Vehicle Internet of Things Data Processing Vehicle Internet of Things OTA Data Processing Method, System, Terminal and Storage Medium

Abstract

The present invention provides a vehicle networking OTA data processing method, system, terminal and storage medium for vehicle networking data processing. The method includes: performing anomaly prediction based on the environmental information and driving information of the target vehicle to obtain an anomaly prediction result; if the anomaly prediction result meets the anomaly condition, sending an OTA upgrade anomaly prompt to the driver; performing driving prediction based on the historical driving information and the current navigation information to obtain a predicted destination, and determining an OTA upgrade recommended area according to the historical driving information; matching the predicted destination with the OTA upgrade recommended area in terms of location, and generating an OTA upgrade recommendation prompt according to the location matching result. In the embodiment of the present invention, when the anomaly prediction result meets the anomaly condition, it is determined that the current OTA upgrade operation is a misoperation. By sending an OTA upgrade anomaly prompt to the driver, the misupgrade of the target vehicle is prevented, and the user experience is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle networking, and particularly to a vehicle networking OTA data processing method, system, terminal and storage medium for vehicle networking data processing. Background Art

[0002] With the development of technologies such as computers, data communication, intelligent sensing, etc. and the ubiquitous use of mobile intelligent terminals, technologies such as big data, cloud computing, Internet of Things, social networks, and artificial intelligence are further integrated into the entire ground traffic management system such as transportation, service recommendation, vehicle management, etc. Vehicle networking is an important part of the Internet of Things. It effectively combines vehicles, roads, people and other buildings through an information center to extract and effectively utilize the attribute information and static / dynamic information of all vehicles (i.e., intelligent vehicles) on an information network platform. As an open fusion network system that can be computed, controlled, managed, guided, and trusted for coordinating multiple people, multiple vehicles, multiple roadside units and an open environment, vehicle networking provides new ideas and ways for the intelligent management and control of vehicles.

[0003] In the existing vehicle networking OTA data processing during vehicle networking data processing, generally, the vehicle is directly OTA upgraded according to the OTA upgrade instruction sent by the user. When the user makes a misoperation, it is easy to cause the vehicle to be temporarily unusable, thereby reducing the user experience. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a vehicle networking OTA data processing method, system, terminal and storage medium for vehicle networking data processing to solve the problem of low user experience during vehicle networking data processing in the prior art.

[0005] The embodiments of the present invention are implemented as follows. A vehicle networking OTA data processing method, the method includes:

[0006] If an OTA upgrade instruction sent by a target vehicle is received, obtain the environment information and driving information of the target vehicle, and perform anomaly prediction on the target vehicle according to the environment information and the driving information to obtain an anomaly prediction result, where the environment information includes environmental images and environmental signals;

[0007] If the anomaly prediction result meets the anomaly condition, send an OTA upgrade anomaly prompt to the driver, and obtain the historical driving information and current navigation information of the target vehicle;

[0008] Perform driving prediction on the target vehicle according to the historical driving information and the current navigation information to obtain a predicted destination, and determine an OTA upgrade recommended area according to the historical driving information;

[0009] Match the predicted destination with the OTA upgrade recommended area in terms of location, and generate an OTA upgrade recommendation prompt according to the location matching result.

[0010] Preferably, perform anomaly prediction on the target vehicle according to the environmental information and the driving information to obtain an anomaly prediction result, including:

[0011] Perform entity recognition on the environmental image, determine the vehicle environmental type according to the entity recognition result, and match the vehicle environmental type with a preset environmental type to obtain an environmental matching result;

[0012] Obtain the driving duration in the driving information, and compare the driving duration with a preset duration to obtain a duration comparison result, where the driving duration is the duration between the start of the target vehicle and the current time;

[0013] Obtain the driving speed and lane change information in the driving information, and perform prediction of the behavior type according to the driving speed, the lane change information, and the driver's face image to obtain a predicted behavior type;

[0014] Compare the signal strength in the environmental signal with a signal threshold to obtain a signal comparison result;

[0015] Combine the signal comparison result, the predicted behavior type, the duration comparison result, and the environmental matching result to obtain the anomaly prediction result.

