Vehicle data reporting method, system and device, vehicle, storage medium and product

Abstract

The invention discloses a vehicle data reporting method, system and device, a vehicle, a storage medium and a product, and relates to the technical field of vehicles. The method comprises the following steps: acquiring a dynamic sensor signal and position information of a vehicle, wherein the dynamic sensor signal is a signal acquired by a sensor in the vehicle; and under the condition that the dynamic sensor signal meets the time sequence condition and the position information meets the space sequence condition, triggering to report the vehicle data, and reporting the vehicle data. A comprehensive triggering condition is constructed by fusing a time sequence condition and a space sequence condition, and vehicle data reporting can be triggered only when the time sequence condition and the space sequence condition are met at the same time, so that intelligent control of vehicle data uploading is realized, reporting of invalid data and redundant data is reduced, and the vehicle data uploading efficiency is improved. The occupation of the network bandwidth and the storage resource of the cloud server is reduced, and the data transmission efficiency is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of vehicle technology, and in particular to a vehicle data reporting method, system, device, vehicle, storage medium, and product. Background Art

[0002] With the rapid development of Internet of Vehicles technology, vehicle data can be reported to cloud servers for analysis and storage.

[0003] In related technologies, vehicle data reporting often uses a timed reporting mechanism. That is, the vehicle packages all sensor signals collected by the vehicle (including acceleration, speed, steering angle, etc.) into complete data frames at a preset interval (such as every 5 seconds or 30 seconds). All sensor signals are then uploaded to a cloud server via a fixed network channel.

[0004] However, this method uploads a lot of invalid and redundant data. For example, when the vehicle is stationary, the zero acceleration value at idle uploaded at regular intervals is invalid information. This high amount of invalid and redundant data not only consumes network bandwidth but also consumes storage resources on the cloud server. Summary of the Invention

[0005] The present application provides a vehicle data reporting method, system, device, vehicle, storage medium and product to solve the problem that the uploaded data contains a large amount of invalid data and redundant data, which not only occupies network bandwidth but also occupies storage resources of cloud servers.

[0006] To achieve the above objectives, this application adopts the following technical solutions:

[0007] In a first aspect, a vehicle data reporting method is provided, comprising: acquiring a vehicle's dynamic sensor signal and location information, wherein the dynamic sensor signal is a signal collected by a sensor in the vehicle. When the dynamic sensor signal satisfies a time sequence condition and the location information satisfies a spatial sequence condition, triggering vehicle data reporting, wherein the time sequence condition is a configured criterion for determining whether the dynamic sensor signal satisfies the trigger condition, and the spatial sequence condition is a configured criterion for determining whether the location information satisfies the trigger condition. The vehicle data is then reported.

[0008] The solution provided in this application constructs a comprehensive trigger condition by integrating time series conditions and space series conditions. Vehicle data reporting will be triggered only when both the time series conditions and the space series conditions are met at the same time. This realizes intelligent control of vehicle data upload, reduces the reporting of invalid data and redundant data, reduces the network bandwidth and storage resources occupied by cloud servers, and improves data transmission efficiency.

[0009] In a possible implementation, the vehicle data reporting method provided in the present application further includes: performing data analysis on the dynamic sensor signal based on a sliding time window; and determining that the dynamic sensor signal meets the time series condition when the dynamic sensor signal within the sliding time window is greater than or equal to the monitoring signal threshold.

[0010] In another possible implementation, the vehicle data reporting method provided in the present application further includes: obtaining a normal distribution of historical dynamic sensor signals; obtaining an abnormal signal threshold based on the normal distribution; and determining that the dynamic sensor signal meets the time series condition when the dynamic sensor signal is greater than or equal to the abnormal signal threshold.

[0011] Another possible implementation method is that the dynamic sensor signal within the above-mentioned sliding time window is greater than or equal to the monitoring signal threshold, which can be specifically implemented as follows: the signal values ​​of the dynamic sensor signal within the sliding time window in a continuous first time period are all greater than or equal to the monitoring signal threshold.

[0012] Another possible implementation method is that the dynamic sensor signal within the above-mentioned sliding time window is greater than or equal to the monitoring signal threshold, which can be specifically implemented as follows: the signal mean of the dynamic sensor signal within the sliding time window is greater than or equal to the monitoring signal threshold, and the signal mean refers to the average value of the signal value of the dynamic sensor signal within the sliding time window.

[0013] In another possible implementation, the monitoring signal threshold and the abnormal signal threshold are both dynamically adjusted, and the adjustment of the monitoring signal threshold and the abnormal signal threshold is associated with the location and scene of the vehicle.

[0014] In another possible implementation, the vehicle data reporting method provided in the present application further includes: analyzing the location information to obtain the scene in which the vehicle is located; when the scene is a risk scene, the location information satisfies the spatial sequence condition.

[0015] In another possible implementation method, the vehicle data reporting method provided in the present application further includes: analyzing the position information to obtain the position parameters of the vehicle's position; when the position parameter is greater than or equal to the position parameter threshold, the position information satisfies the spatial sequence condition.

[0016] In another possible implementation, the vehicle data reporting method provided in the present application further includes: after triggering the reporting, performing data processing on the dynamic sensor signal and the location information to obtain the vehicle data.

[0017] In another possible implementation, the data processing includes at least one of the following: filtering invalid signals in the dynamic sensor signal and the location information; comparing the dynamic sensor signal of the current period with the dynamic sensor signal of the previous period, comparing the location information of the current period with the location information of the previous period, and filtering out difference signals from the dynamic sensor signal and the location information of the current period; sampling the dynamic sensor signal and the location information; or adding timestamps and / or positioning coordinates to the dynamic sensor signal and the location information.

[0018] In another possible implementation, the above-mentioned reporting of the vehicle data may be specifically implemented as follows: when reporting the vehicle data, different transmission channels are selected to report the vehicle data according to attribute parameters of the vehicle data or the network status of the vehicle.

[0019] In another possible implementation, the above-mentioned selecting different transmission channels to report the vehicle data according to the attribute parameters of the vehicle data can be specifically implemented as follows: selecting transmission channels with different transmission speeds to report the vehicle data according to the urgency of the vehicle data.

[0020] In another possible implementation, the above-mentioned reporting of the vehicle data may be specifically implemented as follows: when the vehicle data contains sensitive data, the vehicle data is encrypted and then reported.

