Driving state monitoring method and device

Abstract

The embodiment of the invention provides a driving state monitoring method and device, and the method comprises the steps: collecting the driving track and driving data of a target vehicle, and when the continuous driving duration in the driving data exceeds a preset continuous driving duration threshold value, and the continuous driving speed of the target vehicle exceeds a preset speed threshold value, carrying out the monitoring of a driving state; the method comprises the steps of determining a driving behavior of a target vehicle as a preliminary fatigue driving behavior, determining a real-time road type corresponding to a driving track, obtaining a vehicle image of the target vehicle under the condition that the road type is a non-congestion type, comparing the vehicle image with registration information of the current target vehicle, and if a comparison result is that the vehicles are consistent, determining that the vehicle image is not consistent with the registration information of the current target vehicle. And determining the driving behavior of the target vehicle as a fatigue driving behavior, generating a fatigue driving data packet from corresponding data, and uploading the fatigue driving data packet to a server. According to the method, the defects of limitation of interference elimination, reliability of evidence solidification and the like are effectively overcome, and the accuracy and reliability of fatigue driving judgment are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and specifically to a driving status monitoring method and device. Background Art

[0002] Fatigue driving is one of the major hidden dangers that cause traffic accidents. In existing technologies, methods for detecting fatigue driving can rely on driver self-reporting or on-board sensor detection methods to determine whether the current driver is driving fatigued. However, the method that relies on driver self-reporting is highly subjective and has low accuracy, while the detection method based on on-board sensors is easily affected by environmental factors and has a high misjudgment rate.

[0003] With the development of intelligent transportation technology, the above-mentioned problems can be solved by determining driving fatigue through multi-source data fusion. However, the existing methods for determining driving fatigue through multi-source data fusion still have deficiencies in terms of data fusion accuracy, limited interference elimination, and reliability of evidence solidification, making it difficult to meet actual determination needs. Summary of the Invention

[0004] In response to the problems in the existing technology, the present application provides a driving status monitoring method and device, which can effectively solve the shortcomings of traditional technologies in determining fatigue driving in terms of the accuracy of data fusion, the limited interference elimination and the reliability of evidence solidification, and significantly improve the accuracy and reliability of fatigue driving judgment.

[0005] In order to solve at least one of the above problems, the present application provides the following technical solutions: In a first aspect, the present application provides a driving state monitoring method, comprising: Collecting the target vehicle's driving trajectory and driving data, and determining the target vehicle's driving behavior as preliminary fatigue driving behavior when the continuous driving duration in the driving data exceeds a preset continuous driving duration threshold and the continuous driving speed of the target vehicle exceeds a preset speed threshold, the driving data including the continuous driving duration and the continuous driving speed; Determine the real-time road type corresponding to the driving trajectory through reverse geocoding. If the road type is non-congested, obtain a vehicle image taken at a checkpoint along the driving trajectory of the target vehicle. Compare the vehicle image with the current registration information of the target vehicle to obtain a comparison result. The road type includes congested and non-congested types, and the comparison results include consistent and inconsistent vehicles. When the comparison result shows that the vehicles are consistent, the preliminary fatigue driving behavior of the target vehicle is updated to the fatigue driving behavior, and the driving trajectory, driving data and vehicle image of the target vehicle are encrypted to generate a fatigue driving data packet and uploaded to the server.

[0006] Further, the method further comprises: inputting the starting coordinate of the driving track and the real-time coordinate into a map interface, obtaining geographical position information corresponding to the driving track with the starting coordinate as a starting point, the real-time coordinate as an ending point, and the driving track as a track, the geographical position information comprising road names corresponding to respective roads and real-time congestion indexes; When the real-time congestion index indicates that the current road is a congested road segment, the initial road type is determined as an initial congestion type, and when the real-time congestion index indicates that the current road is a non-congested road segment, the initial road type is determined as an initial non-congestion type; The method further comprises: determining each initial road type corresponding to the driving track, and when there are more than a preset congestion quantity threshold of initial congestion types in the initial road types, determining the road type corresponding to the current driving track as a congestion type, and updating the driving behavior of the target vehicle to a non-fatigue driving behavior.

[0007] Further, the method further comprises: identifying license plate information in the vehicle image, and comparing the license plate information with license plate information in the registration information to obtain a license plate comparison result. When the license plate comparison result is consistent, vehicle appearance features in the vehicle image are extracted, and the vehicle appearance features are compared with vehicle appearance features in the registration information to obtain a comparison result, the vehicle appearance features comprising a vehicle body color, a vehicle model, and a vehicle identification.

[0008] Further, after obtaining the license plate comparison result, the method further comprises: When the license plate comparison result is inconsistent, historical driving images of the target vehicle are obtained, and the historical driving images, the vehicle image, and the registration information are compared to obtain a fake license plate comparison result, the fake license plate comparison result being a suspicious vehicle and an incorrectly identified vehicle. When the fake license plate comparison result is the suspicious vehicle, the vehicle image, the historical driving image, and the registration information are processed into a fake license plate data packet, and the fake license plate data packet is sent to a corresponding fake license plate processing platform. When the fake license plate comparison result is the incorrectly identified vehicle, the step of obtaining the vehicle image captured by the camera at the midway of the driving track of the target vehicle is re-executed until the comparison result is obtained.

[0009] Further, the method further comprises: when the continuous driving speed is lower than a preset speed threshold, determining a low-speed driving duration corresponding to the continuous driving speed being lower than the preset speed threshold. When the low-speed driving duration exceeds a preset low-speed driving duration threshold, the driving track and the driving data of the target vehicle are initialized to re-determine the driving track and the driving data of the target vehicle. When the low-speed driving duration does not exceed the preset low-speed driving duration threshold, the low-speed driving duration is removed from the continuous driving duration without resetting the record of the continuous driving duration.

[0010] Further, after collecting the driving trajectory and driving data of the target vehicle, further comprising: In the case of continuous missing data points in the driving trajectory, determining the missing start time, missing end time, missing duration and missing position of the missing data points; In the case where the missing duration exceeds the preset missing duration threshold, determining the missing area based on the missing start time, missing end time and missing position; Extracting the last valid data point before the missing start time from the driving trajectory, extracting the last valid data point after the missing end time from the driving trajectory, and determining the spatial distance between the valid data points before and after the missing data points; In the case where the spatial distance exceeds the missing area, determining the data segment corresponding to the continuous missing data points as an invalid data segment, and skipping the invalid data segment in the continuous driving duration.

[0011] Further, further comprising: encrypting the driving trajectory, driving data and vehicle image of the target vehicle by a preset encryption algorithm to generate a ciphertext data block; Performing a secure hash algorithm operation on the ciphertext data block to generate a hash value, generating a fatigue driving data packet based on the hash value and the ciphertext data block, and uploading the fatigue driving data packet to the server.

