Gung identification method and system based on GPS

Abstract

The invention relates to the technical field of information processing, in particular to a risk gang identification method based on a GPS (Global Positioning System), which comprises the following steps: a data acquisition step: acquiring GPS data from equipment, and performing data preprocessing on the GPS data to obtain standardized GPS data; a GPS network construction step: constructing a GPS network of the target area based on the degradation scale; a clustering analysis step: performing clustering analysis processing on the standardized GPS data based on a GPS network to obtain a risk GPS data list; and a gang identification step: completing risk gang identification based on the risk GPS data list.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and more specifically, to a GPS-based gang identification method and system. Background Art

[0002] Existing GPS-based gang identification methods, such as gang identification based on the K-means algorithm and the DBSCAN algorithm, rely on calculating the distance between each individual GPS to perform clustering calculations, and based on the clustering calculation results, identify groups of individuals with a high degree of aggregation, that is, identify potential gang behavior.

[0003] However, existing GPS-based gang identification methods have certain limitations:

[0004] The algorithm model is highly dependent on the accuracy of GPS positioning data. If the GPS data is inaccurate, the calculated distance will be inaccurate, which will mislead the judgment of the algorithm model and may cause unrelated individuals to be misjudged as associated, or ignore individuals that are actually associated.

[0005] The existing algorithm model needs to process a large amount of data to calculate the distance between each two GPSs, which can easily lead to a decrease in computing efficiency and even performance bottlenecks. At the same time, due to the limitation of distance measurement, the existing algorithm model cannot identify gangs in real time and does not have real-time performance.

[0006] Different business scenarios have different requirements for distance thresholds, but existing clustering methods based on distance metrics require a preset distance threshold when performing group mining, and cannot be dynamically adjusted for different business scenarios.

[0007] Therefore, it is an urgent problem to provide a GPS-based gang identification method and system with accuracy, efficiency, real-time and robustness. Summary of the invention

[0008] The present invention provides a GPS-based group identification method and system, so as to at least solve the problems that the existing GPS-based group identification method has low accuracy, low efficiency and cannot adapt to different business scenarios.

[0009] The present invention aims to provide a GPS-based group identification method and system, which realizes GPS-based group identification with high efficiency, accuracy, real-time and robustness by degrading GPS data into the same grid and setting different degradation scales and clustering trigger and termination thresholds based on different business scenarios.

[0010] To achieve the above object, the present invention provides a method for identifying risk groups based on GPS, which is characterized by comprising:

[0011] Data acquisition step: acquiring GPS data from a device, and performing data preprocessing on the GPS data to obtain standardized GPS data;

[0012] GPS network construction steps: construct the GPS network of the target area based on the degradation scale;

[0013] Cluster analysis step: performing cluster analysis on the standardized GPS data based on the GPS network to obtain a risk GPS data list;

[0014] Group identification step: complete risk group identification based on the risk GPS data list.

[0015] Furthermore, the GPS network construction step includes:

[0016] A degradation scale is preset based on different business scenarios, a GPS grid is constructed based on the degradation scale, and a GPS grid adjacency relationship between the GPS grid and its adjacent grids is constructed to obtain a GPS network in a target area.

[0017] Furthermore, the cluster analysis step includes:

[0018] GPS data degradation step: degrading the standardized GPS data based on the degradation scale;

[0019] Risk counting step: using the GPS grid as a risk analysis unit, obtaining the number of times each risk analysis unit in the GPS network is degraded, which is recorded as GPS count.

[0020] Furthermore, the cluster analysis step further comprises:

[0021] Preset clustering trigger thresholds and clustering termination thresholds based on different business scenarios;

[0022] If the GPS count of the current risk analysis unit exceeds the cluster triggering threshold, the risk analysis unit is marked as a risk unit.

[0023] Furthermore, the cluster analysis step further comprises:

[0024] Calculating the GPS count difference between the GPS count of the risk unit and the GPS count of its adjacent risk analysis unit;

[0025] If the GPS count difference is less than the cluster termination threshold, the risk unit performs risk propagation and clustering, and the adjacent risk analysis unit is marked as a risk unit;

[0026] If the GPS count difference satisfies the clustering termination threshold, the risk unit terminates clustering;

[0027] After traversing the risk analysis unit, a risk GPS data list is returned.

