Road vehicle searching method and system based on regional rasterization

Abstract

The invention discloses a road vehicle searching method and system based on area rasterization, and the method comprises the following steps: drawing a standard driving route on a map, defining a driving time period, and obtaining the driving data of all vehicles whose driving time is within the driving time period; calculating the size of a grid block of each vehicle according to the precipitation, the road visibility and the environment to which the road belongs when the vehicle runs, rasterizing the standard running route according to the size of the grid block, generating a search area of the vehicle, and matching the running data of the vehicle with the corresponding search area to obtain the road matching length of the vehicle; taking the ratio of the road matching length of the vehicle to the length of the standard driving route as the road matching rate of the vehicle; the vehicles with the road matching rates larger than the first matching threshold value serve as full matching vehicles, the vehicles with the road matching rates larger than the second matching threshold value and smaller than the first matching threshold value serve as partial matching vehicles, and vehicle information is output according to matching requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and in particular to a road vehicle search method and system based on regional rasterization. Background Art

[0002] In the field of modern traffic management and logistics optimization, road vehicle search technology plays a vital role. This technology can not only help car owners quickly locate the vehicle position and improve the operational efficiency of parking lots, but also optimize traffic flow, reduce congestion, and provide decision support for urban planning and traffic management through the analysis of vehicle driving data. Obtaining matching vehicle information is crucial for analyzing the economic, dynamic, and emission performance of vehicles at different times and under different working conditions. The accumulation and analysis of these data help to improve the commodity characteristics of vehicles and optimize vehicle design and performance. In addition, by comparing different driving information at the same starting point and end point, the most economically efficient route can be identified, which is of great significance for logistics companies to plan transportation routes, reduce costs, and improve efficiency. It also provides valuable guidance for urban traffic planning and road construction.

[0003] Existing road car search technology mainly relies on navigation routes provided by map manufacturers, and uses core technologies such as data collection and binding, real-time positioning technology, maps and navigation to achieve real-time tracking and navigation of vehicle locations. These technologies include the use of Bluetooth beacons, GPS positioning and other technologies combined with parking lot map engines to achieve real-time tracking of vehicle locations. In addition, there is also a technology that allows fuzzy searches by entering license plate numbers and parking space numbers, and the system quickly matches and displays vehicle locations.

[0004] Although the existing technology has made some progress in car search services, there are still some problems. First, the existing technology mainly relies on fully matched driving information and does not introduce the concept of partial matching. As a result, in the actual vehicle operation process, when drivers choose different routes due to factors such as road congestion and highway tolls, these vehicles are often eliminated by the existing technology, resulting in inaccurate car search results. Secondly, for long-distance commercial vehicles, the existing technology cannot take into account the behavior of vehicles deviating from the original route such as loading and unloading goods, drivers resting, refueling, etc. These vehicles will also be mistakenly eliminated, resulting in inaccurate or no car search. These problems limit the application scope and accuracy of car search technology. Summary of the invention

[0005] The present invention proposes a road vehicle search method and system based on regional rasterization, which solves the problem that the existing road vehicle search method does not take into account the route deviation of the vehicle when it is traveling.

[0006] In order to solve the above technical problems, the present invention provides a road vehicle search method based on regional rasterization, comprising the following steps:

[0007] Step S1: draw a standard driving route on a map, define a driving time period, obtain driving data of all vehicles whose driving time is within the driving time period, and form a vehicle driving data set;

[0008] Step S2: for all vehicles in the vehicle driving data set: the grid block size of the vehicle is calculated according to the precipitation, road visibility and road environment when the vehicle is driving, the standard driving route is rasterized according to the grid block size, a search area for the vehicle is generated, the driving data of the vehicle is matched with the search area, and the road matching length of the vehicle is obtained;

[0009] Step S3: taking the ratio of the road matching length of the vehicle to the length of the standard driving route as the road matching rate of the vehicle;

[0010] Step S4: The vehicle whose road matching rate is greater than the first matching threshold is regarded as a fully matched vehicle, and the vehicle whose road matching rate is greater than the second matching threshold and less than the first matching threshold is regarded as a partially matched vehicle, and the vehicle information is output according to the matching requirements.

