Coverage model accurate vision field analysis method based on digital map

Abstract

The invention discloses a coverage model accurate vision field analysis method based on a digital map, and the method comprises the following steps: 1, taking the latitude and longitude coordinates of a certain alternative camera installation position as a central point O, initializing a two-dimensional grid array A [n] [n], and storing the latitude and longitude coordinate information and height information of the central point of each grid; 2, solving whether each grid of the initialized two-dimensional grid array A [n] [n] obtained in the step 1 is shielded and visible or not one by one; 3, traversing, analyzing and outputting the two-dimensional grid array A [n] [n] in the step 2; 4, analyzing and outputting information of the two-dimensional grid array A [n] [n], and connecting the information with an online map to realize geographic information system display and analysis; 5, calculating the coverage rate of the camera at the position of the point O according to the information of each grid of the A [n] [n]; and 6, traversing the camera coverage rates corresponding to the to-be-selected central point positions, and obtaining the optimal camera site selection position through the highest camera coverage rate.

Description

Technical Field

[0001] The present invention relates to the fields of wireless communication, smart city construction, geographic information system (GIS) analysis, and public security, and particularly relates to a precise field of view analysis method for a coverage model based on a digital map. Background Art

[0002] With the continuous development of the wireless communication field, the construction of signal towers has been ongoing. Signal towers cover various regions, and there are more scenarios for the utilization of signal tower resources. Fire prevention scenarios in forests, grasslands, rivers, etc. (smart cities); public security, border defense and coastal defense, early warning, and emergency command, etc. By installing surveillance cameras of different specifications on signal towers, real-time monitoring of the areas within 3 km, 5 km, and 10 km around the installation points can be achieved all-weather and all-angle. However, due to factors such as terrain and landforms (such as slopes and hills), building blockages, and camera hanging heights, there are blind spots in the field of view of the cameras at the monitoring points. Based on such a background, a rapid visualization tool for the coverage range of signal tower cameras is needed, which can quickly find the coverage blind spots in large areas on the map, so as to plan and construct in a timely manner to check for omissions and make up for deficiencies. Assist in adding new monitoring camera points and gradually simulate the full-coverage scenarios of forests, grasslands, rivers, etc. within the region.

[0003] The existing technologies have the following disadvantages:

[0004] (1) Low accuracy in camera planning: The location and hanging height of cameras are based on experience and manual survey methods. The accuracy of camera height setting depends on the experience of engineers. Usually, multiple on-site surveys and height adjustments are required, which is highly subjective and it is difficult to ensure the consistency of the plan.

[0005] (2) Low efficiency in camera planning: Due to the lack of a simulation tool for reference, only manual operations such as climbing and feeling the height are available, resulting in a high trial-and-error cost and low completion efficiency.

[0006] (3) Difficulty in identifying camera blind spots: It can only be achieved through real-time debugging of cameras. Since the coverage range of cameras is 3, 5, and 10 km, it is extremely difficult to distinguish visible blind spots by visual observation. Summary of the Invention

[0007] Object of the Invention: In view of the deficiencies of the existing technologies, the present invention provides a method for precise field of view analysis of a coverage model based on a digital map as the basic data method, combined with camera parameters, and through geometric algorithms to simulate and calculate the coverage area range of cameras, as well as the calculation of coverage blind spots affected by factors such as terrain and landforms within the area. It can be visually output and displayed in the form of an online map (such as Gaode, Baidu, etc.).

[0008] To solve the above technical problems, the present invention discloses a precise field of view analysis method for a coverage model based on a digital map, including the following steps:

[0009] Step 1: Taking the longitude and latitude coordinates of a certain alternative camera installation position as the center point O, and the farthest coverage distance of the camera as the coverage radius R. According to the accuracy g of the digital map, with the length of each grid equal to the accuracy g of the digital map, initialize a two-dimensional grid array A[n][n]. Each grid stores the longitude and latitude coordinate information and height information of the center point of this grid.

[0010] Step 2: Taking the center point O of the coverage area as the center of the circle, traverse the initialized two-dimensional grid array A[n][n] obtained in Step 1 in sequence, and solve whether each grid is blocked and visible one by one.

