down. In the relocation scenario, as the number of police station construction increases, the quartiles and medians of the dispatch time gradually decrease; and when 40 police stations are relocated, the dispatch time efficiency is no less than the dispatch efficiency of the existing police station layout. Although the candidate police station locations used in this study are simulated locations generated based on the road network dataset of the district, the results of this study fully demonstrate that there is still much room for improvement in the current police station layout in the district.

[0085] Compared with the prior art, the police force deployment method based on multi-source data fusion provided by the embodiment of the present application has the following significant advantages:

[0086] 1) Multi-source data fusion

[0087] By using ArcGIS to integrate multi-source data such as POI point data, regional administrative planning maps, travel time data, traffic conditions, road network data, etc. into geographic raster data, the scalability and compatibility of the method are improved.

[0088] 2) Data-driven optimization modeling

[0089] The present invention is based on geographic raster data formed by the fusion of multi-source data, and combined with the relevant requirements of urban police stations for police rescue, it optimizes the site selection layout and police force deployment of urban police stations in stages, which has significant advantages over the traditional method of dividing the area based on the jurisdiction.

[0090] 3) Migration and replication

[0091] In addition to being applied to the construction planning layout of urban police stations, the present invention can be migrated to other facility site selection and planning fields, especially emergency facility fields, including but not limited to fire protection, hospitals, shelters, etc., by appropriately fine-tuning relevant parameters.

[0092] Corresponding to the above method embodiment, this specification also provides an embodiment of a police force deployment device based on multi-source data fusion, Figure 4 The following is a schematic diagram of the structure of a police force deployment device based on multi-source data fusion provided in an embodiment of the present application. Figure 4 As shown, the device comprises:

[0093] An acquisition module 402 is configured to acquire road network data of a target area;

[0094] The first determination module 404 is configured to determine a candidate police station set by analyzing the road network data, wherein the candidate police station set carries existing police station locations in the target area;

[0095] The second determination module 406 is configured to determine a set of demand points and demand weights corresponding to each demand point by analyzing the regional POI data corresponding to the target area, and determine the police force requirements under each demand level based on the demand weights;

[0096] The first generating module 408 is configured to calculate the travel time from each police station in the candidate police station set to each demand point in the demand point set in different time periods based on the geographical locations of the candidate police station set and the demand point set, and generate a travel time set, wherein the travel time is the travel time corresponding to the shortest path from each police station to each demand point;

[0097] The second generating module 410 is configured to analyze the service efficiency of each police station for each demand point in different scenarios based on the travel time set, and generate a service efficiency set;

[0098] The third determination module 412 is configured to determine the police force layout based on the service effectiveness set, wherein the police force layout includes the police station layout and the police force allocation corresponding to each police station.

[0099] In an optional embodiment, the first determining module 404 is further configured to:

[0100] By analyzing the road network data, road nodes and existing police station locations are determined;

[0101] The road nodes and the existing police station locations are analyzed based on a target algorithm to determine the minimum number of candidate police stations and generate a candidate police station set, wherein the minimum number of police stations is not less than the total number of demand points.

[0102] In an optional embodiment, the second determining module 406 is further configured to:

[0103] Acquire regional POI data corresponding to the target area, and perform grid processing on the POI data to obtain grid center point data;

[0104] Based on the grid center point data, a demand point set is determined, wherein the demand point set includes a daytime demand point set and a nighttime demand point set;

[0105] Based on the demand point set, the POI data is spatially analyzed to obtain a first demand weight corresponding to each demand point in the daytime demand point set and a second demand weight corresponding to each demand point in the nighttime demand point set.

[0106] In an optional embodiment, the second determining module 406 is further configured to:

[0107] The minimum number of police stations required for each demand point is obtained from the demand weight, the minimum number is input into the multi-objective of the asymptotic maximum coverage model, and the police station layout and police force allocation are output, wherein the police force allocation is the minimum number of police officers corresponding to each police station.

