Vehicle identification method and device, computer equipment, storage medium and program product
By obtaining and comparing the running trajectories of vehicles within a preset period, and using similarity algorithms to identify target vehicles, the problem of low efficiency of traditional vehicle identification technology is solved and more efficient and accurate vehicle identification is achieved.
Patent Information
- Application Number
- CN202411324572.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-23
- Publication Date
- 2025-01-21
AI Technical Summary
Traditional vehicle identification technology is low in efficiency and cannot effectively improve the speed and accuracy of vehicle identification.
By obtaining the running trajectory of multiple first candidate vehicles within the preset cycle, and identifying according to the trajectory similarity of different time periods, the target vehicle is determined. The specific steps include the use of the preset similarity algorithm comparative trajectory similarity, screen the trajectory with a degree of similarity less than the threshold, and determine the target vehicle when the target time period is greater than the threshold.
It improves the efficiency of vehicle identification, reduces manpower investment, and ensures the accuracy of the identification process, thereby improving the identification efficiency of target vehicles.
Smart Images
Figure CN119337141A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of vehicle identification technology, and in particular to a vehicle identification method, apparatus, computer equipment, storage medium, and program product. Background Technology
[0002] With the large number of vehicles put into use, identifying and classifying them will help traffic management departments to manage them in a timely manner.
[0003] In traditional technologies, vehicle identification mainly involves setting up checkpoints at certain locations or intersections to check each passing vehicle one by one.
[0004] However, traditional technologies suffer from low vehicle recognition efficiency. Summary of the Invention
[0005] Therefore, it is necessary to provide a vehicle identification method, device, computer equipment, storage medium, and program product that can improve vehicle identification efficiency in response to the above-mentioned technical problems.
[0006] In a first aspect, this application provides a vehicle identification method, including:
[0007] Acquire the running trajectories of multiple first candidate vehicles within a preset period;
[0008] The target vehicle is determined by identifying each first candidate vehicle based on its running trajectory at different time periods within the preset period.
[0009] In one embodiment, the step of identifying the first candidate vehicles based on their operating trajectories at different time periods within the preset period, and determining the target vehicle, includes:
[0010] Based on a preset similarity algorithm, the degree of similarity of the running trajectories of each of the first candidate vehicles in different time periods within the preset period is determined;
[0011] Determine the target time period corresponding to the running trajectory whose similarity is less than a preset similarity threshold, and if the target time period is greater than the preset time period threshold, determine the target vehicle based on the first candidate vehicle corresponding to the running trajectory.
[0012] In one embodiment, determining the target vehicle based on the first candidate vehicle corresponding to the running trajectory includes:
[0013] The first candidate vehicle corresponding to the running trajectory is determined as the second candidate vehicle;
[0014] The trajectory of each second candidate vehicle is analyzed to determine the location information of the destination of each second candidate vehicle.
[0015] The target vehicle is determined based on the location information of the destination of each of the second candidate vehicles.
[0016] In one embodiment, determining the target vehicle based on the location information of the destination of each of the second candidate vehicles includes:
[0017] Based on the location information of the destination of each second candidate vehicle, determine the second candidate vehicles with different destinations;
[0018] The second candidate vehicle, which has a different destination, is identified as the target vehicle.
[0019] In one embodiment, the method further includes:
[0020] Obtain basic information about multiple third-candidate vehicles within the area to be analyzed;
[0021] Based on the basic information of each of the third candidate vehicles, the multiple third candidate vehicles are screened to determine the multiple first candidate vehicles.
[0022] In one embodiment, the step of filtering the plurality of third candidate vehicles based on the basic information of each of the third candidate vehicles to determine the plurality of first candidate vehicles includes:
[0023] Based on the basic information of each of the third candidate vehicles, determine the vehicle type of each of the third candidate vehicles;
[0024] The third candidate vehicle that meets the preset vehicle type is determined as the first candidate vehicle; the preset vehicle type is a vehicle that has not obtained operating rights.
[0025] Secondly, this application also provides a vehicle identification device, comprising:
[0026] The first candidate module is used to acquire the running trajectories of multiple first candidate vehicles within a preset period;
[0027] The identification module is used to identify each of the first candidate vehicles based on their running trajectories at different time periods within the preset period, and to determine the target vehicle.
[0028] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:
[0029] Acquire the running trajectories of multiple first candidate vehicles within a preset period;
[0030] The target vehicle is determined by identifying each first candidate vehicle based on its running trajectory at different time periods within the preset period.
[0031] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:
[0032] Acquire the running trajectories of multiple first candidate vehicles within a preset period;
[0033] The target vehicle is determined by identifying each first candidate vehicle based on its running trajectory at different time periods within the preset period.