[0016] Preferably, performing prediction of the behavior type according to the driving speed, the lane change information, and the driver's face image to obtain a predicted behavior type includes:

[0017] Determine the number of acceleration anomalies according to the driving speed, and determine the lane change frequency according to the lane change information;

[0018] Extract features from the face image to obtain face features, and perform vector conversion on the face features, the number of acceleration anomalies, and the lane change frequency to obtain a face vector, an acceleration anomaly vector, and a lane change vector;

[0019] Combine the face vector, the acceleration anomaly vector, and the lane change vector to obtain a combined vector, and calculate the similarity between the combined vector and a preset vector to obtain a vector similarity;

[0020] Determine the behavior type corresponding to the maximum vector similarity as the predicted behavior type.

[0021] Preferably, after performing anomaly prediction on the target vehicle according to the environmental information and the driving information to obtain an anomaly prediction result, it further includes:

[0022] If the predicted behavior type is a preset type, it is determined that the abnormal prediction result meets the abnormal condition;

[0023] If in the duration comparison result, the driving duration is less than the preset duration, it is determined that the abnormal prediction result meets the abnormal condition;

[0024] If in the environment matching result, the vehicle environment type matches successfully with the preset environment type, it is determined that the abnormal prediction result meets the abnormal condition.

[0025] Preferably, driving prediction is performed on the target vehicle according to the historical driving information and the current navigation information to obtain a predicted destination, including:

[0026] Obtain the navigation end point in the current navigation information, and perform parking position matching between the navigation end point and the historical driving information to obtain a matching parking position;

[0027] Obtain the matching times of the matching parking position, and determine the matching parking position corresponding to the maximum matching times as the predicted destination.

[0028] Preferably, determining an OTA upgrade recommendation area according to the historical driving information includes:

[0029] Obtain the historical parking positions in the historical driving information, and obtain the parking times and average parking durations of the historical parking positions;

[0030] If the parking times of any of the historical parking positions are greater than the times threshold, and the average parking duration is greater than the duration threshold, determine the historical parking position as a candidate upgrade position;

[0031] Obtain the scene type of the candidate upgrade position, and perform position screening on the candidate upgrade position according to the scene type;

[0032] Obtain the position distance between the candidate upgrade position after position screening and the data base station, and determine the candidate upgrade position with a position distance less than the distance threshold as the target upgrade position;

[0033] Perform area division according to the target upgrade position to obtain the OTA upgrade recommendation area.

[0034] Another object of the embodiments of the present invention is to provide a vehicle networking data processing vehicle networking OTA data processing system, the system includes:

[0035] Anomaly prediction module, which is used to obtain the environmental information and driving information of the target vehicle if an OTA upgrade instruction sent by the target vehicle is received, and perform anomaly prediction on the target vehicle according to the environmental information and the driving information to obtain an anomaly prediction result, where the environmental information includes environmental images and environmental signals;

[0036] Information acquisition module, which is used to send an OTA upgrade anomaly prompt to the driver if the anomaly prediction result meets the anomaly condition, and obtain the historical driving information and current navigation information of the target vehicle;

[0037] Driving prediction module, which is used to perform driving prediction on the target vehicle according to the historical driving information and the current navigation information to obtain a predicted destination, and determine an OTA upgrade recommended area according to the historical driving information;

[0038] Upgrade recommendation module, which is used to match the predicted destination with the OTA upgrade recommended area, and generate an OTA upgrade recommendation prompt according to the position matching result.

[0039] Preferably, the anomaly prediction module is further used to:

[0040] Perform entity recognition on the environmental image, determine the vehicle environment type according to the entity recognition result, and match the vehicle environment type with a preset environment type to obtain an environment matching result;

[0041] Obtain the driving duration in the driving information, and compare the driving duration with a preset duration to obtain a duration comparison result, where the driving duration is the duration between the start of the target vehicle and the current time;

[0042] Obtain the driving speed and lane change information in the driving information, and perform behavior type prediction according to the driving speed, the lane change information and the driver's face image to obtain a predicted behavior type;

[0043] Compare the signal strength in the environmental signal with a signal threshold to obtain a signal comparison result;

[0044] Combine the signal comparison result, the predicted behavior type, the duration comparison result and the environment matching result to obtain the anomaly prediction result.

