Blind area identification method for gridding map

Abstract

The invention relates to the field of blind area recognition, in particular to a blind area recognition method for a gridding map. Comprising the following steps: breaking a road network according to a set division interval length, and generating a basic data table containing endpoint coordinates and road information; patrolling track points and case details of patrolling personnel are extracted, and longitude and latitude points and supervisor identifiers or case identifiers are obtained to serve as primary keys; coordinate system conversion of the data points is carried out, and latitude and longitude coordinates of the data points are converted into urban construction coordinates; dividing the analysis area into a plurality of grids; performing intersection analysis on the road points, the supervisor track points and the case points in each grid by adopting a multi-thread method, calculating the shortest distances between the supervisor track points and the case points and the road points, and outputting a patrol road result; and selecting a road complementary to the patrol road result from all the roads in the basic data table as a blind area, and generating a blind area recognition result. According to the invention, data processing speed, analysis efficiency and identification accuracy can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of blind spot recognition, and in particular to a blind spot recognition method for a grid map. Background Art

[0002] The identification of blind spots mainly involves the intersection analysis of cases and roads, and obtaining uncovered areas as blind spots (ignored areas). Usually, the intersection of the two can be performed using the buffer analysis method in a spatial manner, which usually relies on geographic information system (GIS) software to perform spatial analysis. However, in large-scale cases, the existing GIS spatial analysis technology has a slow processing speed and takes a long time for large-area data sets. When performing blind spot identification, it is difficult to process large-scale data, and the blind spot identification effect is poor. Summary of the invention

[0003] The purpose of the present invention is to provide a blind spot identification method for a grid map to solve the above technical problems;

[0004] The technical problem solved by the present invention can be achieved by adopting the following technical solutions:

[0005] A blind spot identification method for a grid map, comprising:

[0006] Step S1, dividing the road network according to the set segmentation interval length, and generating a basic data table containing endpoint coordinates and road information;

[0007] Step S2, extracting the patrol track points and case details of the patrol officer, obtaining the latitude and longitude points and the supervisor identifier or case identifier as the primary key;

[0008] Step S3, converting the coordinate system of the data points, converting the data points including the road points, the inspection track points and the case points from longitude and latitude coordinates to urban construction coordinates;

[0009] Step S4, dividing the complete map area into a plurality of grids according to the coordinate range of the analysis area;

[0010] Step S5, using a multi-threaded method to perform intersection analysis on the road points, the supervisor track points and the case points in each grid, calculate the shortest distance between the supervisor track points and the case points and the road points, and output the inspection road results;

[0011] Step S6, selecting a road complementary to the inspection road result from all the roads in the basic data table as a blind spot, and generating a blind spot recognition result.

[0012] Preferably, in step S1, the segmentation interval length is set, and the road points separated by the segmentation interval length are batch-generated by cyclically calling the element selection and segmentation method.

[0013] Preferably, in step S1, after the road point is generated, it is determined whether the road point is a road intersection point, and if it is a road intersection point, the road point is removed; if it is not a road intersection point, the road point is retained.

[0014] Preferably, in step S2, the road points are used as basic data, and a one-time batch processing method is used to convert the road points from latitude and longitude coordinates to urban construction coordinates.

[0015] Preferably, in step S2, the inspection trajectory points and the case points are used as real-time data, and after accessing the data of the inspection trajectory points and the case points through a real-time interface, the inspection trajectory points and the case points are converted from latitude and longitude coordinates to urban construction coordinates in real time.

[0016] Preferably, step S4 comprises,

[0017] Step S41, obtaining the coordinates and quantity of all the data points;

[0018] Step S42, finding the coordinate extreme point to determine the range of the analysis area;

[0019] Step S43, dividing the analysis area into a plurality of rectangular grids, wherein the amount of data N in each grid is within a preset range of magnitude; wherein N=CC×RC, CC represents the number of the inspection trajectory points and the case points, and RC represents the number of the road points.

[0020] Preferably, in step S5, the original grid is subjected to boundary expansion processing using a preset boundary expansion parameter to obtain the expanded grid, the data points located on the boundary of the original grid are located within the expanded grid, and the segmentation interval length is used as the boundary expansion parameter.