[0011] Step 3: Traverse and parse the calculated two-dimensional grid array A[n][n] obtained in Step 2 for output.

[0012] Step 4: Connect the parsed output information of the two-dimensional grid array A[n][n] to the online map to realize the display of the geographic information system and the analysis of necessary conditions.

[0013] Step 5: For the rapid visualization planning of the camera coverage area, the camera coverage rate at the position of point O can be calculated from the information of each grid in A[n][n]. Define the camera coverage rate at the position of point O as f o :

[0014]

[0015] In the formula, 0, 1, and -1 are the attribute values of whether the grid is visible. 1 is the visible value, 0 is the blocked and invisible value, -1 is the out-of-range and invisible value. n is the length of the two-dimensional grid array. A[i][j] represents any one in the two-dimensional grid array A[n][n]. δ(A[i][j], 1) means that when A[i][j]=1, the function value is 1, otherwise it is 0. δ(A[i][j], -1) means that when A[i][j]= -1, the function value is 1, otherwise it is 0.

[0016] Step 6: Traverse the camera coverage rates corresponding to each candidate center point position, and obtain the optimal camera location selection through the highest camera coverage rate.

[0017] In Step 1, for the initialization of a two-dimensional grid array A[n][n], where n is the length of the array, the specific steps are as follows:

[0018] The length of the two-dimensional grid array is equal to the value obtained by rounding up the result of dividing the coverage radius R by the accuracy g of the digital map and then multiplying by 2 and adding 1. The calculation formula is as follows:

[0019]

[0020] Setting the center point O of the coverage area as the grid A[x][y], where x and y are the array subscripts of the center point O, the value can be obtained as:

[0021]

[0022] g is the length of each grid, which is also equal to the distance between the central points of each adjacent grid. Through the array subscript of the central point O of the coverage area and the longitude and latitude coordinates of the central point O of the coverage area, the longitude and latitude coordinates of the central points of all grids in A[n][n] can be calculated, so as to initialize and set the longitude and latitude coordinates of the central points of each grid. The longitude and latitude coordinates of the central point O are the candidate positions for suspending the camera.

[0023] In step 1, according to the longitude and latitude coordinates of the central points of each grid, the altitude and building height are obtained in the digital map to obtain the height information of each grid. For each grid, the height of the central point O grid plus the hanging height of the camera, that is, the height of the camera relative to the ground plane. The height of the ground plane relative to the camera is an externally input value, and the height is equal to the height of the camera suspension support. The camera suspension support is a signal tower existing in the reuse planning area.

[0024] Step 2 includes:

[0025] Step 2-1: The grid to be solved is the target grid, and the central point of the target grid is defined as S. The corresponding target grid is represented as A[x s [y s in the grid array. x s and y s are the array subscripts of the central point S of the target grid. Connect the central point O of the coverage area with the central point S of the target grid, and all the grids passed in the middle are defined as influencing grids. Solve the set of influencing grids {A[x i [y i}, where x i and y i are the array subscripts of any influencing grid;

[0026] Step 2-2: Traverse A[n][n] in sequence to solve whether each grid area is blocked and visible one by one.

[0027] In step 2-1, to solve the set of influencing grids corresponding to any target grid, the specific steps are as follows:

[0028] Step 2-1-1: According to the grid position A[x][y] of point O, the grid A[x s [y s of point S, and the array subscripts of these two points, O-S is the connection line between the central point O of the coverage area and the central point S of the target grid. Calculate the straight-line equation of O-S to determine the slope and intercept, and calculate the horizontal direction increment d x and the vertical direction increment d y, the calculation formula is:

[0029] d x = x s - x

[0030] d y = y s - y;

[0031] Step 2-1-2: Use the Bresenham line algorithm to calculate the grids passed by the line connecting points O and S, and solve to obtain the subscripts of the two-dimensional grid array of all the passed grids, which are the affected grids.