[0021] In a second aspect, a vehicle data reporting system is provided, which includes: a signal collector, a processor, and a communication component, wherein the signal collector is communicatively connected to the communication component via the processor.

[0022] In a possible implementation, the signal collector is used to obtain dynamic sensor signals and position information of a vehicle, where the dynamic sensor signals are signals collected by sensors in the vehicle.

[0023] In another possible implementation, the above-mentioned processor is used to trigger the reporting of vehicle data when the dynamic sensor signal meets the time series condition and the position information meets the spatial sequence condition, wherein the time series condition is a configured standard for judging whether the dynamic sensor signal meets the trigger condition, and the spatial sequence condition is a configured standard for judging whether the position information meets the trigger condition.

[0024] In another possible implementation, the communication component is used to report the vehicle data.

[0025] In another possible implementation, the processor is further configured to perform data analysis on the dynamic sensor signal based on a sliding time window; and when the dynamic sensor signal within the sliding time window is greater than or equal to a monitoring signal threshold, it is determined that the dynamic sensor signal satisfies the time series condition.

[0026] In another possible implementation, the processor is further configured to obtain a normal distribution of historical dynamic sensor signals; obtain an abnormal signal threshold based on the normal distribution; and determine that the dynamic sensor signal satisfies the time series condition when the dynamic sensor signal is greater than or equal to the abnormal signal threshold.

[0027] In another possible implementation, the processor is further configured to determine that the dynamic sensor signal within the sliding time window satisfies the time series condition when the signal values ​​of the dynamic sensor signal within the sliding time window are greater than or equal to the monitoring signal threshold in consecutive first time periods.

[0028] In another possible implementation, the above-mentioned processor is further used to determine that the dynamic sensor signal satisfies the time series condition when the signal mean of the dynamic sensor signal within the sliding time window is greater than or equal to the monitoring signal threshold, and the signal mean refers to the average value of the signal value of the dynamic sensor signal within the sliding time window.

[0029] In another possible implementation, the monitoring signal threshold and the abnormal signal threshold are both dynamically adjusted, and the adjustment of the monitoring signal threshold and the abnormal signal threshold is associated with the location and scene of the vehicle.

[0030] In another possible implementation, the processor is further configured to analyze the location information to obtain the scene in which the vehicle is located; and when the scene is a risky scene, the location information satisfies the spatial sequence condition.

[0031] In another possible implementation, the processor is further configured to analyze the position information to obtain position parameters of the vehicle; when the position parameters are greater than or equal to a position parameter threshold, the position information satisfies the spatial sequence condition.

[0032] In another possible implementation, the processor is further configured to perform data processing on the dynamic sensor signal and the location information to obtain the vehicle data after triggering the report.

[0033] In another possible implementation, the data processing includes at least one of the following: filtering invalid signals in the dynamic sensor signal and the location information; comparing the dynamic sensor signal of the current period with the dynamic sensor signal of the previous period, comparing the location information of the current period with the location information of the previous period, and filtering out difference signals from the dynamic sensor signal and the location information of the current period; sampling the dynamic sensor signal and the location information; or adding timestamps and / or positioning coordinates to the dynamic sensor signal and the location information.

[0034] In another possible implementation, the communication component is further configured to select different transmission channels to report the vehicle data according to attribute parameters of the vehicle data or the network status of the vehicle when reporting the vehicle data.

[0035] In another possible implementation, the communication component is further configured to select transmission channels with different transmission speeds to report the vehicle data according to the urgency of the vehicle data.

[0036] In another possible implementation, the communication component is further configured to encrypt the vehicle data and then report it when the vehicle data contains sensitive data.

[0037] In a third aspect, a vehicle data reporting device is provided, which is applied to a vehicle data reporting engine. The device includes: an acquisition module, a parsing module, and a driving module.

[0038] The acquisition module is configured to acquire a vehicle's dynamic sensor signals and location information. The dynamic sensor signals are signals collected by sensors in the vehicle. If the dynamic sensor signals meet a time sequence condition and the location information meets a spatial sequence condition, the module triggers the reporting of vehicle data. The time sequence condition is the configured criteria for determining whether the dynamic sensor signals meet the trigger condition, and the spatial sequence condition is the configured criteria for determining whether the location information meets the trigger condition. The vehicle data is then reported.

[0039] The solution provided in this application constructs a comprehensive trigger condition by integrating time series conditions and space series conditions. Vehicle data reporting will be triggered only when both the time series conditions and the space series conditions are met at the same time. This realizes intelligent control of vehicle data upload, reduces the reporting of invalid data and redundant data, reduces the network bandwidth and storage resources occupied by cloud servers, and improves data transmission efficiency.

[0040] In a possible implementation, the vehicle data reporting method provided in the present application further includes: performing data analysis on the dynamic sensor signal based on a sliding time window; and determining that the dynamic sensor signal meets the time series condition when the dynamic sensor signal within the sliding time window is greater than or equal to the monitoring signal threshold.

[0041] In another possible implementation, the vehicle data reporting method provided in the present application further includes: obtaining a normal distribution of historical dynamic sensor signals; obtaining an abnormal signal threshold based on the normal distribution; and determining that the dynamic sensor signal meets the time series condition when the dynamic sensor signal is greater than or equal to the abnormal signal threshold.

[0042] Another possible implementation method is that the dynamic sensor signal within the above-mentioned sliding time window is greater than or equal to the monitoring signal threshold, which can be specifically implemented as follows: the signal values ​​of the dynamic sensor signal within the sliding time window in a continuous first time period are all greater than or equal to the monitoring signal threshold.

[0043] Another possible implementation method is that the dynamic sensor signal within the above-mentioned sliding time window is greater than or equal to the monitoring signal threshold, which can be specifically implemented as follows: the signal mean of the dynamic sensor signal within the sliding time window is greater than or equal to the monitoring signal threshold, and the signal mean refers to the average value of the signal value of the dynamic sensor signal within the sliding time window.

[0044] In another possible implementation, the monitoring signal threshold and the abnormal signal threshold are both dynamically adjusted, and the adjustment of the monitoring signal threshold and the abnormal signal threshold is associated with the location and scene of the vehicle.

[0045] In another possible implementation, the vehicle data reporting method provided in the present application further includes: analyzing the location information to obtain the scene in which the vehicle is located; when the scene is a risk scene, the location information satisfies the spatial sequence condition.

