Vehicle monitoring method and device based on beidou navigation, electronic equipment and medium

Abstract

This application relates to the field of safety monitoring technology, and in particular to a vehicle monitoring method, device, electronic device, and medium based on BeiDou navigation. The method includes acquiring a BeiDou video stream of the area to be monitored; identifying at least one vehicle feature in the BeiDou video stream and determining the vehicle speed information corresponding to each vehicle feature; identifying vehicles whose acceleration exceeds a preset standard acceleration and whose speed exceeds a preset standard speed as abnormal driving vehicles; determining the anomaly type of the abnormal driving vehicle from historical anomaly data; when the anomaly type is an unconventional anomaly, determining the current location and transfer area location of the abnormal driving vehicle based on the BeiDou video stream, determining a transfer route based on the current location and transfer area location, and sending the transfer route to the terminal device corresponding to the abnormal driving vehicle; when the anomaly type is a conventional anomaly, generating a speed reduction warning. This application can improve the safety of vehicle driving.

Description

Technical Field

[0001] This application relates to the field of security monitoring technology, and in particular to a vehicle monitoring method, device, electronic equipment and medium based on Beidou navigation. Background Technology

[0002] The development of modern transportation has brought endless convenience to people's travel, but it has also brought many safety hazards. Among them, highways are not only long, but also have complex and ever-changing road conditions. In addition to straight sections, they also include different types of road sections such as tunnels, bridges, and curves. Drivers need to adjust their driving status in a timely manner according to the latest road conditions to adapt to different types of road sections. Furthermore, in order to prevent vehicles from driving too slowly in the highway traffic flow, causing traffic jams and accidents, most highways have set minimum driving speeds. Therefore, highways are the road conditions with the highest number of traffic accidents.

[0003] Traffic accidents generally cause significant losses to people's lives and property, so a vehicle monitoring method that can improve vehicle safety during driving is particularly important. Summary of the Invention

[0004] To improve vehicle driving safety, this application provides a vehicle monitoring method, device, electronic device, and medium based on BeiDou navigation.

[0005] Firstly, this application provides a vehicle monitoring method based on BeiDou navigation, employing the following technical solution:

[0006] A vehicle monitoring method based on BeiDou navigation includes:

[0007] Acquire BeiDou video streams of the area to be monitored;

[0008] Identify at least one vehicle feature in the BeiDou video stream and determine the vehicle speed information corresponding to each vehicle feature. The vehicle speed information includes the vehicle acceleration and vehicle speed of the corresponding vehicle within a first preset time period.

[0009] Vehicles whose acceleration exceeds the preset standard acceleration and whose speed exceeds the preset standard speed are identified as abnormal driving vehicles;

[0010] Based on the vehicle characteristics corresponding to the abnormally driving vehicle, the abnormal type of the abnormally driving vehicle is determined from historical abnormal data. The abnormal type includes unconventional abnormalities and regular abnormalities.

[0011] When the anomaly type is an unconventional anomaly, the current location and transfer area of ​​the abnormally moving vehicle are determined according to the Beidou video stream, and a transfer route is determined according to the current location and transfer area, and the transfer route is sent to the terminal device corresponding to the abnormally moving vehicle.

[0012] When the anomaly type is a regular anomaly, a speed reduction warning is generated.

[0013] By adopting the above technical solution, real-time monitoring of the BeiDou video stream corresponding to the monitored area is conducted to promptly detect abnormal vehicles within the monitored area. Timely assistance is provided to these abnormal vehicles to reduce the probability of traffic accidents. When determining whether a vehicle is abnormal, the system simultaneously checks whether the vehicle's acceleration and speed exceed their respective standard values ​​to improve accuracy. Since different drivers have different driving habits, when abnormal acceleration and speed are detected, historical anomaly data is used to determine whether the anomaly is normal. Precise assistance is provided to drivers based on the type of anomaly, rather than using a uniform approach, thus reducing the probability of traffic accidents and improving driving safety.

[0014] In one possible implementation, determining the current location and relocation area of ​​the abnormally moving vehicle based on the BeiDou video stream includes:

[0015] Based on the abnormal vehicle features contained in each BeiDou image in the BeiDou video stream, each BeiDou image is marked to obtain a corresponding feature image;

[0016] The direction of travel and current position of the abnormally moving vehicle are determined based on multiple feature images;

[0017] Based on the driving direction, the image of the area to be transferred is determined from the Beidou video stream, and the sub-region address and type contained in the image of the area to be transferred are identified. The sub-region with the preset type is determined as the initial sub-region.

[0018] When the number of initial sub-regions is one, the initial sub-region is determined as the transfer region, and the position of the initial sub-region is determined as the position of the transfer region;

[0019] When there are at least two initial sub-regions, the initial sub-region with the closest interval distance to the current position is determined as the transfer region, and the position of the initial sub-region is determined as the position of the transfer region.

[0020] By adopting the above technical solution, when the driving abnormality of a vehicle is an unusual abnormality, it indicates that the vehicle may be in an uncontrollable state. At this time, the abnormal vehicle can be guided to a transfer area to slow down, thereby ensuring the safety of the driver and the vehicle. Furthermore, the transfer is carried out by selecting a suitable sub-region from multiple sub-regions based on the type and spacing, rather than randomly transferring the abnormal vehicle to a nearby sub-region. This improves the safety of the vehicle during driving while reducing the probability of abnormal driving vehicles causing loss of life and property to others.

[0021] In one possible implementation, after determining the transfer route based on the current location and the transfer area location, the method further includes:

[0022] The transfer length of the transfer route and the number of vehicles in the transfer route are determined based on the BeiDou video stream, and the vehicle density in the transfer route is determined based on the transfer length and the number of vehicles.

[0023] When the vehicle density is higher than the preset standard vehicle density, the standby position of at least one UAV device is obtained, and the target UAV device is determined based on the transfer route and the standby position of the at least one UAV device;

[0024] Generate a dispersal command based on the transfer route, and control the UAV equipment to disperse the vehicles on the transfer route according to the dispersal command.

[0025] By adopting the above technical solution, when there is a large amount of traffic on the transfer route, drones can be used to disperse the remaining vehicles, so that the remaining vehicles on the transfer route can give way, which can improve the safety of the remaining vehicles during the driving process. In addition, the decision on whether to start the drone is based on the vehicle density on the transfer route can reduce unnecessary data calculations, thereby reducing the workload of computer equipment when processing data.

[0026] In one possible implementation, after generating a dispersal command based on the transfer route and controlling the UAV device to disperse vehicles on the transfer route according to the dispersal command, the method further includes:

[0027] Based on the speed of the abnormally moving vehicle, adjust the dispersal speed of the drone equipment so that the drone equipment is positioned in front of the abnormally moving vehicle.

[0028] When the distance between the drone device and the abnormally moving vehicle is greater than the preset distance, an electronic fence is set for the path between the drone device and the abnormally moving vehicle.

[0029] When other vehicles are detected within the electronic fence, an alarm signal is generated and sent to the vehicle terminal corresponding to the other vehicles.

[0030] By adopting the above technical solution, the drone equipment is moved by controlling the speed of the abnormally moving vehicle, so that the drone equipment can disperse vehicles in front of the abnormally moving vehicle to ensure that the vehicles in front can avoid them in time. However, since the drone equipment will continue to move forward, it may not be able to disperse vehicles that later enter the transfer path in time. Therefore, an electronic fence can be set up on the movement path that has already been dispersed to promptly remind vehicles that have entered the electronic fence area, thereby facilitating the protection of the driving safety of vehicles that later enter.

[0031] In one possible implementation, adjusting the dispersal speed of the drone device based on the speed of the abnormally moving vehicle includes:

[0032] Based on the current position of the abnormally moving vehicle, its speed and acceleration, and the standby position of the drone equipment, the first dispersal speed and the first acceleration of the drone equipment are determined.

