Vehicle monitoring method, system, computer device and readable storage medium

Abstract

The present application provides a vehicle monitoring method, system, computer device and readable storage medium, wherein a user terminal sends a vehicle number to a cloud server; the cloud server determines a positioning device based on the location of the destination address in the actual road network; the on-board processor locates the vehicle through the positioning device to generate an initial driving trajectory; the initial trajectory length of the initial driving trajectory is de-noised to obtain a candidate driving trajectory; the candidate driving trajectory is thinned out based on the deviation area comparison result formed between the initial driving trajectory, the candidate driving trajectory and the nearest route to obtain a preferred driving trajectory; the cloud server determines the driving state of the vehicle based on the preferred driving trajectory, and sends the driving state and driving trajectory to the user terminal; the user terminal displays the driving state and driving trajectory of the vehicle to the user. The above method is used to effectively and accurately monitor the driving state of the vehicle, avoid safety hazards, and reduce the risk of traffic accidents.

Description

Technical Field

[0001] The present invention relates to the field of traffic management, and in particular to a vehicle monitoring method, system, computer equipment and readable storage medium. Background Art

[0002] With the development of economy and society, living standards have gradually improved, and people's travel has become inseparable from the help of transportation. While related transportation tools bring convenience to people, they also bring various problems such as traffic congestion and traffic safety. Aggressive driving behavior: such as sudden acceleration and deceleration, is one of the main causes of traffic accidents. Therefore, accurately identifying abnormal driving conditions of moving vehicles and effectively and accurately monitoring vehicles are of great significance to vehicle safety and traffic safety.

[0003] In the prior art, when monitoring the driving state of a vehicle, the driver usually makes a subjective analysis based on his driving state or the passengers make a subjective analysis based on their riding experience to determine whether abnormal driving behavior or abnormal driving occurs. When the driver or the passengers feel that abnormal driving occurs, they manually alert the outside world and ask for help through communication devices such as mobile phones.

[0004] However, the study found that due to the different perceptions of different people, when the driver or the people in the car subjectively analyze and judge the driving status based on their feelings, different analysis results may appear. For some drivers or passengers with poor perception, abnormal driving may have occurred but they did not perceive it, which will lead to the inability to effectively and accurately monitor the driving status of the vehicle. Different people's experience analysis standards will also be different. For some driving phenomena, some people may judge them as normal driving while others may judge them as abnormal driving, so it is impossible to achieve unified standard monitoring of the driving status of the vehicle. In addition, if the driver or passenger is fatigued, distracted, or has an accident during the driving of the vehicle, they cannot know the driving status of the vehicle in time, let alone call the police or ask for help from the outside world in time, which will increase the safety hazards of the vehicle when driving and increase the risk of traffic accidents. Summary of the invention

[0005] In view of this, an object of the present invention is to provide a vehicle monitoring method, system, computer equipment and readable storage medium to effectively and accurately monitor the driving status of a vehicle, avoid safety hazards when the vehicle is driving, and reduce the risk of traffic accidents.

[0006] In a first aspect, an embodiment of the present application provides a vehicle monitoring method, which is applied to a vehicle monitoring system, wherein the system includes an on-board processor, several on-board positioning devices, a cloud server, and a user terminal, and the method includes:

[0007] The user terminal responds to the vehicle monitoring instruction input by the user, obtains the vehicle number of the target vehicle indicated by the vehicle monitoring instruction, and sends the vehicle number of the target vehicle to the cloud server;

[0008] The cloud server obtains the actual road network, sends a travel query instruction to the target vehicle-mounted processor of the target vehicle, and determines a target positioning device for positioning the target vehicle from a plurality of vehicle-mounted positioning devices based on the location of the destination address fed back by the target vehicle-mounted processor in response to the travel query instruction in the actual road network;

[0009] The target vehicle-mounted processor locates the target vehicle through the target positioning device to obtain a plurality of positioning trajectory points of the target vehicle, and generates an initial driving trajectory of the target vehicle based on each positioning trajectory point;

[0010] The target vehicle-mounted processor calculates an initial trajectory length of the initial driving trajectory, determines a target denoising level for denoising the initial driving trajectory according to the initial trajectory length, and denoises the initial driving trajectory according to a denoising strategy corresponding to the target denoising level to obtain a candidate driving trajectory;

[0011] The target vehicle-mounted processor respectively calculates a first deviation area formed between the initial driving trajectory and the nearest route, and a second deviation area formed between the candidate driving trajectory and the nearest route, and compares the first deviation area with the second deviation area to obtain a deviation area comparison result;

[0012] The target vehicle-mounted processor determines a target thinning intensity for thinning the candidate driving trajectory according to the deviation area comparison result, and performs thinning on the candidate driving trajectory according to a thinning strategy corresponding to the target thinning intensity to obtain a preferred driving trajectory;

[0013] The cloud server calculates the positioning time difference and distance between every two adjacent track points in the preferred driving track, determines whether the positioning time difference between every two adjacent track points exceeds a preset time difference, and determines whether the distance between every two adjacent track points exceeds a preset distance;

[0014] If the positioning time difference between each two adjacent trajectory points does not exceed the preset time difference, and the distance between each two adjacent trajectory points does not exceed the preset distance, the cloud server marks the driving state of the target vehicle as normal, and sends the driving state of the target vehicle and the preferred driving trajectory to the user terminal;

[0015] If there are two time abnormal trajectory points whose positioning time difference exceeds the preset time difference, or there are two distance abnormal trajectory points whose distance exceeds the preset distance, the cloud server marks the driving state of the target vehicle as abnormal, and performs trajectory point compensation on the preferred driving trajectory based on the two time abnormal trajectory points and / or the two distance abnormal trajectory points to obtain a target driving trajectory, and sends the driving state of the target vehicle and the target driving trajectory to the user terminal;

[0016] The user terminal displays the driving status of the target vehicle and the preferred driving trajectory or the target driving trajectory to the user.

[0017] Optionally, the target positioning device for positioning the target vehicle is determined from a plurality of vehicle positioning devices based on the location of the destination address fed back by the target vehicle-mounted processor in response to the trip query instruction in the actual road network, including:

[0018] determining a destination area based on the location of the destination address in the actual road network;

[0019] Calculating a road density of the destination area, and determining a target positioning accuracy level based on the road density;

[0020] The target positioning device is determined based on the target positioning accuracy level.

[0021] Optionally, determining a target denoising level for denoising the initial driving trajectory according to the initial trajectory length includes:

[0022] Configure data mapping intervals for each denoising level;

[0023] Normalizing the initial trajectory length to determine a target data mapping interval within which the normalized processing result falls;

[0024] The denoising level corresponding to the target data mapping interval is determined as the target denoising level.

[0025] Optionally, determining a target thinning intensity for thinning the candidate driving trajectory according to the deviation area comparison result includes:

[0026] If the deviation area comparison result is that the first deviation area is greater than the second deviation area, the target thinning intensity is determined as the maximum thinning intensity;

[0027] If the deviation area comparison result is that the first deviation area is smaller than the second deviation area, the target thinning intensity is determined as the minimum thinning intensity;

[0028] If the deviation area comparison result is that the first deviation area is equal to the second deviation area, the target thinning intensity is determined to be a medium thinning intensity.