[0012] In a second aspect, the application provides a driving state monitoring device, comprising: A first processing module for collecting the driving trajectory and driving data of the target vehicle, and determining the driving behavior of the target vehicle as a preliminary fatigue driving behavior when the continuous driving duration in the driving data exceeds the preset continuous driving duration threshold and the continuous driving speed of the target vehicle exceeds the preset speed threshold. The driving data includes continuous driving duration and continuous driving speed; A second processing module for determining the real-time road type corresponding to the driving trajectory by reverse geocoding, obtaining the vehicle image taken by the camera in the driving trajectory of the target vehicle, and comparing the vehicle image with the registration information of the current target vehicle to obtain a comparison result. The road type includes congested type and non-congested type, and the comparison result includes vehicle consistency and vehicle inconsistency; A third processing module for updating the preliminary fatigue driving behavior of the target vehicle to fatigue driving behavior when the comparison result is vehicle consistency, and encrypting the driving trajectory, driving data and vehicle image of the target vehicle to generate a fatigue driving data packet and upload it to the server.

[0013] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the driving state monitoring method when executing the program.

[0014] In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executable on a processor to implement the steps of the driving state monitoring method.

[0015] In a fifth aspect, the present application provides a computer program product comprising computer programs / instructions, wherein the computer programs / instructions are executable on a processor to implement the steps of the driving state monitoring method.

[0016] According to the above technical solution, the present application provides a driving state monitoring method and device. The driving trajectory and driving data of a target vehicle are innovatively collected. When the continuous driving duration in the driving data exceeds a preset continuous driving duration threshold and the continuous driving speed of the target vehicle exceeds a preset speed threshold, the driving behavior of the target vehicle is determined as a preliminary fatigue driving behavior. The real-time road type corresponding to the driving trajectory is determined by means of reverse geocoding. When the road type is a non-congestion type, the vehicle image captured by a checkpoint in the middle of the driving trajectory of the target vehicle is obtained, and the vehicle image is compared with the registration information of the target vehicle to obtain a comparison result. When the comparison result is consistent with the vehicle, the preliminary fatigue driving behavior of the target vehicle is updated to a fatigue driving behavior. The driving trajectory, driving data, and vehicle image of the target vehicle are encrypted to generate a fatigue driving data packet and uploaded to a server. This method effectively solves the problems of the traditional technology in terms of the accuracy of data fusion, the limitation of interference exclusion, and the reliability of evidence solidification in determining fatigue driving, and significantly improves the accuracy and reliability of fatigue driving determination. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0018] Figure 1 The flowchart of the driving state monitoring method in the embodiments of the present application; Figure 2 The structural diagram of the driving state monitoring device in the embodiments of the present application; Figure 3 The structural diagram of the electronic device in the embodiments of the present application.

[0019] Reference signs: Electronic device 9600, central processor 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage section 9142, data storage section 9143, driver program storage section 9144, antenna 9111, speaker 9131, microphone 9132. DETAILED DESCRIPTION

[0020] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0021] The acquisition, storage, use, processing and the like of data in the technical solutions of the present application comply with relevant provisions of national laws and regulations.

[0022] In view of the problems in the prior art, the present application provides a driving state monitoring method and device, which integrates multi-dimensional data such as driving trajectory, geographic information, and images taken by passing through a toll gate, constructs a closed-loop evidence chain, accurately determines the fatigue driving behavior of a target vehicle, and generates a reliable fatigue driving data package to provide strong support.

[0023] In order to effectively solve the deficiencies of the prior art in the accuracy of data fusion, the limitation of interference exclusion, and the reliability of evidence solidification in determining fatigue driving, and significantly improve the accuracy and reliability of fatigue driving determination, the present application provides an embodiment of a driving state monitoring method, as shown in Figure 1 , which specifically includes the following contents: Step S101: collecting driving trajectory and driving data of a target vehicle, and determining the driving behavior of the target vehicle as a preliminary fatigue driving behavior when the continuous driving duration in the driving data exceeds a preset continuous driving duration threshold and the continuous driving speed of the target vehicle exceeds a preset speed threshold.

[0024] Among them, the driving data includes continuous driving duration and continuous driving speed.

[0025] Optionally, the embodiment collects the driving trajectory and driving data of the target vehicle, wherein the driving trajectory of the target vehicle can be acquired through the Global Positioning System (GPS) in the vehicle terminal of the target vehicle, the GPS trajectory data of the target vehicle can be acquired at a preset time interval (for example, once per second), and the trajectory data can include but is not limited to time stamp, latitude and longitude, and speed information.

[0026] The time stamp is used to record the position and speed of the target vehicle at each moment, the latitude and longitude are used to determine the geographical position of the target vehicle, and the speed information is used to determine whether the target vehicle is in a driving state. The driving data further includes the continuous driving duration and the continuous driving speed of the target vehicle.

[0027] In addition, when the continuous driving duration in the driving data exceeds the preset continuous driving duration threshold, and the continuous driving speed of the target vehicle exceeds the preset speed threshold, the driving behavior of the target vehicle is determined as a preliminary fatigue driving behavior, that is, the driving behavior of the target vehicle is determined as a preliminary fatigue driving behavior only when the continuous driving duration exceeds the preset driving duration threshold and the continuous driving speed exceeds the preset speed threshold during the continuous driving duration.

[0028] The preset continuous driving duration threshold is set based on relevant regulations and driving safety standards, for example, 8 hours, which is used to determine whether the driver of the target vehicle continuously drives for more than the corresponding preset continuous driving duration, and the preset speed threshold is used to exclude the misjudgment of the target vehicle in the low-speed driving scene such as a parking lot and a road construction area, wherein the preset speed threshold can be 3 kilometers per hour.

[0029] The embodiment realizes that whether the driver in the target vehicle has a preliminary fatigue driving behavior can be determined through the driving trajectory and driving data of the target vehicle, wherein the driving trajectory and driving data are obtained through multiple source sensors, which improves the accuracy and reliability of the obtained preliminary fatigue driving behavior.

[0030] Step S102: determining the real-time road type corresponding to the driving trajectory through the reverse geocoding mode, acquiring the vehicle image captured by the camera in the middle of the driving trajectory of the target vehicle in the case of a non-congestion type road type, and comparing the vehicle image with the registration information of the current target vehicle to obtain a comparison result.

[0031] The road type includes a congestion type and a non-congestion type, and the comparison result includes vehicle consistency and vehicle inconsistency.