[0032] In Step 2-1-2, the specific steps for solving the subscripts of the two-dimensional grid array of all the passed grids are as follows:

[0033] Define s x as the horizontal step size, and s y as the vertical step size. The subscripts of the A[n][n] array are consecutive natural numbers with a step size of 1. Let x i and y i be the array subscripts of any affected grid:

[0034] Case 1: |d x | ≥ |d y |, indicating that the horizontal increment is large. Then, the horizontal step size s x has priority:

[0035] Set:

[0036] where s x = 1 means that point S is in the lower direction of point O, and s x = -1 means that point S is in the upper direction of point O;

[0037] The solution is:

[0038]

[0039] Iterative derivation: Through the calculated d x , s x , s y , starting from point O and moving towards point S with step sizes s x , s y iteratively, the subscripts x i and y i of the affected grid array can be obtained one by one. Since the step size is 1, the total number of iterations is (|d x | - 1). Based on the subscripts x and y of point O, add the step sizes s x , s y for each iteration. The specific formula is as follows:

[0040]

[0041] Finally, the set of influencing grids corresponding to the target grid S point at any point is obtained as {A[x i [y i};

[0042] Case 2: |d x | < |d y |, indicating that the vertical increment is large. Then the vertical step size s y takes precedence:

[0043] Set:

[0044] Among them, s y = 1 means that the S point is in the right direction of the O point, and s y = -1 means that the S point is in the left direction of the O point;

[0045] The solution is as follows:

[0046]

[0047] Iterative derivation: Through the calculated d y , s x , s y , starting from the O point and moving towards the S point with step sizes s x , s y for iteration, the subscripts x i and y i of the influencing grid array can be obtained one by one. Since the step size is 1, the total number of iterations is (|d y | - 1). Based on the subscripts x and y of the O point, add the step sizes s x , s y for each iteration. The specific formula is as follows:

[0048]

[0049] Finally, the set of influencing grids corresponding to the target grid S point at any point is obtained as {A[x i [y i}.

[0050] In step 2-2, to solve whether each grid area is blocked and visible, the specific steps are as follows:

[0051] Define the center point of any grid in the set of influencing grids as A i ;

[0052] Obtain the grid height information of the O point, S point, and A i point from step 1. Define h o as the grid height of the O point, h s as the grid height of the S point, hi is A i Dot grid height, h max is A i The maximum grid height at which the grid at point A does not block point S:

[0053] It can be obtained through the right trapezoid waist line length calculation formula:

[0054]

[0055] OS is the distance from point O to point S. The distance between two points can be calculated by obtaining the longitude and latitude information of the two points through A[n][n]. Similarly, A i S is A i The distance from point A to point S. Whether point S is blocked by point A i There are four cases where point S is blocked:

[0056] Case 1: OS≥R, no calculation is performed. The distance exceeds the maximum coverage radius of the camera, and it is directly marked as out of range and invisible;

[0057] Case 2: h o <h s , no calculation is performed. The height at the target grid point S is greater than the height at point O, and it is treated as blocked;

[0058] Case 3: h o ≥h s &h max ≥h i , the grid A i has no influence on the target grid point S and does not block it;

[0059] Case 4: h o ≥h s &h max <h i , the grid A i has an influence on the target grid point S, and point S is blocked and invisible;

[0060] Iterative derivation: Calculate and judge each point A in the set of influencing grids {A[x i [y i} corresponding to point S. When each point A i does not block point S, point S is marked as visible. Otherwise, as long as one of the influencing grids blocks point S, then point S is marked as blocked and invisible; i Finally, a two-dimensional grid array A[n][n] is calculated. A[n][n] contains the longitude and latitude information, height information, and whether it is blocked and visible information of each grid.

[0061] Beneficial effects:

[0062] Beneficial effects:

[0063] 1. Improve the accuracy of camera planning: The method of the present invention can quickly obtain the analysis of the coverage fields of all alternative positions within the planned area by cameras, as well as the coverage rate of each camera's field of view, and perform optimal site selection. It does not require multiple on-site surveys and height adjustments, and can accurately calculate the field of view range based on the digital map without relying on the site, improving the planning accuracy and ensuring the consistency of the results.

[0064] 2. Improve the efficiency of camera planning: Since there is no need for operations such as manual climbing and groping, the trial-and-error cost is low and the completion efficiency is high. Through this technical solution, users can quickly complete the analysis of the camera coverage field of view without leaving home, assisting users in quickly planning the scheme.

[0065] 3. Quickly identify camera blind spots: By using the method of the present invention for calculating the field of view analysis, the coverage blind spots can be directly displayed graphically on the interface, which is simple and intuitive.

