Traveling vehicle path planning method based on real-time traceability

Abstract

The invention discloses a navigation vehicle path planning method based on real-time traceability, and relates to the technical field of environment monitoring, and the method specifically comprises the following steps: collecting VOCs hotspot grids, industrial park limit values and limits, and historical navigation monitoring data; calculating a VOCs concentration high value point of the control area; generating a VOCs concentration space thermodynamic diagram and displaying the VOCs concentration space thermodynamic diagram on a GIS map; a key VOCs management and control area is visually marked with a GIS map as the background; establishing medium-scale and small-scale air pollution transmission simulation in combination with continuously updated precise regional pollution source lists; the pollution source and the contribution ratio are analyzed in real time, and the navigation path is planned according to the pollution source and the contribution ratio. According to the invention, through analysis of monitoring station data, pollution concentration hot spots and key management and control areas are identified and monitored, pollution sources are further searched based on a traceability model, and the problem of lack of planning of a navigation area is reduced; by designing the underway path planning and optimization algorithm, the underway route is automatically generated in the background, and the dependence of underway command on experience is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a method for planning a path of a traveling vehicle based on real-time tracing. Background Art

[0002] The UAV uses vehicle-mounted rapid monitoring equipment to continuously and automatically monitor while moving, and combines fixed-point monitoring to conduct qualitative and quantitative analysis of pollutants, and displays the spatial continuous distribution of pollutants along the route based on geographic location information. The UAV is a mobile monitoring device for environmental monitoring, mainly used to monitor pollutants in the atmosphere. Function: It can monitor a variety of pollutants in the air in real time, such as volatile organic compounds (VOCs), particulate matter (PM2.5 and PM10), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2) and carbon monoxide (CO).

[0003] At present, the published technical solutions have not proposed solutions to the problems of lack of planning in the navigation area, reliance on experience in navigation command, and lack of mining of navigation data. At the same time, the results of navigation source tracing analysis are relatively professional and should be reasonably visualized to assist relevant managers in carrying out precise pollution control work.

[0004] However, during the implementation of the above technical solution, it was found that there were at least the following technical problems: 1. There is a lack of planning for the navigation area. For large-scale parks, counties and cities, the range of navigation options is relatively large. The current regional planning of daily navigation is highly subjective and lacks scientific and objective support; 2. Navigation command relies on experience. For emergency navigation scenarios, on-site personnel are required to have a high degree of familiarity with the pollution sources in the target area, and to make subjective judgments on possible problem points, which requires a high level of professionalism from personnel. To this end, we propose a navigation vehicle path planning method based on real-time traceability. Summary of the invention

[0005] 1. Technical issues to be resolved In view of the deficiencies in the prior art, the present invention provides a method for path planning of a navigation vehicle based on real-time tracing, which solves the technical problems of lack of planning in the navigation area and reliance on experience in navigation command.

[0006] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for path planning of a UAV based on real-time traceability, the specific steps are as follows: Step 1. Collect VOCs hotspot grids, industrial park limits and historical cruise monitoring data; Step 2. Calculate the high-value points of VOCs concentration in the control area; Step 3. Generate a spatial heat map of VOCs concentration and display it on a GIS map; Step 4. Use the GIS map as the background to visually mark the key VOCs control areas; Step 5. Combined with the continuously updated and accurate regional pollution source list, establish mesoscale and small-scale air pollution transmission simulation; Step 6. Analyze pollution sources and contribution ratios in real time, and plan the navigation route based on them.

[0007] Preferably, basic information collection in step 1: Collect basic information and data of the control area, including data information of various monitoring sites ( ),in, is the latitude and longitude coordinates of site i, The pollutant concentration monitored by monitoring station i at time t in hour, and the data information of various pollution sources ( ),in, is the latitude and longitude coordinates of the pollution source s, is the pollution source height, is the emission intensity of pollution sources at hour t, the total number of monitoring stations is N, and the total number of pollution sources is M.

[0008] Preferably, for example: for City A, there are N1 national control points, N2 provincial control points, and N3 municipal control points, that is, the total number of monitoring sites is N=N1+N2+N3; the same is true for the total number of pollution sources M.

[0009] Preferably, in step 2, the high-value points of VOCs concentration in the control area are calculated. For any longitude and latitude point in the control area, , the current pollutant concentration can be estimated based on the monitoring data using interpolation methods , taking inverse distance interpolation as an example, the calculation formula is: ; Where N is the number of all sites in the control area, is the position from site i to point ( ), usually in meters. Note that the algorithm mainly uses inverse distance interpolation, while the Kriging interpolation method is used here.