[0034] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:
[0035] Acquire the running trajectories of multiple first candidate vehicles within a preset period;
[0036] The target vehicle is determined by identifying each first candidate vehicle based on its running trajectory at different time periods within the preset period.
[0037] The aforementioned vehicle identification method, device, computer equipment, storage medium, and program product are relatively simple to acquire the running trajectories of multiple first candidate vehicles within a preset period, and can quickly acquire the running trajectories of multiple first candidate vehicles within a preset period. In addition, the amount of data on the running trajectories of the first candidate vehicles at different time periods within the preset period is also relatively small. Thus, the amount of data that needs to be processed in the process of identifying each first candidate vehicle based on the running trajectories of each first candidate vehicle at different time periods within the preset period is also relatively small, thereby enabling the target vehicle to be quickly identified from the first candidate vehicles and improving the efficiency of target vehicle identification. Attached Figure Description
[0038] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0039] Figure 1 is an application environment diagram of the vehicle recognition method in one embodiment;
[0040] Figure 2 is a flowchart illustrating a vehicle identification method in one embodiment;
[0041] Figure 3 is a flowchart illustrating the vehicle identification method in another embodiment;
[0042] Figure 4 is a flowchart illustrating the vehicle identification method in another embodiment;
[0043] Figure 5 is a flowchart illustrating the vehicle identification method in another embodiment;
[0044] Figure 6 is a flowchart illustrating the vehicle identification method in another embodiment;
[0045] Figure 7 is a flowchart illustrating the vehicle identification method in another embodiment;
[0046] Figure 8 is a structural block diagram of a vehicle identification device in one embodiment;
[0047] Figure 9 is a structural block diagram of the vehicle identification device in another embodiment;
[0048] Figure 10 is a structural block diagram of a vehicle identification device in another embodiment;
[0049] Figure 11 is a structural block diagram of a vehicle identification device in another embodiment;
[0050] Figure 12 is an internal structure diagram of a computer device in one embodiment. Detailed Implementation
[0051] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0052] The vehicle recognition method provided in this application embodiment can be applied to the application environment shown in Figure 1. The computer device 102 communicates with the server 104 via a network. A data storage system can store the data that the server 104 needs to process. The data storage system can be integrated onto the server 104, or it can be located in the cloud or on other network servers. The computer device 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, etc. The server 104 can be the server of the traffic management department of the area to be analyzed. The server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.
[0053] In an exemplary embodiment, as shown in FIG2, a vehicle recognition method is provided. Taking the application of this method to the computer device in FIG1 as an example, the method includes the following steps:
[0054] S201, acquire the running trajectories of multiple first candidate vehicles within a preset period.
[0055] First, it should be noted that in this embodiment, vehicles can be identified at the city, county, or district level. That is, the multiple first candidate vehicles in this embodiment can be vehicles within a specific city, county, or district. Furthermore, it should be noted that the first candidate vehicles in this embodiment are motor vehicles within the area to be analyzed, including cars, vans, motorcycles, and other motor vehicles.
[0056] Optionally, in this embodiment, the computer device may first determine the areas where multiple first candidate vehicles are located, and then send an acquisition request to the server of the traffic management department in the areas where the multiple first candidate vehicles are located, requesting to obtain the running trajectory of the multiple first candidate vehicles within a preset period from the server of the traffic management department in the areas where the multiple first candidate vehicles are located. Alternatively, the computer device may also send an acquisition request to the server of the traffic management department in the areas where the multiple first candidate vehicles are located, requesting to obtain the location information of the checkpoints passed by the multiple first candidate vehicles within the preset period from the server of the traffic management department in the areas where the multiple first candidate vehicles are located, thereby determining the running trajectory of each first candidate vehicle within the preset period based on the location information of the checkpoints passed by each first candidate vehicle within the preset period.
[0057] Optionally, the preset period can be a period set according to actual needs. For example, the preset period can be the most recent 30 days, or the most recent 15 days, or other time periods within the most recent time. This embodiment does not limit the specific time of the preset period.
[0058] S202, Identify each first candidate vehicle based on its running trajectory at different time periods within a preset period, and determine the target vehicle.
[0059] First, it should be noted that the target vehicle in this application embodiment refers to a vehicle that does not have the qualifications for operation.
[0060] The different time periods within the preset period can be divided in units of days, such as adjacent days within the preset period; or in units of weeks, such as adjacent weeks within the preset period; or in units of hours, such as adjacent hours within the preset period. This embodiment does not impose limitations on the different time periods within the preset period; the specific criteria for division can be determined based on actual analytical needs.