[0033] When the driving position of the drone equipment is consistent with the driving position of the abnormally driving vehicle, the first acceleration is adjusted to obtain the second acceleration, and the second dispersal speed of the drone equipment is determined based on the second acceleration.

[0034] By adopting the above technical solution, the first dispersal speed of the drone equipment is determined by the vehicle speed and acceleration of the abnormally moving vehicle, so that the drone equipment can reach the abnormally moving vehicle in the shortest possible time, so as to carry out the dispersal work in a timely manner, that is, to facilitate the timely safety protection of other vehicles. When the drone equipment has reached the location of the abnormally moving vehicle, the dispersal speed of the drone equipment can be adjusted to maintain a relative distance between the drone equipment and the abnormally moving vehicle, so as to monitor the actual driving status of the abnormally moving vehicle in real time while carrying out the dispersal work, thereby improving the safety of the abnormally moving vehicle during its driving process.

[0035] In one possible implementation, determining the anomaly type of the abnormally driving vehicle from historical anomaly data includes:

[0036] Identify the facial features of multiple abnormal drivers and the corresponding abnormal vehicle features within a second preset time period from historical abnormal data;

[0037] An abnormal feature matrix is ​​obtained by integrating multiple sets of abnormal driver facial features and corresponding abnormal vehicle features. The abnormal feature matrix includes at least one abnormal vehicle feature corresponding to each of the multiple abnormal driver facial features.

[0038] Facial features of abnormal drivers whose number of abnormal vehicle features exceeds a preset standard number of abnormal features are identified as abnormal features of interest; based on the abnormal feature matrix, it is determined whether the facial features of abnormal drivers corresponding to the vehicle features of the abnormally driving vehicle are the abnormal features of interest.

[0039] If so, then the abnormality type of the abnormally driving vehicle is determined to be a normal abnormality type;

[0040] If not, then the abnormal type of the abnormally driving vehicle is determined to be an unconventional abnormal type.

[0041] By adopting the above technical solution, since the drivers of the same vehicle may be different, and correspondingly, the vehicles driven by the same driver may also be different, it is necessary to perform matrix integration of the drivers corresponding to the vehicles that have historically experienced driving abnormalities, to obtain the number of abnormalities that the same driver has experienced in the historical driving process, and thereby determine the driving habits of each driver. Then, based on the driving habits, it is possible to determine the abnormal driving type of the driver of the abnormal vehicle in the current driving process, which is conducive to improving the accuracy of the abnormality type judgment.

[0042] In one possible implementation, before determining vehicles with acceleration exceeding a preset standard acceleration and vehicle speed exceeding a preset standard speed as abnormal driving vehicles, the method further includes:

[0043] The vehicle information for each vehicle is determined based on the BeiDou video stream, including the vehicle model, vehicle brand, and vehicle series.

[0044] Based on the correspondence between vehicle information and emissions, the target emissions for each vehicle are determined;

[0045] Based on the correspondence between emissions and standard speed, the preset standard acceleration and preset standard driving speed corresponding to each vehicle characteristic are determined.

[0046] By adopting the above technical solution, since different vehicle models, series and brands have different driving speed limits, the abnormal driving judgment standards for different vehicles on the highway are also different. By determining the corresponding abnormal judgment standard based on the vehicle information of each vehicle before judging whether there is a driving abnormality, it is easier to reduce the probability of false judgment and thus improve the accuracy of judging driving abnormalities.

[0047] Secondly, this application provides a vehicle monitoring device based on BeiDou navigation, which adopts the following technical solution:

[0048] A vehicle monitoring device based on BeiDou navigation includes:

[0049] The video stream acquisition module is used to acquire the BeiDou video stream of the area to be monitored.

[0050] The vehicle speed information determination module is used to identify at least one vehicle feature in the Beidou video stream and determine the vehicle speed information corresponding to each vehicle feature. The vehicle speed information includes the vehicle acceleration and vehicle speed of the corresponding vehicle within a first preset time period.

[0051] The abnormal vehicle identification module is used to identify vehicles whose acceleration exceeds a preset standard acceleration and whose speed exceeds a preset standard speed as abnormal driving vehicles.

[0052] The anomaly type determination module is used to determine the anomaly type of the abnormally driving vehicle from historical anomaly data based on the vehicle characteristics corresponding to the abnormally driving vehicle. The anomaly type includes unconventional anomalies and regular anomalies.

[0053] The transfer route determination module is used to determine the current location and transfer area of ​​the abnormal vehicle based on the Beidou video stream when the anomaly type is an unconventional anomaly, and to determine the transfer route based on the current location and transfer area, and send the transfer route to the terminal device corresponding to the abnormal vehicle.

[0054] The speed reduction warning module is used to generate a speed reduction warning when the anomaly type is a regular anomaly.

[0055] By adopting the above technical solution, real-time monitoring of the BeiDou video stream corresponding to the monitored area is conducted to promptly detect abnormal vehicles within the monitored area. Timely assistance is provided to these abnormal vehicles to reduce the probability of traffic accidents. When determining whether a vehicle is abnormal, the system simultaneously checks whether the vehicle's acceleration and speed exceed their respective standard values ​​to improve accuracy. Since different drivers have different driving habits, when abnormal acceleration and speed are detected, historical anomaly data is used to determine whether the anomaly is normal. Precise assistance is provided to drivers based on the type of anomaly, rather than using a uniform approach, thus reducing the probability of traffic accidents and improving driving safety.

[0056] In one possible implementation, when the relocation route determination module determines the current location and relocation area of ​​the abnormally moving vehicle based on the BeiDou video stream, it is specifically used for:

[0057] Based on the abnormal vehicle features contained in each BeiDou image in the BeiDou video stream, each BeiDou image is marked to obtain a corresponding feature image;

[0058] The direction of travel and current position of the abnormally moving vehicle are determined based on multiple feature images;

[0059] Based on the driving direction, the image of the area to be transferred is determined from the Beidou video stream, and the sub-region address and type contained in the image of the area to be transferred are identified. The sub-region with the preset type is determined as the initial sub-region.

[0060] When the number of initial sub-regions is one, the initial sub-region is determined as the transfer region, and the position of the initial sub-region is determined as the position of the transfer region;

[0061] When there are at least two initial sub-regions, the initial sub-region with the closest interval distance to the current position is determined as the transfer region, and the position of the initial sub-region is determined as the position of the transfer region.

[0062] In one possible implementation, the device further includes:

[0063] The vehicle density determination module is used to determine the transfer length of the transfer route and the number of vehicles in the transfer route based on the Beidou video stream, and to determine the vehicle density in the transfer route based on the transfer length and the number of vehicles.

[0064] The target drone equipment module is used to obtain the standby position of at least one drone equipment when the vehicle density is higher than the preset standard vehicle density, and to determine the target drone equipment based on the transfer route and the standby position of the at least one drone equipment.

[0065] The dispersal module is used to generate dispersal instructions based on the transfer route, and control the UAV equipment to disperse vehicles on the transfer route according to the dispersal instructions.

[0066] In one possible implementation, the device further includes:

[0067] The speed adjustment module is used to adjust the dispersal speed of the drone device based on the driving speed of the abnormally moving vehicle, so that the drone device is positioned in front of the abnormally moving vehicle.

[0068] An electronic fence module is set up to set up an electronic fence for the path between the drone device and the abnormally moving vehicle when the distance between the drone device and the abnormally moving vehicle is greater than a preset distance.

[0069] The warning module is used to generate a warning signal when other vehicles are detected within the electronic fence, and to send the warning signal to the vehicle terminal corresponding to the other vehicles.

[0070] In one possible implementation, when adjusting the dispersal speed of the drone device based on the speed of the abnormally moving vehicle, the speed adjustment module is specifically used for:

[0071] Based on the current position of the abnormally moving vehicle, its speed and acceleration, and the standby position of the drone equipment, the first dispersal speed and the first acceleration of the drone equipment are determined.