[0108] In an optional embodiment, the second determining module 406 is further configured to:

[0109] When the police force demand of each demand point is covered in the set time period, calculate the satisfaction of each police station with the police dispatch time coverage of each demand point in the set time period;

[0110] Calculate the number of police officers deployed in each police station during a set period of time;

[0111] Calculate the police force demand weights of each demand point in the set time period;

[0112] Based on the coverage satisfaction of the police response time, the number of configured police forces and the police force demand weight, determine the maximum performance of public security management services of each police station for each demand point in each time period;

[0113] In the case of providing public security management services to demand points, the number of dispatched police officers that each police station is capable of dispatching to meet the needs of the demand points in each time period is calculated, wherein the number of dispatched police officers is greater than the number of police officers required at each demand point in the target time period;

[0114] The layout of police stations and allocation of police force are determined based on the maximum performance of public security management services provided by each police station to each demand point in each time period and the number of dispatched police forces.

[0115] In an optional embodiment, the first generating module 408 is further configured to:

[0116] Based on the police force allocation and road traffic status information, the shortest path from each police station to each demand point is calculated, wherein the road traffic status information includes first road traffic status information and second road traffic status information;

[0117] Analyze the congestion of the shortest path under the first road traffic state, determine the first travel sub-time from each police station to each daytime demand point based on the congestion of each road section, generate a first travel sub-time set, and determine the first travel time set corresponding to the daytime demand point based on the first travel sub-time set;

[0118] Analyze the congestion of the shortest path under the second road traffic state, determine the second travel sub-time from each police station to each night demand point based on the congestion of each road section, generate a second travel sub-time set, and determine the second travel time set corresponding to the night demand point based on the second travel sub-time set;

[0119] A set of transit times is generated based on the first set of transit times and the second set of transit times.

[0120] In an optional embodiment, the second generation module 410 is further configured to: obtain a first layout corresponding to each police station in the first scene, and a second layout corresponding to each police station in the second scene;

[0121] Calculate the first police service time from each police station to each demand point under the first layout, and the second police service time from each police station to each demand point under the second layout;

[0122] A service performance set is generated based on the first police service time and the second police service time.

[0123] In an optional embodiment, after the acquisition module 402, it is further configured to:

[0124] The road network data is processed to generate geographic raster data.

[0125] By using the police force deployment device based on multi-source data fusion provided in the embodiment of the present application, on the one hand, ArcGIS is used to classify multi-source data such as POI point data, regional administrative planning maps, travel time data, traffic conditions, road network data, etc. into geographic raster data, thereby improving the scalability and compatibility of the method; on the other hand, based on the geographic raster data formed after the fusion of multi-source data, and combined with the relevant requirements of the urban police station’s police dispatch and rescue site, the site selection layout and police force deployment of the urban police station are optimized in stages, which has significant advantages over the traditional method of dividing the area based on the jurisdiction, thereby shortening the police dispatch time of the urban police station and improving the efficiency of the police station’s public security prevention and control.

[0126] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the police force deployment device based on multi-source data fusion, since it is basically similar to the police force deployment method embodiment based on multi-source data fusion, the description is relatively simple, and the relevant parts can be referred to the partial description of the police force deployment method embodiment based on multi-source data fusion.

[0127] Figure 5 This is a block diagram of a computing device provided in an embodiment of the present application. The components of the computing device 500 include but are not limited to a memory 510 and a processor 520. The processor 520 is connected to the memory 510 via a bus 530, and the database 550 is used to store data.

[0128] The computing device 500 also includes an access device 540, which enables the computing device 500 to communicate via one or more networks 560. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 540 may include one or more of any type of network interface (e.g., a network interface card (NIC)) of wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a world-wide interoperability for microwave access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, and a near field communication (NFC).