[0061] Optionally, in this embodiment, the destination of each first candidate vehicle in different time periods within a preset period can be determined based on its running trajectory. Multiple first candidate vehicles are then identified based on their destinations in different time periods to determine the target vehicle. Alternatively, as another optional implementation, the similarity of the running trajectories of each first candidate vehicle in different time periods within a preset period can be obtained, and the first candidate vehicles can be identified based on the similarity of their running trajectories in different time periods within the preset period to determine the target vehicle.
[0062] Optionally, in this embodiment, after the target vehicle is identified,
[0063] To more intuitively display the characteristics of the target vehicle, the identified target vehicle can be presented in a visual form. Specifically, the driving points of the target vehicle within a preset period can be marked on a map, and the trajectory of the target vehicle can be visualized.
[0064] In the aforementioned vehicle identification method, the process of acquiring the running trajectories of multiple first candidate vehicles within a preset period is relatively simple, enabling rapid acquisition of these trajectories. Furthermore, the amount of data on the running trajectories of the first candidate vehicles at different time periods within the preset period is relatively small. This means that the amount of data processed during the identification of each first candidate vehicle based on its running trajectories at different time periods within the preset period is also relatively small, thus enabling rapid identification of the target vehicle from the first candidate vehicles and improving the efficiency of target vehicle identification. Additionally, the identification process can be performed based on the running trajectories of each first candidate vehicle at different time periods within the preset period, minimizing human intervention and avoiding excessive manpower investment. This also ensures accuracy during the identification process, thereby guaranteeing the accuracy of the ultimately identified target vehicle.
[0065] This embodiment will describe in detail the process of identifying the target vehicle based on the running trajectory of each first candidate vehicle at different time periods within a preset period. In an exemplary embodiment, as shown in FIG3, the above-mentioned S202 includes:
[0066] S301, Based on a preset similarity algorithm, determine the degree of similarity of the running trajectories of each first candidate vehicle in different time periods within a preset period.
[0067] Optionally, the preset similarity algorithm in this embodiment can be the Jaccard algorithm, or it can be other similarity algorithms.
[0068] Optionally, in this embodiment, the running trajectories of each first candidate vehicle at different time periods within a preset period can be compared according to a preset similarity algorithm to determine the degree of similarity of the running trajectories of each first candidate vehicle at different time periods within the preset period; alternatively, the checkpoint locations passed by each first candidate vehicle at different time periods within the preset period can be determined based on the running trajectories of each first candidate vehicle at different time periods within the preset period, and the degree of similarity of the running trajectories of each first candidate vehicle at different time periods within the preset period can be determined based on the checkpoint locations passed by each first candidate vehicle at different time periods within the preset period and the preset similarity algorithm. It can be understood that if the checkpoint locations passed by the first candidate vehicle at different time periods within the preset period are more similar according to the preset similarity algorithm, the degree of similarity of the trajectories of the first candidate vehicle at different time periods within the preset period is higher; if the checkpoint locations passed by the first candidate vehicle at different time periods within the preset period are less similar according to the preset similarity algorithm, the degree of similarity of the trajectories of the first candidate vehicle at different time periods within the preset period is lower.
[0069] S302, determine the target time period corresponding to the running trajectory with a similarity level less than a preset similarity threshold, and if the target time period is greater than the preset time period threshold, determine the target vehicle based on the first candidate vehicle corresponding to the running trajectory.
[0070] Understandably, if the similarity of the running trajectories of the first candidate vehicle in different time periods within a preset period is less than a preset similarity threshold, it means that the running trajectories of the first candidate vehicle in different time periods within a preset period are relatively scattered, irregular, and without fixed routes. Based on this, the computer equipment can determine the target time period corresponding to the running trajectory with a similarity less than the preset similarity threshold based on the similarity of the running trajectories of each first candidate vehicle in different time periods within a preset period. For example, taking a preset period of the most recent 30 days as an example, and different time periods within the preset period as adjacent days within the 30 days, the computer device can determine the similarity of the running trajectories of the first candidate vehicle on two adjacent days within the 30 days. If it is determined that the similarity of the running trajectories on the 1st and 2nd, 5th and 6th, 7th and 8th, 10th and 11th, 16th and 17th, 21st and 22nd, and 23rd and 24th days within these 30 days is less than a preset similarity threshold, then the 1st, 2nd, 5th, 6th, 7th, 8th, 10th, 11th, 16th, 17th, 21st, 22nd, 23rd, and 24th days within these 30 days can be determined as the target time period.