[0072] When the driving position of the drone equipment is consistent with the driving position of the abnormally driving vehicle, the first acceleration is adjusted to obtain the second acceleration, and the second dispersal speed of the drone equipment is determined based on the second acceleration.

[0073] In one possible implementation, when determining the anomaly type from historical anomaly data, the anomaly type determination module is specifically used for:

[0074] Identify the facial features of multiple abnormal drivers and the corresponding abnormal vehicle features within a second preset time period from historical abnormal data;

[0075] An abnormal feature matrix is ​​obtained by integrating multiple sets of abnormal driver facial features and corresponding abnormal vehicle features. The abnormal feature matrix includes at least one abnormal vehicle feature corresponding to each of the multiple abnormal driver facial features.

[0076] Facial features of abnormal drivers whose number of abnormal vehicle features exceeds a preset standard number of abnormal features are identified as abnormal features of interest; based on the abnormal feature matrix, it is determined whether the facial features of abnormal drivers corresponding to the vehicle features of the abnormally driving vehicle are the abnormal features of interest.

[0077] If so, then the abnormality type of the abnormally driving vehicle is determined to be a normal abnormality type;

[0078] If not, then the abnormal type of the abnormally driving vehicle is determined to be an unconventional abnormal type.

[0079] In one possible implementation, the device further includes:

[0080] The vehicle information determination module is used to determine the vehicle information of each vehicle based on the Beidou video stream. The vehicle information includes the vehicle model, vehicle brand, and vehicle series.

[0081] The emissions determination module is used to determine the target emissions for each vehicle based on the correspondence between vehicle information and emissions.

[0082] The standard speed determination module is used to determine the preset standard acceleration and preset standard driving speed for each vehicle characteristic based on the correspondence between emissions and standard speed.

[0083] Thirdly, this application provides an electronic device that adopts the following technical solution:

[0084] An electronic device comprising:

[0085] At least one processor;

[0086] Memory;

[0087] At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: execute the above-described vehicle monitoring method based on BeiDou navigation.

[0088] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0089] A computer-readable storage medium includes: a computer program stored thereon that can be loaded by a processor and execute the above-described vehicle monitoring method based on BeiDou navigation.

[0090] In summary, this application includes at least one of the following beneficial technical effects:

[0091] 1. By monitoring the BeiDou video stream corresponding to the monitored area in real time, abnormal vehicles within the monitored area can be detected in a timely manner. Assistance can be provided to these abnormal vehicles promptly to reduce the probability of traffic accidents. When determining whether a vehicle is abnormal, the vehicle's acceleration and speed are simultaneously assessed to improve accuracy. Since different drivers have different driving habits, when abnormal acceleration and speed are detected, historical anomaly data should be used to determine if the anomaly is normal. Precise assistance should be provided to drivers based on the type of anomaly, rather than using a uniform approach, to reduce the probability of traffic accidents and improve driving safety.

[0092] 2. The drone is moved by controlling the speed of the abnormally moving vehicle, so that it can disperse vehicles in front of the abnormally moving vehicle to ensure that the vehicles in front can avoid them in time, thus ensuring the driving safety of the vehicles in front. However, since the drone needs to keep moving forward, it may not be able to disperse vehicles that later enter the transfer path in time. Therefore, an electronic fence can be set up on the movement path that has been dispersed to protect the driving safety of vehicles that later enter. Attached Figure Description

[0093] Figure 1 This is an electronic device architecture diagram according to an embodiment of this application;

[0094] Figure 2 This is a flowchart illustrating a vehicle monitoring method based on BeiDou navigation in an embodiment of this application.

[0095] Figure 3 This is an example diagram of a region to be transferred in an embodiment of this application;

[0096] Figure 4 This is a schematic diagram of a process for generating a warning signal in an embodiment of this application;

[0097] Figure 5 This is a schematic diagram of the structure of a vehicle monitoring device based on Beidou navigation in an embodiment of this application;

[0098] Figure 6 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0099] The following is in conjunction with the appendix Figure 1-6 This application will be described in further detail.

[0100] After reading this specification, those skilled in the art may make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0101] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0102] Specifically, this application provides a vehicle monitoring method based on BeiDou navigation, executed by an electronic device. This electronic device can be a server or a terminal device. The server 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. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, and this application does not impose any limitations on this connection.

[0103] For ease of understanding, please refer to Figure 1 , Figure 1 This is an electronic device architecture diagram applicable to embodiments of this application. In order to transmit information with each driving vehicle, each vehicle terminal can communicate with the electronic device. When an abnormal driving vehicle is detected, and the abnormal driving vehicle needs to be dispersed by a drone device to disperse other driving vehicles, the electronic device can send control commands to the drone device. Therefore, the electronic device and the drone device can communicate with each other. Since the drone device can monitor the driving status of the abnormal driving vehicle in real time while dispersing other vehicles, each vehicle terminal can also communicate with the drone device. The method of communication between devices is not specifically limited in this embodiment of the application, as long as communication between devices can be established.

[0104] refer to Figure 2 , Figure 2 This is a flowchart illustrating a vehicle monitoring method based on BeiDou navigation in an embodiment of this application. The method includes steps S210, S220, S230, S240, S250, and S260, wherein: Step S210: Obtain the BeiDou video stream of the area to be monitored.

[0105] Specifically, the area to be monitored is any area on the highway. Using BeiDou navigation technology, functions such as vehicle positioning, road condition monitoring, intelligent navigation, and vehicle networking can be realized. In addition, BeiDou navigation technology can provide functions such as precise time synchronization, navigation, telemetry, and remote control. The BeiDou video stream is the road surface video stream collected by BeiDou satellites when monitoring the road surface of the highway to be monitored. After the video stream is collected, it is transmitted back to the ground so that it can be received by electronic devices.

[0106] Step S220: Identify at least one vehicle feature in the BeiDou video stream and determine the vehicle speed information corresponding to each vehicle feature.

[0107] The vehicle speed information includes the vehicle acceleration and vehicle speed of the corresponding vehicle within a first preset time period.

[0108] Specifically, since this application embodiment requires vehicle monitoring, the BeiDou video stream within the monitoring area must contain at least one vehicle feature to facilitate monitoring of the vehicle's driving process. The vehicle feature can be a license plate feature, which facilitates vehicle identification from the video stream. When identifying vehicle features from the BeiDou video stream, the BeiDou video stream can be frame-stripped to obtain a BeiDou image. This BeiDou image is then imported into a vehicle feature recognition model to obtain at least one vehicle feature corresponding to the BeiDou video stream. The vehicle feature recognition model is trained using a large number of samples containing license plate images and manually labeled data. The method for identifying at least one vehicle feature appearing in the BeiDou video stream is not specifically limited in this application embodiment, as long as at least one vehicle feature contained in the BeiDou video stream can be identified.

[0109] Vehicle acceleration refers to the rate of change of a vehicle's velocity per unit time, while vehicle speed refers to the distance a vehicle travels per unit time. Based on the extracted frame images, the distance traveled by the same vehicle per unit time can be easily determined. Furthermore, based on the correspondence between the image and the proportion of highways within the actual monitored area, the distance traveled by the vehicle per unit time can be determined, thus determining the vehicle's speed. At least two vehicle speeds can be determined using the same method. The rate of change of velocity per unit time can be determined based on the change of speed between these at least two vehicle speeds, thus determining the vehicle's acceleration. The method for determining vehicle acceleration and vehicle speed is not specifically limited in this embodiment. The first preset time period can be 5 minutes or 10 minutes. The specific time period is not specifically limited in this embodiment and can be set by relevant technical personnel.

[0110] Step S230: Vehicles whose acceleration exceeds the preset standard acceleration and whose speed exceeds the preset standard speed are identified as abnormal driving vehicles.