[0045] In an embodiment of the present invention, abnormal prediction of a target vehicle can be effectively performed through environmental information and driving information to detect whether the target vehicle meets the OTA upgrade condition. When the abnormal prediction result meets the abnormal condition, it is determined that the target vehicle does not meet the OTA upgrade condition, and the current OTA upgrade operation is a misoperation. By sending an OTA upgrade abnormal prompt to the driver, misupgrading of the target vehicle is prevented, effectively improving the user experience. Driving prediction of the target vehicle can be effectively performed through historical driving information and current navigation information to automatically determine the predicted destination of the target vehicle. Based on the historical driving information, the OTA upgrade recommended area can be effectively determined. By matching the predicted destination with the OTA upgrade recommended area, an OTA upgrade recommendation prompt for the target vehicle can be automatically generated. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a flowchart of a vehicle networking OTA data processing method for vehicle networking data processing provided by the first embodiment of the present invention;

[0047] Figure 2 is a schematic structural diagram of a vehicle networking OTA data processing system for vehicle networking data processing provided by the second embodiment of the present invention;

[0048] Figure 3 is a schematic structural diagram of a terminal device provided by the third embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0050] In order to illustrate the technical solutions described in the present invention, the following will be described through specific embodiments.

[0051] Embodiment 1

[0052] Please refer to Figure 1 , which is a flowchart of a vehicle networking OTA data processing method for vehicle networking data processing provided by the first embodiment of the present invention. This vehicle networking OTA data processing method can be applied to any device or system. This vehicle networking OTA data processing method includes the following steps:

[0053] Step S10, if an OTA upgrade instruction sent by a target vehicle is received, obtain the environmental information and driving information of the target vehicle, and perform abnormal prediction on the target vehicle according to the environmental information and the driving information to obtain an abnormal prediction result;

[0054] Among them, the environmental information includes environmental images and environmental signals. The environmental image is an image of the environment where the target vehicle is currently located. The environmental signals include the network download speed of the target vehicle. The target vehicle is abnormally predicted based on the environmental information and driving information to predict whether both the environment where the target vehicle is currently located and the driving behavior of the driver meet the OTA upgrade conditions. When it is detected that the predicted environment where the target vehicle is currently located and / or the driving behavior of the driver do not meet the OTA upgrade conditions, it is determined that there is an abnormality in the current OTA upgrade of the target vehicle, and the current OTA upgrade is a misoperation by the user.

[0055] Optionally, abnormal prediction is performed on the target vehicle according to the environmental information and the driving information to obtain an abnormal prediction result, including:

[0056] Perform entity recognition on the environmental image, determine the vehicle environment type according to the entity recognition result, and match the vehicle environment type with a preset environment type to obtain an environment matching result; among them, entity recognition is performed on the environmental image to determine the entity type of the image entity in the environmental image, an environmental entity matrix is constructed according to the entity type and image coordinates of the image entity, vector conversion is performed on the environmental entity matrix to obtain an environmental entity vector, the similarity between the environmental entity vector and the environmental type vector in the environmental type database is calculated to obtain a type similarity, and the environmental type corresponding to the maximum type similarity is determined as the vehicle environment type. The preset environment type can be set according to requirements. For example, the preset environment type can be set as a highway environment type, a viaduct environment type, or a driving lane environment type, etc.;

[0057] Obtain the driving duration in the driving information and compare the driving duration with a preset duration to obtain a duration comparison result; among them, the driving duration is the duration between the start of the target vehicle and the current time, and the preset duration can be set according to requirements;

[0058] Obtain the driving speed and lane change information in the driving information, and perform behavior type prediction according to the driving speed, the lane change information, and the face image of the driver to obtain a predicted behavior type;

[0059] Compare the signal strength in the environmental signal with a signal threshold to obtain a signal comparison result; among them, the signal threshold can be set according to requirements;

[0060] Combine the signal comparison result, the predicted behavior type, the duration comparison result, and the environment matching result to obtain the abnormal prediction result.

