A method for generating mobile target activity heat map based on geographic grid

Abstract

The present invention relates to the field of computer technology, and discloses a method for generating a mobile target activity heat map based on a geographic grid, comprising the following steps: 1) establishing a geographic grid space; 2) establishing a storage structure corresponding to the geographic grid space; 3) calculating the effective time weight of the mobile target in the geographic grid space; 4) traversing the historical trajectory points of the mobile target, mapping each trajectory point to positions p and q in a corresponding two-dimensional array A, where p is the column number and q is the row number, calculating the weight r of the trajectory point, and accumulating r to A[p][q]; 5) producing weighted trajectory points, and rendering and generating a mobile target activity heat map. In the case of massive data, the present invention uses the geographic grid space to segment the geographic coordinate space, and the segmentation granularity of the geographic grid space is calculated according to the heat map pixel points and the spatial span of the historical trajectory points, which greatly improves the heat map generation speed of the mobile target, and makes the heat map generation operation become a ready-to-use operation.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method for generating a mobile target activity heat map based on a geographic grid. Background Art

[0002] The activity heat map of mobile targets is a data visualization tool that can intuitively display the activity heat of mobile targets in different geographical locations. The geographical coordinates of mobile targets often change. In order to evaluate the activity heat of a single mobile target in different geographical locations, the general practice is to map the historical trajectory points of the mobile target into the geographical coordinate space, generate a circle with a count gradient descending to the edge with each point as the center, and then calculate the counts of the overlapping parts of multiple circular areas respectively, and finally render it into a picture to form a heat map.

[0003] There are two problems with the heat map generated in this way: first, when there are many historical trajectory points, the time overhead of generating the heat map is relatively large; second, it reflects the density of trajectory points. When the historical trajectory points are evenly distributed in time, it can reflect the heat of the activity area of ​​the mobile target. When the historical trajectory points are unevenly distributed in time, the activity heat statistics will be distorted, that is, the area where the trajectory points with short time intervals are located has more points, and the activity heat obtained by statistics is relatively large; the area where the trajectory points with long time intervals are located has fewer points, and the activity heat obtained by statistics is relatively small, which will lead to deviations from the global distribution. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a method for generating a mobile target activity heat map based on a geographic grid, which realizes the mapping of mobile target trajectory points to Cartesian space. The mapping value can reflect the time weight of the trajectory point, ensuring that the heat map truly reflects the heat of the activity area of ​​the mobile target. At the same time, the method can reduce the number of pixels that need to be rendered, and has the advantage of controllable complexity of the heat map generation time, thereby solving the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for generating a mobile target activity heat map based on a geographic grid, comprising the following steps:

[0006] 1): Establish geographic grid space;

[0007] 2): Use the two-dimensional array A[i][j] to establish a storage structure corresponding to the geographic grid space;

[0008] 3): Calculate the effective time weight of the mobile target in the geographic grid space;

[0009] 4): Traverse the historical trajectory points of the mobile target, map each trajectory point to the corresponding position p and q in the two-dimensional array A[i][j], then calculate the weight r of the trajectory point in combination with the effective time weight, and add r to A[p][q]; where p is the column number and q is the row number;

[0010] 5): Traverse the values ​​in the array A[p][q] in sequence, generate weighted trajectory points, and render to generate a mobile target activity heat map.

[0011] In step 1), the historical trajectory points of the moving target are traversed to obtain the maximum longitude Lon among the trajectory points. max and minimum longitude Lon min , if the number of pixels in the given heat map direction is p x , then the grid side length of the geographic grid space Δdegree=(Lon max -Lon min ) / (p x / c-2), where c is the pixel side length of a grid, and its value range can be controlled in the interval [4,20].

[0012] In step 2), a two-dimensional array A[i][j] is used to establish a storage structure corresponding to the geographic grid space, that is, in the storage structure:

[0013] Number of columns i = p x / c, row number j = [(Lat max -Lat min ) / Δdegree]+2, Lat max is the maximum latitude, Lat min It is the minimum latitude, and the initial storage value of all positions in the array is 0.