[0011] Preferably, in step S2, the standard driving route is gridded according to the grid block size, comprising the following steps:

[0012] Step S21: dividing the standard driving route into a plurality of straight line segments;

[0013] Step S22: Calculate the grid block size Y of vehicle i according to the precipitation, road visibility and road environment when vehicle i is traveling, obtain equal grid size distance points on different straight line segments according to the grid block size Y, and for each straight line segment, take the two endpoints and all equal grid size distance points as the center point;

[0014] Step S23: For each center point: take the center point as the origin, use the horizontal line and the vertical line as the coordinate axis, and construct a rectangular coordinate system. Establish multiple lines parallel to the coordinate axis and with a length of The line segments form a grid area;

[0015] Step S24: merging the search areas formed by all the center points to complete the rasterization of the standard driving route.

[0016] Preferably, step S21 includes the following steps:

[0017] Step S211: setting a distance threshold, and collecting a number of position points on the standard driving route according to the set distance threshold;

[0018] Step S212: using the map service APP to obtain the longitude and latitude coordinates of each collected location point, and marking each collected location point on the cloud map;

[0019] Step S213: Use a straight line to connect two adjacent position points, and divide the standard driving route into a plurality of straight line segments.

[0020] Preferably, the expression for calculating the grid block size Y in step S22 is:

[0021]

[0022] Where H is the road width; V is the maximum speed limit; Z is the road visibility during the vehicle driving time period; and W is the precipitation during the vehicle driving time period.

[0023] The present invention also provides a road vehicle search system based on regional rasterization, which is implemented based on the above-mentioned road vehicle search method based on regional rasterization, and includes: a standard route drawing module, a vehicle driving data collection module, a search area generation module, a vehicle matching module and a matching result output module;

[0024] The standard route drawing module: selects a number of location points, uses the map service APP to generate a route between two adjacent location points, and obtains a standard driving route;

[0025] The vehicle driving data collection module is used to define a driving time period and obtain driving data of all vehicles whose driving time is within the driving time period;

[0026] The search area generation module calculates the grid block size of the vehicle according to the precipitation, road visibility and road environment when the vehicle is traveling, and rasterizes the standard driving route according to the grid block size to form a search area for each vehicle;

[0027] The vehicle matching module matches the driving data of the vehicle with the corresponding search area to obtain the road matching length of each vehicle, and uses the ratio of the road matching length of the vehicle to the length of the standard driving route as the matching rate of the vehicle;

[0028] The matching result output module stores the matching rates of all vehicles and outputs the detailed information of the matching vehicles according to the matching requirements.

[0029] Preferably, the system further comprises a user interface module, wherein the user interface module is used to provide a user interaction interface, allowing a user to input a vehicle search request, and displaying a vehicle search result including matching vehicle information and a matching type.

[0030] Preferably, the system further comprises a rights management module, which is used to manage user rights, prevent unauthorized users from accessing, record and monitor system operations, and facilitate problem tracking and system maintenance.

[0031] Preferably, the search area generation module rasterizes the standard driving route according to the grid block size to form a search area for each vehicle, comprising the following steps:

[0032] Step 1: Divide the standard driving route into a number of straight line segments;

[0033] Step 2: Calculate the grid block size Y of vehicle i according to the precipitation, road visibility and road environment when vehicle i is traveling, obtain equal grid size distance points on different straight line segments according to the grid block size Y, and for each straight line segment, take the two endpoints and all equal grid size distance points as the center point;

[0034] Step 3: For each center point: Take the center point as the origin, the horizontal line and the vertical line as the coordinate axis, and construct a rectangular coordinate system. Establish multiple lines parallel to the coordinate axis and with a length of The line segments form a grid area;

[0035] Step 4: Merge the search areas formed by all center points to form the search area for each vehicle.

[0036] Preferably, the expression for calculating the grid block size Y in step 2 is:

[0037]

[0038] Where H is the road width; V is the maximum speed limit; Z is the road visibility during the vehicle driving time period; and W is the precipitation during the vehicle driving time period.

[0039] Preferably, the vehicle driving data collection module obtains the vehicle driving data through the vehicle's controller area network (CAN).

[0040] The benefits of the present invention include at least:

[0041] 1. Taking into account the weather conditions and road conditions during actual driving, the search grid size of each vehicle is calculated, so that the generated search area is more in line with the actual situation and the matching accuracy is improved;

[0042] 2. Provide matching rate as decision support to help managers or users make more reasonable decisions based on the degree of matching between vehicles and standard routes; by setting matching thresholds, vehicles can be divided into fully matched vehicles and partially matched vehicles, and vehicle information can be output according to different needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of a method flow of an embodiment of the present invention;

[0044] Figure 2 Schematic diagram of the relationship between the signal error region width and the grid block size in an embodiment of the present invention;

[0045] Figure 3 Schematic diagram of a search area for a vehicle in an embodiment of the present invention;

[0046] Figure 4 The figure is a schematic diagram of the application process of an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings 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 belong to the protection scope of the present invention.