[0061] Furthermore, performing behavior type prediction according to the driving speed, the lane change information, and the face image of the driver to obtain a predicted behavior type, including:

[0062] Determine the number of abnormal accelerations according to the driving speed, and determine the lane-changing frequency according to the lane-changing information; wherein, if the difference in driving speed within a preset time interval is greater than the difference threshold, it is determined that the current preset time interval is the abnormal acceleration duration, obtain the number of abnormal acceleration durations to get the number of abnormal accelerations, and calculate the lane-changing frequency according to the number of lane-changes and driving duration in the lane-changing information;

[0063] Extract features from the face image to obtain face features, and perform vector conversion on the face features, the number of abnormal accelerations, and the lane-changing frequency to obtain a face vector, an abnormal acceleration vector, and a lane-changing vector; wherein, by performing key-point recognition on the face image, face features are obtained, and the face features are used to represent the facial expression of the driver;

[0064] Combine the face vector, the abnormal acceleration vector, and the lane-changing vector to obtain a combined vector, and calculate the similarity between the combined vector and a preset vector to obtain the vector similarity; wherein, the preset vector can be set according to requirements;

[0065] Determine the behavior type corresponding to the maximum vector similarity as the predicted behavior type.

[0066] Furthermore, after obtaining the abnormal prediction result by performing abnormal prediction on the target vehicle according to the environmental information and the driving information, it further includes:

[0067] If the predicted behavior type is a preset type, it is determined that the abnormal prediction result meets the abnormal condition; wherein, the preset type can be set according to requirements. For example, the preset type can be set as a violent driving type or an angry expression type, etc. When the predicted behavior type is a violent driving type, it is determined that the driver currently has a violent driving situation. Therefore, the abnormal prediction result meets the abnormal condition, and the current target vehicle does not meet the OTA upgrade condition;

[0068] If in the duration comparison result, the driving duration is less than the preset duration, it is determined that the abnormal prediction result meets the abnormal condition; wherein, the preset duration can be set according to requirements;

[0069] If in the environmental matching result, the vehicle environmental type matches successfully with the preset environmental type, it is determined that the abnormal prediction result meets the abnormal condition; wherein, if the vehicle environmental type matches successfully with the preset environmental type, it is determined that the environment where the target vehicle is currently located cannot meet the OTA upgrade condition. Therefore, the abnormal prediction result meets the abnormal condition.

[0070] Step S20: If the abnormal prediction result meets the abnormal condition, send an OTA upgrade abnormal prompt to the driver, and obtain the historical driving information and current navigation information of the target vehicle.

[0071] Among them, when the abnormal prediction result meets the abnormal condition, it is determined that the target vehicle does not meet the OTA upgrade condition, and the current OTA upgrade operation is a misoperation. By sending an OTA upgrade abnormal prompt to the driver, the misupgrade of the target vehicle is prevented, effectively improving the user experience.

[0072] In this step, when the abnormal prediction result does not meet the abnormal condition, it is determined that the current environment of the target vehicle and the driving behavior of the driver meet the OTA upgrade condition, and an OTA upgrade operation is performed on the target vehicle.

[0073] Step S30: Perform driving prediction on the target vehicle according to the historical driving information and the current navigation information to obtain a predicted destination, and determine an OTA upgrade recommended area according to the historical driving information.

[0074] Among them, the driving prediction of the target vehicle can be effectively performed through the historical driving information and the current navigation information to automatically determine the predicted destination of the target vehicle. The OTA upgrade recommended area is used to represent the area where the target vehicle is recommended to perform OTA upgrade.

[0075] Optionally, performing driving prediction on the target vehicle according to the historical driving information and the current navigation information to obtain a predicted destination includes:

[0076] Obtain the navigation end point in the current navigation information, and match the navigation end point with the historical driving information to obtain a matching parking position; among them, the historical driving information includes the corresponding relationship between the historical end point and the corresponding parking position, match the navigation end point with the historical end point, and determine the parking position corresponding to the matched historical end point as the matching parking position.