[0010] Preferably, for the above interpolation method, for example, the monitoring point A coordinates (x1, y1) monitors the concentration c1; the monitoring point B coordinates (x2, y2) monitors the concentration c2, then the concentration of any point coordinates (x0, y0) between the two points can be calculated based on the interpolation.

[0011] Preferably, in step 3, the VOCs concentration spatial heat map is generated The control area is divided into rectangular grids with an interval of 50 or 100 meters in the X and Y directions. , based on the formula in step 2, the pollutant concentration at the grid point is estimated, and finally a two-dimensional rectangular heat map is made according to the concentration, where K={1,2,3,...,nx}, L={1,2,3,...,ny}, nx and ny are the number of grids in the X and Y directions respectively.

[0012] Preferably, in step 4, key VOCs control areas are visually marked right To filter, Greater than the pollutant concentration control threshold When the coordinates of the point are marked as key control points, the points with higher concentration in the area can be found according to the grid map. Intuitively, they are the red positions in the heat map. In terms of the algorithm, they are the grid points. Carry out high-value screening, that is, monitor high concentration values ​​= key control points.

[0013] Preferably, in step 5, the air pollution transmission simulation traceability Consider that the small-scale air quality model (AERMOD, CALPUFF) is actually a mapping , which reflects the contribution of pollution source s to the pollution concentration of point G at time t. For the key control point G, the contribution of each pollution source to it can be calculated as follows: .

[0014] Preferably, in step 6, the navigation traceability path planning 6.1 Selection of points to be moved: For any key control point G, the contribution of M pollution sources to its pollution concentration can be calculated, R={ ,..., }, sort the set R, take the first P points for navigation analysis, and obtain the set of points to be navigated U={p0,p1,p2,...,pP}, P=3, where p0 is the current position of the navigation vehicle, and p1 to pP are the pollution sources to be navigated.

[0015] Preferably, 6.2 Optimize the navigation path: The optimal navigation path is an optimal order of reaching points, which makes the navigation path shortest. Since p0 must be sorted first, there are a total of P factorial P! sorting methods for the P points that need to be navigated. When the value of P is small, such as P=3, the exhaustive method can be used to calculate the length of the navigation path required by the 3! sortings respectively, and the one with the shortest length is taken as the optimal one.

[0016] Preferably, 6.3 generates the optimal navigation path: according to the optimal navigation path, the order is reached, for example, p0->p3->p1->p2, the map API is called, the starting point p0 and the end point p2 are input, and other points (p3, p1) are added as waypoints in sequence, the path track is generated, and displayed on the front end, and task instructions are issued to relevant navigation personnel.

[0017] (III) Beneficial effects The present invention analyzes the data from monitoring sites to identify hot spots of pollution concentration and key control areas, further searches for pollution sources based on a tracing model, provides a scientific reference for navigation positions, and reduces the problem of lack of planning in navigation areas; by designing navigation path planning and optimization algorithms, the background automatically generates navigation routes, reducing the reliance of navigation command on experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings.

[0019] Figure 1 This is a technical solution flow chart of a method for path planning of a mobile vehicle based on real-time tracing of the present invention; Figure 2 This is a pollution concentration interpolation result diagram in a method for route planning of a UAV based on real-time tracing of the present invention; Figure 3 It is a tracing effect for point G in a method for UAV path planning based on real-time tracing of the present invention, and a ranking diagram of contributing pollution sources and contribution rates; Figure 4 This is an implementation effect diagram of a method for UAV path planning based on real-time tracing of the present invention. DETAILED DESCRIPTION

[0020] The embodiments of the present application provide a method for route planning of a UAV based on real-time traceability to solve the problems in the prior art of lack of planning of the navigation area and reliance on experience in navigation command. By analyzing the data from monitoring sites, the pollution concentration hotspots and key control areas are identified, and the source of pollution is further found based on the traceability model, providing a scientific reference for the navigation position and reducing the problem of lack of planning in the navigation area.

[0021] Example 1 The technical solution in the embodiment of the present application is to solve the problem that the navigation area lacks planning and the navigation command relies on experience. The overall idea is as follows: In view of the problems existing in the prior art, the present invention provides a method for path planning of a UAV based on real-time tracing, and the specific steps are as follows: Step 1. Collect VOCs hotspot grids, industrial park limits and historical cruise monitoring data; Step 2. Calculate the high-value points of VOCs concentration in the control area; Step 3. Generate a spatial heat map of VOCs concentration and display it on a GIS map; Step 4. Use the GIS map as the background to visually mark the key VOCs control areas; Step 5. Combined with the continuously updated and accurate regional pollution source list, establish mesoscale and small-scale air pollution transmission simulation; Step 6. Analyze pollution sources and contribution ratios in real time, and plan the navigation route based on them.