[0071] Furthermore, if the similarity of the operating trajectories of the first candidate vehicle is relatively low across different time periods within the preset period, it indicates that the first candidate vehicle's operating trajectory is irregular, suggesting that its travel is not on a fixed route and is highly random. This indirectly suggests that the first candidate vehicle is likely not qualified for operation. Conversely, if the similarity of the operating trajectories of the first candidate vehicle is relatively high across different time periods within the preset period, it indicates that the first candidate vehicle generally follows a fixed route, suggesting that the probability of the first candidate vehicle not being qualified for operation is relatively low. Based on this judgment criterion, when the target time period is longer than a preset time period threshold, the target vehicle can be determined based on the first candidate vehicle corresponding to the operating trajectory with a similarity lower than the preset similarity threshold.
[0072] Continuing with the example of the target time period being the 1st, 2nd, 5th, 6th, 7th, 8th, 10th, 11th, 16th, 17th, 21st, 22nd, 23rd, and 24th days of the aforementioned 30 days, assuming the preset time period threshold is 10 days, then the number of days in the above-determined target time period is greater than the preset time period threshold. In this case, the computer equipment can determine the target vehicle based on the first candidate vehicle corresponding to the running trajectory of these 10 days.
[0073] Optionally, as an embodiment, the first candidate vehicle corresponding to the running trajectory with a similarity less than a preset similarity threshold can be directly determined as the target vehicle. Alternatively, the first target vehicle can be determined from these first candidate vehicles based on the driving destinations of the first candidate vehicles corresponding to the running trajectory with a similarity less than a preset similarity threshold at different time periods within a preset period.
[0074] In this embodiment, the process of determining the similarity of the running trajectories of each first candidate vehicle in different time periods within a preset period according to a preset similarity algorithm is relatively simple. It can quickly determine the similarity of the running trajectories of each first candidate vehicle in different time periods within a preset period, thereby quickly determining the target time period corresponding to the running trajectory with a similarity less than a preset similarity threshold. Furthermore, if the target time period is greater than the preset time period threshold, the target vehicle can be quickly determined based on the first candidate vehicle corresponding to the running trajectory. Since the amount of computation in the whole process is small, the target vehicle can be quickly determined based on the running trajectories of each first candidate vehicle in different time periods within a preset period, thereby improving the recognition efficiency of the target vehicle.
[0075] This embodiment will describe the process of determining the target vehicle in detail. In one embodiment, as shown in FIG4, the above-mentioned S302 includes:
[0076] S401, determine the first candidate vehicle corresponding to the running trajectory as the second candidate vehicle.
[0077] In this embodiment, if the target time period corresponding to the determined running trajectory with a similarity level less than a preset similarity threshold is greater than a preset time period threshold, the computer device can determine the first candidate vehicle corresponding to the above running trajectory as the second candidate vehicle.
[0078] S402, Analyze the trajectory of each second candidate vehicle to determine the location information of the destination of each second candidate vehicle.
[0079] In this embodiment, after the computer device determines the second candidate vehicle, it can analyze the running trajectory of each second candidate vehicle to extract the destination information of each second candidate vehicle. Based on the destination information of each second candidate vehicle, the location information of the destination of each second candidate vehicle is determined. Optionally, the computer device can map the running trajectory of each second candidate vehicle onto a corresponding map and determine the location information of the destination of each second candidate vehicle from the map; or, the computer device can extract the location coordinates of the destination of each second candidate vehicle from the running trajectory of each second candidate vehicle and determine the location information of the destination of each second candidate vehicle based on the location coordinates of the destination of each second candidate vehicle.
[0080] S403: Determine the target vehicle based on the location information of the destination of each second candidate vehicle.
[0081] Optionally, in this embodiment, the computer device can compare the location information of the destinations of each second candidate vehicle to determine whether the location information of the destinations of each second candidate vehicle is the same. If the location information of the destinations of the second candidate vehicles is the same, it means that although the running trajectories of the second candidate vehicles are different at different time periods within the preset period, the destinations are the same. This indicates that the second candidate vehicles may have taken detours or changed their driving routes at different time periods within the preset period, resulting in a small degree of similarity in the running trajectories of the second candidate vehicles at different time periods within the preset period, but the final destinations are the same. In this case, the computer device can determine that the second candidate vehicles arrive at the same place at different time periods within the preset period, indicating that the probability of the second candidate vehicle being the target vehicle is small.