[0129] In one embodiment of the present specification, the above components of the computing device 500 and Figure 5 Other components not shown in the figure may also be connected to each other, for example, via a bus. It should be understood that Figure 5 The computing device structure block diagram shown is only for the purpose of illustration, and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0130] The computing device 500 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 500 may also be a mobile or stationary server.

[0131] The processor 520 is used to execute the following computer executable instructions, which, when executed by the processor, implement the steps of the above-mentioned police force deployment method based on multi-source data fusion.

[0132] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computing device embodiment, since it is basically similar to the police force deployment method embodiment based on multi-source data fusion, the description is relatively simple, and the relevant parts can be referred to the partial description of the police force deployment method embodiment based on multi-source data fusion.

[0133] An embodiment of the present specification also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the above-mentioned police force deployment method based on multi-source data fusion.

[0134] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computer-readable storage medium embodiment, since it is basically similar to the police force deployment method embodiment based on multi-source data fusion, the description is relatively simple, and the relevant parts can be referred to the partial description of the police force deployment method embodiment based on multi-source data fusion.

[0135] An embodiment of the present specification also provides a computer program, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned police force deployment method based on multi-source data fusion.

[0136] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computer program embodiment, since it is basically similar to the police force deployment method embodiment based on multi-source data fusion, the description is relatively simple, and the relevant parts can be referred to the partial description of the police force deployment method embodiment based on multi-source data fusion.

[0137] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0138] The computer instructions include computer program codes, which may be in source code form, object code form, executable files or some intermediate forms, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0139] It should be noted that the above is a description of a specific embodiment of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of the present specification.

[0140] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0141] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The optional embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that technicians in the relevant technical field can well understand and use this specification. This specification is only limited by the claims and their full scope and equivalents.

Claims

1. A police force deployment method based on multi-source data fusion, characterized in that: include: Obtain road network data for the target area; By analyzing the road network data, a candidate police station set is determined, wherein the candidate police station set carries the locations of existing police stations in the target area; By analyzing the regional POI data corresponding to the target area, a set of demand points and the demand weights corresponding to each demand point are determined, and based on the demand weights, the police force requirements under each demand level are determined; Based on the geographical locations of the candidate police station set and the demand point set, the travel time from each police station in the candidate police station set to each demand point in the demand point set is calculated in different time periods to generate a travel time set, wherein the travel time is the travel time corresponding to the shortest path from each police station to each demand point; Based on the travel time set, the service efficiency of each police station for each demand point in different scenarios is analyzed to generate a service efficiency set; Based on the service effectiveness set, the police force layout is determined, wherein the police force layout includes the police station layout and the police force allocation corresponding to each police station.

2. The method according to claim 1, characterized in that The step of analyzing the road network data to determine a set of candidate police stations includes: By analyzing the road network data, road nodes and existing police station locations are determined; The road nodes and the existing police station locations are analyzed based on a target algorithm to determine the minimum number of candidate police stations and generate a candidate police station set, wherein the minimum number of police stations is not less than the total number of demand points.

3. The method according to claim 1, characterized in that The method of analyzing the regional POI data corresponding to the target area to determine the demand point set and the demand weight corresponding to each demand point includes: Acquire regional POI data corresponding to the target area, and perform grid processing on the POI data to obtain grid center point data; Based on the grid center point data, a demand point set is determined, wherein the demand point set includes a daytime demand point set and a nighttime demand point set; Based on the demand point set, the POI data is spatially analyzed to obtain a first demand weight corresponding to each demand point in the daytime demand point set and a second demand weight corresponding to each demand point in the nighttime demand point set.

4. The method according to claim 1, characterized in that: Determining the police force requirements at each requirement level based on the requirement weights includes: The minimum number of police stations required for each demand point is obtained from the demand weight, the minimum number is input into the multi-objective of the asymptotic maximum coverage model, and the police station layout and police force allocation are output, wherein the police force allocation is the minimum number of police officers corresponding to each police station.