[0022] Basic information collection in step 1: Collect basic information and data of the control area, including data information of various monitoring sites ( ).in, is the latitude and longitude coordinates of site i, is the pollutant concentration monitored by monitoring station i at hour t. Data information of various pollution sources ( ),in, is the latitude and longitude coordinates of the pollution source s, is the pollution source height, is the emission intensity of pollution sources at hour t. The total number of monitoring sites is N, and the total number of pollution sources is M.

[0023] For example, for City A, there are N1 national monitoring points, N2 provincial monitoring points, and N3 municipal monitoring points, that is, the total number of monitoring sites is N=N1+N2+N3; the same is true for the total number of pollution sources M. The monitoring data is provided by the local authorities or by our big data center.

[0024] Calculate the high-value points of VOCs concentration in the control area in step 2 For any longitude and latitude point in the control area, , the current pollutant concentration can be estimated based on the monitoring data using interpolation methods , taking inverse distance interpolation as an example, the calculation formula is: ; Where N is the number of all sites in the control area, is the position from site i to point ( ), usually in meters. Note that the algorithm using inverse distance interpolation is mainly shown here, while the Kriging interpolation method is used here.

[0025] For the above interpolation method, for example, the monitoring point A coordinates (x1, y1) monitors the concentration c1; the monitoring point B coordinates (x2, y2) monitors the concentration c2, then the concentration of any point coordinates (x0, y0) between the two points can be calculated based on the interpolation.

[0026] In step 3, the VOCs concentration spatial heat map is generated (such as Figure 2 ), green indicates excellent air quality level, yellow indicates light pollution, orange indicates toxic pollution, red indicates heavy pollution, and purple indicates severe pollution. The concentration data refers to the technical regulations of the ambient air quality index (AQI).

[0027] The control area is divided into rectangular grids with an interval of 50 or 100 meters in the X and Y directions. At the point, the pollutant concentration at the grid point is estimated based on the formula in step 2, and finally a two-dimensional rectangular heat map is made according to the concentration, where K={1,2,3,...,nx}, L={1,2,3,...,ny}, nx and ny are the number of grids in the X and Y directions, respectively.

[0028] Step 4: Visually mark key VOCs control areas right To filter, Greater than the pollutant concentration control threshold When the coordinates of the point are marked as key control points, the grid map can be used to find the points with higher concentration in the area. Intuitively, they are the red positions in the heat map. In terms of the algorithm, they are the grid points. Carry out high-value screening, that is, monitor high concentration values ​​= key control points.

[0029] Air pollution transmission simulation traceability in step 5 Consider that the small-scale air quality model (AERMOD, CALPUFF) is actually a mapping , which reflects the contribution of pollution source s to the pollution concentration of point G at time t. For the key control point G, the contribution of each pollution source to it can be calculated as follows: ; Step 6: Route planning for navigation and tracing (combined with Figure 3 shown) 6.1 Selection of points to be moved: For any key control point G, the contribution of M pollution sources to its pollution concentration can be calculated, R={ ,..., }, sort the set R, take the first P points for navigation analysis, and obtain the set of points to be navigated U = {p0, p1, p2, ..., pP}, P = 3. Among them, p0 is the current position of the navigation vehicle, and p1 to pP are the pollution sources to be navigated.

[0030] 6.2 Optimize the navigation path: The optimal navigation path is the best order of points to reach, which makes the navigation path shortest. Since p0 must be ranked first, there are a total of P factorial P! ways to sort the P points that need to be navigated. When the value of P is small, such as P=3, the exhaustive method can be used to calculate the length of the navigation path required by the 3! sorts respectively, and the one with the shortest length is the best.

[0031] 6.3 Generate the optimal navigation path: According to the order of the optimal navigation path, for example, p0->p3->p1->p2, call the map API, enter the starting point p0 and the end point p2, and add other points (p3, p1) as waypoints in sequence, generate the path trajectory, and display it on the front end, and issue task instructions to relevant navigation personnel.

[0032] The present invention analyzes the data of monitoring sites, identifies the hot spots of monitoring pollution concentration and key control areas, and further finds the source of pollution based on the tracing model, providing a scientific reference for the navigation position and reducing the problem of lack of planning in the navigation area. The present invention realizes automatic generation of a navigation route in the background by designing a navigation path planning and optimization algorithm, thereby reducing the reliance of navigation command on experience.

[0033] Finally, it should be noted that: Obviously, the above embodiments are only examples for clearly explaining the present invention, and are not intended to limit the implementation methods. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from this are still within the scope of protection of the present invention.