[0032] Optionally, after determining that the target vehicle has preliminary fatigue driving behavior, this embodiment can convert the starting point coordinates and the end point coordinates of the driving trajectory into specific geographic location information, such as the name of a highway, the name of a city road, etc., through reverse geocoding. By calling the map interface, the geographic location information corresponding to the driving trajectory can be obtained, thereby determining the real-time road type of the driving trajectory, wherein the starting point coordinates and the end point coordinates can be combined with a preset road type database to perform a comparison to obtain the real-time road type, wherein the road type includes a congested type and a non-congested type. The congested type means that the target vehicle may drive slowly for a long time due to traffic congestion, and the non-congested type means that the target vehicle will not drive slowly for a long time due to traffic congestion.

[0033] In addition, when the road type is non-congested, a vehicle image taken when the target vehicle passes through the checkpoint during its driving trajectory is further obtained, wherein the vehicle appearance features and license plate information in the vehicle image can be extracted by image recognition and compared with the target vehicle registration information, wherein the vehicle appearance features include but are not limited to the body color, vehicle model, and vehicle identification, and the comparison results include vehicle consistency and vehicle inconsistency.

[0034] Furthermore, multi-dimensional information such as the target vehicle's driving posture and the interior conditions of the cab can be compared. In low-light or bad weather conditions, the vehicle image can be pre-processed through image enhancement to improve the vehicle image quality, thereby improving the accuracy of the comparison results.

[0035] This embodiment determines the real-time road type through the driving trajectory, and obtains a comparison result based on the real-time road type, vehicle image and registration information of the target vehicle, thereby further determining the accuracy and reliability of the initial fatigue driving behavior.

[0036] Step S103: When the comparison result shows that the vehicles are consistent, the preliminary fatigue driving behavior of the target vehicle is updated to the fatigue driving behavior, and the driving trajectory, driving data and vehicle image of the target vehicle are encrypted to generate a fatigue driving data packet and uploaded to the server.

[0037] Optionally, in this embodiment, when the comparison result between the vehicle image and the registration information of the target vehicle is that the vehicles are consistent, the preliminary fatigue driving behavior of the target vehicle is updated to fatigue driving behavior, and the driving trajectory, driving data and vehicle image of the target vehicle are encrypted to generate a fatigue driving data packet, and the fatigue driving data packet is uploaded to the server.

[0038] Among them, encryption processing can be performed through an asymmetric encryption algorithm (such as the RSA algorithm), and the fatigue driving data packet generated includes but is not limited to the timestamp, hash value and other information of the driving data to ensure the integrity and authenticity of the driving data.

[0039] Further, the fatigue driving data packet can be encrypted and stored through the blockchain mode to ensure the non-tamperability and traceability of the fatigue driving data packet. In addition, the data can be classified through a multi-level encryption mechanism, and the data of different levels (such as the driving trajectory, driving data, and vehicle image) can be encrypted respectively to improve the data security.

[0040] In the case where it is determined that the driving behavior of the target vehicle is fatigue driving behavior, the driving trajectory, driving data, and vehicle image of the target vehicle are encrypted to generate a fatigue driving data packet, thereby enhancing the security and traceability of the data and improving the overall security and reliability.

[0041] The embodiment realizes a fatigue driving determination method that is accurate, efficient, and secure by collecting the driving trajectory and driving data of the target vehicle, combining the reverse geocoding mode, vehicle image, and data encryption mode, and can effectively determine fatigue driving behavior and generate reliable evidence. In addition, the determination of fatigue driving is the result of multi-source data fusion, thereby reducing the finiteness of interference elimination when excluding interference and improving the reliability of the evidence and the accuracy and reliability of fatigue driving behavior determination.

[0042] In some embodiments, the real-time road type corresponding to the driving trajectory is determined through the reverse geocoding mode, including: The starting coordinates and real-time coordinates of the driving trajectory are input into a map interface to obtain geographical location information corresponding to the driving trajectory with the starting coordinates as the starting point and the real-time coordinates as the ending point. The geographical location information includes the road names and real-time congestion indexes of the roads. When the real-time congestion index indicates that the current road is a congested road section, the initial road type is determined as an initial congestion type, and when the real-time congestion index indicates that the current road is a non-congested road section, the initial road type is determined as an initial non-congested type. Each initial road type corresponding to the driving trajectory is determined. When there are more than a preset congestion quantity threshold of initial congestion types in the initial road types, the road type corresponding to the current driving trajectory is determined as a congestion type, and the driving behavior of the target vehicle is updated to a non-fatigue driving behavior.

[0043] Optionally, the starting coordinates and real-time coordinates of the driving trajectory are input into a map interface to obtain geographical location information corresponding to the driving trajectory with the starting coordinates as the starting point and the real-time coordinates as the ending point through the map interface. The map interface is a map application programming (API) interface, and the geographical location information includes but is not limited to road names and real-time congestion indexes of the roads.

[0044] The map API interface can be an interface of a high-precision map service provider, and the map API interface can provide detailed geographic location and traffic condition data, and can more accurately reflect the congestion state of a road, thereby improving the accuracy of determining the fatigue driving behavior.

[0045] In addition, according to the real-time congestion index, it is determined whether the current road is a congested road section. The real-time congestion index is usually a numerical value indicating the congestion degree of the current road, for example, 0 indicates smoothness, and 100 indicates severe congestion.

[0046] If the real-time congestion index indicates that the current road is a congested road section, the initial road type is determined as an initial congestion type, and if the real-time congestion index indicates that the current road is a non-congested road section, the initial road type is determined as an initial non-congestion type.

[0047] In addition, statistical analysis is performed on each initial road type corresponding to the driving trajectory. If there are more than a preset congestion quantity threshold of initial congestion types in the initial road types, for example, three initial road types are initial congestion types in succession, the road type corresponding to the current driving trajectory is determined as a congestion type, and the driving behavior of the target vehicle is updated as a non-fatigue driving behavior, so as to exclude the misjudgment caused by traffic congestion and ensure that the determination of the fatigue driving behavior is more accurate.

[0048] Further, real-time traffic data such as traffic flow and accident information can be combined to dynamically adjust the judgment logic of the road type, so as to better cope with sudden traffic events and reduce misjudgment.

[0049] The embodiment realizes determination of the real-time road type corresponding to the driving trajectory through the reverse geocoding technology, and determines the road type in combination with the real-time congestion index, so as to timely reflect the actual traffic condition of the road, improve the real-time performance and adaptability of determining the fatigue driving behavior, and effectively exclude the misjudgment caused by traffic congestion, thereby improving the accuracy of determining the fatigue driving behavior.