[0082] In this embodiment, since the computer device determines the first candidate vehicle corresponding to the running trajectory as the second candidate vehicle when the target time period corresponding to the running trajectory with a similarity level less than a preset similarity threshold is greater than a preset time period threshold, the determination process of the second candidate vehicle is relatively simple. Therefore, the second candidate vehicle can be quickly determined, and the running trajectory of each second candidate vehicle can be analyzed to quickly determine the location information of the driving end point of each second candidate vehicle. Then, based on the location information of the driving end point of each second candidate vehicle, the target vehicle can be quickly determined, ensuring the efficiency of target vehicle determination.
[0083] This embodiment will explain the detailed process of determining the target vehicle based on the location information of the destination of each second candidate vehicle. In one embodiment, as shown in FIG5, the above S403 includes:
[0084] S501, based on the location information of the destination of each second candidate vehicle, determine the second candidate vehicles with different destinations.
[0085] Optionally, in this embodiment, the computer device can compare the location information of the destinations of each second candidate vehicle to determine the second candidate vehicles with different destinations. For example, if the location information of the destination of the second candidate vehicle is coordinate information, the computer device can compare the coordinate information of the destinations of the second candidate vehicles and determine the second candidate vehicles with different coordinate information as having different destinations; or, if the location information of the destination of the second candidate vehicle is location information, the computer device can compare the location information of the destinations of the second candidate vehicles and determine the second candidate vehicles with different location information as having different destinations.
[0086] S502 identifies the second candidate vehicle, which has a different destination, as the target vehicle.
[0087] In this embodiment, after the computer device identifies second candidate vehicles with different destinations, it can directly designate these second candidate vehicles with different destinations as the target vehicle. For example, if the identified second candidate vehicles with different destinations are three vehicles A, B, and C, the computer device can designate these three vehicles as the target vehicle.
[0088] In this embodiment, the computer device further determines second candidate vehicles with different destinations based on the location information of the destinations of each second candidate vehicle. The second candidate vehicles with different destinations are then identified as target vehicles. Through further screening and elimination, the target vehicle is further identified from the identified second candidate vehicles, which improves the accuracy of the identified target vehicle and ensures the accuracy of the target vehicle identified by the computer device.
[0089] In the scenario described above, where multiple first candidate vehicles are acquired within a preset period, it is necessary to first determine the multiple first candidate vehicles. This embodiment will describe the process of determining the first candidate vehicles in detail. In one embodiment, as shown in Figure 6, the method further includes:
[0090] S601, obtain basic information about multiple third candidate vehicles within the area to be analyzed.
[0091] The area to be analyzed can be a city, a county, or a district. Optionally, in this embodiment, the third candidate vehicle can be all motor vehicles within the area to be analyzed. As an optional implementation, the basic information of the third candidate vehicle in this embodiment may include the vehicle's license plate number, license plate type, license plate color, vehicle owner's name, vehicle owner's identity information, vehicle owner's contact number, vehicle owner's registered address, vehicle engine identification number, vehicle body color, vehicle model, vehicle Chinese brand, and vehicle operation identification, etc.
[0092] In this embodiment, as an optional implementation, the computer device can obtain basic information about multiple third candidate vehicles from the server of the traffic management department of the area to be analyzed.
[0093] S602, based on the basic information of each third candidate vehicle, screens multiple third candidate vehicles to determine multiple first candidate vehicles.
[0094] In this embodiment, the first candidate vehicle is a vehicle suspected of not having obtained operating rights, selected from the third candidate vehicles. Optionally, in this embodiment, based on the basic information of each third candidate vehicle, all third candidate vehicles within the area to be analyzed can be determined as the first candidate vehicle; alternatively, the first candidate vehicle can be selected from the third candidate vehicles within the area to be analyzed. As an optional implementation, in this embodiment, the motor vehicle operation identification number (MDI) of each third candidate vehicle can be extracted from its basic information. Then, the MDIs of each third candidate vehicle are compared, and the third candidate vehicle with a different MDI is determined as the first candidate vehicle.
[0095] In this embodiment, by accurately obtaining the basic information of multiple third candidate vehicles in the area to be analyzed, multiple third candidate vehicles can be accurately screened based on their accurate basic information, and multiple first candidate vehicles can be determined from among them, thereby ensuring the accuracy of the determined multiple first candidate vehicles.
[0096] This embodiment will explain the specific process of screening multiple third candidate vehicles based on the basic information of each third candidate vehicle to determine multiple first candidate vehicles. In one embodiment, as shown in FIG7, the above S602 includes:
[0097] S701, based on the basic information of each third candidate vehicle, determine the vehicle type of each third candidate vehicle.