[0077] Obtain the matching times of the matching parking position, and determine the matching parking position corresponding to the maximum matching times as the predicted destination.

[0078] Furthermore, determining the OTA upgrade recommended area according to the historical driving information includes:

[0079] Obtain the historical parking positions in the historical driving information, and obtain the parking times and average parking duration of the historical parking positions.

[0080] If the number of parking times at any of the historical parking positions is greater than the number threshold, and the average parking duration is greater than the duration threshold, then determine the historical parking position as a candidate upgrade position; where the number threshold and the duration threshold can be set according to requirements;

[0081] Obtain the scene type of the candidate upgrade position, and perform position screening on the candidate upgrade position according to the scene type; where, match the candidate upgrade position with a type query table to obtain the scene type. The type query table stores the corresponding relationships between different candidate upgrade positions and the corresponding scene types. Match the scene type of the candidate upgrade position with a scene blacklist. If the scene type of the candidate upgrade position matches the scene blacklist successfully, then delete the candidate upgrade position. The scene blacklist stores specified scene types, and the specified scene types can be set according to requirements;

[0082] Obtain the position distance between the candidate upgrade position after position screening and the data base station, and determine the candidate upgrade position with a position distance less than the distance threshold as the target upgrade position; where the distance threshold can be set according to requirements;

[0083] Perform area division according to the target upgrade position to obtain the OTA upgrade recommended area; where, during the area division process, the image and size of the area can be set according to requirements.

[0084] Step S40, match the predicted destination with the OTA upgrade recommended area, and generate an OTA upgrade recommendation prompt according to the position matching result;

[0085] Among them, match the predicted destination with the OTA upgrade recommended area to determine whether the predicted destination is within the OTA upgrade recommended area. If the predicted destination is within the OTA upgrade recommended area, the content of the generated OTA upgrade recommendation prompt includes "It is recommended to perform OTA upgrade at the predicted destination". If the predicted destination is not within the OTA upgrade recommended area, the content of the generated OTA upgrade recommendation prompt includes "It is not recommended to perform OTA upgrade at the predicted destination".

[0086] In this embodiment, the target vehicle can be effectively predicted for anomalies through environmental information and driving information to detect whether the target vehicle meets the OTA upgrade conditions. When the anomaly prediction result meets the anomaly condition, it is determined that the target vehicle does not meet the OTA upgrade conditions, and the current OTA upgrade operation is a misoperation. By sending an OTA upgrade anomaly prompt to the driver, the misupgrade of the target vehicle is prevented, effectively improving the user experience. The driving of the target vehicle can be effectively predicted through historical driving information and current navigation information to automatically determine the predicted destination of the target vehicle. Based on the historical driving information, the OTA upgrade recommended area can be effectively determined. By matching the predicted destination with the OTA upgrade recommended area, an OTA upgrade recommendation prompt for the target vehicle can be automatically generated.

[0087] Embodiment 2

[0088] Please refer to Figure 2 , which is a schematic structural diagram of the vehicle networking OTA data processing system 100 provided by the second embodiment of the present invention, including:

[0089] Anomaly prediction module 10, configured to, if receiving an OTA upgrade instruction sent by the target vehicle, obtain the environmental information and driving information of the target vehicle, and perform anomaly prediction on the target vehicle according to the environmental information and the driving information to obtain an anomaly prediction result, where the environmental information includes environmental images and environmental signals.

[0090] Optionally, the anomaly prediction module 10 is further configured to: perform entity recognition on the environmental image, determine the vehicle environmental type according to the entity recognition result, and match the vehicle environmental type with a preset environmental type to obtain an environmental matching result;

[0091] Obtain the driving duration in the driving information, and compare the driving duration with a preset duration to obtain a duration comparison result, where the driving duration is the duration between the start of the target vehicle and the current time;

[0092] Obtain the driving speed and lane change information in the driving information, and perform prediction of the behavior type according to the driving speed, the lane change information, and the facial image of the driver to obtain a predicted behavior type;

[0093] Compare the signal strength in the environmental signal with a signal threshold to obtain a signal comparison result;

[0094] Combine the signal comparison result, the predicted behavior type, the duration comparison result, and the environmental matching result to obtain the anomaly prediction result.