[0050] In some embodiments, the vehicle image is compared with the registration information of the current target vehicle to obtain a comparison result, including: License plate information in the vehicle image is recognized, and the license plate information is compared with the license plate information in the registration information to obtain a license plate comparison result. When the license plate comparison result is consistent, vehicle appearance features in the vehicle image are extracted, and the vehicle appearance features are compared with the vehicle appearance features in the registration information to obtain a comparison result. The vehicle appearance features include a vehicle body color, a vehicle model, and a vehicle identification.

[0051] Optionally, the embodiment extracts license plate information from the vehicle image by optical character recognition (OCR), wherein the OCR technology can automatically identify the characters on the license plate and convert them into readable text format.

[0052] In addition, the extracted license plate information is compared with the license plate information in the registration information of the target vehicle, and if the license plate information is consistent, the vehicle appearance features in the vehicle image are extracted, wherein the registration information is stored in a preset database, including but not limited to the license plate number, vehicle owner information and vehicle appearance of the target vehicle.

[0053] Among them, the vehicle image can be analyzed by image recognition to extract the above-mentioned vehicle appearance features, for example, the color of the target vehicle is determined by color recognition algorithm, the vehicle model is determined by vehicle model recognition algorithm, and the vehicle logo is recognized by logo recognition algorithm.

[0054] In addition, the extracted vehicle appearance features are compared with the vehicle appearance features in the registration information to obtain a comparison result, wherein the vehicle appearance features in the registration information are recorded when the vehicle is registered, and usually include the color of the vehicle body, the vehicle model and the vehicle logo, etc.

[0055] Further, the driver information can also be compared, and when the comparison result is that the driver information is inconsistent and the comparison result of other data is consistent, the driving trajectory and driving data of the current target vehicle are reset, that is, it is determined that the target vehicle has changed the driver and there is no fatigue driving behavior.

[0056] When the comparison result is that the driver information is consistent and the comparison result of other data is consistent, the identification of the fatigue driving behavior of the current target vehicle is maintained.

[0057] The embodiment realizes that by comparing the vehicle image with the registration information of the target vehicle, it can be verified twice, reduce misjudgment, ensure the fairness of the fatigue driving behavior judgment, and accurately identify the vehicle information of the target vehicle, so that it can work normally even in complex environment, and can quickly process and analyze vehicle image data, reduce response time, and can timely discover and handle fatigue driving behavior.

[0058] In some embodiments, after obtaining the license plate comparison result, it further includes: When the license plate comparison result is inconsistent, the historical driving image of the target vehicle is obtained, the historical driving image, the vehicle image and the registration information are compared to obtain a fake license plate comparison result, and the fake license plate comparison result is a suspicious vehicle and an incorrectly identified vehicle; When the license plate comparison result is a suspicious vehicle, the vehicle image, historical driving image, and registration information are processed into a license plate data packet, and the license plate data packet is sent to the corresponding license plate processing platform. When the license plate comparison result is an error-identified vehicle, the step of obtaining the vehicle image of the target vehicle passing through the camera halfway along the driving track is re-executed until a consistent comparison result is obtained.

[0059] Optionally, when the license plate comparison result is inconsistent, the historical driving image of the target vehicle is retrieved, wherein the historical driving image can be stored in the monitoring system to record the driving situation of the target vehicle at different times and locations.

[0060] The historical driving image can be obtained by querying the database of the monitoring system, which stores the snapshot images of the target vehicle at each camera.

[0061] In addition, the current snapshot vehicle image is compared with the historical driving image, and a comprehensive analysis is performed in combination with the registration information of the target vehicle to obtain the license plate comparison result, wherein the comparison content includes but is not limited to license plate information, vehicle appearance characteristics (such as vehicle color, vehicle model, vehicle identification).

[0062] If the vehicle information in the historical driving image is consistent with the current snapshot vehicle image but inconsistent with the registration information, the license plate comparison result of the target vehicle is determined as a suspicious vehicle, and if the historical driving image is inconsistent with the current snapshot vehicle image, the license plate comparison result of the target vehicle is determined as an error-identified vehicle.

[0063] In addition, when the license plate comparison result is a suspicious vehicle, the vehicle image, historical driving image, and registration information are processed into a license plate data packet, and the license plate data packet is sent to the corresponding license plate processing platform, wherein the license plate data packet includes but is not limited to detailed vehicle information and license plate comparison result, facilitating further investigation and processing by relevant management departments.

[0064] In addition, when the license plate comparison result is an error-identified vehicle, the step of obtaining the vehicle image of the target vehicle passing through the camera halfway along the driving track is re-executed until a consistent comparison result is obtained, i.e., comparing the vehicle images and historical driving images captured by each camera or comparing different vehicles in the same image, to avoid errors in the determination of fatigue driving behavior caused by errors in identifying the target vehicle, i.e., the purpose of re-obtaining the vehicle image is to ensure the accuracy of the determination of fatigue driving behavior and avoid misjudgment caused by image quality problems or errors in the recognition algorithm.

[0065] The embodiment realizes multi-dimensional comparison by comparing historical driving images, vehicle images and registration information, reduces misjudgment, effectively identifies a fake vehicle or identification error, ensures fairness, accurately identifies a target vehicle in a complex environment, and improves reliability.

[0066] In some embodiments, further comprising: In a case where the continuous driving speed is lower than the preset speed threshold, determining a low-speed driving duration corresponding to the continuous driving speed being lower than the preset speed threshold; In a case where the low-speed driving duration exceeds a preset low-speed driving duration threshold, initializing a driving trajectory and driving data of the target vehicle to re-determine the driving trajectory and the driving data of the target vehicle; In a case where the low-speed driving duration does not exceed the preset low-speed driving duration threshold, removing the low-speed driving duration from the continuous driving duration and not resetting a record of the continuous driving duration.

[0067] Optionally, in a case where the continuous driving speed of the target vehicle is lower than the preset speed threshold, the low-speed driving duration is recorded, where the low-speed driving duration refers to a duration in which the target vehicle continuously drives at a speed lower than the speed threshold, and the record of the low-speed driving duration can be achieved by analyzing the driving data of the target vehicle, that is, continuously monitoring the speed change of the target vehicle and starting timing when the continuous driving speed is lower than the preset speed threshold.

[0068] In addition, if the low-speed driving duration exceeds a preset low-speed driving duration threshold, for example, 10 minutes, the driving trajectory and the driving data of the target vehicle are initialized, that is, the recording of the driving trajectory and the driving data of the target vehicle is restarted, to ensure that the determination of fatigue driving behavior is based on accurate data.

[0069] The initialization is to exclude the influence of long-time low-speed driving on the determination of fatigue driving, for example, moving a car in a parking lot or slowly driving in traffic congestion, to ensure the accuracy of fatigue driving behavior.