[0048] like Figure 1 As shown, an embodiment of the present invention provides a road vehicle search method based on regional rasterization, comprising the following steps:

[0049] Step S1: draw a standard driving route on a map, define a driving time period, obtain driving data of all vehicles within the driving time period, and form a vehicle driving data set.

[0050] Specifically, the standard driving route of the embodiment of the present invention is drawn based on a large amount of research and driving experience. After determining two points on the map, the map service APP is called to automatically generate a route between the two points. That is, when the operator completes drawing two geographic location information points on the map, the map service APP will automatically generate a route between the two points. Repeat the above steps to generate the entire standard driving route.

[0051] For Company A that needs to search for vehicles on the road, the embodiment of the present invention uses MySQL to build a vehicle driving database, which includes the driving data of all vehicles of Company A, and the data comes from the controller area network CAN of the vehicles of Company A. A driving time period is set, and the driving data of all vehicles within the driving time period are obtained from the vehicle driving database of Company A to form a vehicle driving data set.

[0052] Step S2: For all vehicles in the vehicle driving data set: calculate the grid block size of the vehicle according to the precipitation, road visibility and road environment when the vehicle is driving, rasterize the standard driving route according to the grid block size, generate the vehicle's search area, match the vehicle's driving data with the search area, and obtain the vehicle's road matching length.

[0053] Step S3: The ratio of the road matching length of the vehicle to the length of the standard driving route is taken as the road matching rate of the vehicle.

[0054] Step S4: The vehicles whose road matching rate is greater than the first matching threshold are regarded as fully matched vehicles, and the vehicles whose road matching rate is greater than the second matching threshold and less than the first matching threshold are regarded as partially matched vehicles, and the vehicle information is output according to the matching requirements.

[0055] In the embodiment of the present invention, the distance threshold is set to 100-300 meters. According to the set distance threshold, several location points are collected on the standard driving route, and the longitude and latitude coordinates of each collected location point are obtained using the map service APP. Each collected location point is marked on the cloud map, and the Nth location point is connected to the N+1th location point by a straight line, where N=1, 2, 3..., and the standard driving route is divided into several straight line segments.

[0056] Calculate the length of different straight line segments, divide the distance of each straight line segment by the grid block size Y, determine the equal grid size distance points on different straight line segments, and for each straight line segment, take the two endpoints and all the equal grid size distance points as the center point.

[0057] The grid block size for each vehicle is determined by:

[0058] According to the name of the road to which the vehicle's route belongs returned by the map service APP, the road width H and the maximum speed of the road are obtained from the road data disclosed by the government and traffic management department based on the returned road name. The signal error width X is calculated based on the information obtained. The maximum speed of the road is used to roughly judge the surrounding environment of the road. For example, when the maximum speed of the road is 100-120km / h, the current road is judged to be a highway, with few surrounding buildings and a narrow signal error zone; when the maximum speed of the road is 60-100km / h, the current road is judged to be a city or rural trunk road and expressway, with few surrounding buildings and a narrow signal error zone; when the maximum speed of the road is 30-60km / h, the current road is judged to be a city street and a specific area, such as a school, residential area, mountain road, with many surrounding GPS signal obstructions and a wide signal error zone. After obtaining the preliminary signal error zone threshold according to the maximum speed, the signal error zone is further divided according to the weather when the vehicle is traveling. If the weather when the vehicle is traveling is heavy rain or foggy weather, the width of the signal error zone is increased on the premise of the preliminary signal error zone to obtain the final grid block size Y. like Figure 2 The figure shows the relationship between the width of the signal error region and the grid block size Y, where the calculation expression of the grid block size Y is:

[0059]

[0060] Where H is the road width in meters; V is the maximum speed limit in kilometers per hour; Z is the visibility when the vehicle is driving in meters; W is the precipitation when the vehicle is driving in millimeters per hour.

[0061] With these center points as the origin, and the horizontal and vertical lines as the coordinate axes, multiple rectangular coordinate systems are constructed. Establish multiple lines parallel to the coordinate axis and with a length of The line segments form a grid area.