[0095] Further, the anomaly prediction module 10 is further configured to: determine the number of acceleration anomalies according to the driving speed, and determine the lane change frequency according to the lane change information;

[0096] Extract features from the face image to obtain face features, and perform vector conversion on the face features, the number of acceleration anomalies, and the lane change frequency to obtain a face vector, an acceleration anomaly vector, and a lane change vector;

[0097] Combine the face vector, the acceleration anomaly vector, and the lane change vector to obtain a combined vector, and calculate the similarity between the combined vector and a preset vector to obtain a vector similarity;

[0098] Determine the behavior type corresponding to the maximum vector similarity as the predicted behavior type.

[0099] Furthermore, the anomaly prediction module 10 is further configured to: if the predicted behavior type is a preset type, determine that the anomaly prediction result meets the anomaly condition;

[0100] If in the duration comparison result, the driving duration is less than the preset duration, determine that the anomaly prediction result meets the anomaly condition;

[0101] If in the environment matching result, the vehicle environment type matches the preset environment type successfully, determine that the anomaly prediction result meets the anomaly condition.

[0102] The information acquisition module 11 is configured to, if the anomaly prediction result meets the anomaly condition, send an OTA upgrade anomaly prompt to the driver, and acquire the historical driving information and the current navigation information of the target vehicle.

[0103] The driving prediction module 12 is configured to perform driving prediction on the target vehicle according to the historical driving information and the current navigation information to obtain a predicted destination, and determine an OTA upgrade recommended area according to the historical driving information.

[0104] Optionally, the driving prediction module 12 is further configured to: acquire the navigation end point in the current navigation information, and perform parking position matching between the navigation end point and the historical driving information to obtain a matching parking position;

[0105] Acquire the number of matching times of the matching parking position, and determine the matching parking position corresponding to the maximum number of matching times as the predicted destination.

[0106] Further, the driving prediction module 12 is further configured to: acquire the historical parking position in the historical driving information, and acquire the parking times and the average parking duration of the historical parking position;

[0107] If the number of parking times at any of the historical parking positions is greater than the number threshold, and the average parking duration is greater than the duration threshold, then determine the historical parking position as a candidate upgrade position;

[0108] Obtain the scenario type of the candidate upgrade position, and perform position screening on the candidate upgrade position according to the scenario type;

[0109] Obtain the position distance between the candidate upgrade position after position screening and the data base station, and determine the candidate upgrade position with a position distance less than the distance threshold as the target upgrade position;

[0110] Perform area division according to the target upgrade position to obtain the OTA upgrade recommended area.

[0111] The upgrade recommendation module 13 is used to match the predicted destination with the OTA upgrade recommended area, and generate an OTA upgrade recommendation prompt according to the position matching result.

[0112] In this embodiment, the target vehicle can be effectively predicted for anomalies through environmental information and driving information to detect whether the target vehicle meets the OTA upgrade conditions. When the anomaly prediction result meets the anomaly condition, it is determined that the target vehicle does not meet the OTA upgrade conditions, and the current OTA upgrade operation is a misoperation. By sending an OTA upgrade anomaly prompt to the driver, the mis-upgrade of the target vehicle is prevented, effectively improving the user experience. The driving of the target vehicle can be effectively predicted through historical driving information and current navigation information to automatically determine the predicted destination of the target vehicle. Based on historical driving information, the OTA upgrade recommended area can be effectively determined. By matching the predicted destination with the OTA upgrade recommended area, an OTA upgrade recommendation prompt for the target vehicle can be automatically generated.

[0113] Embodiment III

[0114] Figure 3 It is a structural block diagram of a terminal device 2 provided in the third embodiment of the present application. As Figure 3 shown, the terminal device 2 of this embodiment includes: a processor 20, a memory 21, and a computer program 22 stored in the memory 21 and executable on the processor 20, such as a program for processing vehicle networking data and OTA data of vehicle networking. When the processor 20 executes the computer program 22, the steps in each of the above embodiments of the vehicle networking data processing and vehicle networking OTA data processing method are implemented.