[0066] 4. The accurate field of view analysis method of the coverage model based on digital map of the present invention is completely programmable, does not require a large amount of input data, occupies less computing resources, can significantly improve the accuracy and efficiency of calculating the camera coverage area, can be visualized and presented through the online map method GIS, and can meet the actual scenario requirements such as current fire prevention, disaster prevention, safety, and emergency. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a flowchart of the method of the present invention.

[0068] Figure 2 It is an initialized two-dimensional grid array diagram.

[0069] Figure 3 It is a schematic diagram of the principle for judging the affected grid.

[0070] Figure 4 It is a schematic diagram of the principle for solving whether the target grid is visible.

[0071] Figure 5 It is a schematic diagram of the principle for the field of view analysis of the coverage area of a single camera.

[0072] Figure 6 Online map GIS display simulation effect diagram. DETAILED DESCRIPTION OF THE INVENTION

[0073] The present invention discloses a method based on a digital map. Geographical basic data information is obtained through the digital map. With the camera coordinates as the center, the initial coverage area is determined by the maximum radius of the camera radiation, and the circumscribed square of the initial coverage area is used as the calculation graph. The core idea of this method is to calculate the visibility of each grid one by one through grid calculation of the area by geometric algorithms. Finally, the camera coverage area and the coverage blind spots are determined, thereby determining an optimal camera layout scheme.

[0074] Figure 2 Among them, O is the center point of the coverage area, and R is the coverage radius;

[0075] Figure 3 Among them, 1 is the influence grid represented by the dark gray grid, 2 is the target grid represented by the light gray grid, O is the center point of the coverage area, and S is the center point of the target grid;

[0076] Figure 4 Among them, x is the horizontal abscissa, y is the vertical ordinate, O is the center point of the coverage area, R is the coverage radius, S is the center point of the target grid, and A i is the corresponding grid center point, h o is the grid height at point O, h s is the grid height at point S, h i is the grid height at point A i point grid height, h max is the grid height at point A i is the maximum grid height at point A where the grid does not block point S;

[0077] Figure 5 Among them, white represents the visible area, light gray represents the occluded invisible area, and dark gray represents the out-of-range invisible area.

[0078] Figure 6 Among them, green represents the visible area, light gray represents the occluded invisible area, and dark gray represents the out-of-range invisible area.

[0079] Embodiment:

[0080] This embodiment is based on resource reuse. A camera for monitoring fire safety is installed on an existing signal tower. The advantages of installing it on an existing signal tower are as follows: First, the base station has a coverage radius and its location is relatively reasonable; Second, the signal tower is convenient for camera installation and is at a high and open place. There are three signal towers, A, B, and C, in the area where the camera needs to be installed. The corresponding longitude and latitude coordinates of the three signal towers are (N118°50'22.6716", E32°4'38.9748'), (N118°50′22.6716", E32°4′38.9748), and (N118°50′16.2888", E32°4′34.8672");

[0081] The installed camera for monitoring fires has a maximum coverage distance, that is, the coverage radius, of 200 meters under the requirement of being able to clearly capture fire hazard points;

[0082] Step 1: Taking the longitude and latitude coordinates of Tower A as the center point O, with the camera coverage radius of 200 meters, according to the accuracy of the digital map of 20 meters, and each grid length being equal to the digital map accuracy of 20 meters, initialize a two-dimensional grid array A[n][n], and each grid stores the longitude and latitude coordinates information and height information of the center point of this grid;

[0083] Step 2: Taking the center point O of the coverage area as the center of the circle, sequentially traverse the initialized two-dimensional grid array A[n][n] obtained in Step 1, and solve whether each grid is blocked and visible one by one;

[0084] Step 3: Traverse and parse the calculated two-dimensional grid array A[n][n] obtained in Step 2 for output;