[0029] Optionally, performing trajectory point compensation on the preferred driving trajectory based on the two time abnormal trajectory points and / or the two distance abnormal trajectory points to obtain a target driving trajectory includes:

[0030] If there are two time-abnormal trajectory points whose positioning time difference exceeds the preset time difference, and there are no two distance-abnormal trajectory points whose distance exceeds the preset distance, then trajectory point compensation is performed on the preferred driving trajectory based on the two time-abnormal trajectory points to obtain the target driving trajectory;

[0031] If there are no two time abnormal trajectory points whose positioning time difference exceeds the preset time difference, and there are two distance abnormal trajectory points whose distance exceeds the preset distance, then trajectory point compensation is performed on the preferred driving trajectory based on the two distance abnormal trajectory points to obtain the target driving trajectory;

[0032] If there are two time-abnormal trajectory points whose positioning time difference exceeds the preset time difference, and there are two distance-abnormal trajectory points whose distance exceeds the preset distance, trajectory point compensation is performed on the preferred driving trajectory based on the two time-abnormal trajectory points and the two distance-abnormal trajectory points to obtain the target driving trajectory.

[0033] Optionally, after the cloud server marks the driving state of the target vehicle as abnormal, the method further includes:

[0034] The cloud server determines an alternative positioning device other than the target positioning device from a plurality of vehicle-mounted positioning devices;

[0035] The target vehicle-mounted processor repositions the target vehicle through the alternative positioning device to obtain a number of alternative trajectory points of the target vehicle, and generates an alternative driving trajectory of the target vehicle based on each alternative trajectory point;

[0036] The cloud server performs road binding processing on the alternative driving trajectory based on the actual road network to obtain an optimized driving trajectory, and sends the optimized driving trajectory to the user terminal;

[0037] The user terminal displays the optimized driving trajectory to the user.

[0038] Optionally, the vehicle-mounted positioning device includes a GPS positioning device, a base station positioning device and a WIFI positioning device.

[0039] In a second aspect, an embodiment of the present application provides a vehicle monitoring system, the system comprising an on-board processor, a plurality of on-board positioning devices, a cloud server and a user terminal;

[0040] The user terminal is used to respond to the vehicle monitoring instruction input by the user, obtain the vehicle number of the target vehicle indicated by the vehicle monitoring instruction, and send the vehicle number of the target vehicle to the cloud server;

[0041] The cloud server is used to obtain the actual road network, send a travel query instruction to the target vehicle-mounted processor of the target vehicle, and determine a target positioning device for positioning the target vehicle from a plurality of vehicle-mounted positioning devices based on the location of the destination address fed back by the target vehicle-mounted processor in response to the travel query instruction in the actual road network;

[0042] The target vehicle-mounted processor is used to locate the target vehicle through the target positioning device to obtain a plurality of positioning trajectory points of the target vehicle, and generate an initial driving trajectory of the target vehicle based on each positioning trajectory point;

[0043] The target vehicle-mounted processor is used to calculate an initial trajectory length of the initial driving trajectory, determine a target denoising level for denoising the initial driving trajectory according to the initial trajectory length, and denoise the initial driving trajectory according to a denoising strategy corresponding to the target denoising level to obtain a candidate driving trajectory;

[0044] The target vehicle-mounted processor is used to respectively calculate a first deviation area formed between the initial driving trajectory and the nearest route, and a second deviation area formed between the candidate driving trajectory and the nearest route, and compare the first deviation area with the second deviation area to obtain a deviation area comparison result;

[0045] The target vehicle-mounted processor is used to determine a target thinning intensity for thinning the candidate driving trajectory according to the deviation area comparison result, and to thin the candidate driving trajectory according to the thinning strategy corresponding to the target thinning intensity to obtain a preferred driving trajectory;

[0046] The cloud server is used to calculate the positioning time difference and distance between every two adjacent track points in the preferred driving track, determine whether the positioning time difference between every two adjacent track points exceeds a preset time difference, and determine whether the distance between every two adjacent track points exceeds a preset distance;

[0047] The cloud server is configured to mark the driving state of the target vehicle as normal if the positioning time difference between each two adjacent trajectory points does not exceed the preset time difference, and the distance between each two adjacent trajectory points does not exceed the preset distance, and send the driving state of the target vehicle and the preferred driving trajectory to the user terminal;

[0048] The cloud server is configured to mark the driving state of the target vehicle as abnormal if there are two time abnormal trajectory points whose positioning time difference exceeds the preset time difference, or there are two distance abnormal trajectory points whose distance exceeds the preset distance, and perform trajectory point compensation on the preferred driving trajectory based on the two time abnormal trajectory points and / or the two distance abnormal trajectory points to obtain a target driving trajectory, and send the driving state of the target vehicle and the target driving trajectory to the user terminal;

[0049] The user terminal is used to display the driving status of the target vehicle and the preferred driving trajectory or the target driving trajectory to the user.

[0050] Optionally, the target positioning device for positioning the target vehicle is determined from a plurality of vehicle positioning devices based on the location of the destination address fed back by the target vehicle-mounted processor in response to the trip query instruction in the actual road network, including:

[0051] determining a destination area based on the location of the destination address in the actual road network;

[0052] Calculating a road density of the destination area, and determining a target positioning accuracy level based on the road density;

[0053] The target positioning device is determined based on the target positioning accuracy level.

[0054] Optionally, determining a target denoising level for denoising the initial driving trajectory according to the initial trajectory length includes:

[0055] Configure data mapping intervals for each denoising level;

[0056] Normalizing the initial trajectory length to determine a target data mapping interval within which the normalized processing result falls;

[0057] The denoising level corresponding to the target data mapping interval is determined as the target denoising level.

[0058] Optionally, determining a target thinning intensity for thinning the candidate driving trajectory according to the deviation area comparison result includes:

[0059] If the deviation area comparison result is that the first deviation area is greater than the second deviation area, the target thinning intensity is determined as the maximum thinning intensity;

[0060] If the deviation area comparison result is that the first deviation area is smaller than the second deviation area, the target thinning intensity is determined as the minimum thinning intensity;

[0061] If the deviation area comparison result is that the first deviation area is equal to the second deviation area, the target thinning intensity is determined to be a medium thinning intensity.