[0115] Exemplarily, the computer program 22 may be divided into one or more modules. The one or more modules are stored in the memory 21 and executed by the processor 20 to complete this application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program 22 in the terminal device 2. The terminal device may include, but is not limited to, a processor 20 and a memory 21.

[0116] The so-called processor 20 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0117] The memory 21 may be an internal storage unit of the terminal device 2, such as the hard disk or memory of the terminal device 2. The memory 21 may also be an external storage device of the terminal device 2, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 2. Further, the memory 21 may also include both the internal storage unit and the external storage device of the terminal device 2. The memory 21 is used to store the computer program and other programs and data required by the terminal device. The memory 21 may also be used to temporarily store data that has been output or will be output.

[0118] In addition, in each embodiment of this application, the various functional modules may be integrated in one processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0119] When an integrated module is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Among them, the computer-readable storage medium can be non-volatile or volatile. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of this application, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable storage medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice within the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0120] The above-mentioned embodiments are only used to illustrate the technical solutions of this application, rather than to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of this application, and should all be included in the protection scope of this application.

Claims

1. A method for processing Internet of Vehicles OTA data, characterized in that: The method comprises: If an OTA upgrade instruction sent by a target vehicle is received, environmental information and driving information of the target vehicle are obtained, and abnormality prediction of the target vehicle is performed according to the environmental information and the driving information to obtain an abnormality prediction result, wherein the environmental information includes an environmental image and an environmental signal; If the abnormal prediction result meets the abnormal condition, an OTA upgrade abnormality prompt is sent to the driver, and the historical driving information and current navigation information of the target vehicle are obtained; Performing driving prediction for the target vehicle according to the historical driving information and the current navigation information to obtain a predicted destination, and determining a recommended area for OTA upgrade according to the historical driving information; Position-matching the predicted destination with the OTA upgrade recommendation area, and generating an OTA upgrade recommendation prompt according to the position matching result; Performing driving prediction on the target vehicle according to the historical driving information and the current navigation information to obtain a predicted destination includes: Acquire a navigation destination in the current navigation information, and match the navigation destination with the historical driving information to obtain a matching parking location; Acquire the matching times of the matching parking positions, and determine the matching parking position corresponding to the maximum matching times as the predicted destination; Determining the OTA upgrade recommended area based on the historical driving information includes: Obtaining historical parking locations in the historical driving information, and obtaining the number of parking times and average parking duration at the historical parking locations; If the number of parking times at any of the historical parking locations is greater than a number threshold, and the average parking duration is greater than a duration threshold, the historical parking location is determined as a candidate upgrade location; Acquiring the scenario type of the candidate upgrade location, and performing location screening on the candidate upgrade location according to the scenario type; Obtaining the location distance between the candidate upgrade location after location screening and the data base station, and determining the candidate upgrade location whose location distance is less than a distance threshold as the target upgrade location; The target upgrade location is divided into regions to obtain the OTA upgrade recommended region.

2. The method for processing Internet of Vehicles OTA data according to claim 1, characterized in that: An abnormality prediction is performed on the target vehicle according to the environmental information and the driving information to obtain an abnormality prediction result, including: Performing entity recognition on the environment image, determining the vehicle environment type according to the entity recognition result, and matching the vehicle environment type with a preset environment type to obtain an environment matching result; Acquire the driving time in the driving information, and compare the driving time with a preset time to obtain a time comparison result, wherein the driving time is the time between the start of the target vehicle and the current time; Acquiring the driving speed and lane change information in the driving information, and performing behavior type prediction based on the driving speed, the lane change information and the driver's facial image to obtain a predicted behavior type; Comparing the signal strength in the environmental signal with the signal threshold to obtain a signal comparison result; The signal comparison result, the predicted behavior type, the duration comparison result and the environment matching result are combined to obtain the abnormal prediction result.