[0046] In another possible implementation method, the vehicle data reporting method provided in the present application further includes: analyzing the position information to obtain the position parameters of the vehicle's position; when the position parameter is greater than or equal to the position parameter threshold, the position information satisfies the spatial sequence condition.

[0047] In another possible implementation, the vehicle data reporting method provided in the present application further includes: after triggering the reporting, performing data processing on the dynamic sensor signal and the location information to obtain the vehicle data.

[0048] In another possible implementation, the data processing includes at least one of the following: filtering invalid signals in the dynamic sensor signal and the location information; comparing the dynamic sensor signal of the current period with the dynamic sensor signal of the previous period, comparing the location information of the current period with the location information of the previous period, and filtering out difference signals from the dynamic sensor signal and the location information of the current period; sampling the dynamic sensor signal and the location information; or adding timestamps and / or positioning coordinates to the dynamic sensor signal and the location information.

[0049] In another possible implementation, the above-mentioned reporting of the vehicle data may be specifically implemented as follows: when reporting the vehicle data, different transmission channels are selected to report the vehicle data according to attribute parameters of the vehicle data or the network status of the vehicle.

[0050] In another possible implementation, the above-mentioned selecting different transmission channels to report the vehicle data according to the attribute parameters of the vehicle data can be specifically implemented as follows: selecting transmission channels with different transmission speeds to report the vehicle data according to the urgency of the vehicle data.

[0051] In another possible implementation, the above-mentioned reporting of the vehicle data may be specifically implemented as follows: when the vehicle data contains sensitive data, the vehicle data is encrypted and then reported.

[0052] The vehicle data reporting device provided in the third aspect is used to execute the vehicle data reporting method provided in the first aspect or any possible implementation method of the first aspect. The technical effects corresponding to any implementation method in the third aspect can be referred to the technical effects corresponding to any implementation method in the first aspect, and will not be repeated here.

[0053] In a fourth aspect, a computer device is provided, comprising: a processor and a memory, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor to implement the vehicle data reporting method described in the first aspect or any possible implementation method of the first aspect.

[0054] In a fifth aspect, a computer-readable storage medium is provided, in which at least one computer program is stored. The at least one computer program is loaded and executed by a processor to implement the vehicle data reporting method as described in the first aspect or any one of the implementation methods of the first aspect.

[0055] In a sixth aspect, a computer program product is provided, which includes a computer program or instructions. When the computer program or instructions are executed by a processor, the vehicle data reporting method described in the first aspect or any one of the implementation methods in the first aspect is implemented.

[0056] In the seventh aspect, an embodiment of the present application provides a chip system comprising at least one processor and at least one interface circuit, wherein the at least one interface circuit is used to perform transceiver functions and send instructions to the at least one processor. When the at least one processor executes the instructions, the at least one processor executes to implement the vehicle data reporting method as described in the first aspect or any one of the implementation methods in the first aspect.

[0057] In an eighth aspect, an embodiment of the present application provides a vehicle, comprising: a processor and a memory, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor to implement the vehicle data reporting method as described in the first aspect or any possible implementation method of the first aspect.

[0058] The solutions provided in aspects 4 to 8 above are used to implement the first aspect or the method provided in aspect 1 above, and their specific implementations are not described in detail here. The technical effects corresponding to any implementation of the solutions provided in aspects 4 to 8 above can be referred to the technical effects corresponding to the first aspect or any implementation of aspect 1 above, and are not described in detail here.

[0059] It should be noted that various possible implementations of any of the above aspects can be combined under the premise that the solutions are not contradictory. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is a schematic diagram of the architecture of a computing system provided by an exemplary embodiment of the present application;

[0061] Figure 2 is a schematic diagram of a vehicle data reporting system provided by an exemplary embodiment of the present application;

[0062] Figure 3 This is a flow chart of a vehicle data reporting method provided by an exemplary embodiment of the present application;

[0063] Figure 4 is a flowchart of another vehicle data reporting method provided by an exemplary embodiment of the present application;

[0064] Figure 5 This is a flowchart of another vehicle data reporting method provided by an exemplary embodiment of the present application;

[0065] Figure 6This is a structural diagram of a vehicle data reporting device provided by an exemplary embodiment of the present application;

[0066] Figure 7 It is a structural diagram of a computer device provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0067] In the embodiments of the present application, in order to clearly describe the technical solutions of the embodiments of the present application, words such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art will understand that words such as "first" and "second" do not limit the quantity or execution order, and words such as "first" and "second" do not necessarily mean different. There is no order of precedence or priority between the technical features described by "first" and "second".

[0068] In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner to facilitate understanding.

[0069] In the embodiments of the present application, at least one can also be described as one or more, and multiple can be two, three, four or more, which is not limited in this application.

[0070] In addition, the network architecture and scenarios described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Ordinary technicians in this field can know that with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0071] It should be noted that the information (including but not limited to device information, personal information of the subject, etc.), data (including but not limited to data used for analysis, storage, and display, etc.), and signals involved in this application are all authorized by the subject or fully authorized by all parties, and the collection, use, and processing of relevant data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the dynamic sensor signals and location information involved in this application are all obtained with full authorization.

[0072] With the rapid development of connected vehicle technology, vehicle data can be reported to cloud servers for analysis and storage. Conventional data reporting methods include scheduled reporting and single signal reporting. The following briefly describes these two methods.

[0073] Timed reporting method. The core components required for the timed reporting method include: controller, timed trigger module, and wireless communication unit. The workflow of timed reporting is: the timed trigger module generates a full upload instruction according to a preset period (such as every 5 seconds, 30 seconds). The controller collects all sensor signals (including acceleration, vehicle speed, steering angle, etc.) and packages all sensor signals into complete data frames. The wireless communication unit uploads the full data to the cloud server through a fixed network channel (such as Long Term Evolution (LTE)).