[0062] Optionally, performing trajectory point compensation on the preferred driving trajectory based on the two time abnormal trajectory points and / or the two distance abnormal trajectory points to obtain a target driving trajectory includes:

[0063] If there are two time-abnormal trajectory points whose positioning time difference exceeds the preset time difference, and there are no two distance-abnormal trajectory points whose distance exceeds the preset distance, then trajectory point compensation is performed on the preferred driving trajectory based on the two time-abnormal trajectory points to obtain the target driving trajectory;

[0064] If there are no two time abnormal trajectory points whose positioning time difference exceeds the preset time difference, and there are two distance abnormal trajectory points whose distance exceeds the preset distance, then trajectory point compensation is performed on the preferred driving trajectory based on the two distance abnormal trajectory points to obtain the target driving trajectory;

[0065] If there are two time-abnormal trajectory points whose positioning time difference exceeds the preset time difference, and there are two distance-abnormal trajectory points whose distance exceeds the preset distance, trajectory point compensation is performed on the preferred driving trajectory based on the two time-abnormal trajectory points and the two distance-abnormal trajectory points to obtain the target driving trajectory.

[0066] Optionally, the cloud server is used to determine an alternative positioning device other than the target positioning device from a plurality of vehicle-mounted positioning devices after marking the driving state of the target vehicle as abnormal;

[0067] The target vehicle-mounted processor is used to reposition the target vehicle through the alternative positioning device to obtain a number of alternative trajectory points of the target vehicle, and generate an alternative driving trajectory of the target vehicle based on each alternative trajectory point;

[0068] The cloud server is used to perform road binding processing on the candidate driving trajectory based on the actual road network to obtain an optimized driving trajectory, and send the optimized driving trajectory to the user terminal;

[0069] The user terminal is used to display the optimized driving trajectory to the user.

[0070] Optionally, the vehicle-mounted positioning device includes a GPS positioning device, a base station positioning device and a WIFI positioning device.

[0071] In a third aspect, an embodiment of the present application provides a computer device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the vehicle monitoring method described in any optional implementation manner of the second aspect are performed.

[0072] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the vehicle monitoring method described in any optional implementation manner in the above-mentioned second aspect are executed.

[0073] The technical solution provided by this application includes but is not limited to the following beneficial effects:

[0074] First, the user terminal responds to the vehicle monitoring instruction input by the user, obtains the vehicle number of the target vehicle indicated by the vehicle monitoring instruction, and sends the vehicle number of the target vehicle to the cloud server; the cloud server obtains the actual road network, sends a travel query instruction to the target vehicle processor of the target vehicle, and determines the target positioning device for positioning the target vehicle from a number of vehicle positioning devices based on the location of the destination address fed back by the target vehicle processor in response to the travel query instruction in the actual road network; the target vehicle processor locates the target vehicle through the target positioning device to obtain a number of positioning trajectory points of the target vehicle, and generates an initial driving trajectory of the target vehicle based on each positioning trajectory point; through the above steps, a positioning device with suitable accuracy is determined according to the vehicle's travel information to locate the trajectory of the vehicle to generate a vehicle driving trajectory, which can improve the accuracy and effectiveness of the obtained vehicle driving trajectory and provide reliable data support for monitoring the vehicle driving status.

[0075] Then, the target vehicle-mounted processor calculates the initial trajectory length of the initial driving trajectory, determines the target denoising level for denoising the initial driving trajectory according to the initial trajectory length, and denoises the initial driving trajectory according to the denoising strategy corresponding to the target denoising level to obtain a candidate driving trajectory; the target vehicle-mounted processor respectively calculates the first deviation area formed between the initial driving trajectory and the nearest line, and the second deviation area formed between the candidate driving trajectory and the nearest line, and compares the first deviation area with the second deviation area to obtain a deviation area comparison result; the target vehicle-mounted processor determines the target thinning intensity for thinning the candidate driving trajectory according to the deviation area comparison result, and thins the candidate driving trajectory according to the thinning strategy corresponding to the target thinning intensity to obtain a preferred driving trajectory; through the above steps, the vehicle-mounted processor performs denoising optimization processing on its own driving trajectory, so as to further improve the accuracy and reliability of the vehicle positioning result.

[0076] Furthermore, the cloud server calculates the positioning time difference and distance between every two adjacent trajectory points in the preferred driving trajectory, determines whether the positioning time difference between every two adjacent trajectory points exceeds the preset time difference, and determines whether the distance between every two adjacent trajectory points exceeds the preset distance; if the positioning time difference between every two adjacent trajectory points does not exceed the preset time difference, and the distance between every two adjacent trajectory points does not exceed the preset distance, the cloud server marks the driving state of the target vehicle as normal, and sends the driving state of the target vehicle and the preferred driving trajectory to the user terminal; if there are two time-abnormal trajectory points whose positioning time difference exceeds the preset time difference, or there are two distance-abnormal trajectory points whose distance exceeds the preset distance, the cloud server marks the driving state of the target vehicle as abnormal, and based on the two time-abnormal trajectory points and / or the two distance-abnormal trajectory points, performs trajectory point compensation on the preferred driving trajectory to obtain the target driving trajectory, and sends the driving state of the target vehicle and the target driving trajectory to the user terminal; through the above steps, the cloud server can analyze the driving state of the vehicle according to the vehicle driving trajectory with a unified standard, and further optimize the vehicle driving trajectory according to the vehicle state to obtain more accurate vehicle driving state information.

[0077] Finally, the user terminal displays the target vehicle's driving status and the preferred driving trajectory or target driving trajectory to the user. It does not need the driver or passenger to manually send vehicle status information. The vehicle's driving status can be analyzed and sent to the outside world directly through the cooperation of various processors and servers in the system.

[0078] By adopting the above scheme, the on-board processor, several on-board positioning devices, the cloud server and the user terminal cooperate with each other, select a suitable positioning device to locate the vehicle and generate a driving trajectory, denoise and optimize the driving trajectory, and then analyze the driving state of the vehicle based on the optimization result, and send the analysis result to the user terminal, so that the user can know the driving state of the vehicle in time, realize effective and accurate monitoring of the driving state of the vehicle, avoid safety hazards when the vehicle is driving, and reduce the risk of traffic accidents.

[0079] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0081] Figure 1 A flow chart of a vehicle monitoring method provided by Embodiment 1 of the present invention is shown;

[0082] Figure 2 A flow chart of a method for determining a target positioning device provided by Embodiment 1 of the present invention is shown;

[0083] Figure 3 A flow chart of a method for determining a target denoising level provided by the first embodiment of the present invention is shown;

[0084] Figure 4 A flow chart of a method for optimizing driving trajectory display provided by Embodiment 1 of the present invention is shown;

[0085] Figure 5 A schematic diagram of the structure of a vehicle monitoring system provided by Embodiment 2 of the present invention is shown;

[0086] Figure 6 A schematic diagram of the structure of a computer device provided in Embodiment 3 of the present invention is shown. DETAILED DESCRIPTION

[0087] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present invention.

[0088] Embodiment 1

[0089] To facilitate understanding of this application, Figure 1 The flowchart of a vehicle monitoring method provided in the first embodiment of the present invention is shown to describe the contents of the first embodiment of the present application in detail.