[0062] Finally, the grid areas formed by all the center points are merged to form Figure 3 The search area shown, Figure 3 The black thick line frame is the search area, and the different colored points on the straight line correspond to different center points.

[0063] like Figure 4 As shown, when performing road matching, a loop is used to determine whether the longitude and latitude information in the search area contains the longitude and latitude information in the vehicle driving position in the second vehicle data set, and multiple longitude and latitude matching is required.

[0064] First, define: V: the total number of vehicles in the database; N: the number of rows of data in the database; D: the length of the road that the vehicle matches; S: the length of the defined route.

[0065] Line matching for a vehicle includes the following steps:

[0066] Step 1: Initialize V=1, N=1, D=0;

[0067] Step 2: Get the driving data of the Nth row of the Vth vehicle from the database;

[0068] Step 3: Determine whether the longitude and latitude information of the Nth row of driving data is included in the search area. If so, mark the geographic coordinate point corresponding to the row of data as a matching point; otherwise, set N=N+1 and return to step 2;

[0069] Step 4: Determine whether the Nth row is the last row of driving data of the vehicle. If so, execute step 5; otherwise, set N=N+1 and return to step 2;

[0070] Step 5: Determine whether D / S is greater than a second matching threshold. In the embodiment of the present invention, the second matching threshold is set to 5%. If yes, execute step 6; otherwise, remove the vehicle and proceed to step 7.

[0071] Step 6: Determine whether D / S is greater than a first matching threshold. In the embodiment of the present invention, the first matching threshold is set to 80%. If so, the vehicle is defined as a fully matching vehicle and the vehicle information is output; otherwise, the vehicle is defined as a partially matching vehicle and the vehicle information is output;

[0072] Step 7: Determine whether the Vth vehicle is the last vehicle in the database. If so, the road matching is completed and the matching vehicle information is output; otherwise, set N=1, V=V+1 and return to step 2.

[0073] An embodiment of the present invention also provides a road vehicle search system based on regional rasterization, which is implemented based on the above-mentioned road vehicle search method based on regional rasterization, and includes: a standard route drawing module, a vehicle driving data collection module, a search area generation module, a vehicle matching module and a matching result output module.

[0074] The standard route drawing module first selects several location points, uses the map service APP to generate the route between two adjacent location points, and obtains the standard driving route.

[0075] The vehicle driving data collection module acquires the driving data of all vehicles within the driving time period by defining the driving time period.

[0076] The search area generation module calculates the grid block size of the vehicle according to the precipitation, road visibility and road environment when the vehicle is traveling, and rasterizes the standard driving route according to the grid block size to form a search area for each vehicle.

[0077] The vehicle matching module matches the vehicle's driving data with the corresponding search area to obtain the road matching length of each vehicle, and takes the ratio of the vehicle's road matching length to the standard driving route length as the vehicle's matching rate.

[0078] The matching result output module stores the matching rates of all vehicles and outputs the detailed information of the matching vehicles according to the matching requirements.

[0079] The road vehicle search method of the present invention improves the accuracy and adaptability of vehicle search through flexible grid block size definition, and automatically processes a large amount of data to improve efficiency. Not only fully matching vehicles but also partially matching vehicles can be identified, making the results more comprehensive and adaptable to different driving conditions and route changes. In addition, the method provides data support for decision-making, optimizes resource allocation, improves the success rate of vehicle search, and reduces the cost of vehicle search. It is suitable for multiple fields such as traffic management and logistics optimization.

[0080] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. Only the preferred embodiments of the present invention are expressed. The description is more specific and detailed, but it cannot be understood as limiting the scope of the present invention. As long as there is no contradiction in the combination of these technical features, they should be considered as within the scope of this specification.

[0081] It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these modifications and improvements all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A road vehicle search method based on regional rasterization, characterized in that: The following steps are involved: Step S1: draw a standard driving route on a map, define a driving time period, obtain driving data of all vehicles whose driving time is within the driving time period, and form a vehicle driving data set; Step S2: for all vehicles in the vehicle driving data set: the grid block size of the vehicle is calculated according to the precipitation, road visibility and road environment when the vehicle is driving, the standard driving route is rasterized according to the grid block size, a search area for the vehicle is generated, the driving data of the vehicle is matched with the search area, and the road matching length of the vehicle is obtained; Step S3: taking the ratio of the road matching length of the vehicle to the length of the standard driving route as the road matching rate of the vehicle; Step S4: The vehicle whose road matching rate is greater than the first matching threshold is regarded as a fully matched vehicle, and the vehicle whose road matching rate is greater than the second matching threshold and less than the first matching threshold is regarded as a partially matched vehicle, and the vehicle information is output according to the matching requirements.