[0074] However, the data uploaded by this method contains a lot of invalid and redundant data. For example, when the vehicle is stationary, the zero acceleration value at idle speed that is uploaded periodically is invalid information. The large amount of invalid and redundant data in the uploaded data will not only occupy network bandwidth, but also occupy the storage resources of the cloud server. Among them, when the vehicle is stationary or driving normally, the regular uploading of a large number of meaningless "heartbeat signals" (such as zero acceleration value at idle speed) is invalid information. According to statistics, more than 70% of the data uploaded by this method is invalid information. In addition, this method cannot distinguish the actual needs of different driving scenarios. For example, if a vehicle collides while driving at low speed in a tunnel, the acceleration threshold is not triggered (the conventional threshold is higher), resulting in data leakage. When the vehicle starts on a slope, the short-term acceleration fluctuation triggers an erroneous upload, generating a large amount of interference data.

[0075] Single signal reporting method. The core components required for the single signal reporting method include: a single sensor (such as an accelerometer), a threshold comparator, and a data upload module. The workflow of the single signal reporting method is as follows: the sensor collects the target signal (such as acceleration) in real time and inputs it into the threshold comparator. When the signal value exceeds a fixed threshold (such as acceleration > 0.5g), the full upload of vehicle data is immediately triggered. The uploaded data contains fixed-length segments before and after the trigger moment (such as 5 seconds before and 5 seconds after the trigger).

[0076] However, the data uploaded by this method does not cover enough scenarios, that is, it relies only on a single signal (such as acceleration) and cannot identify potential risks in complex driving environments. For example, missed scenarios: when a vehicle collides at low speed in a tunnel, the acceleration may not reach the normal threshold (such as 0.5g), resulting in key data not being uploaded. Misjudgment scenarios: The vehicle is triggered to upload due to short-term signal fluctuations in non-dangerous scenarios (such as normal acceleration on slippery roads, starting on a slope), resulting in a large amount of invalid data. Fixed thresholds lack flexibility: The trigger threshold cannot be dynamically adjusted according to the road type (such as tunnels, highways, and urban roads), resulting in insufficient sensitivity in high-risk scenarios or frequent false triggering of low-risk scenarios.

[0077] Based on this, the present application provides a vehicle data reporting method to obtain the dynamic sensor signal and location information of the vehicle. When the dynamic sensor signal meets the time series condition and the location information meets the space sequence condition, the vehicle data reporting is triggered. The vehicle data is reported. This solution constructs a comprehensive trigger condition by integrating the time series condition and the space sequence condition. Only when the time series condition and the space sequence condition are met at the same time, the vehicle data reporting is triggered, realizing intelligent control of vehicle data upload, reducing the reporting of invalid data and redundant data, reducing the network bandwidth and storage resources of the cloud server, and improving data transmission efficiency.

[0078] The solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0079] The solution provided in this application can be applied to Figure 1 In the computing system shown in FIG. Figure 1 As shown, the computing system includes: a vehicle and a cloud server. The vehicle includes a vehicle data reporting system, which reports the vehicle data to the cloud server for storage or calculation.

[0080] Optionally, the cloud server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides cloud computing services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and other basic cloud computing services.

[0081] Optionally, the vehicle can be a passenger car, a commercial vehicle, a special vehicle, an autonomous driving vehicle, a motorcycle, a tricycle, an autonomous driving logistics vehicle, etc., but is not limited thereto, and the embodiments of the present application do not make specific limitations on this.

[0082] Among them, the vehicle supports networking, that is, the vehicle can communicate with the cloud server.

[0083] For example, the solution provided in this application can be applied to the following vehicle scenarios.

[0084] (1) Passenger cars. The solution provided in this application can obtain the dynamic sensor signals and position information of the vehicle. When the dynamic sensor signal meets the time series condition and the position information meets the space series condition, the vehicle data is triggered to be reported. In addition, by dynamically adjusting the thresholds in the time series condition or the space series condition, for example, the lateral acceleration threshold of 0.3g on urban roads, the driving behavior of the vehicle can be monitored in real time, the occurrence of problems such as false triggering of sudden acceleration / sudden braking can be reduced, and the accuracy of the vehicle data can be improved. If the passenger car is a new energy vehicle (such as an electric car), this solution can focus on monitoring the three-electric system (such as battery temperature and motor speed), and combine the time series condition or the space series condition to trigger the upload of abnormal data, thereby supporting battery health management and fault warning.

[0085] (2) Commercial vehicles. For some commercial vehicles used for heavy loads, by dynamically adjusting the thresholds in the time series conditions or spatial series conditions (e.g., increasing the longitudinal acceleration threshold to 0.6g when making sharp turns on a highway), redundant data transmission during normal driving can be reduced, thus lowering fleet management costs. For some commercial vehicles that require high-precision map matching in sensitive areas (e.g., mountain bends, school roads), by dynamically adjusting the thresholds in the time series conditions or spatial series conditions, early warning mechanisms can be activated to improve safety.

[0086] (3) Autonomous driving vehicles. For autonomous driving vehicles, it is necessary to process the data obtained by various sensors in real time. The amount of data processing is large. By integrating the time series conditions and the space series conditions, a comprehensive trigger condition is constructed. Only when the time series conditions and the space series conditions are met at the same time, the vehicle data will be triggered and reported. This realizes the intelligent control of vehicle data upload, reduces the reporting of invalid data and redundant data, reduces the network bandwidth and storage resources of the cloud server, and improves the data transmission efficiency. In addition, by dynamically adjusting the thresholds in the time series conditions or the space series conditions (such as reducing the low-speed collision threshold in the park to 0.2g), the safe data collection of autonomous driving vehicles during driving is ensured.

[0087] Figure 2 A vehicle data reporting system provided in an embodiment of the present application may be Figure 1 The vehicle data reporting system in the illustrated vehicle includes a signal collector 201 , a processor 202 and a communication component 203 . The signal collector 201 is in communication with the communication component 203 via the processor 202 .

[0088] The signal collector 201 is used to obtain the dynamic sensor signals and position information of the vehicle.

[0089] The processor 202 is configured to trigger the reporting of vehicle data when the dynamic sensor signal satisfies a time sequence condition and the position information satisfies a space sequence condition.

[0090] The communication component 203 is used to report vehicle data.

[0091] Optionally, the signal collector 201 includes: an acceleration sensor, a vehicle speed sensor, a steering angle sensor, a locator and a map interface.

[0092] Among them, the map interface is used to connect to the on-board map system to obtain spatial attributes such as sensitive area coordinates and road types.

[0093] The locator includes a Global Navigation Satellite System (GNSS) receiver and an Inertial Navigation System (INS) module, or a GNSS receiver and an Ultra-Wideband (UWB) positioning module.