[0021] Preferably, in step S5, the grids are intersected and analyzed in a multi-threaded manner to find the data points in each grid, and the condition formula that needs to be satisfied to determine that the inspection trajectory point and the case point are located in the grid is:

[0022] CX≥minXG-m

[0023] CX<maxXG+m

[0024] CY≥minYG-m

[0025] CY<maxYG+m

[0026] Wherein, CX represents the urban construction X coordinate value of the inspection trajectory point and the case point, CY represents the urban construction Y coordinate value of the inspection trajectory point and the case point, maxXG represents the maximum urban construction X coordinate value within the range of the grid, minXG represents the minimum urban construction X coordinate value within the range of the grid, maxYG represents the maximum urban construction Y coordinate value within the range of the grid, minYG represents the minimum urban construction Y coordinate value within the range of the grid, and m is the length of the segmentation interval;

[0027] The conditional formula that needs to be satisfied to determine that the road point is located in the grid is:

[0028] RX≥minXG-m

[0029] RX<maxXG+m

[0030] RY≥minYG-m

[0031] RY<maxYG+m

[0032] Wherein, RX represents the urban construction X coordinate value of the road point, and RY represents the urban construction Y coordinate value of the road point.

[0033] Preferably, in step S5, the calculation formula for the distance Dis between the supervisor trajectory point and the case point and the road point is,

[0034]

[0035] Wherein, CX represents the urban construction X coordinate value of the inspection trajectory point and the case point, CY represents the urban construction Y coordinate value of the inspection trajectory point and the case point, RX represents the urban construction X coordinate value of the road point, and RY represents the urban construction Y coordinate value of the road point;

[0036] The supervisor trajectory points and the case points are screened out during the distance calculation with respect to the road points. During the calculation process, each time the same supervisor identifier or case identifier is encountered, it is determined whether the newly calculated distance result is smaller than the previously obtained distance result. If so, the distance record is updated; if not, the previously obtained distance result is retained.

[0037] Preferably, in step S6, the blind spot includes a patrol blind spot and a case candidate blind spot, and according to the patrol road result calculated in step S5, the road among all roads that is complementary to the patrol road result is used as the blind spot, the road among the blind spot that is associated with the supervisor identifier is used as the patrol blind spot, and the road that is associated with the case identifier is used as the case candidate blind spot.

[0038] Beneficial effects of the present invention: Due to the adoption of the above technical scheme, the present invention pre-processes the road network and case data, and adopts coordinate transformation and grid analysis, so that the blind spot identification method provided can effectively improve the data processing speed, analysis efficiency and identification accuracy when processing large-scale data. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A schematic diagram of the steps of a blind spot identification method for a grid map in an embodiment of the present invention;

[0040] Figure 2 It is a flow chart of a blind spot identification method of a grid map in an embodiment of the present invention;

[0041] Figure 3 Schematic diagram of step S4 in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0044] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0045] A blind spot identification method for grid maps, such as Figure 1 , Figure 2 As shown, including,

[0046] Step S1, dividing the road network according to the set segmentation interval length, and generating a basic data table containing endpoint coordinates and road information;

[0047] Step S2, extracting the patrol track points and case details of the patrol officer, obtaining the latitude and longitude points and the supervisor identifier or case identifier as the primary key;

[0048] Step S3, converting the coordinate system of the data points, converting the data points including the road points, patrol track points and case points from longitude and latitude coordinates to urban construction coordinates;

[0049] Step S4, dividing the complete map area into a plurality of grids according to the coordinate range of the analysis area;

[0050] Step S5, using a multi-threaded method to perform intersection analysis on the road points, supervisor track points and case points in each grid, calculate the shortest distance between the supervisor track points and case points and the road points, and output the inspection road results;

[0051] Step S6, selecting a road complementary to the patrol road result from all the roads in the basic data table as a blind spot, and generating a blind spot recognition result.

[0052] Specifically, the present invention mainly considers how to simulate the effect of buffer zone analysis and perform fast calculations in the absence of GIS software during the analysis phase.

[0053] In a preferred embodiment, in step S1, the segmentation interval length is set, and a cyclic call feature selection and segmentation method is used to batch generate road points with the segmentation interval length.

[0054] In a preferred embodiment, in step S1, after the road point is generated, it is determined whether the road point is a road intersection point. If it is a road intersection point, the road point is removed; if it is not a road intersection point, the road point is retained.