[0070] In addition, if the low-speed driving duration does not exceed the preset low-speed driving duration threshold, the low-speed driving duration is removed when determining the continuous driving duration, but the record of the continuous driving duration is not reset, that is, the calculation of the continuous driving duration is reasonably adjusted without completely ignoring the low-speed driving, so as to more accurately reflect the actual driving situation of the target vehicle.

[0071] Further, since the reasonable duration of low-speed driving may be different under different traffic conditions, the preset speed threshold can be dynamically adjusted to flexibly handle the low-speed driving state according to the actual situation, thereby avoiding misjudgment caused by a fixed preset speed threshold.

[0072] Further, real-time data processing can be combined to quickly process and analyze the vehicle speed data of the road where the target vehicle is located, and a feedback mechanism is established to improve the accuracy and reliability of the fatigue driving behavior determination.

[0073] The embodiment can effectively exclude misjudgment caused by low-speed driving and improve the accuracy of fatigue driving behavior determination by monitoring the low-speed driving time and taking different processing methods according to whether the low-speed driving time exceeds the preset speed threshold.

[0074] In some embodiments, after collecting the driving trajectory and driving data of the target vehicle, the method further comprises: In the case that there are continuous missing data points in the driving trajectory, the missing start time, the missing end time, the missing duration and the missing position of the missing data points are determined; In the case that the missing duration exceeds the preset missing duration threshold, the missing area is determined based on the missing start time, the missing end time and the missing position; The nearest pre-missing valid data point before the missing start time and the nearest post-missing valid data point after the missing end time are extracted from the driving trajectory, and the spatial distance between the pre-missing valid data point and the post-missing valid data point is determined; In the case that the spatial distance exceeds the missing area, the data segment corresponding to the continuous missing data points is determined as an invalid data segment, and the invalid data segment is skipped in the continuous driving duration.

[0075] Optionally, in the case that there are continuous missing data points in the driving trajectory, the missing start time, the missing end time, the missing duration and the missing position of the missing data points are determined.

[0076] The missing start time and the missing end time represent the start and end time of the missing driving trajectory, respectively, the missing duration is the time difference between the two time points, and the missing position is the approximate geographic position of the target vehicle during the data missing period.

[0077] In addition, if the missing duration exceeds the preset missing duration threshold, for example, 10 minutes, the missing area is determined based on the missing start time, the missing end time and the missing position, wherein the missing area refers to the range where the target vehicle can travel within the missing duration, which can be estimated by analyzing the pre-missing and post-missing valid data points and the missing duration.

[0078] In addition, the nearest pre-missing valid data point before the missing start time and the nearest post-missing valid data point after the missing end time can be extracted from the driving trajectory, and the spatial distance between the pre-missing valid data point and the post-missing valid data point is determined to determine the distance that the vehicle can travel during the missing period.

[0079] If the spatial distance between the preceding valid data point and the succeeding valid data point exceeds the range of the loss area, the data segment corresponding to the continuous lost data points is determined as an invalid data segment, and when determining the continuous driving duration of the target vehicle, the invalid data segment is skipped to ensure that the calculation of the continuous driving duration is not affected by data loss.

[0080] The embodiment realizes that after collecting the driving trajectory and driving data of the target vehicle, the situation of driving data loss is processed, which can effectively avoid misjudgment caused by data loss, and at the same time, the fatigue driving behavior determination accuracy and reliability are improved.

[0081] In some embodiments, the driving trajectory, driving data and vehicle image of the target vehicle are encrypted to generate a fatigue driving data packet and uploaded to a server, comprising: The driving trajectory, driving data and vehicle image of the target vehicle are encrypted by a preset encryption algorithm to generate a ciphertext data block; A secure hash algorithm operation is performed on the ciphertext data block to generate a hash value, and a fatigue driving data packet is generated based on the hash value and the ciphertext data block, and the fatigue driving data packet is uploaded to the server.

[0082] Optionally, the embodiment encrypts the driving trajectory, driving data and vehicle image of the target vehicle by a preset encryption algorithm to generate a ciphertext data block, wherein the preset encryption algorithm can be an AES-256 encryption algorithm or an RSA encryption algorithm, and the selection of the preset encryption algorithm is based on the security and efficiency of the algorithm itself to ensure that the data to be encrypted is not tampered with or stolen during transmission and storage.

[0083] The encryption process involves converting the original data into ciphertext so that only the recipient with the correct key can decrypt and restore the data to obtain accurate data in the ciphertext data block.

[0084] In addition, a secure hash algorithm (such as SHA-256) operation is performed on the generated ciphertext data block to generate a hash value, wherein the hash value is a digital fingerprint of the data, which is unique and irreversible, and is used to verify the integrity and authenticity of the data. The generation process of the hash value ensures that even if the data has a slight change, the hash value will change significantly, so that it can be detected whether the data has been tampered with.

[0085] In addition, a fatigue driving data packet is generated based on the hash value and the ciphertext data block, wherein the hash value and the ciphertext data block are included in the fatigue driving data packet to ensure the integrity and traceability of the fatigue driving data packet. The safety and verifiability of the fatigue driving data packet are also considered, so that when the fatigue driving data packet is received, the authenticity of the data can be quickly verified according to the hash value.

[0086] In addition, the generated fatigue driving data packet is uploaded to a server, where the server can be a special server of a relevant department or a cloud storage platform. During the uploading process, a secure communication protocol can be used to ensure the safety of data transmission. The server receives and stores the fatigue driving data packet, providing reliable evidence support for subsequent related operations.

[0087] Further, the fatigue driving data packet in the server can be regularly backed up so that the server can quickly recover in the event of data loss or damage. The backup data can be stored in multiple geographic locations to prevent data loss due to natural disasters or human factors.

[0088] The embodiment realizes the generation of a fatigue driving data packet through encryption processing after collecting the driving trajectory, driving data, and vehicle image of the target vehicle, and uploading it to the server and verifying it through a hash value, so that the fatigue driving data packet cannot be tampered with or stolen during transmission and storage, ensuring the safety, reliability, integrity, and traceability of the fatigue driving data.