[0028] Furthermore, the adjacent GPS grids include: a northern neighbor GPS grid, a southern neighbor GPS grid, a western neighbor GPS grid, an eastern neighbor GPS grid, a northwest neighbor GPS grid, a northeast neighbor GPS grid, a southwest neighbor GPS grid and a southeast neighbor GPS grid.

[0029] Furthermore, the risk counting step includes:

[0030] Based on the GPS network, taking the lower left corner vertex of the GPS grid as the center point, merging the west neighboring GPS grid, the southwest neighboring GPS grid and the south neighboring GPS grid of the GPS grid into a GPS virtual large grid;

[0031] The GPS virtual large grid is used as a risk analysis unit, and the number of times each risk analysis unit in the GPS network is degraded is obtained and recorded as GPS count.

[0032] The present invention provides a risk group identification system based on GPS grid, characterized in that the risk group identification method applied to the above-mentioned GPS grid includes:

[0033] Data acquisition module: acquires GPS data from the device, and performs data preprocessing on the GPS data to obtain standardized GPS data;

[0034] GPS network construction module: constructs the GPS network of the target area based on the degradation scale;

[0035] Cluster analysis module: performs cluster analysis on the standardized GPS data based on the GPS network to obtain a risk GPS data list;

[0036] Group identification module: completes risk group identification based on the risk GPS data list.

[0037] The present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned GPS-based risk group identification method when executing the computer program.

[0038] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, the GPS-based risk group identification method as described above is implemented.

[0039] Compared with the prior art, the advantages and positive effects of the present invention are:

[0040] The present invention reduces the number of distances to be calculated by degenerating GPS data into the same grid, thereby greatly reducing the computational complexity and improving the processing speed of the system. Compared with the traditional model, the present invention has a particularly significant improvement in efficiency in large-scale data processing.

[0041] The present invention realizes and meets the business needs of group clustering and risk propagation in different business scenarios by setting different degradation accuracy scales, different risk propagation start and stop thresholds, and different grid center point positioning methods.

[0042] The present invention can effectively carry out risk propagation and gang cluster identification by means of adjacent grid risk propagation, can quickly respond to risk propagation clustering and early warning needs in real-time scenarios, and solves the shortcomings of traditional methods in real-time data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flowchart of the method for identifying risk groups based on GPS of the present invention;

[0044] Figure 2 A schematic diagram of GPS network construction in an embodiment of the present invention;

[0045] Figure 3 This is a schematic diagram of standardized GPS data degradation in an embodiment of the present invention;

[0046] Figure 4 A schematic diagram of cluster analysis in an embodiment of the present invention;

[0047] Figure 5 A schematic diagram of constructing a GPS virtual large grid in an embodiment of the present invention;

[0048] Figure 6 This is a schematic diagram of the adjacency relationship of a GPS virtual large grid in an embodiment of the present invention;

[0049] Figure 7 This is a schematic diagram of the GPS-based risk group identification system of the present invention;

[0050] Figure 8 A schematic diagram of a computer device provided in an embodiment of the present invention.

[0051] In the above picture:

[0052] 40. bus; 41. processor; 42. memory; 43. communication interface; 100. data acquisition module; 200. GPS network construction module; 300. cluster analysis module; 400. gang identification module. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0054] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "liquid level", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. The terms "first" and "second" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, "multiple" means two or more.

[0055] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0056] like Figure 1-Figure 8 As shown, the present invention provides a GPS-based risk group identification method and system to achieve GPS-based group identification with high efficiency, accuracy, real-time and robustness. Figure 1-Figure 8 The GPS-based gang identification method and system provided by the present invention are described in detail.