[0085] Step 4: Connect the parsed output information of the two-dimensional grid array A[n][n] to the online map to realize the display of the geographic information system and the analysis of necessary conditions. The result is as Figure 6 shown. Relative to the signal tower at the center position of the figure, that is, the center point A, the green visible area, the light gray blocked and invisible area, and the dark gray out-of-range invisible area. By analyzing Figure 6 if the invisible area contains the key fire prevention positions, then it is not advisable to use point A as the camera suspension point. On the contrary, if the key fire prevention positions are located in the visible area of the figure, then it is advisable to use point A as the camera suspension point; by traversing whether all the coordinates of the key fire prevention positions in A[n][n] are visible, to determine whether the key fire prevention positions are included;

[0086] Step 5: Rapid visualization planning of the camera coverage area. From the information of each grid in A[n][n], the camera coverage rate at point O can be calculated. Define the camera coverage rate at point O as f o :

[0087]

[0088] It can be calculated that the camera coverage rate at point A is 75.0%.

[0089] Step 6: Iteratively calculate with the same calculation steps. Calculate the camera coverage rates corresponding to the candidate center point positions of Tower B and Tower C as 68.3% and 62.5% respectively. And for the candidate center point positions of Tower A, Tower B, and Tower C, in the occlusion result map, it is satisfied that the key fire prevention positions are not blocked. Thus, by selecting the highest camera coverage rate, the optimal camera location is point A.

[0090] Among them, in Step 1, when initializing the two-dimensional grid array A[n][n], n is the array length, and the specific steps are as follows:

[0091] The length of the two-dimensional grid array is equal to the ceiling of the result of dividing the coverage radius R by the accuracy g of the digital map, multiplied by 2 and then added by 1. According to the following formula, n can be calculated to be 21.

[0092]

[0093] Set the center point O of the coverage area as the grid A[x][y], where x and y are the array subscripts of the center point O. The values can be obtained as 10 and 10 respectively:

[0094]

[0095] g is the length of each grid, which is also equal to the distance between the center points of each adjacent grid. Through the array subscripts of the center point O of the coverage area and the longitude and latitude coordinates of the center point O of the coverage area, the longitude and latitude coordinates of the center points of all grids in A[n][n] can be calculated, so as to initialize the longitude and latitude coordinates of the center points of each grid.

[0096] In step 1, according to the longitude and latitude coordinates of the center point of each grid, obtain the altitude and building height in the digital map to get the height information of each grid. For each grid, the height of the center point O grid plus the hanging height of the camera, that is, the height of the camera relative to the ground plane. The hanging height of the camera is equal to the height of the signal tower where the camera is suspended, which is 40 meters.

[0097] Step 2 includes:

[0098] Step 2-1: The grid to be solved is the target grid. The center point of the target grid is defined as S. The corresponding target grid is represented as A[x s [y s , where x s and y s are the array subscripts of the center point S of the target grid. Connect the center point O of the coverage area with the center point S of the target grid. All the grids passed through in the middle are defined as influencing grids, and solve the set of influencing grids {A[x i [y i}, where x i and y i are the array subscripts of any influencing grid;

[0099] Step 2-2: Traverse A[n][n] in sequence and solve whether each grid area is blocked and visible one by one.

[0100] In step 2-1, to solve the set of influencing grids corresponding to any target grid, the specific steps are as follows:

[0101] Step 2-1-1: According to the grid position A[x][y] of point O and the grid A[x s[y s , and the array subscripts of these two points. The line connecting the center point O of the coverage area and the center point S of the target grid is O-S. Calculate the equation of the O-S line, determine the slope and intercept, and calculate the horizontal increment d of the O-S line segment x and the vertical increment d y . The calculation formula is:

[0102] d x = x w - x

[0103] d y = y s - y;

[0104] Step 2-1-2: Use the Bresenham line algorithm to calculate the grids passed by the line connecting the two points O and S, and solve to obtain the subscripts of the two-dimensional grid array of all the passed grids, that is, the affected grids

[0105] In Step 2-1-2, the specific steps for solving the subscripts of the two-dimensional grid array of all the passed grids are as follows:

[0106] Define s x as the horizontal step size, s y as the vertical step size. The subscripts of the A[n][n] array are consecutive natural numbers with a step size of 1. x i and y i are the array subscripts of any affected grid:

[0107] Case 1: |d x | ≥ |d y |, indicating that the horizontal increment is large. Then the horizontal step size s x takes precedence:

[0108] Set:

[0109] Among them, s x = 1 means that the S point is below the O point, and s x = -1 means that the S point is above the O point;