[0089] In order to effectively solve the deficiencies of traditional technology in terms of data fusion accuracy, limited interference exclusion, and reliable evidence solidification in determining fatigue driving, and significantly improve the accuracy and reliability of fatigue driving determination, the present application provides an embodiment of a driving state monitoring device for implementing all or part of the driving state monitoring, as shown in Figure 2 , the driving state monitoring device specifically includes the following contents: The first processing module 10 is used to collect the driving trajectory and driving data of the target vehicle. When the continuous driving time in the driving data exceeds the preset continuous driving time threshold and the continuous driving speed of the target vehicle exceeds the preset speed threshold, the driving behavior of the target vehicle is determined as a preliminary fatigue driving behavior. The driving data includes continuous driving time and continuous driving speed. The second processing module 20 is used to determine the real-time road type corresponding to the driving trajectory through the reverse geocoding method. When the road type is a non-congestion type, the vehicle image captured by the target vehicle passing through the kiosk in the driving trajectory is obtained, and the vehicle image is compared with the registration information of the current target vehicle to obtain a comparison result. The road type includes a congestion type and a non-congestion type, and the comparison result includes a consistent vehicle and an inconsistent vehicle. The third processing module 30 is used to update the preliminary fatigue driving behavior of the target vehicle to a fatigue driving behavior when the comparison result is a consistent vehicle. The driving trajectory, driving data, and vehicle image of the target vehicle are encrypted to generate a fatigue driving data packet and uploaded to the server.

[0090] From the above description, the driving state monitoring device provided by the embodiment of the application can collect the driving track and driving data of the target vehicle innovatively, determine the driving behavior of the target vehicle as a preliminary fatigue driving behavior when the continuous driving duration in the driving data exceeds the preset continuous driving duration threshold and the continuous driving speed of the target vehicle exceeds the preset speed threshold, determine the real-time road type corresponding to the driving track through the reverse geocoding mode, acquire the vehicle image of the target vehicle passing through the camera halfway in the driving track when the road type is a non-congestion type, compare the vehicle image with the registration information of the current target vehicle to obtain a comparison result, update the preliminary fatigue driving behavior of the target vehicle to a fatigue driving behavior when the comparison result is consistent with the vehicle, and encrypt the driving track, driving data and vehicle image of the target vehicle to generate a fatigue driving data packet and upload the fatigue driving data packet to a server. The method effectively solves the deficiencies of the prior art in the accuracy of data fusion, the limitation of interference exclusion and the reliability of evidence solidification in determining fatigue driving, and significantly improves the accuracy and reliability of fatigue driving determination.

[0091] From the hardware level, in order to effectively solve the deficiencies of the prior art in the accuracy of data fusion, the limitation of interference exclusion and the reliability of evidence solidification in determining fatigue driving, and significantly improve the accuracy and reliability of fatigue driving determination, the application provides an embodiment of an electronic device for implementing all or part of the contents of the driving state monitoring method, which specifically includes the following contents: A processor, a memory, a communications interface and a bus; wherein the processor, the memory and the communications interface complete mutual communication through the bus; the communications interface is used for realizing information transmission between the driving state monitoring device and a core business system, a user terminal and a related database and other related devices; the logic controller can be a desktop computer, a tablet computer and a mobile terminal, and the embodiment is not limited thereto. In the embodiment, the logic controller can be implemented with reference to the embodiments of the driving state monitoring method and the embodiments of the driving state monitoring device, the contents of which are incorporated herein, and repeated descriptions are omitted.

[0092] It can be understood that the user terminal can include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device and the like. The smart wearable device can include smart glasses, a smart watch, a smart bracelet and the like.

[0093] In practical applications, portions of the driving state monitoring method may be executed on the electronic device side as described above, or all operations may be performed on the client device. The specific selection may be based on the processing capabilities of the client device and the limitations of the user's usage scenario. This application does not impose any restrictions on this. If all operations are performed on the client device, the client device may also include a processor.

[0094] The aforementioned client device may include a communication module (i.e., a communication unit) capable of establishing a communication connection with a remote server to facilitate data transmission with the server. The server may include a server at the task scheduling center or, in other implementation scenarios, a server on an intermediate platform, such as a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may comprise a single computer device, a server cluster consisting of multiple servers, or a distributed server configuration.

[0095] Figure 3 Schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Figure 3 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that the Figure 3 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0096] In one embodiment, the driving state monitoring method function may be integrated into the central processing unit 9100. The central processing unit 9100 may be configured to perform the following control: Step S101: collecting a driving trajectory and driving data of a target vehicle. When the continuous driving duration in the driving data exceeds a preset continuous driving duration threshold and the continuous driving speed of the target vehicle exceeds a preset speed threshold, the driving behavior of the target vehicle is determined to be preliminary fatigue driving behavior. The driving data includes the continuous driving duration and the continuous driving speed. Step S102: Determine the real-time road type corresponding to the driving trajectory through reverse geocoding. If the road type is non-congested, obtain a vehicle image captured by a checkpoint while the target vehicle is on the driving trajectory, and compare the vehicle image with the current registration information of the target vehicle to obtain a comparison result. The road type may include congested type and non-congested type, and the comparison result may include consistent vehicles or inconsistent vehicles. Step S103: in the case of consistent comparison results, the preliminary fatigue driving behavior of the target vehicle is updated as the fatigue driving behavior, the driving trajectory, driving data and vehicle image of the target vehicle are encrypted to generate a fatigue driving data packet and uploaded to the server.

[0097] From the above description, the electronic device provided by the embodiments of the present application can effectively solve the problems of the prior art, such as the limited accuracy of data fusion, the limited interference exclusion, and the limited reliability of evidence solidification, in determining fatigue driving. The accuracy and reliability of fatigue driving determination are significantly improved.

[0098] In another embodiment, the driving state monitoring device can be configured separately from the central processor 9100, for example, the driving state monitoring device can be configured as a chip connected with the central processor 9100, and the driving state monitoring method function is realized through the control of the central processor.

[0099] As shown in Figure 3 , the electronic device 9600 can also include a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It should be noted that the electronic device 9600 does not necessarily include all the components shown in Figure 3 ; in addition, the electronic device 9600 can also include components not shown in Figure 3 , which can be referred to prior art.

[0100] As shown in Figure 3 , the central processor 9100 is sometimes also referred to as a controller or an operation control, which can include a microprocessor or other processor device and / or a logic device, the central processor 9100 receives input and controls the operation of each component of the electronic device 9600.

[0101] The memory 9140, for example, can be one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The above-mentioned information related to failure can be stored, and in addition, a program for executing the information related to failure can be stored. The central processing unit 9100 can execute the program stored in the memory 9140 to achieve information storage or processing, and the like.

[0102] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to supply power to the electronic device 9600. The display 9160 is used to display display objects such as images and characters. The display can be, for example, an LCD display, but is not limited thereto.

[0103] The memory 9140 can be a solid state memory such as a read only memory (ROM), a random access memory (RAM), a SIM card, and the like. It can also be a memory that retains information even when power is off, can be selectively erased, and is provided with more data, and examples of such a memory are sometimes referred to as an EPROM, and the like. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 can include an application / function storage section 9142 for storing application programs and function programs or for storing a flow for executing operations of the electronic device 9600 by the central processing unit 9100.