Claims

1. A method for path planning of a mobile vehicle based on real-time tracing, characterized in that: The specific steps are as follows: Step 1. Collect VOCs hotspot grids, industrial park limits and historical cruise monitoring data; Step 2. Calculate the high-value points of VOCs concentration in the control area; Step 3. Generate a spatial heat map of VOCs concentration and display it on a GIS map; Step 4. Use the GIS map as the background to visually mark the key VOCs control areas; Step 5. Combined with the continuously updated and accurate regional pollution source list, establish mesoscale and small-scale air pollution transmission simulation; Step 6. Analyze pollution sources and contribution ratios in real time, and plan the navigation route based on them.

2. A method for path planning of a mobile vehicle based on real-time tracing as claimed in claim 1, characterized in that: Basic information collection in step 1: Collect basic information and data of the control area, including data information of various monitoring sites ( ),in, is the latitude and longitude coordinates of site i, The pollutant concentration monitored by monitoring station i at time t in hour, and the data information of various pollution sources ( ),in, is the latitude and longitude coordinates of the pollution source s, is the pollution source height, is the emission intensity of pollution sources at hour t, the total number of monitoring stations is N, and the total number of pollution sources is M.

3. A method for path planning of a mobile vehicle based on real-time tracing as claimed in claim 1, characterized in that: In step 2, the high-value points of VOCs concentration in the control area are calculated For any longitude and latitude point in the control area, , using interpolation methods to estimate the current pollutant concentration based on monitoring data , taking inverse distance interpolation as an example, the calculation formula is: ； Where N is the number of all sites in the control area, is the position from site i to point ( ), usually in metres.

4. A method for path planning of a mobile vehicle based on real-time tracing as claimed in claim 3, characterized in that: The VOCs concentration spatial heat map generation in step 3 The control area is divided into rectangular grids with an interval of 50 or 100 meters in the X and Y directions. At the point, the pollutant concentration at the grid point is estimated based on the formula in step 2, and finally a two-dimensional rectangular heat map is made according to the concentration. Among them, K={1,2,3,...,nx}, L={1,2,3,...,ny}, nx and ny are the number of grids in the X and Y directions respectively, and the inverse distance interpolation algorithm is used. Here, the Kriging interpolation method is used. For the above interpolation method, the monitoring point A coordinates (x1, y1) monitors the concentration c1; the monitoring point B coordinates (x2, y2) monitors the concentration c2, then the concentration of any point coordinates (x0, y0) between the two points can be calculated based on the interpolation.

5. A method for path planning of a mobile vehicle based on real-time tracing as claimed in claim 4, characterized in that: In step 4, visually mark the key VOCs control areas right To filter, Greater than the pollutant concentration control threshold When the point is marked as a key control point, the points with higher concentration in the area can be found according to the grid map.

6. A method for path planning of a mobile vehicle based on real-time tracing as claimed in claim 5, characterized in that: The air pollution transmission simulation and tracing in step 5 Consider that the small-scale air quality model (AERMOD, CALPUFF) is actually a mapping , which reflects the contribution of pollution source s to the pollution concentration of point G at time t. For the key control point G, the contribution of each pollution source to it can be calculated as follows: 。 7. A method for path planning of a UAV based on real-time tracing as claimed in claim 6, characterized in that: The specific steps of the navigation tracing path planning in step 6 are as follows: 6.1 Select the points to be traveled, 6.2 Optimize the travel path, 6.3 Generate the optimal travel path.

8. A method for path planning of a mobile vehicle based on real-time tracing as claimed in claim 7, characterized in that: 6.1 Selection of points to be moved: For any key control point G, the contribution of M pollution sources to its pollution concentration can be calculated, R={ ,..., }, sort the set R, take the first P points for navigation analysis, and obtain the set of points to be navigated U={p0,p1,p2,...,pP}, P=3, where p0 is the current position of the navigation vehicle, and p1 to pP are the pollution sources to be navigated.

9. A method for path planning of a mobile vehicle based on real-time tracing as claimed in claim 7, characterized in that: 6.2 Optimizing the navigation path: The optimal navigation path is the best order of reaching points, which makes the navigation path the shortest. Since p0 must be ranked first, there are a total of P factorial P! sorting methods for the P points that need to be navigated. When the value of P is small, such as P=3, the exhaustive method can be used to calculate the length of the navigation path required by the 3! sortings respectively, and the one with the shortest length is the best.

10. A method for path planning of a UAV based on real-time tracing as claimed in claim 7, characterized in that: 6.3 Generate the optimal navigation path: According to the order of the optimal navigation path, for example, p0->p3->p1->p2, call the map API, enter the starting point p0 and the end point p2, and add other points (p3, p1) as waypoints in sequence, generate the path trajectory, and display it on the front end, and issue task instructions to relevant navigation personnel.

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