[0110] The solution is:

[0111]

[0112] Iterative derivation: Through the calculated d x , s x , s y , starting from the O point and iterating towards the S point according to the step sizes s x , s y , the subscripts x i and y i of the affected grid array can be obtained one by one, since the step size is 1, the total number of iterations is (|d x | - 1). Based on the subscripts x and y of point O, add the step size s x and s y each time an iteration is performed. The specific formula is as follows:

[0113]

[0114] Finally, the set of influence grids {A[x i [y i} corresponding to the target grid S at any point is obtained;

[0115] Case 2: |d x | < |d y |, indicating that the vertical increment is large. Then the vertical step size s y takes precedence:

[0116] Set:

[0117] Among them, s y = 1 means that point S is in the right direction of point O, and s y = -1 means that point S is in the left direction of point O;

[0118] It can be solved that:

[0119]

[0120] Iterative derivation: Through the calculated d y , s x , s y , starting from point O and iterating towards point S with step sizes s x and s y , the subscripts x i and y i of the influence grid array can be obtained one by one. Since the step size is 1, the total number of iterations is (|d y | - 1). Based on the subscripts x and y of point O, add the step sizes s x and s y each time an iteration is performed. The specific formula is as follows:

[0121]

[0122] Finally, the set of influence grids {A[x i [y i} corresponding to the target grid S at any point is obtained.

[0123] In step 2 - 2, to solve whether each grid area is occluded and visible, the specific steps are as follows:

[0124] Define the center point of any grid in the set of influence grids as Ai ;

[0125] Obtain the grid height information of point O, point S, and point A from step 1 i Define h as the grid height of point O o h as the grid height of point O, h s h as the grid height of point S, h i for point A i h as the grid height of point A, h max for point A i The maximum grid height at which the grid at point A does not block point S is:

[0126] The principle is as Figure 4 shown, and it can be obtained through the right trapezoid waist length calculation formula:

[0127]

[0128] OS is the distance between point O and point S. The distance between two points can be calculated by obtaining the longitude and latitude information of the two points through A[n][n]. Similarly, AS is the distance between point A and point S. There are four cases for whether point S is blocked by point A: i for point A i to point S. Whether point S is blocked by point A i has four cases:

[0129] Case 1: OS ≥ R, no calculation is performed. The distance exceeds the maximum coverage radius of the camera, and it is directly marked as out of range and invisible;

[0130] Case 2: h o < h s , no calculation is performed. The height at the target grid point S is greater than the height at point O, and it is treated as blocked;

[0131] Case 3: h o ≥ h s & h max ≥ h i , the grid at point A i has no effect on the target grid point S and does not block it;

[0132] Case 4: h o ≥ h s & h max < h i , the grid at point A i has an effect on the target grid point S, and point S is blocked and invisible;

[0133] Iterative derivation: Calculate and judge each point A in the set of influencing grids {A[x i [y i} corresponding to point S. When each point A i is calculated and judged, when each point A iIf there is no occlusion at all, point S is marked as visible; otherwise, as long as one of the influencing grids occludes point S, then point S is marked as occluded and invisible.

[0134] Finally, a two-dimensional grid array A[n][n] is calculated. A[n][n] contains the longitude and latitude information, altitude information, and occlusion and visibility information of each grid. The result map of occlusion is as Figure 5 shown. Relative to the signal tower at the center position of the figure, that is, the center point A, white represents the visible area, light gray represents the occluded and invisible area, and dark gray represents the out-of-range invisible area.

[0135] In this embodiment, the visual field analysis of all cameras in the area is quickly and batch completed, and visualization assists users in quickly planning the scheme. Given the position coordinates of all signal towers and the signal tower height data information in the area, through the method of the present invention and in a cyclic iteration manner, the specific coverage and coverage rate of the cameras at the positions of each signal tower can be quickly calculated and analyzed. The coverage and coverage rate of all cameras are visually presented through the online map method GIS.

[0136] According to the coverage information displayed to the user through the interface, cameras with low coverage rates can be deleted based on the local area coverage situation, cameras with large overlapping coverage areas can be deleted, cameras can be newly planned in the uncovered areas, and the coverage situation of the cameras can be recalculated and analyzed in real time by manually adjusting cameras of different specifications (different specifications have different coverage radii). Finally, it assists users in quickly and efficiently planning the best camera layout scheme.