[0057] Embodiment 1:

[0058] like Figure 1 As shown, the present invention provides a method for identifying risk groups based on GPS, comprising:

[0059] Data acquisition step S100: acquiring GPS data from a device, and performing data preprocessing on the GPS data to obtain standardized GPS data;

[0060] Preferably, GPS data collected from devices in different business environments are collected, and data preprocessing is performed on the collected GPS data to obtain standardized GPS data. The data preprocessing method includes but is not limited to data cleaning and data conversion, so that the collected GPS data has a unified data format to ensure data quality.

[0061] In some embodiments, a distributed database or a big data platform may be used to store the large amount of standardized GPS data to ensure the scalability and high availability of the data.

[0062] like Figure 2 As shown, the GPS network construction step S200: constructing a GPS network in the target area based on the degradation scale;

[0063] Preferably, a degradation scale is preset based on different business scenarios, a GPS grid is constructed based on the degradation scale, and a GPS grid adjacency relationship between the GPS grid and its adjacent grids is constructed to obtain a GPS network in a target area.

[0064] Preferably, the adjacent GPS grids include: a northern neighboring GPS grid, a southern neighboring GPS grid, a western neighboring GPS grid, an eastern neighboring GPS grid, a northwest neighboring GPS grid, a northeast neighboring GPS grid, a southwest neighboring GPS grid and a southeast neighboring GPS grid.

[0065] In some embodiments, a degradation scale is selected according to actual needs of the business scenario, such as 0.001, 0.002, 0.003, 0.0001, etc. The degradation scale can be selected according to actual needs. When the degradation scale is selected as 0.002, the degradation method based on the gps_blur degradation function is implemented as follows:

[0066] def gps_blur(lon,lat,acc=0.002):

[0067] lon_new = lon - lon%acc

[0068] lat_new = lat - lat%acc

[0069] return lon_new,lat_new

[0070] After the above degradation, a GPS grid can be obtained. In the GPS grid space, each GPS grid has 8 adjacent grids. These 8 grids are directly associated with each other by adding and subtracting the degradation scale. For example, when the scale is 0.002, the northern neighbor grid of grid G0 (0.002, 0.004) is (0.002, 0.006), the eastern neighbor grid is (0.004, 0.004), and the southwest neighbor grid is (0.000, 0.002). The calculation methods of other adjacent grids are similar. Based on this, the GPS network composed of GPS grids in the target area is obtained.

[0071] like Figure 3-Figure 4 As shown, cluster analysis step S300: performing cluster analysis on standardized GPS data based on the GPS network to obtain a risk GPS data list;

[0072] like Figure 3 As shown, preferably, the standardized GPS data is first degraded based on a preset degradation scale. After the degradation is completed, the GPS grid is used as a risk analysis unit, and the number of times each risk analysis unit in the GPS network is degraded is obtained and recorded as the GPS count.

[0073] Preferably, clustering trigger thresholds and clustering termination thresholds are preset based on different business scenarios; if the GPS count of a certain risk analysis unit exceeds the clustering trigger threshold set in the business scenario and the current risk analysis unit, i.e., the GPS grid, has never triggered risk propagation and clustering, risk propagation and grid clustering will be triggered, the risk analysis unit will be marked as a risk unit, and risk propagation and clustering will be performed to the surrounding adjacent risk analysis units with the risk unit as the center;

[0074] Preferably, the GPS count difference between the GPS count of the risk unit and the GPS count of its adjacent risk analysis unit is calculated. If the GPS count difference is less than the preset cluster termination threshold, the risk unit performs risk propagation and clustering, and the adjacent risk analysis unit is marked as a risk unit, that is, the two grids are merged into one category, thereby achieving clustering, and then diffused outward until the GPS count difference meets the cluster termination threshold, then the risk unit terminates clustering, and risk propagation and clustering stop. After traversing the risk analysis unit, i.e., the GPS grid, a risk GPS data list is returned.

[0075] like Figure 4 As shown, in some embodiments, the marked ones in the figure are risk units, i.e., clustered GPS grids, and the dotted lines indicate that the risk propagation conditions are not met, and the unmarked GPS grids are not risky. The final return (G0, G2, G5, G6, G8) is the result of the latest clustering grid, and the corresponding risk GPS data list information, and a real-time gang risk warning is performed.