3. The method for processing Internet of Vehicles OTA data as claimed in claim 2, characterized in that: Predicting a behavior type according to the vehicle speed, the lane change information, and the driver's facial image to obtain a predicted behavior type includes: determining the number of abnormal accelerations according to the driving speed, and determining the lane changing frequency according to the lane changing information; Extracting features from the facial image to obtain facial features, and performing vector conversion on the facial features, the number of abnormal accelerations, and the lane-changing frequency to obtain a facial vector, an abnormal acceleration vector, and a lane-changing vector; The face vector, the acceleration abnormality vector and the lane change vector are combined to obtain a combined vector, and similarity calculation is performed between the combined vector and a preset vector to obtain vector similarity; The behavior type corresponding to the maximum vector similarity is determined as the predicted behavior type.

4. The method for processing Internet of Vehicles data and Internet of Vehicles OTA data processing as claimed in claim 2, characterized in that: After performing abnormal prediction on the target vehicle according to the environmental information and the driving information and obtaining the abnormal prediction result, the method further includes: If the predicted behavior type is a preset type, determining that the abnormal prediction result meets the abnormal condition; If, in the duration comparison result, the driving duration is less than the preset duration, it is determined that the abnormal prediction result meets the abnormal condition; If, in the environment matching result, the vehicle environment type successfully matches the preset environment type, it is determined that the abnormal prediction result meets the abnormal condition.

5. A vehicle networking data processing vehicle networking OTA data processing system, characterized in that: The system comprises: An abnormality prediction module is used for obtaining environmental information and driving information of the target vehicle if an OTA upgrade instruction sent by the target vehicle is received, and performing abnormality prediction on the target vehicle according to the environmental information and the driving information to obtain an abnormality prediction result, wherein the environmental information includes an environmental image and an environmental signal; An information acquisition module, configured to send an OTA upgrade abnormality prompt to the driver if the abnormal prediction result meets the abnormal condition, and obtain the historical driving information and current navigation information of the target vehicle; A driving prediction module, configured to perform driving prediction on the target vehicle according to the historical driving information and the current navigation information, obtain a predicted destination, and determine a recommended area for OTA upgrade according to the historical driving information; An upgrade recommendation module, configured to perform position matching between the predicted destination and the OTA upgrade recommendation area, and generate an OTA upgrade recommendation prompt according to the position matching result; The driving prediction module is further used to: obtain a navigation destination in the current navigation information, and match the navigation destination with the historical driving information to obtain a matching parking location; Acquire the matching times of the matching parking positions, and determine the matching parking position corresponding to the maximum matching times as the predicted destination; The driving prediction module is also used to: obtain the historical parking positions in the historical driving information, and obtain the number of parking times and average parking duration at the historical parking positions; If the number of parking times at any of the historical parking locations is greater than a number threshold, and the average parking duration is greater than a duration threshold, the historical parking location is determined as a candidate upgrade location; Acquiring the scenario type of the candidate upgrade location, and performing location screening on the candidate upgrade location according to the scenario type; Obtaining the location distance between the candidate upgrade location after location screening and the data base station, and determining the candidate upgrade location whose location distance is less than a distance threshold as the target upgrade location; The target upgrade location is divided into regions to obtain the OTA upgrade recommended region.

6. The vehicle networking data processing vehicle networking OTA data processing system as claimed in claim 5, characterized in that: The abnormal prediction module is also used for: Performing entity recognition on the environment image, determining the vehicle environment type according to the entity recognition result, and matching the vehicle environment type with a preset environment type to obtain an environment matching result; Acquire the driving time in the driving information, and compare the driving time with a preset time to obtain a time comparison result, wherein the driving time is the time between the start of the target vehicle and the current time; Acquiring the driving speed and lane change information in the driving information, and performing behavior type prediction based on the driving speed, the lane change information and the driver's facial image to obtain a predicted behavior type; Comparing the signal strength in the environmental signal with the signal threshold to obtain a signal comparison result; The signal comparison result, the predicted behavior type, the duration comparison result and the environment matching result are combined to obtain the abnormal prediction result.

7. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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