[0111] Specifically, vehicle acceleration and vehicle speed can intuitively represent whether a driver is speeding or is about to speed. Since speeding increases the probability of traffic accidents, vehicles exhibiting speeding behavior are identified as abnormal driving vehicles. To determine whether a vehicle is speeding, it can be determined whether the vehicle's acceleration and speed exceed preset standards. These preset standards include vehicle acceleration exceeding a preset standard acceleration and vehicle speed exceeding a preset standard speed. These preset standards can be added, deleted, or modified by relevant technical personnel according to actual needs. The preset standard acceleration and preset standard speed are not specifically limited in this embodiment.

[0112] Step S240: Based on the vehicle characteristics corresponding to the abnormally driving vehicle, determine the abnormal type of the abnormally driving vehicle from the historical abnormal data.

[0113] Among them, the types of anomalies include unconventional anomalies and conventional anomalies.

[0114] Specifically, when speeding is determined based on vehicle acceleration and speed, the type of anomaly must also be determined based on the driver's driving habits. Unusual anomalies are those where the driver has never used the current vehicle acceleration and speed in their past driving history, or has used them infrequently. Regular anomalies are those where the driver has used the current vehicle acceleration and speed frequently in their past driving history.

[0115] Historical anomaly data contains multiple abnormal driving vehicles and anomaly types corresponding to vehicle features. By traversing through historical anomaly data using vehicle features, the number of different anomaly types when the vehicle feature was abnormal in a historical period can be determined, and the anomaly type of the abnormal driving vehicle at the current moment can be determined accordingly.

[0116] Step S250: When the anomaly type is an unconventional anomaly, determine the current location and transfer area of ​​the abnormally moving vehicle based on the Beidou video stream, determine the transfer route based on the current location and transfer area, and send the transfer route to the terminal device corresponding to the abnormally moving vehicle.

[0117] Specifically, when the anomaly type is unconventional, it indicates that the vehicle may have experienced an uncontrollable error. In this case, it is difficult for the driver to control the vehicle's speed reduction using its onboard braking system, and the probability of the vehicle experiencing a traffic malfunction is relatively high. When the vehicle cannot reduce speed using its own braking system, a safe zone is determined from the monitored area using BeiDou video streams. This allows the vehicle to slow down within the safe zone, ensuring its safety and reducing damage to other vehicles. The type of safe zone is not specifically limited in this embodiment, as long as it enables the vehicle to slow down. The transfer route is the shortest path between the vehicle's current location and the nearest transfer area. The generated transfer route can be directly sent by the electronic device to the terminal device corresponding to the vehicle, reminding the driver to transfer the vehicle according to the transfer route.

[0118] When determining the location of the transfer area based on the BeiDou video stream, it can be done through regional feature recognition. For details, please refer to the embodiment of identifying vehicle features from the BeiDou video stream in step S220 above, which will not be elaborated here.

[0119] Step S260: When the anomaly type is a regular anomaly, generate a speed reduction warning.

[0120] Specifically, when the abnormal type is an unusual anomaly, it indicates that the vehicle has experienced multiple abnormal driving behaviors in its history. The speeding behavior at the current moment may be related to the driver's historical driving habits. In this case, a speed reduction reminder can be generated to remind the driver to slow down. The speed reduction reminder can be given to the driver by establishing communication between the electronic device and the terminal device corresponding to the vehicle, or by reminding the driver through traffic broadcast. The specific reminder method is not specifically limited in this application embodiment, as long as it can remind the driver to slow down in a timely manner.

[0121] In this embodiment, real-time monitoring of the BeiDou video stream corresponding to the monitored area facilitates the timely detection of abnormal vehicles within the monitored area. Timely assistance is provided to these abnormal vehicles to reduce the probability of traffic accidents. When determining whether a vehicle is abnormal, the system simultaneously checks whether its acceleration and speed exceed their respective standard values ​​to improve accuracy. Since different drivers have different driving habits, when abnormal acceleration and speed are detected, historical anomaly data is used to determine if the anomaly is normal. Precise assistance is provided to drivers based on the anomaly type, rather than using a uniform approach, thus reducing the probability of traffic accidents and improving driving safety.

[0122] Furthermore, in step S250, the current location and relocation area of ​​the abnormally moving vehicle are determined based on the BeiDou video stream, specifically including:

[0123] Based on the abnormal vehicle features contained in each BeiDou image in the BeiDou video stream, each BeiDou image is marked to obtain a corresponding feature image; the driving direction and current position of the abnormal vehicle are determined based on multiple feature images; the area to be transferred is determined from the BeiDou video stream based on the driving direction, and the sub-region address and type contained in the area to be transferred are identified, and the sub-region of the preset type is determined as the initial sub-region; when there is only one initial sub-region, the initial sub-region is determined as the transfer area, and the position of the initial sub-region is determined as the transfer area position; when there are at least two initial sub-regions, the initial sub-region with the closest interval distance to the current position is determined as the transfer area, and the position of the initial sub-region is determined as the transfer area position.

[0124] Specifically, multiple BeiDou images can be extracted from the BeiDou video stream by frame extraction. The vehicles contained in each BeiDou image are identified and marked. When marking BeiDou images containing vehicle features, the same vehicle in different BeiDou images should be marked with the same identifier. Similarly, when the same BeiDou image contains multiple vehicles, different vehicles should be marked with different identifiers. In this embodiment, the identifier used when marking BeiDou images is not specifically limited, as long as it can distinguish different vehicles.

[0125] Since multiple BeiDou images are extracted from the BeiDou video stream according to preset frame extraction rules, there is a temporal relationship between the extracted BeiDou images. When multiple BeiDou images contain the same vehicle, after sorting the multiple BeiDou images in chronological order, the displacement of the vehicle can be determined based on the multiple BeiDou images. Then, the direction of travel of the vehicle can be determined by comparing the displacement of the vehicle with the roadbed of the highway. The current position of the vehicle is the position of the vehicle in the BeiDou image corresponding to the last moment in the multiple extracted frames. For example, after extracting frames from the BeiDou video stream according to preset frame extraction rules, four BeiDou images are obtained. The acquisition times of the four BeiDou images are 10:20:01, 10:20:03, 10:20:05, and 10:20:07. Since 10:20:07 is the last moment in the four BeiDou images, the position of the vehicle in the fourth BeiDou image is determined as the current position of the vehicle.

[0126] A transfer area needs to be determined only when the anomaly type of a vehicle is classified as an unconventional anomaly. A transfer route is then determined based on this transfer area to allow the abnormal vehicle to slow down. Therefore, not all areas except highways can be designated as transfer areas. Furthermore, since the vehicle is still in motion, determining the transfer area must also consider whether the vehicle can reach the transfer area without disrupting traffic rules. The image of the area to be transferred contains the abnormal vehicle and multiple sub-regions. The transfer area is determined based on the type of each sub-region and the distance between each sub-region and the abnormal vehicle. Specifically, when determining the area to be transferred from the BeiDou video stream, the area corresponding to the highway at a preset distance in the vehicle's direction of travel is designated as the transfer area, starting from the vehicle. Figure 3As shown, the area to be transferred contains sub-regions, with preset types that can be used as transfer areas, such as wasteland or grassland. Feature recognition is performed on the image to be transferred to determine all sub-regions contained in the image and the region type corresponding to each sub-region. This can be achieved by recognizing the color of each sub-region and determining the region type of each sub-region based on the correspondence between color and type. For example, green represents vegetation, brown represents arid or bare land, and gray or white represents urban buildings, etc. The correspondence between color and type can be set by relevant technical personnel and is not specifically limited in this embodiment. Sub-regions that do not belong to the preset type can be eliminated based on the region type of each sub-region. Finally, the path length between the sub-region and the current position of the vehicle is calculated, and the sub-region corresponding to the shortest path length is determined as the transfer area.