[0014] In step 3), the effective time weight is defined as the minimum time length T of the mobile target represented by a discrete point being active in the grid when it is mapped to the geographic grid space. min =Δdegree×6371000×π / 180 / V, where V is the average moving speed of the moving target.

[0015] In step 4), the mapping method between the moving target trajectory point and the two-dimensional array A is as follows:

[0016] p=(Lon-Lon min ) / Δdegree

[0017] q=(Lat-Lat min ) / Δdegree

[0018] r=min(T min ,ΔTback / 2+ΔT front / 2)

[0019] A[p][q]=A[p][q]+r

[0020] Where, ΔT front is the time interval between the current trajectory point and the previous trajectory point, ΔT back is the time interval between the current trajectory point and the next trajectory point.

[0021] In step 5), the values ​​in array A are traversed one by one, non-zero values ​​are selected, and their subscripts are converted into trajectory point coordinates [x, y]. The values ​​in the array are used as the weights of the trajectory points, and then these weighted trajectory points are used as input to render and generate the mobile target activity heat map, that is:

[0022]

[0023] The beneficial effects are as follows:

[0024] The method for generating a heat map of dynamic target activities based on a geographic grid of the present invention uses a geographic grid space to divide the geographic coordinate space in the case of massive data. The segmentation granularity of the geographic grid space is calculated according to the spatial span of the heat map pixel points and the historical trajectory points, which greatly improves the heat map generation speed of the mobile target and makes the heat map generation operation ready for use.

[0025] The method for generating a dynamic target activity heat map based on a geographic grid of the present invention utilizes a mapping method of mobile target trajectory points to Cartesian space, so that the mapping value can include the time weight of the trajectory points, while reducing the number of pixels that need to be rendered. The heat map generated in this way can quantitatively calculate the activity duration of the mobile target in a specified area, and the calculation process is accurate and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a mapping from geographic grid space to a two-dimensional array;

[0027] Figure 2 It is the calculation principle diagram of effective time weight;

[0028] Figure 3 is the trajectory point mapping diagram;

[0029] Figure 4 It is a flow chart of a method for generating a dynamic target activity heat map based on a geographic grid;

[0030] Figure 5 is a scatter plot of the original trajectory points in the simulation experiment;

[0031] Figure 6 is the heat map generated in the simulation experiment;

[0032] Figure 7 It is a comparison of the heat maps generated by the traditional method and this method. DETAILED DESCRIPTION

[0033] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0034] The present invention provides a technical solution: a method for generating a mobile target activity heat map based on a geographic grid, comprising the following steps:

[0035] 1): Establish a geographic grid space, traverse the historical trajectory points of the mobile target, and obtain the maximum longitude Lon among the trajectory points max and minimum longitude Lon min , if the number of pixels in the given heat map direction is p x , then the grid side length of the geographic grid space Δdegree=(Lon max -Lon min ) / (p x / c-2), where c is the pixel side length of a grid, and the value range can be controlled in the interval [4,20]. Generally speaking, the spatial granularity of the geographic grid needs to be determined based on the number of pixels in the generated heat map and the spatial span of the historical trajectory points of the mobile target. The more pixels in the generated heat map, the finer the grid granularity; the larger the spatial span of the historical trajectory points of the mobile target, the coarser the grid granularity.

[0036] 2): Establish a storage structure corresponding to the geographic grid space, using a two-dimensional array A[i][j] to establish a storage structure corresponding to the geographic grid space, where the number of columns i = p x / c, row number j = [(Lat max -Lat min ) / Δdegree]+2, Lat max is the maximum latitude, Lat min is the minimum latitude, and the initial storage value of all positions in the array is 0; the mapping from geographic grid space to two-dimensional array is as follows Figure 1 shown.