[0104] The memory 9140 can also include a data storage section 9143 for storing data such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. A driver storage section 9144 of the memory 9140 can include various drivers of the electronic device for a communication function and / or for executing other functions of the electronic device such as a messaging application, an address book application, and the like.

[0105] The communication module 9110 is a transmitter / receiver that transmits and receives signals via an antenna 9111. The communication module 9110 (transmitter / receiver) is coupled to the central processing unit 9100 to provide input signals and receive output signals, which can be the same as in the case of a conventional mobile communication terminal.

[0106] Based on different communication technologies, multiple communication modules 9110, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module, etc., can be provided in the same electronic device. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and to receive audio input from the microphone 9132, thereby enabling typical telecommunication functions. The audio processor 9130 can include any suitable buffers, decoders, amplifiers, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling the recording of audio on board via the microphone 9132 and the playing of stored audio on board via the speaker 9131.

[0107] Embodiments of the present application also provide a computer readable storage medium capable of implementing all steps of the driving state monitoring method in the above-mentioned embodiments in which the execution subject is a server or a client. The computer readable storage medium stores a computer program which, when executed by a processor, implements all steps of the driving state monitoring method in the above-mentioned embodiments in which the execution subject is a server or a client. For example, when the processor executes the computer program, the following steps are implemented: Step S101: Collecting the driving trajectory and driving data of the target vehicle, and determining the driving behavior of the target vehicle as a preliminary fatigue driving behavior when the continuous driving duration in the driving data exceeds a preset continuous driving duration threshold and the continuous driving speed of the target vehicle exceeds a preset speed threshold. The driving data includes the continuous driving duration and the continuous driving speed. Step S102: Determining the real-time road type corresponding to the driving trajectory by reverse geocoding. When the road type is a non-congestion type, obtaining a vehicle image of the target vehicle taken by a camera in the middle of the driving trajectory, and comparing the vehicle image with the registration information of the current target vehicle to obtain a comparison result. The road type includes a congestion type and a non-congestion type. The comparison result includes vehicle consistency and vehicle inconsistency. Step S103: When the comparison result is vehicle consistency, updating the preliminary fatigue driving behavior of the target vehicle to a fatigue driving behavior, and encrypting the driving trajectory, the driving data and the vehicle image of the target vehicle to generate a fatigue driving data packet and uploading it to the server.

[0108] From the above description, the computer readable storage medium provided by the embodiments of the application can collect the driving trajectory and driving data of the target vehicle innovatively, determine the driving behavior of the target vehicle as a preliminary fatigue driving behavior when the continuous driving duration in the driving data exceeds the preset continuous driving duration threshold and the continuous driving speed of the target vehicle exceeds the preset speed threshold, determine the real-time road type corresponding to the driving trajectory by the reverse geocoding method, acquire the vehicle image of the target vehicle passing through the camera halfway in the driving trajectory when the road type is a non-congestion type, compare the vehicle image with the registration information of the current target vehicle to obtain a comparison result, update the preliminary fatigue driving behavior of the target vehicle to a fatigue driving behavior when the comparison result is consistent with the vehicle, and encrypt the driving trajectory, driving data and vehicle image of the target vehicle to generate a fatigue driving data packet and upload it to the server. This method effectively solves the deficiencies of the traditional technology in the accuracy of data fusion, the limitation of interference exclusion and the reliability of evidence solidification in determining fatigue driving, and significantly improves the accuracy and reliability of fatigue driving determination.

[0109] The embodiments of the application also provide a computer program product capable of implementing all steps of the driving state monitoring method in the above-mentioned embodiments, wherein the execution subject is a server or a client. The computer program / instruction is executed by a processor to implement the steps of the driving state monitoring method, for example, the computer program / instruction implements the following steps: Step S101: collecting the driving trajectory and driving data of the target vehicle, and determining the driving behavior of the target vehicle as a preliminary fatigue driving behavior when the continuous driving duration in the driving data exceeds the preset continuous driving duration threshold and the continuous driving speed of the target vehicle exceeds the preset speed threshold, wherein the driving data includes the continuous driving duration and the continuous driving speed; Step S102: determining the real-time road type corresponding to the driving trajectory by the reverse geocoding method, acquiring the vehicle image of the target vehicle passing through the camera halfway in the driving trajectory when the road type is a non-congestion type, comparing the vehicle image with the registration information of the current target vehicle to obtain a comparison result, and the road type includes a congestion type and a non-congestion type, and the comparison result includes a consistent vehicle and an inconsistent vehicle; Step S103: updating the preliminary fatigue driving behavior of the target vehicle to a fatigue driving behavior when the comparison result is consistent with the vehicle, and encrypting the driving trajectory, driving data and vehicle image of the target vehicle to generate a fatigue driving data packet and upload it to the server.

[0110] From the above description, the computer program product provided by the embodiment of the present application can determine the driving behavior of the target vehicle as the preliminary fatigue driving behavior when the continuous driving time length in the driving data exceeds the preset continuous driving time length threshold and the continuous driving speed of the target vehicle exceeds the preset speed threshold, determine the real-time road type corresponding to the driving track by the reverse geocoding mode, acquire the vehicle image of the target vehicle passing through the camera in the middle of the driving track when the road type is the non-congestion type, compare the vehicle image with the registration information of the current target vehicle to obtain a comparison result, update the preliminary fatigue driving behavior of the target vehicle as the fatigue driving behavior when the comparison result is consistent with the vehicle, and encrypt the driving track, the driving data and the vehicle image of the target vehicle to generate a fatigue driving data packet and upload the fatigue driving data packet to the server. The method effectively solves the problems of the traditional technology in the accuracy of data fusion, the limitation of interference elimination and the reliability of evidence solidification in determining fatigue driving, and significantly improves the accuracy and reliability of fatigue driving determination.

[0111] Those skilled in the art will understand that the embodiments of the present application can be provided as methods, apparatuses, or computer program products. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0112] The present application is described with reference to flowcharts and / or block diagrams of the methods, apparatuses (devices), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the flow Figure 1 The function specified in one flow or multiple flows and / or blocks Figure 1 The device that implements the function specified in one flow or multiple flows and / or blocks.

[0113] These computer program instructions can also be stored in a computer-readable memory that can guide the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including instruction devices that implement the flow Figure 1 The function specified in one flow or multiple flows and / or blocks Figure 1the function specified in the one or more blocks.

[0114] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operational steps are generated to realize the computer-implemented process, and the instructions executed on the computer or other programmable devices provide a process for implementing the flowchart Figure 1 the flowchart or flowcharts and / or a block Figure 1 the steps of the function specified in the one or more blocks.