[0127] In this embodiment of the application, a sub-region with suitable type and spacing is selected from multiple sub-regions based on the region type, rather than randomly transferring the abnormally driving vehicle to a nearby sub-region. This is to improve the safety of the vehicle during driving while reducing the probability of abnormally driving vehicles causing loss of life and property to others.

[0128] After planning the transfer route based on the transfer area and the vehicle, in order to reduce the probability of the abnormally moving vehicle causing damage to other vehicles during the transfer process, the following measures are also included:

[0129] The transfer route length and the number of vehicles in the transfer route are determined based on the BeiDou video stream, and the vehicle density in the transfer route is determined based on the transfer length and the number of vehicles. When the vehicle density is higher than the preset standard vehicle density, the standby position of at least one UAV device is obtained, and the target UAV device is determined based on the transfer route and the standby position of at least one UAV device. A dispersal command is generated based on the transfer route, and the UAV device is controlled to disperse the vehicles on the transfer route according to the dispersal command.

[0130] Specifically, vehicle density refers to the number of vehicles per unit distance. A higher vehicle density indicates a larger number of vehicles traveling on the transfer route. Since the transfer route is only planned when an abnormal situation occurs during the journey, a higher vehicle density on the transfer route increases the probability of traffic accidents during the transfer. In this case, it is necessary to warn other vehicles traveling on the transfer route to give way in order to improve the safety of other vehicles on the transfer route. The preset vehicle density is not specifically limited in this embodiment of the application and can be set by relevant technical personnel.

[0131] When issuing avoidance warnings to other vehicles in motion, a broadcast warning can be delivered via drone equipment. When identifying the target drone from multiple drones, the distance between the idle drone and the current position of the abnormally moving vehicle can be used. The target drone's standby position may be in front of or behind the abnormally moving vehicle. When the drone is behind the abnormally moving vehicle, steps Sa, Sb, and Sc are also included, as follows: Figure 4 ,in:

[0132] Step Sa: Based on the speed of the abnormally moving vehicle, adjust the dispersal speed of the drone equipment so that the drone equipment is positioned in front of the abnormally moving vehicle.

[0133] Specifically, since the relocation route is planned from the current position of the abnormally moving vehicle, when using drone equipment to warn vehicles in motion, the warning is given to vehicles in front of the abnormally moving vehicle. When the drone equipment is behind the abnormally moving vehicle, the drone equipment's start-up speed needs to be adjusted to ensure that the drone equipment reaches the abnormally moving vehicle in the shortest possible time. Adjusting the drone equipment's dispersal speed based on the abnormally moving vehicle's speed specifically includes:

[0134] Based on the current position, speed, and acceleration of the abnormally moving vehicle, and the standby position of the drone equipment, determine the first dispersal speed and the first acceleration of the drone equipment; when the drone equipment's position matches the abnormally moving vehicle's position, adjust the first acceleration to obtain the second acceleration, and determine the second dispersal speed of the drone equipment based on the second acceleration.

[0135] Specifically, based on the current position of the abnormally moving vehicle and the standby position of the drone equipment, the distance between the abnormally moving vehicle and the drone equipment can be determined. Based on the abnormally moving vehicle's speed and acceleration, the maximum speed it can reach and the time required to reach that speed can be obtained. Then, by inputting the distance between the abnormally moving vehicle and the drone equipment, the abnormally moving vehicle's acceleration, its maximum speed, and the time required to reach the maximum speed into the first calculation formula, the shortest time required for the drone equipment to catch up with the abnormally moving vehicle is obtained. The first calculation formula is:

[0136]

[0137] Where s is used to characterize the distance between the abnormally moving vehicle and the drone equipment;

[0138] a a Used to characterize the acceleration of vehicles moving abnormally;

[0139] t maxa Used to characterize the time required for an abnormally moving vehicle to reach its maximum speed;

[0140] v maxa Used to characterize the maximum speed reached by a vehicle with abnormal driving behavior;

[0141] v b The initial startup speed of the drone device is used to characterize the initial startup speed of the drone device. In this embodiment of the application, the default initial startup speed of the drone device is 0.

[0142] By incorporating the shortest time required for the drone to catch up with the abnormally moving vehicle and the first acceleration required for the drone to start into the second calculation formula, the first dispersion velocity of the drone can be obtained. The second calculation formula is: v b驱散 =v b +a b *t catch ;

[0143] Among them, v b驱散 The first dispersion velocity used to characterize the drone equipment;

[0144] a b The first acceleration used to characterize the drone equipment;

[0145] t catch Used to characterize the shortest time required for a drone device to catch up with an abnormally moving vehicle.

[0146] Combining the first and second formulas facilitates the determination of the first dispersal speed and the first acceleration of the drone device. The method for calculating the first dispersal speed and the first acceleration is not specifically limited in this embodiment, as long as it allows the drone device to move from its standby position to the abnormally moving vehicle in the shortest possible time. Since the drone device is in a pursuit phase when it moves to the abnormally moving vehicle, continuing to move according to the first dispersal speed and the first acceleration may cause the distance between the drone device and the abnormally moving vehicle to increase. Therefore, it is necessary to reduce the first acceleration to obtain a second acceleration, and then determine the second dispersal speed of the drone device based on the second acceleration. The drone device then disperses other vehicles according to the second dispersal speed. By controlling the drone device to move according to the second dispersal speed, a shorter relative distance is maintained between the drone device and the abnormally moving vehicle. This allows for real-time monitoring of the abnormally moving vehicle's actual driving status while the drone device is dispersing vehicles, thereby improving the safety of the abnormally moving vehicle during its movement.

[0147] Step Sb: When the distance between the drone device and the abnormally moving vehicle is greater than the preset distance, set up an electronic fence for the path between the drone device and the abnormally moving vehicle.

[0148] Specifically, the preset interval distance can be 5 meters or 10 meters, and is not specifically limited in this application embodiment. When the interval distance between the drone device and the abnormally driving vehicle exceeds the preset interval distance, it indicates that the drone device has completed the dispersal and warning work within the interval distance between the drone device and the abnormally driving vehicle. At this time, an electronic fence needs to be set up in the area where the dispersal and warning work has been completed, but the abnormally driving vehicle has not yet arrived, so as to prevent other vehicles behind or next to the abnormally driving vehicle from mistakenly entering the area where the dispersal and warning work has been completed, thereby improving the safety of other vehicles during the driving process.

[0149] Step Sc: When other vehicles are detected within the electronic fence, an alarm signal is generated and sent to the vehicle terminals corresponding to the other vehicles.

[0150] Specifically, the warning signal is used to remind vehicles that have mistakenly entered the electronic fence to leave the electronic fence area. When the generated warning signal is sent to the vehicle terminal corresponding to other vehicles, a communication connection can be established between the drone device and the other vehicles, or a communication connection can be established between the electronic device and the other vehicles. In this embodiment of the application, no specific limitation is made, as long as the drivers of other vehicles can leave the electronic fence area in a timely manner after receiving the warning signal.

[0151] The anomaly type corresponding to vehicles exhibiting abnormal driving speeds is related to the driver's usage habits. Therefore, when determining the anomaly type of abnormally driving vehicles from historical anomaly data based on the vehicle characteristics corresponding to the abnormal driving, it includes:

[0152] From historical anomaly data, identify multiple sets of facial features of abnormal drivers and corresponding abnormal vehicle features within a second preset time period; integrate these multiple sets of facial features of abnormal drivers and corresponding abnormal vehicle features to obtain an anomaly feature matrix, wherein the anomaly feature matrix includes at least one abnormal vehicle feature corresponding to each of the multiple abnormal driver facial features; identify facial features of abnormal drivers whose number of abnormal vehicle features exceeds a preset standard number of anomalies as anomaly attention features; based on the anomaly feature matrix, determine whether the facial features of abnormal drivers corresponding to the vehicle features of the abnormally driving vehicle are the anomaly attention features; if yes, determine that the anomaly type of the abnormally driving vehicle is a regular anomaly type; if no, determine that the anomaly type of the abnormally driving vehicle is an unconventional anomaly type.