[0076] Group identification step S400: completing risk group identification based on the risk GPS data list.

[0077] In some embodiments, the identified risk groups may be further analyzed and characterized in combination with relationship maps.

[0078] Embodiment 2:

[0079] like Figure 5-Figure 6 As shown, in some business scenarios, there is a need for cross-regional risk monitoring. Therefore, based on the above-mentioned GPS grid-based risk group identification method, the above-mentioned risk analysis units can be further expanded and merged into a GPS virtual large grid. The GPS virtual large grid is used as the risk analysis unit for cluster analysis and cluster construction to achieve real-time group risk propagation and cluster identification in overlapping scenarios.

[0080] Preferably, based on the GPS network, the lower left corner vertex of the GPS grid is used as the center point, and the western neighboring GPS grid, the southwest neighboring GPS grid and the south neighboring GPS grid of the GPS grid are merged to construct a GPS virtual large grid; due to the natural properties of GPS, in my country, after GPS is degraded, the center point will automatically shift to the lower left, that is, each GPS grid will eventually gather at the vertex in the lower left corner, so the lower left corner fixed point of the GPS grid is selected as the center point.

[0081] Preferably, the GPS virtual large grid is used as the risk analysis unit, and the number of times each risk analysis unit in the GPS network is degraded is obtained and recorded as the GPS count. The subsequent cluster analysis method is consistent with that in the first embodiment.

[0082] like Figure 6 As shown in the figure, there will be overlapping parts between the GPS virtual large grids corresponding to each grid center point. The overlapping area can capture the gangs active at the junction of multiple grids and improve the accuracy of identifying these gangs. Further grid merging and network construction based on the degraded GPS grid can more comprehensively identify risk groups across multiple small grids, improving the breadth and depth of risk identification. In areas with geographical or logical overlap, the virtual large grid can ensure the continuity and consistency of risk analysis and avoid information loss or duplication caused by grid division. This method can flexibly adjust the scale and boundaries of the virtual large grid according to different business scenarios and needs, and has strong adaptability. By merging adjacent grids, the impact of grid boundaries on risk analysis is reduced, and the boundary effect is reduced. In multi-regional or cross-jurisdictional risk management, the virtual large grid helps promote collaboration and information sharing between different departments or institutions.

[0083] Embodiment three:

[0084] The present invention provides a risk group identification system based on GPS grid, and a risk group identification method applied to the above-mentioned GPS grid, comprising:

[0085] Data acquisition module 100: acquires GPS data from a device, and performs data preprocessing on the GPS data to obtain standardized GPS data;

[0086] GPS network construction module 200: constructs a GPS network in a target area based on a degradation scale;

[0087] Cluster analysis module 300: performs cluster analysis on standardized GPS data based on the GPS network to obtain a risk GPS data list;

[0088] Group identification module 400: completes risk group identification based on the risk GPS data list.

[0089] Embodiment 4:

[0090] As shown in FIG. X, this embodiment discloses a specific implementation of a computer device. The computer device may include a processor 41 and a memory 42 storing computer program instructions.

[0091] Specifically, the processor 41 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0092] Among them, the memory 42 may include a large-capacity memory for data or instructions. By way of example and not limitation, the memory 42 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 42 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 42 may be inside or outside the data processing device. In a specific embodiment, the memory 42 is a non-volatile memory. In a specific embodiment, the memory 42 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, referred to as PROM), an erasable PROM (Erasable ProgrammableRead-Only Memory, referred to as EPROM), an electrically erasable PROM (Electrically Erasable ProgrammableRead-Only Memory, referred to as EEPROM), an electrically alterable ROM (Electrically Alterable Read-Only

[0093] The RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM may be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0094] The memory 42 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 41 .

[0095] The processor 41 implements the GPS-based risk group identification method in the above embodiment by reading and executing computer program instructions stored in the memory 42 .

[0096] In some of the embodiments, the computer device may further include a communication interface 43 and a bus 40. Figure 7 As shown, the processor 41, the memory 42, the communication interface 43 and the bus 40 are connected and communicate with each other.