[0098] In this embodiment, as an optional implementation, the computer device can determine the vehicle type information of each third candidate vehicle from the basic information of each third candidate vehicle, and thus determine the vehicle type of each third candidate vehicle based on the vehicle type information. Optionally, in this embodiment, the vehicle type of the third candidate vehicle can be a vehicle that has obtained operating rights, or it can be a vehicle that has not obtained operating rights.
[0099] S702, the third candidate vehicle that meets the preset vehicle type is determined as the first candidate vehicle; the preset vehicle type is a vehicle that has not obtained operating rights.
[0100] The preset vehicle type is the type of vehicle that needs to be identified in this embodiment. Furthermore, the preset vehicle type in this embodiment refers to a vehicle that has not obtained operating rights.
[0101] Optionally, in this embodiment, after the computer device determines the vehicle type of each third candidate vehicle, it can compare the vehicle type of each third candidate vehicle with a preset vehicle type, and determine the third candidate vehicle that matches the preset vehicle type as the first candidate vehicle.
[0102] In this embodiment, since the basic information of each third candidate vehicle includes vehicle type information, the vehicle type of each third candidate vehicle can be accurately determined based on the basic information of each third candidate vehicle. The first candidate vehicle is selected from the third candidate vehicles and the third candidate vehicle that meets the preset vehicle type is determined as the first candidate vehicle. Since the accuracy of the vehicle type of the third candidate vehicles is improved, the accuracy of the first candidate vehicle determined from the third candidate vehicles is also improved.
[0103] To facilitate understanding by those skilled in the art, the vehicle identification method provided in this application will be described in detail below. This method may include:
[0104] S1: Obtain basic information about multiple third candidate vehicles within the area to be analyzed.
[0105] S2, based on the basic information of each third candidate vehicle, determine the vehicle type of each third candidate vehicle.
[0106] S3, the third candidate vehicle that meets the preset vehicle type is determined as the first candidate vehicle; the preset vehicle type is a vehicle that has not obtained operating rights.
[0107] S4: Obtain the running trajectories of multiple first candidate vehicles within a preset period.
[0108] S5. Based on a preset similarity algorithm, determine the degree of similarity of the running trajectories of each first candidate vehicle in different time periods within a preset period.
[0109] S6, determine the target time period corresponding to the running trajectory with a similarity level less than a preset similarity threshold, and if the target time period is greater than a preset time period threshold, determine the first candidate vehicle corresponding to the above running trajectory as the second candidate vehicle.
[0110] S7. Analyze the trajectory of each second candidate vehicle to determine the location information of the destination of each second candidate vehicle.
[0111] S8, based on the location information of the destination of each second candidate vehicle, determine the second candidate vehicles with different destinations.
[0112] S9 identifies the second candidate vehicle, which has a different destination, as the target vehicle.
[0113] It should be noted that the descriptions in S1-S9 above can be found in the relevant descriptions in the above embodiments, and their effects are similar, so they will not be repeated here.
[0114] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.
[0115] Based on the same inventive concept, this application also provides a vehicle identification device for implementing the vehicle identification method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more vehicle identification device embodiments provided below can be found in the limitations of the vehicle identification method described above, and will not be repeated here.
[0116] In an exemplary embodiment, as shown in FIG8, a vehicle recognition device is provided, comprising: a first candidate module 10 and a recognition module 11, wherein:
[0117] The first candidate module 10 is used to acquire the running trajectories of multiple first candidate vehicles within a preset period.
[0118] The identification module 11 is used to identify each first candidate vehicle based on the running trajectory of each first candidate vehicle in different time periods within a preset period, and to determine the target vehicle.
[0119] The vehicle identification device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so they will not be described again here.
[0120] In one embodiment, as shown in FIG9, the identification module 11 includes: a first determining unit 111 and a second determining unit 112, wherein:
[0121] The first determining unit 111 is used to determine the similarity of the running trajectories of each first candidate vehicle in different time periods within a preset period according to a preset similarity algorithm.
[0122] The second determining unit 112 is used to determine the target time period corresponding to the running trajectory whose similarity is less than a preset similarity threshold, and to determine the target vehicle based on the first candidate vehicle corresponding to the running trajectory if the target time period is greater than the preset time period threshold.
[0123] The vehicle identification device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so they will not be described again here.
[0124] In one embodiment, referring to Figure 9, the second determining unit 112 is specifically used to: determine the first candidate vehicle corresponding to the running trajectory as the second candidate vehicle; analyze the running trajectory of each second candidate vehicle to determine the location information of the destination of each second candidate vehicle; and determine the target vehicle based on the location information of the destination of each second candidate vehicle.
[0125] The vehicle identification device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so they will not be described again here.