[0094] Optionally, the processor 202 includes: an integrated microprocessor (such as NXP S32G) and a storage module to support real-time data processing and algorithm execution.

[0095] Alternatively, the integrated microprocessor can be replaced with a field-programmable gate array (FPGA). Specifically, the process of determining whether to trigger vehicle data reporting based on the vehicle's dynamic sensor signals and location information can be fixed to the FPGA. Alternatively, the process of determining whether to trigger vehicle data reporting based on the vehicle's dynamic sensor signals and location information can be split into multiple low-power MCUs (such as STM32), each processing sensor data, positioning matching, and decision logic. These MCUs work together via the SPI / I2C bus to reduce the load on the single chip.

[0096] Optionally, the communication component 203 includes a 5G T-BOX (supporting 5G URLLC and LTE-M multi-mode). A T-BOX generally refers to a telematics box or a telematics control unit (TCU). This is an electronic device installed on a vehicle, primarily used for vehicle communications (telematics). Its core function is to provide mobile network connectivity (such as 4G / 5G), enabling the vehicle to exchange data with the outside world (such as the manufacturer's cloud platform, other vehicles, traffic management systems, etc.).

[0097] The above briefly introduces the modules in the vehicle data reporting system. The following will explain the vehicle data reporting method in detail. It should be noted that the content executed by the modules in the above vehicle data reporting system can be found in the description of the method.

[0098] Figure 3 A vehicle data reporting method provided in an embodiment of the present application is applied to a vehicle, or a vehicle data reporting system in a vehicle. The vehicle data reporting system can be Figure 1 The vehicle data reporting system in the illustrated computing system can also be Figure 2 Schematic diagram of the vehicle data reporting system.

[0099] like Figure 3 As shown, the vehicle data reporting method may include:

[0100] Step 302: Acquire the vehicle's dynamic sensor signals and location information.

[0101] The dynamic sensor signal is a signal collected by a sensor in the vehicle. Both the dynamic sensor signal and the position information are collected by a signal collector in the vehicle.

[0102] Specifically, the vehicle uses the acceleration sensor, speed sensor, and steering angle sensor in the signal collector to collect vehicle dynamic signals (such as acceleration, speed, and steering angle) in real time. The vehicle is positioned using the GNSS+INS / UWB in the signal collector to obtain real-time position information (meter-level positioning accuracy).

[0103] Alternatively, a vehicle's real-time location can be calculated using the received signal strength indicator (RSSI) of Bluetooth beacons, replacing high-precision map matching and reducing costs. For example, in a parking lot, parking management has installed Bluetooth beacons on every floor, and even in every area or above parking spaces. Vehicles equipped with sensors capable of receiving Bluetooth signals will begin receiving signals from surrounding Bluetooth beacons and measuring the RSSI value of each beacon. Suppose the vehicle receives a strong signal (high RSSI value) from the beacon in "B2 Floor - Area A," a moderate signal from the beacon in "B2 Floor - Area B," and a weak signal (low RSSI value) from the beacon in "B1 Floor." By analyzing these varying signal strengths, the vehicle can roughly determine that the vehicle is currently located near Area A on the B2 floor. While this may not be accurate to the centimeter level, it is sufficient to quickly locate the approximate area when retrieving the vehicle. This process does not require complex cameras and lidar, nor does it require real-time matching with expensive high-precision maps, significantly reducing costs.

[0104] Alternatively, a vehicle's real-time location information can be compiled into a location fingerprint database using cellular network base station signal characteristics (such as signal strength and TA values). This allows for meter-level positioning in urban areas, suitable for urban canyon scenarios where GNSS signals are unavailable. Specifically, a vehicle slowly drives along multiple streets. At each fixed point (e.g., every few or ten meters), the vehicle records the signals received from the mobile base stations, the strength of each signal, and the TA value. The precise coordinates of each point are then recorded using high-precision GPS. This extensive data collection creates a "cellular signal location fingerprint database" covering multiple street areas. For example, the database may contain a record: "Near coordinates (121.5, 31.2), base station A signal strength is -65dBm, TA = 3; base station B signal strength is -72dBm, TA = 5; base station C signal strength is -80dBm, TA = 8..." During the positioning phase, the vehicle measured the following signal strength: Base Station A signal strength of -64dBm, TA = 3; Base Station B signal strength of -73dBm, TA = 5; Base Station C signal strength of -81dBm, TA = 8, etc. The vehicle then compared this measurement data with a pre-built "location fingerprint library" and found that the current measurement data was most similar to the records in the library near the coordinates (121.5005, 31.2003). Therefore, the vehicle's current location is approximately (121.5005, 31.2003), with an error of within a few meters.

[0105] After acquiring the location information, the vehicle uses the onboard mapping system interface in the signal collector to obtain the spatial attributes of the current location, including the coordinates of the geofences of sensitive areas (tunnels, schools, construction sections, etc.) and location parameters (road type, slope, curvature radius, etc., such as ramps with a slope greater than 15° and curves with a curvature radius less than 50 meters).

[0106] In some embodiments, after obtaining the dynamic sensor signal and position information, the Kalman filter algorithm is used to perform noise reduction processing on the original data (dynamic sensor signal and position information) to remove the noise interference of the sensor in the signal collector, thereby improving data reliability and providing clean and accurate spatiotemporal basic data for the subsequent process, ensuring the accuracy of the trigger condition judgment.

[0107] Step 304: When the dynamic sensor signal meets the time sequence condition and the position information meets the space sequence condition, triggering the reporting of vehicle data.

[0108] The time sequence condition is a configured standard for determining whether the dynamic sensor signal meets the trigger condition. The spatial sequence condition is a configured standard for determining whether the position information meets the trigger condition.

[0109] Specifically, a time series condition is considered satisfied when a dynamic signal continuously exceeds a threshold value within a continuous time window. Satisfying the time series condition means that at least one of the dynamic sensor signals meets the time series condition. For example, acceleration > 0.5g for three consecutive seconds (in a normal scenario), or > 0.3g for two consecutive seconds (in a tunnel scenario, where the threshold is dynamically lowered).

[0110] In some embodiments, the situations in which it is determined that the dynamic sensor signal meets the time series condition include at least one of the following, but are not limited to these. The embodiments of the present application do not specifically limit this. The following methods can avoid the fixed window from misjudging short-term strong noise and improve the robustness under complex working conditions.