[0090] See also Figure 1 As shown, Figure 1 A flow chart of a vehicle monitoring method provided by Embodiment 1 of the present invention is shown, wherein the method is applied to a vehicle monitoring system, the system includes an on-board processor, several on-board positioning devices, a cloud server and a user terminal, and the method includes steps S101 to S110:

[0091] S101: The user terminal responds to a vehicle monitoring instruction input by a user, obtains the vehicle number of a target vehicle indicated by the vehicle monitoring instruction, and sends the vehicle number of the target vehicle to the cloud server.

[0092] Specifically, the user inputs the vehicle number of the vehicle he wants to monitor through the user terminal, and the user terminal sends the vehicle number from the local to the cloud server, which stores the actual road network information. The cloud server can also receive the vehicle information sent by each user terminal and integrate and analyze the vehicle information of each vehicle.

[0093] S102: The cloud server obtains the actual road network, sends a travel query instruction to the target on-board processor of the target vehicle, and determines a target positioning device for positioning the target vehicle from a plurality of on-board positioning devices based on the position of the destination address fed back by the target on-board processor in response to the travel query instruction in the actual road network.

[0094] Specifically, the cloud server sends a trip query instruction to the target vehicle's onboard processor, for example, by displaying the trip query information on the display panel of the onboard processor or broadcasting the trip query information through the speaker of the onboard processor. After receiving the trip query instruction, the person in the vehicle inputs the trip destination address through the display panel or inputs the trip destination address through the onboard microphone, and the user terminal feeds back the collected trip destination address to the cloud server.

[0095] After receiving the destination address, the cloud server determines the target positioning device suitable for monitoring the trip from a number of vehicle-mounted positioning devices based on the location of the destination address in the actual road network and the road density of the actual road network.

[0096] S103: The target vehicle-mounted processor locates the target vehicle through the target positioning device to obtain a plurality of positioning trajectory points of the target vehicle, and generates an initial driving trajectory of the target vehicle based on each positioning trajectory point.

[0097] Specifically, the on-board processor obtains the positioning data of the target vehicle at each time through the target positioning device in real time, generates a number of positioning trajectory points, and linearly fits the positioning trajectory points collected at each time to obtain the initial driving trajectory of the target vehicle.

[0098] S104: The target vehicle-mounted processor calculates an initial trajectory length of the initial driving trajectory, determines a target denoising level for denoising the initial driving trajectory according to the initial trajectory length, and denoises the initial driving trajectory according to a denoising strategy corresponding to the target denoising level to obtain a candidate driving trajectory.

[0099] Specifically, the longer the trajectory length is, the more noise points may exist in it. Therefore, the initial trajectory length is positively correlated with the target denoising level, and the denoising level is positively correlated with the denoising strength. That is, the longer the trajectory length is, the higher the denoising level is, and the greater the denoising strength and degree of the corresponding denoising strategy is.

[0100] S105: The target vehicle-mounted processor respectively calculates a first deviation area formed between the initial driving trajectory and the nearest line, and a second deviation area formed between the candidate driving trajectory and the nearest line, and compares the first deviation area with the second deviation area to obtain a deviation area comparison result.

[0101] Specifically, the nearest route is the route with the shortest distance from the initial position of the target vehicle to its destination position, that is, the straight line between the two points. The starting point and the end point of the initial driving trajectory are the same as those of the nearest route. As long as the two lines are not completely overlapped, no matter whether there is an intersection in the middle, at least one area will be enclosed, and the area and value of these areas are calculated to obtain the first deviation area. Similarly, the second deviation area of ​​the area formed between the candidate driving trajectory and the nearest route can be obtained. The first deviation area can indicate the deviation of the initial driving trajectory compared to the nearest route, and the second deviation area can indicate the deviation of the candidate driving trajectory after denoising compared to the nearest route. By comparing the first deviation area with the second deviation area, the difference between the deviation of the initial driving trajectory compared to the nearest route and the deviation of the candidate driving trajectory compared to the nearest route can be determined according to the deviation area comparison result, and the difference in the degree of proximity between the initial driving trajectory and the candidate driving trajectory and the nearest route can be analyzed.

[0102] S106: The target vehicle-mounted processor determines a target thinning intensity for thinning the candidate driving trajectories according to the deviation area comparison result, and thins the candidate driving trajectories according to a thinning strategy corresponding to the target thinning intensity to obtain a preferred driving trajectory.

[0103] Specifically, the deviation area comparison result can indicate the degree of similarity between the driving trajectory and the nearest route. The higher the target thinning intensity, the fewer trajectory points the corresponding thinning strategy retains, and the more obvious the thinning effect.

[0104] If the first deviation area is larger than the second deviation area, it means that the initial driving trajectory is closer to the nearest route than the candidate driving trajectory, and the candidate driving trajectory is closer to the nearest route. The closer the candidate driving trajectory is to the nearest route, the closer the candidate driving trajectory is to a straight line, the smaller the fluctuation, and the shorter the length. Therefore, the trajectory points in the candidate driving trajectory are denser and the changes are smaller. Therefore, it is necessary to perform a higher level of thinning processing, retaining only a small number of trajectory points, removing redundant and meaningless trajectory points, and configuring a higher target thinning intensity for it.

[0105] If the first deviation area is smaller than the second deviation area, it means that the initial driving trajectory is closer to the nearest route than the candidate driving trajectory is to the nearest route. The initial driving trajectory is closer to the nearest route, and the candidate driving trajectory is less similar to the nearest route. The more the candidate driving trajectory is less similar to the nearest route, the less close the candidate driving trajectory is to a straight line, the greater the fluctuation, and the longer the length. Therefore, the trajectory points in the candidate driving trajectory are sparser and more variable, so it cannot be subjected to high-intensity thinning. More trajectory points need to be retained, so the target thinning intensity is configured as the minimum thinning intensity.

[0106] If the first deviation area is equal to the second deviation area, it means that the degree of similarity between the initial driving trajectory and the nearest route is equal to the degree of similarity between the candidate driving trajectory and the nearest route, so the target thinning intensity is configured as a medium thinning intensity.

[0107] S107: The cloud server calculates the positioning time difference and distance between every two adjacent trajectory points in the preferred driving trajectory, determines whether the positioning time difference between every two adjacent trajectory points exceeds a preset time difference, and determines whether the distance between every two adjacent trajectory points exceeds a preset distance.

[0108] Specifically, for every two adjacent trajectory points, the positioning time difference and distance between the two adjacent trajectory points are calculated respectively. If the positioning time difference is too large, it means that there may be a situation where the trajectory point has not been collected, and if the distance is too large, it means that there may be a situation where the trajectory point data is lost. Therefore, it is judged whether the positioning time difference between the two adjacent trajectory points exceeds the preset time difference, and whether the distance between every two adjacent trajectory points exceeds the preset distance, so as to determine whether the above-mentioned positioning abnormality occurs according to the judgment result.

[0109] S108: If the positioning time difference between each two adjacent trajectory points does not exceed the preset time difference, and the distance between each two adjacent trajectory points does not exceed the preset distance, the cloud server marks the driving status of the target vehicle as normal, and sends the driving status of the target vehicle and the preferred driving trajectory to the user terminal.