[0126] In one embodiment, referring to Figure 9, the second determining unit 112 is specifically used to: determine second candidate vehicles with different destinations based on the location information of the destinations of each second candidate vehicle; and determine the second candidate vehicles with different destinations as target vehicles.
[0127] The vehicle identification device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so they will not be described again here.
[0128] In one embodiment, as shown in FIG10, the above-mentioned device further includes: a second candidate module 12 and a determining module 13, wherein:
[0129] The second candidate module 12 is used to obtain basic information about multiple third candidate vehicles in the area to be analyzed.
[0130] The determination module 13 is used to filter multiple third candidate vehicles based on the basic information of each third candidate vehicle, and determine multiple first candidate vehicles.
[0131] The vehicle identification device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so they will not be described again here.
[0132] In one embodiment, as shown in FIG11, the determining module 13 includes: a third determining unit 131 and a fourth determining unit 132, wherein:
[0133] The third determining unit 131 is used to determine the vehicle type of each third candidate vehicle based on the basic information of each third candidate vehicle.
[0134] The fourth determining unit 132 is used to determine the third candidate vehicle that meets the preset vehicle type as the first candidate vehicle; the preset vehicle type is a vehicle that has not obtained the right to operate.
[0135] The vehicle identification device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so they will not be described again here.
[0136] Each module in the aforementioned vehicle recognition device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.
[0137] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram is shown in Figure 12. The computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores basic information of a third candidate vehicle and the trajectory data of a first candidate vehicle. The I / O interfaces of the computer device are used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communication with an external terminal via a network connection. When the computer program is executed by the processor, it implements a vehicle identification method.
[0138] Those skilled in the art will understand that the structure shown in Figure 12 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0139] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:
[0140] Acquire the running trajectories of multiple first candidate vehicles within a preset period;
[0141] The target vehicle is determined by identifying each first candidate vehicle based on its running trajectory at different time periods within a preset period.
[0142] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0143] Based on a preset similarity algorithm, the degree of similarity of the running trajectories of each first candidate vehicle in different time periods within a preset period is determined.
[0144] The target time period corresponding to the running trajectory with a similarity level less than a preset similarity threshold is determined, and if the target time period is greater than the preset time period threshold, the target vehicle is determined based on the first candidate vehicle corresponding to the running trajectory.
[0145] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0146] The first candidate vehicle corresponding to the running trajectory is determined as the second candidate vehicle;
[0147] The trajectory of each second candidate vehicle is analyzed to determine the location information of the destination of each second candidate vehicle.
[0148] The target vehicle is determined based on the location information of the destination of each second candidate vehicle.
[0149] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0150] Based on the location information of the destination of each second candidate vehicle, determine the second candidate vehicles with different destinations;
[0151] The second candidate vehicle, which has a different destination, is selected as the target vehicle.
[0152] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0153] Obtain basic information about multiple third-candidate vehicles within the area to be analyzed;
[0154] Based on the basic information of each third candidate vehicle, multiple third candidate vehicles are screened to determine multiple first candidate vehicles.
[0155] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0156] Based on the basic information of each third candidate vehicle, determine the vehicle type of each third candidate vehicle;
[0157] The third candidate vehicle that meets the preset vehicle type is determined as the first candidate vehicle; the preset vehicle type is a vehicle that has not obtained operating rights.
[0158] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:
[0159] Acquire the running trajectories of multiple first candidate vehicles within a preset period;
[0160] The target vehicle is determined by identifying each first candidate vehicle based on its running trajectory at different time periods within a preset period.
[0161] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0162] Based on a preset similarity algorithm, the degree of similarity of the running trajectories of each first candidate vehicle in different time periods within a preset period is determined.
[0163] The target time period corresponding to the running trajectory with a similarity level less than a preset similarity threshold is determined, and if the target time period is greater than the preset time period threshold, the target vehicle is determined based on the first candidate vehicle corresponding to the running trajectory.
[0164] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0165] The first candidate vehicle corresponding to the running trajectory is determined as the second candidate vehicle;
[0166] The trajectory of each second candidate vehicle is analyzed to determine the location information of the destination of each second candidate vehicle.
[0167] The target vehicle is determined based on the location information of the destination of each second candidate vehicle.
[0168] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0169] Based on the location information of the destination of each second candidate vehicle, determine the second candidate vehicles with different destinations;
[0170] The second candidate vehicle, which has a different destination, is selected as the target vehicle.
[0171] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0172] Obtain basic information about multiple third-candidate vehicles within the area to be analyzed;
[0173] Based on the basic information of each third candidate vehicle, multiple third candidate vehicles are screened to determine multiple first candidate vehicles.