[0055] Specifically, the preprocessing of the road network in the present invention is mainly to break the road network at certain intervals and generate a corresponding basic data table containing endpoint coordinates and road information.

[0056] ArcGIS software is used to perform road network segmentation. The "Edit" tool is used to break the line, and a certain element is selected for "Segmentation". The segmentation method selects to split the element at a specific distance. This method cannot be interrupted in batches and does not meet the requirements of preprocessing. Therefore, it is necessary to call feature selection and segmentation through programming loops to batch generate road points with uniform interval distances. At the same time, considering that road intersections cannot accurately determine the road they belong to, road intersections are removed during generation.

[0057] Specifically, the purpose of the road preprocessing in step S1 of the present invention is to interrupt the road network at certain intervals to facilitate subsequent analysis and calculation. Interruption will generate new endpoints. In ArcGIS, the commonly used interruption method is to use the "Split" function of the "Edit" tool. When selecting a certain element for splitting, the element can be set to split at a specific distance. When using the "Split" tool of ArcGIS, it is impossible to batch process multiple elements and satisfactorily meet the need for a large number of roads to be interrupted at fixed intervals; the solution of the present invention is to interrupt multiple road elements in batches through programming, such as Python looping to call the element selection and splitting functions, and automatically generate multiple points according to the specified splitting interval length to adapt to different road conditions.

[0058] A road intersection is an intersection of multiple roads. Since it is difficult to determine which road a road intersection belongs to, the present invention needs to remove the road intersection when interrupting the road and only retain the valid road endpoints.

[0059] Among them, the number of road points is related to the subsequent calculation. A larger interval length will reduce the number of points generated, increase the calculation speed, but also reduce the accuracy; a smaller interval length can improve the accuracy of the result, but will increase the complexity of the calculation. The selection of the segmentation interval length m of the present invention needs to balance the calculation speed and the accuracy of the calculation result, and can be adjusted according to actual needs to achieve the most balanced calculation efficiency and result accuracy.

[0060] Taking the operation using Python script as an example, a loop is created to process each road, traverse each line feature in the road network, calculate the length of the road line and determine how many segmentation points are needed. For each road, the logic of segmentation is to calculate the position of each segmentation point (coordinates along the line), and arcpy.Point and arcpy.Geometry classes can be used to create new point features. Use spatial query to determine whether the new point is near the intersection. If the new point is close to the intersection, skip the point, and finally export the data. The present invention creates a basic data table to record the endpoint coordinates and road information.

[0061] In a preferred embodiment, in step S2, the road points are used as basic data, and a one-time batch processing method is used to convert the road points from latitude and longitude coordinates to urban construction coordinates.

[0062] In a preferred embodiment, in step S2, the patrol track points and case points are used as real-time data, and the patrol track points and case points are accessed through a real-time interface to convert the latitude and longitude coordinates of the patrol track points and case points into urban construction coordinates in real time.

[0063] Specifically, the present invention divides blind spots into two categories: one is patrol blind spots, i.e. blind spots that supervisors have not patrolled, and the other is case candidate blind spots (blind spots that have not been actively discovered), i.e. road blind spots that supervisors have visited but have not found any problems. The data correspond to supervisor patrol track points and case details respectively. The present invention extracts key data information, including latitude and longitude points and supervisor identifiers or case identifiers (IDs), as the primary key for subsequent associated detailed information.

[0064] Specifically, the original data of road points, inspection trajectory points and case points are all longitude and latitude, and the distance calculation formula between longitude and latitude is relatively complicated. In order to reduce the calculation time, the present invention needs to convert the coordinates into urban construction coordinates first.

[0065] The road points are basic data and only need to be converted once. After the road network is subjected to step S1, ie, pre-processing of road network segmentation, the longitude and latitude coordinates are converted to urban construction coordinates.

[0066] The data of patrol trajectory points and case points are real-time data, so coordinate conversion is required in real time when the data is accessed, and the interface is used to convert longitude and latitude coordinates to urban construction coordinates.

[0067] The latitude and longitude coordinates in the present invention are WGS84 coordinate system, and the urban construction coordinates are plane coordinates of local urban planning and construction. The conversion process includes the following steps:

[0068] Step 1, calculate the three-dimensional coordinates of a point on the earth's surface from the longitude and latitude.

[0069] Step 2: Apply projection transformation to map the three-dimensional coordinates to urban construction plane coordinates.