[0110] Specifically, if the positioning time difference between each two adjacent trajectory points does not exceed the preset time difference, and the distance between each two adjacent trajectory points does not exceed the preset distance, it means that the trajectory point data has been effectively collected and effectively retained, then the driving status of the target vehicle is marked as normal, and the driving status (normal) and preferred driving trajectory of the target vehicle are sent to the user terminal.

[0111] S109: If there are two time-abnormal trajectory points whose positioning time difference exceeds the preset time difference, or if there are two distance-abnormal trajectory points whose distance exceeds the preset distance, the cloud server marks the driving state of the target vehicle as abnormal, and performs trajectory point compensation on the preferred driving trajectory based on the two time-abnormal trajectory points and / or the two distance-abnormal trajectory points to obtain a target driving trajectory, and sends the driving state of the target vehicle and the target driving trajectory to the user terminal.

[0112] Specifically, if there are two time-abnormal trajectory points whose positioning time difference exceeds the preset time difference, or two distance-abnormal trajectory points whose distance exceeds the preset distance, it means that the trajectory point data may not be effectively collected or effectively retained, and the cloud server marks the driving state of the target vehicle as abnormal. For the two time-abnormal trajectory points that have not been effectively collected or the distance-abnormal trajectory points that have not been effectively retained, trajectory point compensation is required to finally obtain the target driving trajectory, and the driving state (abnormal) of the target vehicle and the target driving trajectory obtained after compensation are sent to the user terminal.

[0113] S110: The user terminal displays the driving status of the target vehicle and the preferred driving trajectory or the target driving trajectory to the user.

[0114] Specifically, when the driving state of the target vehicle is normal, the user terminal displays the driving state of the target vehicle (normal) and the preferred driving trajectory it receives to the user; when the driving state of the target vehicle is abnormal, the user terminal displays the driving state of the target vehicle (abnormal) and the target driving trajectory it receives to the user.

[0115] In one possible embodiment, see Figure 2 As shown, Figure 2 A flow chart of a method for determining a target positioning device provided in the first embodiment of the present invention is shown, wherein the target positioning device for positioning the target vehicle is determined from a plurality of vehicle-mounted positioning devices based on the location of the destination address fed back by the target vehicle-mounted processor in response to the trip query instruction in the actual road network, including steps S201 to S203:

[0116] S201: Determine a destination area based on the location of the destination address in the actual road network.

[0117] Specifically, the destination area is obtained by expanding an area of ​​a preset size with the location of the destination address in the actual road network as the center point. The destination area is an area near the destination address that may have a spatial association and influence on the destination.

[0118] S202: Calculate the road density of the destination area, and determine the target positioning accuracy level based on the road density.

[0119] Specifically, road density refers to the ratio of the total mileage of the road network in a certain area to the area of ​​the area. The greater the road density, the denser the roads in the area. Therefore, the road density of the destination area is calculated based on the total mileage of the road network in the destination area and the area of ​​the destination area. The greater the road density of the destination area, the denser the internal roads are, so it is necessary to configure it with higher positioning accuracy (corresponding to a higher positioning accuracy level) so that different roads can be located and distinguished more accurately.

[0120] S203: Determine the target positioning device based on the target positioning accuracy level.

[0121] Specifically, a target positioning device that matches the target positioning accuracy level is selected. The higher the target positioning accuracy is, the higher the positioning accuracy of the selected positioning device needs to be.

[0122] In one possible embodiment, see Figure 3 As shown, Figure 3 A flow chart of a method for determining a target denoising level provided by the first embodiment of the present invention is shown, wherein the method for determining a target denoising level for denoising the initial driving trajectory according to the initial trajectory length comprises steps S301 to S303:

[0123] S301: configuring a data mapping interval for each denoising level.

[0124] S302: performing normalization processing on the initial trajectory length, and determining a target data mapping interval into which the normalization processing result falls.

[0125] S303: Determine the denoising level corresponding to the target data mapping interval as the target denoising level.

[0126] Specifically, five denoising levels are configured, namely, the first denoising level, the second denoising level, the third denoising level, the fourth denoising level and the fifth denoising level. The data mapping interval (0, 0.2] is configured for the first denoising level, the data mapping interval (0.2, 0.4] is configured for the second denoising level, the data mapping interval (0.4, 0.6] is configured for the third denoising level, the data mapping interval (0.6, 0.8] is configured for the fourth denoising level, and the data mapping interval (0.8, 1] is configured for the fifth denoising level. The initial trajectory length is normalized to determine which of the above data mapping intervals the target data mapping interval in which the normalized result falls is, and the denoising level corresponding to the target data mapping interval is determined as the target denoising level of the initial trajectory length. For example, after the initial trajectory length is normalized, 0.55 is obtained, which falls within the data mapping interval (0.4, 0.6], and the target denoising level is configured as the third denoising level.

[0127] The denoising strategy of the first denoising level is no denoising, the denoising strategy of the second denoising level is to remove trajectory points with a positioning accuracy lower than 500, the denoising strategy of the third denoising level is to remove trajectory points with a positioning accuracy lower than 100, the denoising strategy of the fourth denoising level is to remove trajectory points with a positioning accuracy lower than 50, and the denoising strategy of the fifth denoising level is to remove trajectory points with a positioning accuracy lower than 20.

[0128] In a feasible implementation manner, determining a target thinning intensity for thinning the candidate driving trajectory according to the deviation area comparison result includes:

[0129] If the deviation area comparison result is that the first deviation area is greater than the second deviation area, the target thinning intensity is determined as the highest thinning intensity; if the deviation area comparison result is that the first deviation area is smaller than the second deviation area, the target thinning intensity is determined as the lowest thinning intensity; if the deviation area comparison result is that the first deviation area is equal to the second deviation area, the target thinning intensity is determined as the medium thinning intensity.

[0130] Specifically, the thinning strategy corresponding to the highest thinning intensity is to retain the first number of trajectory points, the thinning strategy corresponding to the medium thinning intensity is to retain the second number of trajectory points, and the thinning strategy corresponding to the lowest thinning intensity is to retain the third number of trajectory points, and the first number < the second number < the third number.

[0131] In a feasible implementation manner, performing trajectory point compensation on the preferred driving trajectory based on the two time abnormal trajectory points and / or the two distance abnormal trajectory points to obtain a target driving trajectory includes:

[0132] If there are two time-abnormal trajectory points whose positioning time difference exceeds the preset time difference, and there are no two distance-abnormal trajectory points whose distance exceeds the preset distance, trajectory point compensation is performed on the preferred driving trajectory based on the two time-abnormal trajectory points to obtain the target driving trajectory.

[0133] Specifically, the two time-abnormal trajectory points are connected by a straight line, and the multiple of the positioning time difference between the two time-abnormal trajectory points and the preset time difference is calculated, the effective value of the multiple is taken as the number of compensation trajectory points, and the compensation trajectory points that meet the number are equally marked on the line segment between the two time-abnormal trajectory points to obtain the target driving trajectory.