2. A road vehicle search method based on regional rasterization according to claim 1, characterized in that: In step S2, the standard driving route is gridded according to the grid block size, including the following steps: Step S21: dividing the standard driving route into a plurality of straight line segments; Step S22: Calculate the grid block size Y of vehicle i according to the precipitation, road visibility and road environment when vehicle i is traveling, obtain equal grid size distance points on different straight line segments according to the grid block size Y, and for each straight line segment, take the two endpoints and all equal grid size distance points as the center point; Step S23: For each center point: take the center point as the origin, use the horizontal line and the vertical line as the coordinate axis, and construct a rectangular coordinate system. Establish multiple lines parallel to the coordinate axis and with a length of The line segments form a grid area; Step S24: merging the search areas formed by all the center points to complete the rasterization of the standard driving route.

3. A road vehicle search method based on regional rasterization according to claim 2, characterized in that: Step S21 includes the following steps: Step S211: setting a distance threshold, and collecting a number of position points on the standard driving route according to the set distance threshold; Step S212: using the map service APP to obtain the longitude and latitude coordinates of each collected location point, and marking each collected location point on the cloud map; Step S213: Use a straight line to connect two adjacent position points, and divide the standard driving route into a plurality of straight line segments.

4. The method for finding a vehicle on a road based on regional rasterization according to claim 2, characterized in that: The expression for calculating the grid block size Y in step S22 is: Where H is the road width; V is the maximum speed limit; Z is the road visibility during the vehicle driving time period; and W is the precipitation during the vehicle driving time period.

5. A road vehicle search system based on regional gridding, implemented based on a road vehicle search method based on regional gridding as claimed in any one of claims 1 to 4, characterized in that: include: Standard route drawing module, vehicle driving data collection module, search area generation module, vehicle matching module and matching result output module; The standard route drawing module: selects a number of location points, uses the map service APP to generate a route between two adjacent location points, and obtains a standard driving route; The vehicle driving data collection module is used to define a driving time period and obtain driving data of all vehicles whose driving time is within the driving time period; The search area generation module calculates the grid block size of the vehicle according to the precipitation, road visibility and road environment when the vehicle is traveling, and rasterizes the standard driving route according to the grid block size to form a search area for each vehicle; The vehicle matching module matches the driving data of the vehicle with the corresponding search area to obtain the road matching length of each vehicle, and uses the ratio of the road matching length of the vehicle to the length of the standard driving route as the matching rate of the vehicle; The matching result output module stores the matching rates of all vehicles and outputs the detailed information of the matching vehicles according to the matching requirements.

6. A road vehicle search system based on regional gridding according to claim 5, characterized in that: The system also includes a user interface module, which is used to provide a user interaction interface, allow a user to input a vehicle search request, and display a vehicle search result including matching vehicle information and matching type.

7. The road vehicle search system based on regional gridding according to claim 5, characterized in that: The system also includes a rights management module, which is used to manage user rights, prevent unauthorized users from accessing, record and monitor system operations, and facilitate problem tracking and system maintenance.

8. The road vehicle search system based on regional gridding according to claim 5, characterized in that: The search area generation module rasterizes the standard driving route according to the grid block size to form a search area for each vehicle, including the following steps: Step 1: Divide the standard driving route into a number of straight line segments; Step 2: Calculate the grid block size Y of vehicle i according to the precipitation, road visibility and road environment when vehicle i is traveling, obtain equal grid size distance points on different straight line segments according to the grid block size Y, and for each straight line segment, take the two endpoints and all equal grid size distance points as the center point; Step 3: For each center point: Take the center point as the origin, the horizontal line and the vertical line as the coordinate axis, and construct a rectangular coordinate system. Establish multiple lines parallel to the coordinate axis and with a length of The line segments form a grid area; Step 4: Merge the search areas formed by all center points to form the search area for each vehicle.

9. A road vehicle search system based on regional gridding according to claim 8, characterized in that: The expression for calculating the grid block size Y in step 2 is: Where H is the road width; V is the maximum speed limit; Z is the road visibility during the vehicle driving time period; and W is the precipitation during the vehicle driving time period.

10. The road vehicle search system based on regional gridding according to claim 5, characterized in that: The vehicle driving data collection module obtains the vehicle driving data through the vehicle's controller area network CAN.

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