[0070] The specific conversion formula and parameters can be adjusted according to the parameters of the urban construction coordinates, such as the center longitude and latitude, scaling and projection parameters, etc. The present invention uses a batch processing method to uniformly convert the WGS84 coordinates of all road points into urban construction coordinates. Since the inspection trajectory points and case points are both real-time data, the present invention realizes real-time coordinate conversion through an interface. The functions of the interface include at least receiving longitude and latitude coordinates as input, calculating according to a defined coordinate conversion algorithm, and returning the converted urban construction coordinates.

[0071] In a preferred embodiment, Figure 3 As shown, step S4 includes,

[0072] Step S41, obtaining the coordinates and quantity of all data points;

[0073] Step S42, finding the coordinate extreme point and determining the range of the analysis area;

[0074] Step S43, dividing the analysis area into a plurality of rectangular grids, wherein the amount of data N in each grid is within a preset range of magnitude; wherein N=CC×RC, CC represents the number of patrol track points and case points, and RC represents the number of road points.

[0075] Specifically, due to the large number of cases and road points in the city, both of which are over 100,000, it is necessary to adopt a grid-slicing method to perform synchronous calculations in blocks to optimize the processing speed.

[0076] First, according to the area to be analyzed, find its range, including the maximum and minimum coordinate values, establish the length and width range of the analysis area, the length is maxX-minX, the width is maxY-minY, and the number of data points, divide the entire area into multiple rectangular grids of size x*y, so that the number of CC×RC in the grid is in the millions, ensuring the calculation speed, thereby optimizing the processing speed;

[0077] Among them, maxX represents the maximum urban construction X-coordinate value of the regional range, minX represents the minimum urban construction X-coordinate value of the regional range, maxY represents the maximum urban construction Y-coordinate value of the regional range, minY represents the minimum urban construction Y-coordinate value of the regional range, CC represents the number of patrol track points and case points, RC represents the number of road points, x represents the length of the grid, and y represents the width of the grid.

[0078] In a preferred embodiment, in step S5, the original grid is subjected to boundary expansion processing using a preset boundary expansion parameter to obtain an expanded grid, and the data points on the boundary of the original grid are located within the expanded grid, and the segmentation interval length is used as the boundary expansion parameter.

[0079] Specifically, after the road points, patrol trajectory points, case points and grids are prepared, the present invention uses multi-threaded grid division to perform intersection analysis to find the data points in the grid. The length and width of each grid are expanded with preset boundary expansion parameters to ensure that the data points on the grid boundary are calculated accurately. The present invention uses the segmentation interval length m as the boundary expansion parameter.

[0080] In a preferred embodiment, in step S5, a multi-threaded method is used to perform intersection analysis on the grids, find the data points in each grid, and determine the condition formula that needs to be satisfied for the inspection trajectory point and the case point to be located in the grid:

[0081] CX≥minXG-m

[0082] CX<maxXG+m

[0083] CY≥minYG-m

[0084] CY<maxYG+m

[0085] Among them, CX represents the urban construction X coordinate value of the patrol trajectory point and the case point, CX represents the urban construction Y coordinate value of the patrol trajectory point and the case point, maxXG represents the maximum urban construction X coordinate value within the range of the grid, minXG represents the minimum urban construction X coordinate value within the range of the grid, maxYG represents the maximum urban construction Y coordinate value within the range of the grid, minYG represents the minimum urban construction Y coordinate value within the range of the grid, and m is the segmentation interval length;

[0086] The formula for determining whether a road point is located in the grid is:

[0087] RX≥minXG-m

[0088] RX<maxXG+m

[0089] RY≥minYG-m

[0090] RY<maxYG+M

[0091] Among them, RX represents the urban construction X coordinate value of the road point, and RY represents the urban construction Y coordinate value of the road point.