[0134] If there are not two time abnormal trajectory points whose positioning time difference exceeds the preset time difference, and there are two distance abnormal trajectory points whose distance exceeds the preset distance, trajectory point compensation is performed on the preferred driving trajectory based on the two distance abnormal trajectory points to obtain the target driving trajectory.

[0135] Specifically, the two distance-abnormal trajectory points are connected by a straight line, and the multiple of the distance between the two distance-abnormal trajectory points and the preset distance is calculated, and the effective value of the multiple is taken as the number of compensation trajectory points. The compensation trajectory points that meet the number are equally divided and marked on the line segment between the two distance-abnormal trajectory points to obtain the target driving trajectory.

[0136] If there are two time-abnormal trajectory points whose positioning time difference exceeds the preset time difference, and there are two distance-abnormal trajectory points whose distance exceeds the preset distance, trajectory point compensation is performed on the preferred driving trajectory based on the two time-abnormal trajectory points and the two distance-abnormal trajectory points to obtain the target driving trajectory.

[0137] Specifically, determine the multiples of the positioning time difference between the two time abnormal trajectory points and the preset time difference and the multiples of the distance between the two distance abnormal trajectory points and the preset distance, select the effective value of the larger multiple as the number of compensation trajectory points, and mark the line segment between the two abnormal trajectory points corresponding to the smaller multiple in equal parts to obtain the target driving trajectory.

[0138] In one possible embodiment, see Figure 4 As shown, Figure 4 A flowchart of a method for optimizing driving trajectory display provided by the first embodiment of the present invention is shown, wherein, after the cloud server marks the driving state of the target vehicle as abnormal, the method further includes steps S401 to S404:

[0139] S401: The cloud server determines a candidate positioning device other than the target positioning device from a plurality of vehicle-mounted positioning devices.

[0140] S402: The target vehicle-mounted processor repositions the target vehicle through the alternative positioning device to obtain a number of alternative trajectory points of the target vehicle, and generates an alternative driving trajectory of the target vehicle based on the alternative trajectory points.

[0141] S403: The cloud server performs road binding processing on the alternative driving trajectory based on the actual road network to obtain an optimized driving trajectory, and sends the optimized driving trajectory to the user terminal.

[0142] S404: The user terminal displays the optimized driving trajectory to the user.

[0143] Specifically, when the driving state of the target vehicle is abnormal, it may be due to a problem with the driving state of the vehicle or an abnormality in the positioning device. Therefore, in order to exclude abnormal results caused by abnormal positioning devices, the cloud server determines alternative positioning devices other than the target positioning device to ensure that the positioning devices used for subsequent positioning are normal. Based on the actual road network, the alternative driving trajectory is bound to the road, and each trajectory point in the alternative driving trajectory is bound to the road to achieve the effects of correcting the offset trajectory, supplementing interrupted trajectory points, supplementing road turning points, etc., to obtain an optimized driving trajectory. The subsequent positioning and trajectory generation process using alternative positioning devices can refer to the aforementioned positioning and trajectory generation process using the target positioning device.

[0144] In a feasible implementation scheme, the vehicle-mounted positioning device includes a GPS positioning device, a base station positioning device and a WIFI positioning device.

[0145] Specifically, GPS positioning equipment has the highest accuracy, which can reach the meter level under good conditions and is generally below 20 meters; WIFI positioning equipment has medium accuracy, at the hundred-meter level, and the positioning accuracy of Wi-Fi positioning with higher accuracy can be less than 50 meters; base station positioning equipment has the lowest accuracy, at the kilometer level, and can reach about 500 meters when the accuracy is high.

[0146] In steps S202-S203, the positioning accuracy level can be divided into three levels according to the road density, namely, the highest positioning accuracy, the medium positioning accuracy and the lowest positioning accuracy. When the target positioning accuracy is the highest positioning accuracy, the selected target positioning device is the GPS positioning device; when the target positioning accuracy is the medium positioning accuracy, the selected target positioning device is the WIFI positioning device; when the target positioning accuracy is the lowest positioning accuracy, the selected target positioning device is the base station positioning device.

[0147] Embodiment 2

[0148] See also Figure 5 As shown, Figure 5 The schematic diagram of the structure of a vehicle monitoring system provided by the second embodiment of the present invention is shown, wherein the system includes a vehicle processor 501, a plurality of vehicle positioning devices 502, a cloud server 503 and a user terminal 504:

[0149] The user terminal is used to respond to the vehicle monitoring instruction input by the user, obtain the vehicle number of the target vehicle indicated by the vehicle monitoring instruction, and send the vehicle number of the target vehicle to the cloud server;

[0150] The cloud server is used to obtain the actual road network, send a travel query instruction to the target vehicle-mounted processor of the target vehicle, and determine a target positioning device for positioning the target vehicle from a plurality of vehicle-mounted positioning devices based on the location of the destination address fed back by the target vehicle-mounted processor in response to the travel query instruction in the actual road network;

[0151] The target vehicle-mounted processor is used to locate the target vehicle through the target positioning device to obtain a plurality of positioning trajectory points of the target vehicle, and generate an initial driving trajectory of the target vehicle based on each positioning trajectory point;

[0152] The target vehicle-mounted processor is used to calculate an initial trajectory length of the initial driving trajectory, determine a target denoising level for denoising the initial driving trajectory according to the initial trajectory length, and denoise the initial driving trajectory according to a denoising strategy corresponding to the target denoising level to obtain a candidate driving trajectory;

[0153] The target vehicle-mounted processor is used to respectively calculate a first deviation area formed between the initial driving trajectory and the nearest route, and a second deviation area formed between the candidate driving trajectory and the nearest route, and compare the first deviation area with the second deviation area to obtain a deviation area comparison result;

[0154] The target vehicle-mounted processor is used to determine a target thinning intensity for thinning the candidate driving trajectory according to the deviation area comparison result, and to thin the candidate driving trajectory according to the thinning strategy corresponding to the target thinning intensity to obtain a preferred driving trajectory;

[0155] The cloud server is used to calculate the positioning time difference and distance between every two adjacent track points in the preferred driving track, determine whether the positioning time difference between every two adjacent track points exceeds a preset time difference, and determine whether the distance between every two adjacent track points exceeds a preset distance;

[0156] The cloud server is configured to mark the driving state of the target vehicle as normal if the positioning time difference between each two adjacent trajectory points does not exceed the preset time difference, and the distance between each two adjacent trajectory points does not exceed the preset distance, and send the driving state of the target vehicle and the preferred driving trajectory to the user terminal;

[0157] The cloud server is configured to mark the driving state of the target vehicle as abnormal if there are two time abnormal trajectory points whose positioning time difference exceeds the preset time difference, or there are two distance abnormal trajectory points whose distance exceeds the preset distance, and perform trajectory point compensation on the preferred driving trajectory based on the two time abnormal trajectory points and / or the two distance abnormal trajectory points to obtain a target driving trajectory, and send the driving state of the target vehicle and the target driving trajectory to the user terminal;

[0158] The user terminal is used to display the driving status of the target vehicle and the preferred driving trajectory or the target driving trajectory to the user.