[0174] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0175] Based on the basic information of each third candidate vehicle, determine the vehicle type of each third candidate vehicle;
[0176] The third candidate vehicle that meets the preset vehicle type is determined as the first candidate vehicle; the preset vehicle type is a vehicle that has not obtained operating rights.
[0177] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:
[0178] Acquire the running trajectories of multiple first candidate vehicles within a preset period;
[0179] The target vehicle is determined by identifying each first candidate vehicle based on its running trajectory at different time periods within a preset period.
[0180] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0181] Based on a preset similarity algorithm, the degree of similarity of the running trajectories of each first candidate vehicle in different time periods within a preset period is determined.
[0182] The target time period corresponding to the running trajectory with a similarity level less than a preset similarity threshold is determined, and if the target time period is greater than the preset time period threshold, the target vehicle is determined based on the first candidate vehicle corresponding to the running trajectory.
[0183] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0184] The first candidate vehicle corresponding to the running trajectory is determined as the second candidate vehicle;
[0185] The trajectory of each second candidate vehicle is analyzed to determine the location information of the destination of each second candidate vehicle.
[0186] The target vehicle is determined based on the location information of the destination of each second candidate vehicle.
[0187] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0188] Based on the location information of the destination of each second candidate vehicle, determine the second candidate vehicles with different destinations;
[0189] The second candidate vehicle, which has a different destination, is selected as the target vehicle.
[0190] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0191] Obtain basic information about multiple third-candidate vehicles within the area to be analyzed;
[0192] Based on the basic information of each third candidate vehicle, multiple third candidate vehicles are screened to determine multiple first candidate vehicles.
[0193] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0194] Based on the basic information of each third candidate vehicle, determine the vehicle type of each third candidate vehicle;
[0195] The third candidate vehicle that meets the preset vehicle type is determined as the first candidate vehicle; the preset vehicle type is a vehicle that has not obtained operating rights.
[0196] It should be noted that the data involved in this application (including but not limited to data used for analysis, data stored, data displayed, etc.) are all information and data that have been fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0197] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.
[0198] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.
[0199] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.
Claims
1. A vehicle identification method, characterized in that: The method comprises: Obtaining the running trajectories of a plurality of first candidate vehicles within a preset period; The first candidate vehicles are identified according to their running trajectories in different time periods within the preset cycle to determine the target vehicle.
2. The method according to claim 1, characterized in that The step of identifying each of the first candidate vehicles according to the running trajectories of each of the first candidate vehicles in different time periods within the preset period to determine the target vehicle includes: Determining the similarity of the running trajectories of each of the first candidate vehicles in different time periods within the preset period according to a preset similarity algorithm; A target time period corresponding to a running trajectory whose similarity is less than a preset similarity threshold is determined, and when the target time period is greater than the preset time period threshold, the target vehicle is determined according to a first candidate vehicle corresponding to the running trajectory.
3. The method according to claim 2, characterized in that The determining the target vehicle according to the first candidate vehicle corresponding to the running trajectory includes: Determining a first candidate vehicle corresponding to the running trajectory as a second candidate vehicle; Analyze the running track of each of the second candidate vehicles to determine the location information of the travel end point of each of the second candidate vehicles; The target vehicle is determined according to the location information of the travel destination of each of the second candidate vehicles.
4. The method according to claim 3, characterized in that The step of determining the target vehicle according to the location information of the travel destination of each of the second candidate vehicles includes: Determining second candidate vehicles with different travel destinations according to the location information of the travel destinations of each of the second candidate vehicles; The second candidate vehicle with a different travel destination is determined as the target vehicle.
5. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: Obtaining basic information of multiple third candidate vehicles in the area to be analyzed; The plurality of third candidate vehicles are screened according to the basic information of each of the third candidate vehicles to determine the plurality of first candidate vehicles.
6. The method according to claim 5, characterized in that The step of screening the plurality of third candidate vehicles according to the basic information of each of the third candidate vehicles to determine the plurality of first candidate vehicles includes: Determining the vehicle type of each of the third candidate vehicles according to the basic information of each of the third candidate vehicles; A third candidate vehicle that meets a preset vehicle type is determined as the first candidate vehicle; the preset vehicle type is a vehicle that has not obtained operating rights.
7. A vehicle identification device, characterized in that: The device comprises: A first candidate module, used to obtain the running trajectories of a plurality of first candidate vehicles within a preset period; The identification module is used to identify each of the first candidate vehicles according to the running tracks of each of the first candidate vehicles in different time periods within the preset period to determine the target vehicle.
8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.
10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.