[0153] Specifically, the second preset time period can be the past month or the past two months. The specific time period is not specifically limited in this embodiment and can be set by relevant technical personnel. Since the same driver may be driving different vehicles at different times, and the same vehicle may be driven by different drivers at different times, determining the anomaly type corresponding to an abnormal vehicle requires judging based on the driver's driving habits at the current time. Specifically, when determining the driver's driving habits, multiple sets of abnormal data within the second preset time period can be organized using the driver's facial features. Abnormal data containing the same facial features are integrated to ultimately obtain an anomaly feature matrix that can express the characteristics of all abnormal vehicles corresponding to the facial features of each driver. When calculating the number of abnormal vehicle features corresponding to each driver's facial features, the number of abnormal vehicle features corresponding to each row in the abnormal feature matrix can be calculated. The preset standard number of abnormal features can be 5 or 8, depending on the length of the second preset time period. This number can be modified by relevant technical personnel according to actual needs. By statistically analyzing the number of abnormal vehicle features corresponding to each driver's facial features, it is easier to classify multiple drivers and determine each driver's driving habits. Determining the abnormal type of the abnormal vehicle based on the driver's driving habits improves the accuracy of abnormal type identification. If there are no driver facial features for abnormal vehicles in the historical abnormal data, the abnormal type of the abnormal vehicle is directly identified as an unconventional abnormality.

[0154] When determining whether a vehicle is exhibiting abnormal behavior on a highway, the primary method is to compare the vehicle's acceleration and speed with corresponding standards. However, different vehicles may have different standards. Therefore, to improve the accuracy of identifying abnormal behavior, the following methods are also included:

[0155] The vehicle information for each vehicle is determined based on the BeiDou video stream, including vehicle model, brand, and series. The target emissions for each vehicle are determined based on the correspondence between vehicle information and emissions. The preset standard acceleration and preset standard driving speed for each vehicle are determined based on the correspondence between emissions and standard speed.

[0156] Specifically, when determining the vehicle model, brand, and series based on the BeiDou video stream, the embodiment for identifying vehicle features in step S220 above can be referred to, and will not be repeated here. The difference is that the obtained BeiDou image is imported into the vehicle information recognition model, which is trained by a large number of samples containing vehicle model, brand, series, and manual labels.

[0157] The correspondence between vehicle information and emissions includes the emissions corresponding to different vehicle information, which can be added, deleted, and modified by relevant technical personnel. The specific correspondence between vehicle information and emissions is not specifically limited in this embodiment. The correspondence between emissions and standard speed includes the standard acceleration and standard speed corresponding to vehicles with different emissions; the specific content of this correspondence can be set by relevant technical personnel.

[0158] In this embodiment of the application, by determining the corresponding abnormality judgment criteria based on the vehicle information corresponding to each vehicle before judging whether there is a driving abnormality, it is easier to reduce the probability of false judgment of abnormality, thereby improving the accuracy of judging driving abnormality.

[0159] The above embodiments describe a vehicle monitoring method based on BeiDou navigation from the perspective of method flow. The following embodiments describe a vehicle monitoring device based on BeiDou navigation from the perspective of virtual modules or virtual units. For details, please refer to the following embodiments.

[0160] This application provides a vehicle monitoring device based on BeiDou navigation, such as... Figure 5 As shown, the device may specifically include a video stream acquisition module 510, a vehicle speed information determination module 520, an anomaly determination module 530, an anomaly type determination module 540, a transfer route determination module 550, and a speed reduction warning module 560, wherein:

[0161] The video stream acquisition module 510 is used to acquire the BeiDou video stream of the area to be monitored.

[0162] The vehicle speed information determination module 520 is used to identify at least one vehicle feature in the Beidou video stream and determine the vehicle speed information corresponding to each vehicle feature. The vehicle speed information includes the vehicle acceleration and vehicle speed of the corresponding vehicle within a first preset time period.

[0163] The abnormality identification module 530 is used to identify vehicles whose acceleration exceeds a preset standard acceleration and whose driving speed exceeds a preset standard driving speed as abnormal driving vehicles.

[0164] The anomaly type determination module 540 is used to determine the anomaly type of the abnormally driving vehicle from historical anomaly data based on the vehicle characteristics corresponding to the abnormally driving vehicle. The anomaly type includes unconventional anomalies and regular anomalies.

[0165] The transfer route determination module 550 is used to determine the current location and transfer area of ​​the abnormal vehicle based on the Beidou video stream when the anomaly type is an unconventional anomaly, and to determine the transfer route based on the current location and transfer area, and send the transfer route to the terminal device corresponding to the abnormal vehicle.

[0166] The speed reduction warning module 560 is used to generate a speed reduction warning when the anomaly type is a regular anomaly.

[0167] In one possible implementation, the route determination module 550, when determining the current location and relocation area of ​​the abnormally moving vehicle based on the BeiDou video stream, is specifically used for:

[0168] Based on the abnormal vehicle features contained in each BeiDou image in the BeiDou video stream, each BeiDou image is labeled to obtain the corresponding feature image;

[0169] The direction of travel and current location of the abnormally moving vehicle are determined based on multiple feature images;

[0170] Based on the direction of travel, the image of the area to be transferred is determined from the Beidou video stream, and the address and type of the sub-region contained in the image of the area to be transferred are identified. The sub-region with the preset type is determined as the initial sub-region.

[0171] When there is only one initial sub-region, the initial sub-region is determined as the transfer region, and the position of the initial sub-region is determined as the position of the transfer region.

[0172] When there are at least two initial sub-regions, the initial sub-region with the closest interval distance to the current position is determined as the transfer region, and the position of the initial sub-region is determined as the position of the transfer region.

[0173] In one possible implementation, the device further includes:

[0174] The vehicle density determination module is used to determine the transfer length of the transfer route and the number of vehicles in the transfer route based on the BeiDou video stream, and to determine the vehicle density in the transfer route based on the transfer length and the number of vehicles.

[0175] The target drone equipment module is used to obtain the standby position of at least one drone equipment when the vehicle density is higher than the preset standard vehicle density, and to determine the target drone equipment based on the transfer route and the standby position of at least one drone equipment; the dispersal module is used to generate dispersal commands based on the transfer route, and to control the drone equipment to disperse the vehicles on the transfer route according to the dispersal commands.

[0176] In one possible implementation, the device further includes:

[0177] The speed adjustment module is used to adjust the dispersal speed of the drone equipment based on the speed of the abnormally moving vehicle, so that the drone equipment is in front of the abnormally moving vehicle.

[0178] The electronic fence module is set up to set up an electronic fence for the path between the drone and the abnormally moving vehicle when the distance between the drone and the abnormally moving vehicle is greater than the preset distance.

[0179] The warning module is used to generate a warning signal when other vehicles are detected within the electronic fence, and then send the warning signal to the vehicle terminal corresponding to the other vehicles.

[0180] In one possible implementation, the speed adjustment module, when adjusting the dispersal speed of the drone equipment based on the speed of the abnormally moving vehicle, is specifically used for:

[0181] Based on the current location, speed, and acceleration of the abnormally moving vehicle, as well as the standby position of the drone equipment, determine the first dispersal speed and the first acceleration of the drone equipment;

[0182] When the drone's position matches that of the abnormally moving vehicle, the first acceleration is adjusted to obtain the second acceleration, and the second dispersal speed of the drone is determined based on the second acceleration.

[0183] In one possible implementation, when determining the anomaly type from historical anomaly data, the anomaly type determination module 540 is specifically used for:

[0184] Identify the facial features of multiple abnormal drivers and the corresponding abnormal vehicle features within a second preset time period from historical abnormal data;

[0185] An abnormal feature matrix is ​​obtained by integrating multiple sets of abnormal driver facial features and corresponding abnormal vehicle features. The abnormal feature matrix includes at least one abnormal vehicle feature corresponding to each of the multiple abnormal driver facial features.