[0159] In a feasible implementation, the target positioning device for positioning the target vehicle is determined from a plurality of vehicle positioning devices based on the location of the destination address fed back by the target vehicle-mounted processor in response to the trip query instruction in the actual road network, including:

[0160] determining a destination area based on the location of the destination address in the actual road network;

[0161] Calculating a road density of the destination area, and determining a target positioning accuracy level based on the road density;

[0162] The target positioning device is determined based on the target positioning accuracy level.

[0163] In a feasible implementation manner, determining a target denoising level for denoising the initial driving trajectory according to the initial trajectory length includes:

[0164] Configure data mapping intervals for each denoising level;

[0165] Normalizing the initial trajectory length to determine a target data mapping interval within which the normalized processing result falls;

[0166] The denoising level corresponding to the target data mapping interval is determined as the target denoising level.

[0167] In a feasible implementation manner, determining a target thinning intensity for thinning the candidate driving trajectory according to the deviation area comparison result includes:

[0168] If the deviation area comparison result is that the first deviation area is greater than the second deviation area, the target thinning intensity is determined as the maximum thinning intensity;

[0169] If the deviation area comparison result is that the first deviation area is smaller than the second deviation area, the target thinning intensity is determined as the minimum thinning intensity;

[0170] If the deviation area comparison result is that the first deviation area is equal to the second deviation area, the target thinning intensity is determined to be a medium thinning intensity.

[0171] In a feasible implementation manner, performing trajectory point compensation on the preferred driving trajectory based on the two time abnormal trajectory points and / or the two distance abnormal trajectory points to obtain a target driving trajectory includes:

[0172] If there are two time-abnormal trajectory points whose positioning time difference exceeds the preset time difference, and there are no two distance-abnormal trajectory points whose distance exceeds the preset distance, then trajectory point compensation is performed on the preferred driving trajectory based on the two time-abnormal trajectory points to obtain the target driving trajectory;

[0173] If there are no two time abnormal trajectory points whose positioning time difference exceeds the preset time difference, and there are two distance abnormal trajectory points whose distance exceeds the preset distance, then trajectory point compensation is performed on the preferred driving trajectory based on the two distance abnormal trajectory points to obtain the target driving trajectory;

[0174] If there are two time-abnormal trajectory points whose positioning time difference exceeds the preset time difference, and there are two distance-abnormal trajectory points whose distance exceeds the preset distance, trajectory point compensation is performed on the preferred driving trajectory based on the two time-abnormal trajectory points and the two distance-abnormal trajectory points to obtain the target driving trajectory.

[0175] In a feasible implementation scheme, the cloud server is used to determine an alternative positioning device other than the target positioning device from a plurality of vehicle-mounted positioning devices after marking the driving state of the target vehicle as abnormal;

[0176] The target vehicle-mounted processor is used to reposition the target vehicle through the alternative positioning device to obtain a number of alternative trajectory points of the target vehicle, and generate an alternative driving trajectory of the target vehicle based on each alternative trajectory point;

[0177] The cloud server is used to perform road binding processing on the candidate driving trajectory based on the actual road network to obtain an optimized driving trajectory, and send the optimized driving trajectory to the user terminal;

[0178] The user terminal is used to display the optimized driving trajectory to the user.

[0179] In a feasible implementation scheme, the vehicle-mounted positioning device includes a GPS positioning device, a base station positioning device and a WIFI positioning device.

[0180] Embodiment 3

[0181] Based on the same application concept, see Figure 6 As shown, Figure 6 FIG. 4 shows a schematic diagram of the structure of a computer device provided by Embodiment 3 of the present invention, wherein: Figure 6 As shown, a computer device 600 provided in Embodiment 3 of the present application includes:

[0182] A processor 601, a memory 602 and a bus 603, wherein the memory 602 stores machine-readable instructions executable by the processor 601. When the computer device 600 is running, the processor 601 communicates with the memory 602 via the bus 603. When the processor 601 is running, the machine-readable instructions execute the steps of the vehicle monitoring method shown in the above-mentioned embodiment 1.

[0183] Embodiment 4

[0184] Based on the same application concept, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the vehicle monitoring method described in any one of the above embodiments are executed.

[0185] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0186] The computer program product for vehicle monitoring provided in the embodiment of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method described in the previous method embodiment. The specific implementation can be found in the method embodiment and will not be repeated here.

[0187] The vehicle monitoring system provided in the embodiment of the present invention can be specific hardware on the device or software or firmware installed on the device. The system provided in the embodiment of the present invention has the same implementation principle and technical effects as the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the system embodiment, reference can be made to the corresponding contents in the aforementioned method embodiment. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the aforementioned method embodiment, and will not be repeated here.

[0188] In the embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some communication interface, device or unit, which can be electrical, mechanical or other forms.

[0189] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0190] In addition, each functional unit in the embodiment provided by the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0191] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a service device, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0192] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0193] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can still modify the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or perform equivalent replacements on some of the technical features thereof; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. They should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A vehicle monitoring method, characterized in that: Applied to a vehicle monitoring system, the system includes a vehicle-mounted processor, several vehicle-mounted positioning devices, a cloud server and a user terminal, and the method includes: The user terminal responds to the vehicle monitoring instruction input by the user, obtains the vehicle number of the target vehicle indicated by the vehicle monitoring instruction, and sends the vehicle number of the target vehicle to the cloud server; The cloud server obtains the actual road network, sends a travel query instruction to the target vehicle-mounted processor of the target vehicle, and determines a target positioning device for positioning the target vehicle from a plurality of vehicle-mounted positioning devices based on the location of the destination address fed back by the target vehicle-mounted processor in response to the travel query instruction in the actual road network; The target vehicle-mounted processor locates the target vehicle through the target positioning device to obtain a plurality of positioning trajectory points of the target vehicle, and generates an initial driving trajectory of the target vehicle based on each positioning trajectory point; The target vehicle-mounted processor calculates an initial trajectory length of the initial driving trajectory, determines a target denoising level for denoising the initial driving trajectory according to the initial trajectory length, and denoises the initial driving trajectory according to a denoising strategy corresponding to the target denoising level to obtain a candidate driving trajectory; The target vehicle-mounted processor respectively calculates a first deviation area formed between the initial driving trajectory and the nearest route, and a second deviation area formed between the candidate driving trajectory and the nearest route, and compares the first deviation area with the second deviation area to obtain a deviation area comparison result; The target vehicle-mounted processor determines a target thinning intensity for thinning the candidate driving trajectory according to the deviation area comparison result, and performs thinning on the candidate driving trajectory according to a thinning strategy corresponding to the target thinning intensity to obtain a preferred driving trajectory; The cloud server calculates the positioning time difference and distance between every two adjacent track points in the preferred driving track, determines whether the positioning time difference between every two adjacent track points exceeds a preset time difference, and determines whether the distance between every two adjacent track points exceeds a preset distance; If the positioning time difference between each two adjacent trajectory points does not exceed the preset time difference, and the distance between each two adjacent trajectory points does not exceed the preset distance, the cloud server marks the driving state of the target vehicle as normal, and sends the driving state of the target vehicle and the preferred driving trajectory to the user terminal; If there are two time abnormal trajectory points whose positioning time difference exceeds the preset time difference, or there are two distance abnormal trajectory points whose distance exceeds the preset distance, the cloud server marks the driving state of the target vehicle as abnormal, and performs trajectory point compensation on the preferred driving trajectory based on the two time abnormal trajectory points and / or the two distance abnormal trajectory points to obtain a target driving trajectory, and sends the driving state of the target vehicle and the target driving trajectory to the user terminal; The user terminal displays the driving status of the target vehicle and the preferred driving trajectory or the target driving trajectory to the user.