5. The method according to claim 4, characterized in that The method of obtaining the minimum number of police stations required for each demand point from the demand weights, inputting the minimum number into the multi-objective of the progressive maximum coverage model, and outputting the layout of police stations and allocation of police force includes: When the police force demand of each demand point is covered in the set time period, calculate the satisfaction of each police station with the police dispatch time coverage of each demand point in the set time period; Calculate the number of police officers deployed in each police station during a set period of time; Calculate the police force demand weights of each demand point in the set time period; Based on the coverage satisfaction of the police response time, the number of configured police forces and the police force demand weight, determine the maximum performance of public security management services of each police station for each demand point in each time period; In the case of providing public security management services to demand points, the number of dispatched police officers that each police station is capable of dispatching to meet the needs of the demand points in each time period is calculated, wherein the number of dispatched police officers is greater than the number of police officers required at each demand point in the target time period; The layout of police stations and allocation of police force are determined based on the maximum performance of public security management services provided by each police station to each demand point in each time period and the number of dispatched police forces.

6. The method according to claim 1 or 2, characterized in that: The step of calculating the travel time from each police station in the candidate police station set to each demand point in the demand point set in different time periods based on the geographical locations of the candidate police station set and the demand point set to generate a travel time set includes: Based on the police force allocation and road traffic status information, the shortest path from each police station to each demand point is calculated, wherein the road traffic status information includes first road traffic status information and second road traffic status information; Analyze the congestion of the shortest path under the first road traffic state, determine the first travel sub-time from each police station to each daytime demand point based on the congestion of each road section, generate a first travel sub-time set, and determine the first travel time set corresponding to the daytime demand point based on the first travel sub-time set; Analyze the congestion of the shortest path under the second road traffic state, determine the second travel sub-time from each police station to each night demand point based on the congestion of each road section, generate a second travel sub-time set, and determine the second travel time set corresponding to the night demand point based on the second travel sub-time set; A set of transit times is generated based on the first set of transit times and the second set of transit times.

7. The method according to claim 1, characterized in that The scenes include a first scene and a second scene. Based on the travel time set, the service efficiency of each police station for each demand point in different scenarios is analyzed to generate a service efficiency set, including: Obtaining a first layout corresponding to each police station in the first scene, and a second layout corresponding to each police station in the second scene; Calculate the first police service time from each police station to each demand point under the first layout, and the second police service time from each police station to each demand point under the second layout; A service performance set is generated based on the first police service time and the second police service time.

8. The method according to claim 1, characterized in that After obtaining the road network data of the target area, the method includes: The road network data is processed to generate geographic raster data.

9. A police force deployment device based on multi-source data fusion, characterized in that: include: An acquisition module is configured to acquire road network data of a target area; A first determination module is configured to determine a candidate police station set by analyzing the road network data, wherein the candidate police station set carries existing police station locations in a target area; The second determination module is configured to determine a set of demand points and demand weights corresponding to each demand point by analyzing regional POI data corresponding to the target area, and determine the police force requirements under each demand level based on the demand weights; The first generation module is configured to calculate the travel time from each police station in the candidate police station set to each demand point in the demand point set in different time periods based on the geographical locations of the candidate police station set and the demand point set, and generate a travel time set, wherein the travel time is the travel time corresponding to the shortest path from each police station to each demand point; The second generation module is configured to analyze the service efficiency of each police station for each demand point in different scenarios based on the travel time set to generate a service efficiency set; The third determination module is configured to determine the police force layout based on the service effectiveness set, wherein the police force layout includes the police station layout and the police force allocation corresponding to each police station.

10. A computing device comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the police force deployment method based on multi-source data fusion as described in any one of claims 1 to 8 are implemented.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an implementation program for information transmission, and when the program is executed by a processor, the steps of the police force deployment method based on multi-source data fusion as described in any one of claims 1 to 8 are implemented.

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