[0137] The present invention provides a precise visual field analysis method for a coverage model based on a digital map. There are many methods and ways to specifically implement this technical solution. The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention. Each component not clearly defined in this embodiment can be implemented using existing technologies.

Claims

1. A digital map-based coverage model accurate viewshed analysis method, characterized in that: The following steps are involved: Step 1: Take the longitude and latitude coordinates of a candidate camera installation location as the center point O, the farthest coverage distance of the camera as the coverage radius R, and according to the accuracy g of the digital map, the length of each grid is equal to the accuracy g of the digital map, initialize a two-dimensional grid array A[n][n], each grid stores the longitude and latitude coordinate information and height information of the center point of this grid; Step 2: With the center point O of the coverage area as the center of the circle, sequentially traverse the initialized two-dimensional grid array A[n][n] obtained in step 1, and determine whether each grid is blocked or visible one by one; Step 3: Traverse and parse the two-dimensional grid array A[n][n] obtained in step 2; Step 4: Connect the parsed output information of the two-dimensional grid array A[n][n] to the online map to realize the display of the geographic information system and the analysis of necessary conditions; Step 5: Quickly visualize the camera coverage area. The camera coverage rate at point O can be calculated from each grid information of A[n][n]. The camera coverage rate at point O is defined as f o : Where 0, 1 and -1 are the values ​​of whether the grid is visible or not, 1 is the visible value, 0 is the invisible value due to occlusion, -1 is the invisible value due to out-of-range, n is the length of the two-dimensional grid array, A[i][j] represents any one of the two-dimensional grid arrays A[n][n], δ(A[i][j],1) means that when A[i][j]=1, the function value is 1, otherwise it is 0, δ(A[i][j],-1) means that when A[i][j]=-1, the function value is 1, otherwise it is 0; Step 6: Traverse the camera coverage rates corresponding to the center points to be selected, and obtain the optimal camera location through the highest camera coverage rate.

2. The method for accurate visual area analysis based on digital map coverage model according to claim 1, characterized in that: In step 1, a two-dimensional grid array A[n][n] is initialized, where n is the length of the array. The specific steps are as follows: The length of the two-dimensional grid array is equal to the coverage radius R divided by the precision g of the digital map, rounded up, multiplied by 2 and then added to 1. The calculation formula is as follows: Assume that the center point O of the coverage area is the A[x][y] grid, and x and y are the array subscripts of the center point O. The value can be obtained as: g is the length of each grid, which is equal to the distance between each adjacent grid center point. By covering the array subscript of the center point O of the area and the longitude and latitude coordinates of the center point O of the area, the longitude and latitude coordinates of the center point of all grids in A[n][n] can be calculated, thereby initializing the longitude and latitude coordinates of the center point of each grid.

3. The method for accurate visual area analysis based on digital map coverage model according to claim 2, characterized in that: The latitude and longitude coordinates of the center point O are the camera hanging position to be selected.

4. The method for accurate visual area analysis based on digital map coverage model according to claim 2, characterized in that: In step 1, the altitude and the building height are acquired in the digital map according to the latitude and longitude coordinates of the center point of each grid, so as to obtain the height information of each grid.

5. The method for accurate visual area analysis based on digital map coverage model according to claim 4, characterized in that: For each grid, the height of the center point O grid plus the hanging height of the camera, that is, the height of the camera relative to the ground plane, is equal to the height of the camera hanging support.

6. The method for accurate visual area analysis based on digital map coverage model according to claim 5, characterized in that: The camera hanging support is a signal tower already existing in the reuse planning area.

7. The method for accurate visual area analysis based on digital map coverage model according to claim 2, characterized in that: Step 2 includes: Step 2-1: The grid to be solved is the target grid. The center point of the target grid is defined as S. The corresponding target grid is represented in the grid array as A[x s ][y s ], x s and s is the array index of the target grid center point S, connects the coverage area center point O with the target grid center point S, and defines all the grids passed through as influence grids. Solve the influence grid set {A[x i ][y i ]}, x i and i Subscript any array that affects the grid; Step 2-2: Traverse A[n][n] sequentially and determine whether each grid area is blocked or visible.