[0037] 3): Calculate the effective time weight of the mobile target in the geographic grid space. The effective time weight is defined as the minimum time length T that the mobile target represented by a discrete point is active in the grid when it is mapped to the geographic grid space. min =Δdegree×6371000×π / 180 / V, where V is the average moving speed of the moving target; the effective time weight calculation principle diagram is as follows Figure 2 shown.

[0038] 4): Traverse the historical trajectory points of the moving target, map each trajectory point to the corresponding position p and q in the two-dimensional array A, p is the column number, q is the row number, then calculate the weight r of the trajectory point, and add r to A[p][q]. The mapping method is as follows:

[0039] p=(Lon-Lon min ) / Δdegree

[0040] q=(Lat-Lat min ) / Δdegree

[0041] r=min(T min ,ΔT back / 2+ΔT front / 2)

[0042] A[p][q]=A[p][q]+r

[0043] Where, ΔT front is the time interval between the current trajectory point and the previous trajectory point, ΔT back is the time interval between the current trajectory point and the next trajectory point; the trajectory point mapping is as follows Figure 3 shown.

[0044] 5): Generate weighted trajectory points and render to generate a mobile target activity heat map. Traverse the values ​​in array A one by one, select non-zero values, convert their subscripts into trajectory point coordinates [x, y], use the values ​​in the array as the weights of the trajectory points, and then use these weighted trajectory points as input to render and generate a mobile target activity heat map. The steps are as follows:

[0045]

[0046] The flowchart of the whole process is as follows Figure 4 shown.

[0047] The simulation results are as follows Figure 5 , Figure 6 As shown. Figure 5 is a scatter plot of the original trajectory points, Figure 6 This is the heat map generated by this method. The comparison between this method and the heat map generated by the traditional method is as follows Figure 7 As shown in the figure, we can see that this method can accurately highlight the area where the moving target stays for a longer time, while the heat map generated by the traditional method can only highlight the area with dense trajectory points; in addition, the generation speed of this method is about 3 times that of the traditional method, and the efficiency is significantly improved.

[0048] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for generating a mobile target activity heat map based on a geographic grid, characterized in that: The following steps are involved: 1): Establish geographic grid space; In step 1), the historical trajectory points of the mobile target are traversed to obtain the maximum longitude in the trajectory points. and minimum longitude , if the given heat map The number of pixels in the direction is , then the grid side length of the geographic grid space is ,in is the pixel side length of a grid, and its value range can be controlled in the interval [4,20]; 2): Using a two-dimensional array Establishing a storage structure corresponding to the geographic grid space; In step 2), use a two-dimensional array Establish a storage structure corresponding to the geographic grid space, that is, in the storage structure: Number of columns , number of rows , is the maximum latitude, It is the minimum latitude, and the initial storage value of all positions in the array is 0; 3): Calculate the effective time weight of the mobile target in the geographic grid space; In step 3), the effective time weight is defined as the minimum time length of activity of a mobile target represented by a discrete point in the geographic grid space when it is mapped to the grid space. ,in is the average speed of the moving target; 4): Traverse the historical trajectory points of the moving target and map each trajectory point to the corresponding two-dimensional array Position in and , and then calculate the weight of the trajectory point in combination with the effective time weight , and Add to middle; in, is the column number, is the row number; moving target trajectory point and two-dimensional array The mapping method between them is as follows: in, is the time interval between the current trajectory point and the previous trajectory point, is the time interval between the current trajectory point and the next trajectory point; 5): Traverse the array one by one The values ​​in are used to generate weighted trajectory points and render the mobile target activity heat map; In step 5), the array is traversed in sequence Select a non-zero value and convert its subscript into the coordinates of the trajectory point ,The values ​​in the array are used as the weights of the trajectory points, and then these weighted trajectory points are used as input to render and generate the mobile target activity heat map, that is: .

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