[0186] Facial features of abnormal drivers whose number of abnormal vehicle features exceeds a preset standard number of abnormal features are identified as abnormal features of interest. Based on the abnormal feature matrix, it is determined whether the facial features of abnormal drivers corresponding to the vehicle features of abnormally driving vehicles are abnormal features of interest.

[0187] If so, then the abnormality type of the abnormally driving vehicle is determined to be a normal abnormality type;

[0188] If not, then the abnormal type of the abnormally driving vehicle is determined to be an unconventional abnormal type.

[0189] In one possible implementation, the device further includes:

[0190] The vehicle information determination module is used to determine the vehicle information of each vehicle based on the Beidou video stream. The vehicle information includes the vehicle model, vehicle brand, and vehicle series.

[0191] The emissions determination module is used to determine the target emissions for each vehicle based on the correspondence between vehicle information and emissions.

[0192] The standard speed determination module is used to determine the preset standard acceleration and preset standard driving speed for each vehicle characteristic based on the correspondence between emissions and standard speed.

[0193] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0194] This application provides an electronic device, such as... Figure 6 As shown, Figure 6 The illustrated electronic device 600 includes a processor 601 and a memory 603. The processor 601 and the memory 603 are connected, for example, via a bus 602. Optionally, the electronic device 600 may also include a transceiver 604. It should be noted that in practical applications, the transceiver 604 is not limited to one type, and the structure of this electronic device 600 does not constitute a limitation on the embodiments of this application.

[0195] Processor 601 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 601 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0196] Bus 602 may include a pathway for transmitting information between the aforementioned components. Bus 602 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 602 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0197] The memory 603 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0198] The memory 603 stores application code that executes the scheme of this application, and its execution is controlled by the processor 601. The processor 601 executes the application code stored in the memory 603 to implement the content shown in the foregoing method embodiments.

[0199] Electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Servers can also be included. Figure 6 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0200] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0201] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by 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 accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0202] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A vehicle monitoring method based on BeiDou navigation, characterized in that, include: Acquire BeiDou video streams of the area to be monitored; Identify at least one vehicle feature in the BeiDou video stream and determine the vehicle speed information corresponding to each vehicle feature. The vehicle speed information includes the vehicle acceleration and vehicle speed of the corresponding vehicle within a first preset time period. Vehicles whose acceleration exceeds the preset standard acceleration and whose speed exceeds the preset standard speed are identified as abnormal driving vehicles; Based on the vehicle characteristics corresponding to the abnormal driving vehicle, the abnormal type of the abnormal driving vehicle is determined from the historical abnormal data. The abnormal type includes unconventional abnormalities and regular abnormalities. Unconventional abnormalities are the number of times that the driver has not used the vehicle acceleration and vehicle speed during the current driving process in the past driving process, or has used the vehicle acceleration and vehicle speed during the current driving process in a small number of times. A common anomaly is when a driver repeatedly uses the vehicle's acceleration and speed during the current driving process in the past driving history. When the anomaly type is an unconventional anomaly, the current location and transfer area of ​​the abnormally driving vehicle are determined according to the Beidou video stream, and a transfer route is determined according to the current location and transfer area, and the transfer route is sent to the terminal device corresponding to the abnormally driving vehicle. When the anomaly type is a regular anomaly, a speed reduction warning is generated; The step of determining the current location and relocation area of ​​the abnormally moving vehicle based on the BeiDou video stream includes: Based on the abnormal vehicle features contained in each BeiDou image in the BeiDou video stream, each BeiDou image is marked to obtain a corresponding feature image; The direction of travel and current position of the abnormally moving vehicle are determined based on multiple feature images; Based on the driving direction, the image of the area to be transferred is determined from the Beidou video stream, and the sub-region address and type contained in the image of the area to be transferred are identified. The sub-region with the preset type is determined as the initial sub-region. When the number of initial sub-regions is one, the initial sub-region is determined as the transfer region, and the position of the initial sub-region is determined as the position of the transfer region; When there are at least two initial sub-regions, the initial sub-region with the closest interval distance to the current position is determined as the transfer region, and the position of the initial sub-region is determined as the position of the transfer region. The step of determining the transfer route based on the current location and the transfer area location further includes: The transfer length of the transfer route and the number of vehicles in the transfer route are determined based on the BeiDou video stream, and the vehicle density in the transfer route is determined based on the transfer length and the number of vehicles. When the vehicle density is higher than the preset standard vehicle density, the standby position of at least one drone device is obtained, and the target drone device is determined based on the transfer route and the standby position of the at least one drone device; Generate a dispersal command based on the transfer route, and control the UAV equipment to disperse the vehicles on the transfer route according to the dispersal command; The process of generating a dispersal command based on the transfer route and controlling the drone equipment to disperse vehicles on the transfer route according to the dispersal command further includes: Based on the speed of the abnormally moving vehicle, adjust the dispersal speed of the drone equipment so that the drone equipment is positioned in front of the abnormally moving vehicle. When the distance between the drone device and the abnormally moving vehicle is greater than the preset distance, an electronic fence is set for the path between the drone device and the abnormally moving vehicle. When other vehicles are detected within the electronic fence, an alarm signal is generated and sent to the vehicle terminal corresponding to the other vehicles. The method of adjusting the dispersal speed of the drone equipment based on the speed of the abnormally moving vehicle includes: Based on the current position of the abnormally moving vehicle and the standby position of the drone equipment, determine the distance between them; based on the abnormally moving vehicle's speed and acceleration, determine its maximum achievable speed and the time required to reach that speed; input the distance between the abnormally moving vehicle and the drone equipment, the abnormally moving vehicle's acceleration, its maximum speed, and the time required to reach that speed into the first calculation formula to obtain the shortest time required for the drone equipment to catch up with the abnormally moving vehicle, where the first calculation formula is: t catch =(s+0.5×a a ×t 2 maxa ) / (v maxa +v b ); Where s is used to characterize the distance between the abnormally moving vehicle and the drone equipment; a a Used to characterize the acceleration of vehicles moving abnormally; t maxa Used to characterize the time required for an abnormally moving vehicle to reach its maximum speed; v maxa Used to characterize the maximum speed reached by a vehicle with abnormal driving behavior; v b Used to characterize the initial startup speed of unmanned aerial vehicle (UAV) equipment; The minimum time required for the drone to catch up with the abnormally moving vehicle and the first acceleration required for the drone to start are input into the second calculation formula to obtain the first dispersal velocity of the drone. The second calculation formula is: v b驱散 =v b +a b ×t catch ; where v b驱散 Used to characterize the first dispersion velocity of the unmanned aerial vehicle (UAV) equipment; a b The first acceleration used to characterize the unmanned aerial vehicle (UAV) equipment; t catch Used to characterize the shortest time required for a drone device to catch up with an abnormally moving vehicle; By combining the first and second formulas, the first dispersion velocity and the first acceleration of the drone equipment are obtained. When the driving position of the drone device is consistent with the driving position of the abnormally driving vehicle, the first acceleration is reduced to obtain the second acceleration. Then, based on the second acceleration, the second dispersion speed of the drone device is determined, and the drone device is controlled to move according to the second dispersion so that the drone device and the abnormally driving vehicle maintain a short relative distance. The step of determining the anomaly type of the abnormally driving vehicle from historical anomaly data includes: Identify the facial features of multiple abnormal drivers and the corresponding abnormal vehicle features within a second preset time period from historical abnormal data; An abnormal feature matrix is ​​obtained by integrating multiple sets of abnormal driver facial features and corresponding abnormal vehicle features. The abnormal feature matrix includes at least one abnormal vehicle feature corresponding to each of the multiple abnormal driver facial features. Facial features of abnormal drivers whose number of abnormal vehicle features exceeds a preset standard number of abnormal features are identified as abnormal features of concern. Based on the abnormal feature matrix, determine whether the facial features of the abnormal driver corresponding to the vehicle features of the abnormally driving vehicle are the abnormal attention features. If so, then the abnormality type of the abnormally driving vehicle is determined to be a normal abnormality type; If not, then the abnormal type of the abnormally driving vehicle is determined to be an unconventional abnormal type.