[0092] Specifically, after the road points, inspection track points, case points and grids are prepared, the present invention uses multi-threaded grid division to perform intersection analysis to find the data points in the grid. The conditional formula for judging the data points in the grid is:

[0093] CX≥minXG-m

[0094] CX<maxXG+m

[0095] CY≥minYG-m

[0096] CY<maxYG+m

[0097] RX≥minXG-m

[0098] RX<maxXG+m

[0099] RY≥minYG-m

[0100] RY<maxYG+M

[0101] Among them, CX represents the urban construction X coordinate value of the patrol trajectory point and the case point, CY represents the urban construction Y coordinate value of the patrol trajectory point and the case point, maxXG represents the maximum urban construction X coordinate value within the range of the grid, minXG represents the minimum urban construction X coordinate value within the range of the grid, maxYG represents the maximum urban construction Y coordinate value within the range of the grid, minYG represents the minimum urban construction Y coordinate value within the range of the grid, and m is the segmentation interval length; RX represents the urban construction X coordinate value of the road point, and RY represents the urban construction Y coordinate value of the road point.

[0102] In a preferred embodiment, in step S5, the calculation formula of the distance Dis between the supervisor's trajectory point and the case point and the road point is,

[0103]

[0104] Among them, CX represents the urban construction X coordinate value of the patrol track point and the case point, CY represents the urban construction Y coordinate value of the patrol track point and the case point, RX represents the urban construction X coordinate value of the road point, and RY represents the urban construction Y coordinate value of the road point;

[0105] The distance between the supervisor's trajectory points and case points and the road points is filtered out during the calculation. During the calculation process, each time the same supervisor identifier or case identifier is encountered, it is determined whether the newly calculated distance result is less than the previously obtained distance result. If it is less, the distance record is updated; if not, the previously obtained distance result is retained. In this way, only the shortest distance record is retained for the same supervisor identifier or case identifier to determine the road inspected by the supervisor and the road to which the case belongs.

[0106] Specifically, the present invention calculates the distance between two points using the following formula:

[0107]

[0108] Among them, CX represents the urban construction X coordinate value of the patrol trajectory point and the case point, CY represents the urban construction Y coordinate value of the patrol trajectory point and the case point, RX represents the urban construction X coordinate value of the road point, and RY represents the urban construction Y coordinate value of the road point. The calculation results are saved, and only the shortest distance record is retained for the same supervisor identifier or case identifier to determine the road inspected by the supervisor and the road to which the case belongs, reducing redundant information. The shortest distance can be used to determine which supervisor inspected which road and which case is most directly related to which road.

[0109] In a preferred embodiment, in step S6, the blind spots include patrol blind spots and case candidate blind spots. According to the patrol road results calculated in step S5, the roads among all roads that are complementary to the patrol road results are taken as blind spots, the roads among the blind spots that are associated with the supervisor identifier are taken as patrol blind spots, and the roads that are associated with the case identifier are taken as case candidate blind spots.

[0110] Specifically, the inspection blind area refers to the area that the supervisor did not cover during the inspection process, and the case candidate blind area refers to the road area where the supervisor has inspected but did not actively find a case. Among all the roads, the road that is complementary to the result generated in step S5 is taken, that is, the inspection blind area that the supervisor did not inspect and the case candidate blind area that was not actively found. Each supervisor and each case has a unique identifier for distinguishing different supervisors and cases, and the supervisor will be used as the primary key for subsequent association detailed information.

[0111] During the intersection analysis, the inspection trajectory points and case points are matched with the road points to obtain the roads with the shortest distance to the inspection trajectory points and case points. In step S1, the basic data table containing all road information is listed; then, it is compared with the identified roads in step S5. The complementary operation is to perform a difference operation on all roads and the inspected roads. The blind spot identification result is the road that is not an inspected road.

[0112] Specifically, the present invention clearly defines and distinguishes different types of blind spots, which facilitates subsequent recording and processing of blind spots, and also facilitates rapid search and association of case and supervisor information, thereby improving case processing efficiency.

[0113] The above description is only a preferred embodiment of the present invention, and does not limit the implementation mode and protection scope of the present invention. For those skilled in the art, it should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the description and illustrations of the present invention should be included in the protection scope of the present invention.

Claims

1. A blind spot identification method for a grid map, characterized in that: include, Step S1, dividing the road network according to the set segmentation interval length, and generating a basic data table containing endpoint coordinates and road information; Step S2, extracting the patrol track points and case details of the patrol officer, obtaining the latitude and longitude points and the supervisor identifier or case identifier as the primary key; Step S3, converting the coordinate system of the data points, converting the data points including the road points, the inspection track points and the case points from longitude and latitude coordinates to urban construction coordinates; Step S4, dividing the complete map area into a plurality of grids according to the coordinate range of the analysis area; Step S5, using a multi-threaded method to perform intersection analysis on the road points, the supervisor track points and the case points in each grid, calculate the shortest distance between the supervisor track points and the case points and the road points, and output the inspection road results; Step S6, selecting a road complementary to the inspection road result from all the roads in the basic data table as a blind spot, and generating a blind spot recognition result.