2. The method according to claim 1, characterized in that The target positioning device for positioning the target vehicle is determined from a plurality of vehicle positioning devices based on the location of the destination address fed back by the target vehicle-mounted processor in response to the travel query instruction in the actual road network, including: determining a destination area based on the location of the destination address in the actual road network; Calculating a road density of the destination area, and determining a target positioning accuracy level based on the road density; The target positioning device is determined based on the target positioning accuracy level.

3. The method according to claim 1, characterized in that The step of determining a target denoising level for denoising the initial driving trajectory according to the initial trajectory length includes: Configure data mapping intervals for each denoising level; Normalizing the initial trajectory length to determine a target data mapping interval within which the normalized processing result falls; The denoising level corresponding to the target data mapping interval is determined as the target denoising level.

4. The method according to claim 1, characterized in that: The step of determining a target thinning intensity for thinning the candidate driving trajectory according to the deviation area comparison result includes: If the deviation area comparison result is that the first deviation area is greater than the second deviation area, the target thinning intensity is determined as the maximum thinning intensity; If the deviation area comparison result is that the first deviation area is smaller than the second deviation area, the target thinning intensity is determined as the minimum thinning intensity; If the deviation area comparison result is that the first deviation area is equal to the second deviation area, the target thinning intensity is determined to be a medium thinning intensity.

5. The method according to claim 1, characterized in that The performing trajectory point compensation on the preferred driving trajectory based on the two time abnormal trajectory points and / or the two distance abnormal trajectory points to obtain a target driving trajectory includes: If there are two time-abnormal trajectory points whose positioning time difference exceeds the preset time difference, and there are no two distance-abnormal trajectory points whose distance exceeds the preset distance, then trajectory point compensation is performed on the preferred driving trajectory based on the two time-abnormal trajectory points to obtain the target driving trajectory; If there are no two time abnormal trajectory points whose positioning time difference exceeds the preset time difference, and there are two distance abnormal trajectory points whose distance exceeds the preset distance, then trajectory point compensation is performed on the preferred driving trajectory based on the two distance abnormal trajectory points to obtain the target driving trajectory; If there are two time-abnormal trajectory points whose positioning time difference exceeds the preset time difference, and there are two distance-abnormal trajectory points whose distance exceeds the preset distance, trajectory point compensation is performed on the preferred driving trajectory based on the two time-abnormal trajectory points and the two distance-abnormal trajectory points to obtain the target driving trajectory.

6. The method according to claim 1, characterized in that After the cloud server marks the driving state of the target vehicle as abnormal, the method further includes: The cloud server determines an alternative positioning device other than the target positioning device from a plurality of vehicle-mounted positioning devices; The target vehicle-mounted processor repositions the target vehicle through the alternative positioning device to obtain a number of alternative trajectory points of the target vehicle, and generates an alternative driving trajectory of the target vehicle based on each alternative trajectory point; The cloud server performs road binding processing on the alternative driving trajectory based on the actual road network to obtain an optimized driving trajectory, and sends the optimized driving trajectory to the user terminal; The user terminal displays the optimized driving trajectory to the user.

7. The method according to claim 1, characterized in that The vehicle-mounted positioning device includes a GPS positioning device, a base station positioning device and a WIFI positioning device.

8. A vehicle monitoring system, characterized in that: The system includes an on-board processor, several on-board positioning devices, a cloud server and a user terminal; The user terminal is used to respond to the vehicle monitoring instruction input by the user, obtain the vehicle number of the target vehicle indicated by the vehicle monitoring instruction, and send the vehicle number of the target vehicle to the cloud server; The cloud server is used to obtain the actual road network, send a travel query instruction to the target vehicle-mounted processor of the target vehicle, and determine a target positioning device for positioning the target vehicle from a plurality of vehicle-mounted positioning devices based on the location of the destination address fed back by the target vehicle-mounted processor in response to the travel query instruction in the actual road network; The target vehicle-mounted processor is used to locate the target vehicle through the target positioning device to obtain a plurality of positioning trajectory points of the target vehicle, and generate an initial driving trajectory of the target vehicle based on each positioning trajectory point; The target vehicle-mounted processor is used to calculate an initial trajectory length of the initial driving trajectory, determine a target denoising level for denoising the initial driving trajectory according to the initial trajectory length, and denoise the initial driving trajectory according to a denoising strategy corresponding to the target denoising level to obtain a candidate driving trajectory; The target vehicle-mounted processor is used to respectively calculate a first deviation area formed between the initial driving trajectory and the nearest route, and a second deviation area formed between the candidate driving trajectory and the nearest route, and compare the first deviation area with the second deviation area to obtain a deviation area comparison result; The target vehicle-mounted processor is used to determine a target thinning intensity for thinning the candidate driving trajectory according to the deviation area comparison result, and to thin the candidate driving trajectory according to the thinning strategy corresponding to the target thinning intensity to obtain a preferred driving trajectory; The cloud server is used to calculate the positioning time difference and distance between every two adjacent track points in the preferred driving track, determine whether the positioning time difference between every two adjacent track points exceeds a preset time difference, and determine whether the distance between every two adjacent track points exceeds a preset distance; The cloud server is configured to mark the driving state of the target vehicle as normal if the positioning time difference between each two adjacent trajectory points does not exceed the preset time difference, and the distance between each two adjacent trajectory points does not exceed the preset distance, and send the driving state of the target vehicle and the preferred driving trajectory to the user terminal; The cloud server is configured to mark the driving state of the target vehicle as abnormal if there are two time abnormal trajectory points whose positioning time difference exceeds the preset time difference, or there are two distance abnormal trajectory points whose distance exceeds the preset distance, and perform trajectory point compensation on the preferred driving trajectory based on the two time abnormal trajectory points and / or the two distance abnormal trajectory points to obtain a target driving trajectory, and send the driving state of the target vehicle and the target driving trajectory to the user terminal; The user terminal is used to display the driving status of the target vehicle and the preferred driving trajectory or the target driving trajectory to the user.

9. A computer device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the vehicle monitoring method as described in any one of claims 1 to 7 are performed.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, executes the steps of the vehicle monitoring method as claimed in any one of claims 1 to 7.

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