2. The vehicle monitoring method based on BeiDou navigation according to claim 1, characterized in that, Before determining vehicles whose acceleration exceeds a preset standard acceleration and whose speed exceeds a preset standard speed as abnormal driving vehicles, the method further includes: The vehicle information for each vehicle is determined based on the BeiDou video stream, including the vehicle model, vehicle brand, and vehicle series. Based on the correspondence between vehicle information and emissions, the target emissions for each vehicle are determined; Based on the correspondence between emissions and standard speed, the preset standard acceleration and preset standard driving speed corresponding to each vehicle characteristic are determined.

3. A vehicle monitoring device based on BeiDou navigation, characterized in that, include: The video stream acquisition module is used to acquire the BeiDou video stream of the area to be monitored. The vehicle speed information determination module is used to identify at least one vehicle feature in the Beidou video stream and determine the vehicle speed information corresponding to each vehicle feature. The vehicle speed information includes the vehicle acceleration and vehicle speed of the corresponding vehicle within a first preset time period. The abnormal vehicle identification module is used to identify vehicles whose acceleration exceeds a preset standard acceleration and whose speed exceeds a preset standard speed as abnormal driving vehicles. The anomaly type determination module is used to determine the anomaly type of the abnormal driving vehicle from historical anomaly data based on the vehicle characteristics corresponding to the abnormal driving vehicle. The anomaly type includes unconventional anomalies and regular anomalies. Unconventional anomalies are the number of times that the driver has not used the vehicle acceleration and vehicle speed during the current driving process in the past driving process, or has used the vehicle acceleration and vehicle speed during the current driving process a small number of times. A common anomaly is when a driver repeatedly uses the vehicle's acceleration and speed during the current driving process in the past driving history. The transfer route determination module is used to determine the current location and transfer area of ​​the abnormal vehicle based on the Beidou video stream when the anomaly type is an unconventional anomaly, and to determine the transfer route based on the current location and transfer area, and send the transfer route to the terminal device corresponding to the abnormal vehicle. The speed reduction warning module is used to generate a speed reduction warning when the anomaly type is a regular anomaly. Specifically, the route determination module, when determining the current location and relocation area of ​​the abnormally moving vehicle based on the BeiDou video stream, is used for: Based on the abnormal vehicle features contained in each BeiDou image in the BeiDou video stream, each BeiDou image is marked to obtain a corresponding feature image; The direction of travel and current position of the abnormally moving vehicle are determined based on multiple feature images; Based on the driving direction, the image of the area to be transferred is determined from the Beidou video stream, and the sub-region address and type contained in the image of the area to be transferred are identified. The sub-region with the preset type is determined as the initial sub-region. When the number of initial sub-regions is one, the initial sub-region is determined as the transfer region, and the position of the initial sub-region is determined as the position of the transfer region; When there are at least two initial sub-regions, the initial sub-region with the closest interval distance to the current position is determined as the transfer region, and the position of the initial sub-region is determined as the position of the transfer region. Also includes: The vehicle density determination module is used to determine the transfer length of the transfer route and the number of vehicles in the transfer route based on the Beidou video stream, and to determine the vehicle density in the transfer route based on the transfer length and the number of vehicles. The target drone equipment module is used to obtain the standby position of at least one drone equipment when the vehicle density is higher than the preset standard vehicle density, and to determine the target drone equipment based on the transfer route and the standby position of the at least one drone equipment. The dispersal module is used to generate dispersal instructions based on the transfer route, and control the UAV equipment to disperse vehicles on the transfer route according to the dispersal instructions; The speed adjustment module is used to adjust the dispersal speed of the drone device based on the driving speed of the abnormally moving vehicle, so that the drone device is positioned in front of the abnormally moving vehicle. An electronic fence module is set up to set up an electronic fence for the path between the drone device and the abnormally moving vehicle when the distance between the drone device and the abnormally moving vehicle is greater than a preset distance. The warning module is used to generate a warning signal when other vehicles are detected within the electronic fence, and to send the warning signal to the vehicle terminal corresponding to the other vehicles. When adjusting the dispersal speed of the drone based on the speed of abnormally moving vehicles, the speed adjustment module is specifically used for: Based on the current position of the abnormally moving vehicle and the standby position of the drone equipment, determine the distance between them; based on the abnormally moving vehicle's speed and acceleration, determine its maximum achievable speed and the time required to reach that speed; input the distance between the abnormally moving vehicle and the drone equipment, the abnormally moving vehicle's acceleration, its maximum speed, and the time required to reach that speed into the first calculation formula to obtain the shortest time required for the drone equipment to catch up with the abnormally moving vehicle, where the first calculation formula is: t catch =(s+0.5×a a ×t 2 maxa ) / (v maxa +v b ); Where s is used to characterize the distance between the abnormally moving vehicle and the drone equipment; a a Used to characterize the acceleration of vehicles moving abnormally; t maxa Used to characterize the time required for an abnormally moving vehicle to reach its maximum speed; v maxa Used to characterize the maximum speed reached by a vehicle with abnormal driving behavior; v b Used to characterize the initial startup speed of unmanned aerial vehicle (UAV) equipment; The minimum time required for the drone to catch up with the abnormally moving vehicle and the first acceleration required for the drone to start are input into the second calculation formula to obtain the first dispersal velocity of the drone. The second calculation formula is: v b驱散 =v b +a b ×t catch ; where v b驱散 Used to characterize the first dispersion velocity of the unmanned aerial vehicle (UAV) equipment; a b The first acceleration used to characterize the unmanned aerial vehicle (UAV) equipment; t catch Used to characterize the shortest time required for a drone device to catch up with an abnormally moving vehicle; By combining the first and second formulas, the first dispersion velocity and the first acceleration of the drone equipment are obtained. When the driving position of the drone device is consistent with the driving position of the abnormally driving vehicle, the first acceleration is reduced to obtain the second acceleration. Then, based on the second acceleration, the second dispersion speed of the drone device is determined, and the drone device is controlled to move according to the second dispersion so that the drone device and the abnormally driving vehicle maintain a short relative distance. The anomaly type determination module, when identifying the anomaly type of a vehicle with abnormal driving behavior from historical anomaly data, is specifically used for: Identify the facial features of multiple abnormal drivers and the corresponding abnormal vehicle features within a second preset time period from historical abnormal data; An abnormal feature matrix is ​​obtained by integrating multiple sets of abnormal driver facial features and corresponding abnormal vehicle features. The abnormal feature matrix includes at least one abnormal vehicle feature corresponding to each of the multiple abnormal driver facial features. Facial features of abnormal drivers whose number of abnormal vehicle features exceeds a preset standard number of abnormal features are identified as abnormal features of concern. Based on the abnormal feature matrix, determine whether the facial features of the abnormal driver corresponding to the vehicle features of the abnormally driving vehicle are the abnormal attention features. If so, then the abnormality type of the abnormally driving vehicle is determined to be a normal abnormality type; If not, then the abnormal type of the abnormally driving vehicle is determined to be an unconventional abnormal type.

4. An electronic device, characterized in that, The electronic device includes: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: perform a vehicle monitoring method based on BeiDou navigation according to any one of claims 1-2.

5. A computer-readable storage medium, characterized in that, include: The computer program is stored and can be loaded by a processor and executed as described in any one of claims 1-2, which is a vehicle monitoring method based on BeiDou navigation.

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