2. The blind spot identification method of the grid map according to claim 1 is characterized in that: In step S1, the segmentation interval length is set, and the road points separated by the segmentation interval length are batch-generated by cyclically calling the element selection and segmentation method.

3. The blind spot identification method of the grid map according to claim 2 is characterized in that: In step S1, after the road point is generated, it is determined whether the road point is a road intersection point. If it is a road intersection point, the road point is removed; if it is not a road intersection point, the road point is retained.

4. The blind spot identification method of a grid map according to claim 1, characterized in that: In step S2, the road points are used as basic data, and a one-time batch processing method is used to convert the road points from latitude and longitude coordinates to urban construction coordinates.

5. The blind spot identification method of the grid map according to claim 1, characterized in that: In step S2, the inspection trajectory points and the case points are used as real-time data. After accessing the data of the inspection trajectory points and the case points through a real-time interface, the inspection trajectory points and the case points are converted from latitude and longitude coordinates to urban construction coordinates in real time.

6. The blind spot identification method of a grid map according to claim 1, characterized in that: Step S4 comprises, Step S41, obtaining the coordinates and quantity of all the data points; Step S42, finding the coordinate extreme point to determine the range of the analysis area; Step S43, dividing the analysis area into a plurality of rectangular grids, wherein the amount of data N in each grid is within a preset range of magnitude; wherein N=CC×RC, CC represents the number of the inspection trajectory points and the case points, and RC represents the number of the road points.

7. The blind spot identification method of a grid map according to claim 1, characterized in that: In step S5, the original grid is subjected to boundary expansion processing using a preset boundary expansion parameter to obtain the expanded grid, the data points located on the boundary of the original grid are located within the expanded grid, and the segmentation interval length is used as the boundary expansion parameter.

8. The blind spot identification method of the grid map according to claim 7, characterized in that: In step S5, the grids are intersected and analyzed in a multi-threaded manner to find the data points in each grid, and the condition formula that needs to be satisfied for determining that the inspection trajectory point and the case point are located in the grid is: CX≥minXG-m CX<maxXG+m CY≥minYG-m CY<maxYG+m Wherein, CX represents the urban construction X coordinate value of the inspection trajectory point and the case point, CY represents the urban construction Y coordinate value of the inspection trajectory point and the case point, maxXG represents the maximum urban construction X coordinate value within the range of the grid, minXG represents the minimum urban construction X coordinate value within the range of the grid, maxYG represents the maximum urban construction Y coordinate value within the range of the grid, minYG represents the minimum urban construction Y coordinate value within the range of the grid, and m is the length of the segmentation interval; The conditional formula that needs to be satisfied to determine that the road point is located in the grid is: RX≥minXG-m RX<maxXG+m RY≥minYG-m RY<maxYG+m Wherein, RX represents the urban construction X coordinate value of the road point, and RY represents the urban construction Y coordinate value of the road point.

9. The blind spot identification method of the grid map according to claim 8, characterized in that: In step S5, the calculation formula of the distance Dis between the supervisor trajectory point and the case point and the road point is, Wherein, CX represents the urban construction X coordinate value of the inspection trajectory point and the case point, CY represents the urban construction Y coordinate value of the inspection trajectory point and the case point, RX represents the urban construction X coordinate value of the road point, and RY represents the urban construction Y coordinate value of the road point; The supervisor trajectory points and the case points are screened out during the distance calculation with respect to the road points. During the calculation process, each time the same supervisor identifier or case identifier is encountered, it is determined whether the newly calculated distance result is smaller than the previously obtained distance result. If so, the distance record is updated; if not, the previously obtained distance result is retained.

10. The blind spot identification method of a grid map according to claim 8, characterized in that: In step S6, the blind spot includes a patrol blind spot and a case candidate blind spot. According to the patrol road result calculated in step S5, the road in all roads that is complementary to the patrol road result is taken as the blind spot, the road in the blind spot associated with the supervisor identifier is taken as the patrol blind spot, and the road associated with the case identifier is taken as the case candidate blind spot.

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