8. The method for accurate visual area analysis based on digital map coverage model according to claim 7, characterized in that: In step 2-1, solve the influence grid set corresponding to any target grid. The specific steps are as follows: Step 2-1-1: According to the grid position A[x][y] of point O, the grid position A[x s ][y s ], and the array subscripts of the two points, OS is the line connecting the center point O of the coverage area and the center point S of the target grid, calculate the equation of the OS line, determine the slope and intercept, and calculate the horizontal increment d of the OS line segment x and the vertical increment d y , the calculation formula is: d x =x w -x d y =y s -y; Step 2-1-2: Use the Bresenham line algorithm to calculate the grids that the line connecting the two points of OS passes through, and solve for all the grids passed through, that is, the subscripts of the two-dimensional grid array that affects the grid.

9. The method for accurate visual area analysis based on digital map coverage model according to claim 8, characterized in that: In step 2-1-2, the subscripts of the two-dimensional grid arrays of all grids passed are solved, and the specific steps are as follows: Definitions x is the horizontal step length, s y is the vertical step length, the A[n][n] array subscripts are continuous natural numbers, the step length is 1, x i and i For any array that affects the grid, the following table: Case 1: |d x |≥|d y |, indicating that the horizontal increment is large, so the horizontal step length s x priority: set up: Among them, s x =1 means point S is below point O, s x =-1 means point S is above point O; The solution is: Iterative derivation: d obtained by calculation x 、s x 、s y , starting from point O and moving towards point S with a step length of s x 、s y Iteration can obtain the affected grid array index x one by one i and i , since the step size is 1, the total number of iterations is (|d x |-1), based on the subscripts x and y of point O, each iteration is incremented by a step size s x 、s y , the specific formula is as follows: Finally, we can obtain the influence grid set {A[x i ][y i ]}; Case 2: |d x |<|d y |, indicating that the vertical increment is large, so the vertical step length s y priority: set up: Among them, s y =1 means point S is to the right of point O, s y =-1 means point S is to the left of point O; The solution is: Iterative derivation: d obtained by calculation y 、s x 、s y , starting from point O and moving towards point S with a step length of s x 、s y Iteration can obtain the affected grid array index x one by one i and i , since the step size is 1, the total number of iterations is (|d y |-1), based on the subscripts x and y of point O, and adding a step length s for each iteration x 、s y , the specific formula is as follows: Finally, we can obtain the influence grid set {A[x i ][y i ]}.

10. The method for accurate visual area analysis based on digital map coverage model according to claim 2, characterized in that: In step 2-2, determine whether each grid area is blocked or visible. The specific steps are as follows: Define any grid center point in the affected grid set as A i ; Obtain points O, S and A from step 1 i Point grid height information, define h o is the grid height at point O, h s is the grid height of point S, h i A i Point grid height, h max A i The maximum grid height that the grid should not block point S is: The calculation formula of the waistline length of a right-angled trapezoid can be obtained: OS is the distance between point O and point S. The distance between the two points can be calculated by obtaining the longitude and latitude information of the two points through A[n][n]. Similarly, A i S is for A i The distance from point A to point S, whether point S is covered by A i There are four cases of point occlusion: Case 1: OS ≥ R, no calculation is performed, the distance exceeds the maximum coverage radius of the camera, and it is directly marked as out of range and invisible; Case 2: h o <h s , no calculation is performed, and the height at point S of the target grid is greater than the height at point O, which is treated as blocked; Case 3: h o ≥h s &h max ≥h i , affecting grid A i The point has no influence on the target grid point S and does not block it; Case 4: h o ≥h s &h max <h i , affecting grid A i Point has an influence on the target grid point S, which is blocked and cannot be seen; Iterative derivation: Through the influence grid set {A[x i ][y i ]} Each A i The points are calculated and judged. When each A i Point S is marked as visible only if none of the influencing grids blocks point S. Otherwise, point S is marked as blocked and invisible as long as one of the influencing grids blocks point S. The final calculation obtains a two-dimensional grid array A[n][n], which contains the latitude and longitude information, altitude information, and whether it is blocked or visible for each grid.

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