[0097] The communication interface 43 is used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0098] The communication interface 43 can also realize data communication with other components such as external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0099] The bus 40 includes hardware, software or both, and couples the components of the computer device to each other. The bus 40 includes but is not limited to at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 40 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SAT) bus, or a Serial Advanced Technology Attachment (SAT) bus.

[0100] The bus 40 may include a SATA bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable bus, or a combination of two or more of these. Where appropriate, the bus 40 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0101] In addition, in combination with the GPS-based risk group identification method in the above embodiment, the embodiment of the present application can provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any one of the GPS-based risk group identification methods in the above embodiment is implemented.

[0102] The above description is only a preferred embodiment of the present invention and does not limit the present invention in other forms. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A GPS-based risk group identification method, characterized in that: include: Data acquisition step: acquiring GPS data from a device, and performing data preprocessing on the GPS data to obtain standardized GPS data; GPS network construction steps: construct the GPS network of the target area based on the degradation scale; Cluster analysis step: performing cluster analysis on the standardized GPS data based on the GPS network to obtain a risk GPS data list; Group identification step: complete risk group identification based on the risk GPS data list.

2. According to the GPS-based risk group identification method of claim 1, it is characterized in that: The GPS network construction steps include: A degradation scale is preset based on different business scenarios, a GPS grid is constructed based on the degradation scale, and a GPS grid adjacency relationship between the GPS grid and its adjacent grids is constructed to obtain a GPS network in a target area.

3. According to the GPS-based risk group identification method of claim 2, it is characterized in that: The cluster analysis step comprises: GPS data degradation step: degrading the standardized GPS data based on the degradation scale; Risk counting step: using the GPS grid as a risk analysis unit, obtaining the number of times each risk analysis unit in the GPS network is degraded, which is recorded as GPS count.

4. According to the GPS-based risk group identification method of claim 3, it is characterized in that: The cluster analysis step further comprises: Preset clustering trigger thresholds and clustering termination thresholds based on different business scenarios; If the GPS count of the current risk analysis unit exceeds the cluster triggering threshold, the risk analysis unit is marked as a risk unit.

5. According to the GPS-based risk group identification method of claim 4, it is characterized in that: The cluster analysis step further comprises: Calculating the GPS count difference between the GPS count of the risk unit and the GPS count of its adjacent risk analysis unit; If the GPS count difference is less than the cluster termination threshold, the risk unit performs risk propagation and clustering, and the adjacent risk analysis unit is marked as a risk unit; If the GPS count difference satisfies the clustering termination threshold, the risk unit terminates clustering; After traversing the risk analysis unit, a risk GPS data list is returned.

6. According to claim 3, the method for identifying risk groups based on GPS is characterized in that: The adjacent GPS grids include: a northern neighbor GPS grid, a southern neighbor GPS grid, a western neighbor GPS grid, an eastern neighbor GPS grid, a northwest neighbor GPS grid, a northeast neighbor GPS grid, a southwest neighbor GPS grid and a southeast neighbor GPS grid.

7. The GPS-based risk group identification method according to claim 6 is characterized in that: The risk counting step comprises: Based on the GPS network, taking the lower left corner vertex of the GPS grid as the center point, merging the west neighboring GPS grid, the southwest neighboring GPS grid and the south neighboring GPS grid of the GPS grid into a GPS virtual large grid; The GPS virtual large grid is used as a risk analysis unit, and the number of times each risk analysis unit in the GPS network is degraded is obtained and recorded as GPS count.

8. A risk group identification system based on GPS grid, characterized in that: The risk group identification method applied to the GPS grid as described in any one of claims 1 to 7 above comprises: Data acquisition module: acquires GPS data from the device, and performs data preprocessing on the GPS data to obtain standardized GPS data; GPS network construction module: constructs the GPS network of the target area based on the degradation scale; Cluster analysis module: performs cluster analysis on the standardized GPS data based on the GPS network to obtain a risk GPS data list; Group identification module: completes risk group identification based on the risk GPS data list.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the GPS-based risk group identification method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the GPS-based risk group identification method as described in any one of claims 1 to 7 is implemented.

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