[0111] Scenario 1: The vehicle's processor performs data analysis on the dynamic sensor signal based on a sliding time window. If the dynamic sensor signal within the sliding time window is greater than or equal to the monitoring signal threshold, the dynamic sensor signal is determined to meet the time series condition.

[0112] Exemplarily, when the signal values ​​of the dynamic sensor signal in the sliding time window are all greater than or equal to the monitoring signal threshold in consecutive first time periods, it is determined that the dynamic sensor signal meets the time series condition.

[0113] For example, the sliding time window is 3 seconds, the monitoring signal threshold is 0.5, and when the acceleration is greater than 0.5 for 3 consecutive seconds, it is determined that the acceleration signal meets the time series condition.

[0114] Exemplarily, when a signal mean value of the dynamic sensor signal within the sliding time window is greater than or equal to the monitoring signal threshold, it is determined that the dynamic sensor signal meets the time series condition.

[0115] The signal mean refers to the average value of the signal value of the dynamic sensor signal within the sliding time window.

[0116] For example, the sliding time window is 5 seconds, and the mean of the dynamic sensor signal within 5 seconds is calculated. When the mean exceeds the monitoring signal threshold, it is determined that the dynamic sensor signal meets the time series condition. This method is suitable for sensor scenarios with large noise.

[0117] Scenario 2: A processor in the vehicle obtains a normal distribution of historical dynamic sensor signals; obtains an abnormal signal threshold based on the normal distribution; and determines that the dynamic sensor signal satisfies a time series condition when the dynamic sensor signal is greater than or equal to the abnormal signal threshold.

[0118] For example, based on the normal distribution of historical data, an abnormal signal threshold is obtained, which is μ+3σ, where μ is the mean and σ is the standard deviation. When the dynamic sensor signal exceeds μ+3σ, it is determined that the dynamic sensor signal meets the time series condition.

[0119] Specifically, the spatial sequence conditions are used to determine whether the vehicle's position is in a sensitive area (such as the coordinate range of a tunnel entrance, a school geofence), or the vehicle's current position parameters (such as slope, curvature radius) are compared with preset thresholds (such as slope > 15°, curvature radius < 50 meters).

[0120] In some embodiments, the situation in which the position information is determined to meet the spatial sequence condition includes at least one of the following, but is not limited thereto, and is not specifically limited in the embodiments of the present application:

[0121] Scenario 1: The processor in the vehicle analyzes the location information to obtain the scene the vehicle is in; if the scene is a risky scene, the location information meets the spatial sequence condition.

[0122] Specifically, when the scene where the vehicle is located belongs to a sensitive area, it is determined that the location information meets the spatial sequence condition. For example, when the vehicle is located at a school, it is determined that the location information meets the spatial sequence condition.

[0123] Scenario 2: The processor in the vehicle analyzes the position information to obtain a position parameter of the vehicle's position; when the position parameter is greater than or equal to a position parameter threshold, the position information satisfies the spatial sequence condition.

[0124] Specifically, if the vehicle's location parameter is greater than or equal to a location parameter threshold, the location information is determined to meet the spatial sequence condition. For example, if the vehicle's slope is greater than 15° and the curvature radius is less than 50 meters, the location information is determined to meet the spatial sequence condition.

[0125] In one possible implementation, the method for determining whether the vehicle's position is in a sensitive area may include at least one of the following, but is not limited to this. The embodiments of the present application do not specifically limit this. The following method can reduce the dependence on high-precision positioning modules and expand to low-cost vehicle-mounted solutions.

[0126] Method 1: When determining whether the vehicle's position is in a sensitive area, the R-tree spatial index algorithm can be used to match the vehicle's real-time coordinates with the sensitive areas preset in the map.

[0127] Method 2: Encode geographic coordinates into grid strings and use prefix matching to quickly determine whether a vehicle has entered a sensitive area (such as tunnel grid prefix matching), improving the efficiency of large-scale area matching. For example, the grid prefix covering tunnel A is "wx4g," the grid prefix covering tunnel B is "wy0q7," and the grid prefix covering tunnel C is "vjs9p." Consider a vehicle with coordinates (39.9042°N, 116.4074°E). First, convert the vehicle's GPS coordinates (39.9042, 116.4074) into a grid string using a grid encoding algorithm. Assume the conversion result is "wx4g0eurz." Extract the prefix from the encoded result. Prefixes of varying lengths can be extracted for matching with varying precision. Assume we extract the first four characters as the primary area identifier, resulting in the prefix "wx4g." Prefix matching: Compare the vehicle's prefix "wx4g" with the prefix "wx4g" of tunnel A: A match! This indicates the vehicle likely entered tunnel A. Comparing the vehicle prefix "wx4g" with the prefix "wy0q7" of Tunnel B, there is no match. Comparing the vehicle prefix "wx4g" with the prefix "vjs9p" of Tunnel C, there is no match. Conclusion: Based on the prefix matching results, we can quickly determine that this vehicle entered Tunnel A.

[0128] Method 3: Use the on-board camera + SLAM algorithm to build an environmental map in real time, matching the visual features of preset sensitive areas (such as tunnel entrance signs). It is suitable for scenarios where GNSS signals are blocked (such as underground garages).

[0129] In some embodiments, both the monitoring signal threshold and the abnormal signal threshold are dynamically adjusted, and their adjustment is tied to the vehicle's location and the scene it's in. By dynamically adjusting the monitoring signal threshold and the abnormal signal threshold, the solution's adaptability to different scenarios can be improved, avoiding the "one-size-fits-all" drawback of fixed thresholds across different road conditions.

[0130] Alternatively, the vehicle's location can be determined based on the type of road it is on, such as whether it is in a tunnel, highway, urban road, city road, or a narrow rural bridge. The vehicle's location can be determined based on the functional area it is in, such as a school, hospital, shopping mall, or construction zone. The scene can be determined based on the weather conditions at the vehicle's location, such as heavy rain or fog.

[0131] In one possible implementation, a "vehicle location, scene, and threshold" mapping model is established based on historical data trained using a Long Short-Term Memory (LSTM) model. This automatically adjusts the monitoring signal threshold or abnormal signal threshold based on the vehicle's current location and scene (tunnel, urban road, highway).