[0115] The principles and implementation manners of the present application are described in the specific embodiments in the present application, and the above embodiment descriptions are only used to help understand the method of the present application and its core idea; meanwhile, for the general skilled in the art, the specific implementation manners and application ranges will be changed according to the idea of the present application, and the above descriptions should not be understood as the limitation of the present application.

Claims

1. A driving state monitoring method, characterized in that: The method comprises: collecting a driving trajectory and driving data of a target vehicle, and determining the driving behavior of the target vehicle as preliminary fatigue driving behavior when a continuous driving duration in the driving data exceeds a preset continuous driving duration threshold and a continuous driving speed of the target vehicle exceeds a preset speed threshold, the driving data including the continuous driving duration and the continuous driving speed; Determining the real-time road type corresponding to the driving trajectory by reverse geocoding, and if the road type is a non-congested type, obtaining a vehicle image of the target vehicle captured at a checkpoint along the driving trajectory, and comparing the vehicle image with the current registration information of the target vehicle to obtain a comparison result, wherein the road type includes a congested type and a non-congested type, and the comparison result includes whether the vehicles are consistent or inconsistent; When the comparison result shows that the vehicles are consistent, the preliminary fatigue driving behavior of the target vehicle is updated to fatigue driving behavior, and the driving trajectory, driving data and vehicle image of the target vehicle are encrypted to generate a fatigue driving data packet and uploaded to the server.

2. The method according to claim 1, characterized in that The determining of the real-time road type corresponding to the driving trajectory by reverse geocoding includes: Inputting the starting coordinates and real-time coordinates of the driving trajectory into a map interface, obtaining geographic location information corresponding to the driving trajectory with the starting coordinates as the starting point, the real-time coordinates as the end point, and the driving trajectory as the trajectory, wherein the geographic location information includes the road name and real-time congestion index corresponding to each road; When the real-time congestion index indicates that the current road is a congested section, the initial road type is determined to be an initial congested type; when the real-time congestion index indicates that the current road is a non-congested section, the initial road type is determined to be an initial non-congested type; Determine each of the initial road types corresponding to the driving trajectory. When there is an initial congestion type among the initial road types that exceeds a preset congestion number threshold, determine the road type corresponding to the current driving trajectory as the congestion type, and update the driving behavior corresponding to the target vehicle to non-fatigue driving behavior.

3. The method according to claim 1, characterized in that The comparing the vehicle image with the current registration information of the target vehicle to obtain a comparison result includes: Identifying license plate information in the vehicle image, comparing the license plate information with the license plate information in the registration information, and obtaining a license plate comparison result; When the license plate comparison result is consistent, the vehicle appearance features in the vehicle image are extracted and compared with the vehicle appearance features in the registration information to obtain the comparison result, where the vehicle appearance features include body color, vehicle model and vehicle identification.

4. The method according to claim 3, characterized in that After obtaining the license plate comparison result, the method further includes: When the license plate comparison result is inconsistent, obtaining a historical driving image of the target vehicle, comparing the historical driving image, the vehicle image, and the registration information to obtain a duplicate license plate comparison result, the duplicate license plate comparison result being a questionable vehicle and an incorrectly identified vehicle; When the duplicate license plate comparison result indicates that the vehicle is in doubt, the vehicle image, the historical driving image, and the registration information are processed into a duplicate license plate data packet, and the duplicate license plate data packet is sent to a corresponding duplicate license plate processing platform; When the duplicate license plate comparison result indicates that a wrong vehicle is identified, the step of obtaining a vehicle image of the target vehicle taken when the target vehicle passes through a checkpoint in the driving trajectory is performed again until the comparison result is obtained.

5. The method according to claim 1, wherein Also includes: When the continuous driving speed is lower than the preset speed threshold, determining a low-speed driving time corresponding to the continuous driving speed being lower than the preset speed threshold; When the low-speed driving duration exceeds a preset low-speed driving duration threshold, initializing the driving trajectory and the driving data of the target vehicle to re-determine the driving trajectory and the driving data of the target vehicle; When the low-speed driving duration does not exceed the preset low-speed driving duration threshold, the low-speed driving duration is removed from the continuous driving duration and the record of the continuous driving duration is not reset.

6. The method according to claim 1, characterized in that After collecting the target vehicle's driving trajectory and driving data, it also includes: In the case where there are continuous lost data points in the driving trajectory, determining the lost start time, lost end time, lost duration and lost location of the lost data points; When the loss duration exceeds a preset loss duration threshold, determining a loss area based on the loss start time, the loss end time, and the loss location; Extracting a valid data point before the loss start time closest to the driving trajectory, extracting a valid data point after the loss end time closest to the driving trajectory, and determining a spatial distance between the valid data point before the loss and the valid data point after the loss; In a case where the spatial distance exceeds the lost area, the data segments corresponding to the consecutive lost data points are determined as invalid data segments, and the invalid data segments are skipped during the continuous driving duration.

7. The method according to claim 1, characterized in that Encrypting the driving trajectory, the driving data, and the vehicle image of the target vehicle to generate a fatigue driving data packet and uploading it to a server, including: Encrypting the driving trajectory, the driving data, and the vehicle image of the target vehicle using a preset encryption algorithm to generate a ciphertext data block; A secure hash algorithm operation is performed on the ciphertext data block to generate a hash value, a fatigue driving data packet is generated based on the hash value and the ciphertext data block, and the fatigue driving data packet is uploaded to a server.

8. A driving status monitoring device, characterized in that: The device comprises: a first processing module, configured to collect a driving trajectory and driving data of a target vehicle, and determine the driving behavior of the target vehicle as preliminary fatigue driving behavior when a continuous driving duration in the driving data exceeds a preset continuous driving duration threshold and a continuous driving speed of the target vehicle exceeds a preset speed threshold, the driving data including the continuous driving duration and the continuous driving speed; A second processing module is configured to determine the real-time road type corresponding to the driving trajectory by reverse geocoding, and if the road type is a non-congested type, obtain a vehicle image of the target vehicle taken at a checkpoint along the driving trajectory, and compare the vehicle image with the current registration information of the target vehicle to obtain a comparison result, wherein the road type includes a congested type and a non-congested type, and the comparison result includes whether the vehicles are consistent or inconsistent; The third processing module is used to update the preliminary fatigue driving behavior of the target vehicle to fatigue driving behavior when the comparison result shows that the vehicles are consistent, encrypt the driving trajectory, the driving data and the vehicle image of the target vehicle to generate a fatigue driving data packet and upload it to the server.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the driving state monitoring method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the driving state monitoring method according to any one of claims 1 to 7 are implemented.

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