[0132] For example, when the vehicle is in a tunnel, the acceleration threshold is reduced from 0.5g to 0.3g, improving sensitivity to low-speed collision scenarios; when the vehicle is on a highway, the acceleration threshold is increased from 0.5g to 0.6g to reduce false triggering of normal acceleration.

[0133] In another possible implementation, it is also possible to preset a multi-scenario threshold table (such as different thresholds for tunnels / highways / urban roads) and directly call the corresponding threshold through real-time map tags (such as road type fields) without the need for model training, thereby reducing computing resource consumption.

[0134] For example, a vehicle obtains the label of the current road A through GPS and mapping services. By querying the threshold table, it finds that the acceleration threshold corresponding to Road A is 0.6g. At this point, the vehicle accelerates normally, and the accelerometer reading reaches 0.55g. Because 0.55g < 0.6g, the vehicle is judged to be driving normally, and vehicle data reporting is not triggered. If the vehicle ahead suddenly brakes, the vehicle's acceleration quickly increases to 0.7g. Because 0.7g > 0.6g, the vehicle is judged to have a potential collision, triggering vehicle data reporting.

[0135] In another possible implementation, the vehicle's current position parameters (such as slope and curvature) and dynamic sensor signals can be input to generate an adaptive threshold through a fuzzy rule base (such as "when the slope is large and the vehicle speed is high, lower the acceleration threshold"), which is suitable for new scenarios without historical data.

[0136] In another possible implementation, the vehicle's current spatial scene (tunnel / school, etc.), time characteristics (day / night), and weather data can be used as input to output the optimal threshold value of each sensor, which is suitable for nonlinear threshold mapping scenarios.

[0137] For example, suppose a vehicle is approaching a school zone during daytime and light rain. The inputs are: spatial scene: school zone; temporal features: daytime; and weather data: light rain. Because the scene is a school zone, the maximum permitted approach speed threshold is lowered to 30 km / h. Because the weather is light rain, the camera image dehazing / enhancement strength is increased to 50%.

[0138] In another possible implementation, the threshold value may be dynamically optimized through a reward function (eg, reduction in invalid data rate, improvement in trigger accuracy).

[0139] Step 306: Report the vehicle data.

[0140] Exemplarily, when reporting vehicle data, different transmission channels are selected to report the vehicle data according to attribute parameters of the vehicle data or the network status of the vehicle.

[0141] Optionally, the attribute parameters include, but are not limited to, urgency, priority, and the like, and are not specifically limited thereto in the embodiments of the present application. The urgency of the vehicle data can be determined based on the event that triggers the report. When the report is triggered by an emergency event, the urgency of the vehicle data is high, such as rapid acceleration in a tunnel, collision warning, etc. When the report is triggered by a routine event, the urgency of the vehicle data is low, such as acceleration changes when starting on a slope, etc.

[0142] For example, the communication components in a vehicle select different transmission channels with different transmission speeds to report vehicle data based on its urgency. In high-urgency scenarios, data is uploaded in real time via the 5G Ultra-Reliable Low-Latency Communication (URLLC) channel to ensure low latency. In low-urgency scenarios, data is transmitted via the Long-Term Evolution for Machines (LTE-M) channel to optimize bandwidth resources.

[0143] Optionally, when the vehicle's network status is good, the 5G URLLC channel is used for uploading. When the vehicle's network status deteriorates, the 5G URLLC channel is switched to LTE-M for uploading.

[0144] In some embodiments, the communication component in the vehicle encrypts the vehicle data before reporting it if the vehicle data contains sensitive data.

[0145] Optionally, the sensitive data includes, but is not limited to, the user's private data, biometric data, etc.

[0146] Specifically, when the vehicle data contains sensitive data, the vehicle data is encrypted using the Transport Layer Security (TLS) protocol and reported.

[0147] In summary, the solution provided by this application constructs a comprehensive trigger condition by fusing time series conditions and space series conditions. Vehicle data reporting is triggered only when both the time series conditions and the space series conditions are met at the same time, thereby realizing intelligent control of vehicle data upload, reducing the reporting of invalid and redundant data, reducing the network bandwidth and storage resources occupied by cloud servers, and improving data transmission efficiency. By fusing time series conditions and space series conditions to construct a comprehensive trigger condition, it is possible to avoid misjudgment of single signal triggering (such as not triggering when the slope is large but there is no abnormal acceleration) and missed judgment (such as lowering the threshold requirement due to activation of spatial conditions during low-speed collisions in tunnels), thereby achieving accurate identification of high-risk scenarios.

[0148] Furthermore, if Figure 4 The flowchart of the vehicle data reporting method shown in the figure, before triggering the vehicle data reporting and before the vehicle data reporting, the present application solution also includes:

[0149] Step 305: After the report is triggered, the dynamic sensor signal and position information are processed to obtain vehicle data.

[0150] Exemplarily, a processor in the vehicle performs data processing on the dynamic sensor signal and position information to obtain vehicle data.

[0151] The vehicle data may be dynamic sensor signals and position information, or may be data obtained by processing the dynamic sensor signals and position information.

[0152] Optionally, data processing includes at least one of the following, but is not limited thereto, and is not specifically limited in this embodiment of the present application:

[0153] (1) Filter invalid signals from dynamic sensor signals and position information. For example, a processor (e.g., NXPS32G microprocessor) filters invalid heartbeat signals and extracts key data fragments before and after the trigger moment (e.g., acceleration, braking signals, and sensor raw values ​​10 seconds before and after the trigger).

[0154] (2) Compare the dynamic sensor signal of the current cycle with the dynamic sensor signal of the previous cycle, and compare the position information of the current cycle with the position information of the previous cycle, and filter out the difference signal from the dynamic sensor signal and position information of the current cycle. Specifically, the dynamic sensor signals of the previous and next cycles are compared by hash check, and only the difference part is transmitted to reduce the data transmission amount, or the position information of the previous and next cycles is compared by hash check, and only the difference part is transmitted to reduce the data transmission amount.

[0155] (3) Perform signal sampling on dynamic sensor signals and position information.

[0156] (4) Add timestamps and / or positioning coordinates to dynamic sensor signals and location information. For example, when uploading dynamic sensor signals and location information, attach a millisecond-level timestamp (time accuracy to milliseconds, location accuracy to meters), so as to achieve data traceability and scene reproduction. For example, the millisecond-level timestamp is: 2025-06-24 14:30:15.001. The positioning coordinates are: longitude 0°, latitude 0°. By adding timestamps and positioning coordinates, the vehicle status and position at the time of the accident can be accurately reproduced, providing key evidence for liability determination (such as the triggering moment and driving trajectory of the collision event in the tunnel can be directly traced).

[0157] The above embodiment describes the vehicle data reporting method. The following will describe the vehicle data reporting method with a specific example.

[0158] like Figure 5 The flowchart of the vehicle data reporting method shown is as follows. The method includes:

[0159] Step 501: Start.

[0160] Step 502: Collect dynamic sensor signals.

[0161] Exemplarily, the dynamic sensor signal is collected by a signal collector in the vehicle.

[0162] Step 503: Collect location information.

[0163] Exemplarily, the location information of the vehicle is collected by a signal collector in the vehicle.

[0164] It should be noted that the collection of dynamic sensor signals and the collection of position information can be performed successively or simultaneously, and the embodiments of the present application do not specifically limit this.

[0165] Step 504: Data preprocessing.

[0166] For example, after acquiring the dynamic sensor signal and position information, the Kalman filter algorithm is used to perform noise reduction processing on the original data (dynamic sensor signal and position information) to remove the noise interference of the sensor in the signal collector, thereby improving data reliability.

[0167] Step 505: Determine whether the time series condition is met.

[0168] Exemplarily, after acquiring the dynamic sensor signal, it is determined whether the dynamic sensor signal meets the time series condition. If the dynamic sensor signal meets the time series condition, step 507 is executed; if the dynamic sensor signal does not meet the time series condition, step 506 is executed.

[0169] Step 506: Do not trigger reporting.

[0170] Step 507: Determine whether the spatial sequence condition is met.

[0171] Exemplarily, when the dynamic sensor signal satisfies the time sequence condition, it is determined whether the position information satisfies the space sequence condition. If the position information satisfies the space sequence condition, step 508 is executed; if the position information does not satisfy the space sequence condition, step 506 is executed.

[0172] Step 508: Trigger reporting of vehicle data.

[0173] Exemplarily, when reporting vehicle data, different transmission channels are selected to report the vehicle data according to attribute parameters of the vehicle data or the network status of the vehicle.

[0174] There are different ways to implement this solution, such as a lightweight solution based on a rule engine and a distributed solution based on blockchain and processor, but this is not limited to these.

[0175] A lightweight solution based on a rule engine.

[0176] The hardware components of this solution include: sensors on the vehicle, processors (low-cost MCUs, such as ESP32) and communication modules. The implementation process is: through predefined time series conditions and spatial sequence conditions (such as "school area + speed > 30km / h + for 10 seconds", that is, triggering upload), no machine learning model is required. The processor only performs rule matching and data screening, and does not include model training functions. In short, the solution of this application is executed through the sensors, processors and communication modules on the vehicle.

[0177] For example, a school bus is equipped with a GPS module (sensor), an ESP32 microcontroller, and a 4G communication board. The system has pre-set rules: "If a vehicle enters the preset school zone and its speed exceeds 30 km / h for 10 seconds, then the vehicle data upload is triggered." The ESP32 is responsible for reading GPS data to determine the location and read the speed data. It does not use any models, but simply checks: "Is the vehicle currently in the school zone? Is the speed exceeding 30? Has this state lasted for 10 seconds?" If all conditions are met, the ESP32 filters out the relevant information about the event (timestamp, location coordinates, current speed, etc.). The ESP32 sends the filtered vehicle data to the cloud server via the 4G module for management personnel to review and record.

[0178] This lightweight solution has a simple design, low cost, and relatively low power consumption because it only communicates when it detects that predefined time sequence conditions and spatial sequence conditions are met, rather than continuously uploading all data.

[0179] Distributed solution of blockchain + processor.

[0180] The hardware components of this solution include: sensors on the vehicle, processors (low-cost MCUs, such as ESP32) and communication modules. Unlike the above solutions, the processor acts as a node in the blockchain, and the reported vehicle data will be encrypted and sent to the blockchain. It will be uploaded to the alliance chain through a consensus mechanism (such as PoST), and the tamper-proof nature of the blockchain will be used to enhance the effectiveness of the data evidence chain. At the same time, threshold adjustment and priority scheduling will be automatically performed through smart contracts. The implementation process is the same as the above process, except for the reporting location of vehicle data. By reporting vehicle data to the blockchain, decentralized storage can be used to improve data security and prevent single point failures of cloud servers. In addition, smart contracts implement trusted execution of trigger rules to avoid human tampering. Applicable scenarios include: commercial fleet management with extremely high requirements for data traceability and security, and compliant storage of autonomous driving data.

[0181] The above mainly introduces the solution provided by this application. Correspondingly, this application also provides a vehicle data reporting device, which is used to implement the above method embodiment.

[0182] In some embodiments, in order to implement the above functions, the vehicle data reporting device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of the various examples described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0183] The embodiment of the present application can divide the vehicle data reporting device into functional modules according to the above method embodiment. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or software functional modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical functional division. In actual implementation, there may be other division methods.

[0184] In some embodiments, the present application provides a vehicle data reporting device, which is used to implement the functions of the vehicle data reporting device in the above vehicle data reporting method embodiment. Figure 6The vehicle data reporting device shown in FIG. 6 may include an acquisition module 601 , a calculation module 602 , and a reporting module 603 .

[0185] Wherein, the acquisition module 601 is used to execute Figure 3 、 Figure 4 The operation of step 302 in the illustrated method. The calculation module 602 is used to perform Figure 3 、 Figure 4 The operations of steps 304 and 305 in the illustrated method. The reporting module 603 is used to perform Figure 3 、 Figure 4 The operation of step 306 in the illustrated method.

[0186] like Figure 7 As shown, the computer device provided in the embodiment of the present application may include a processor 701, a bus 702, a communication interface 703, and a memory 704. The processor 701, the memory 704, and the communication interface 703 communicate with each other via the bus 702. It should be understood that the present application does not limit the number of processors and memories in the computer device.

[0187] The bus 702 may be a PCI bus or an extended industry standard architecture (EISA) bus, or a USB bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 The bus 702 may include a path for transmitting information between various components of a computer device (eg, memory 704, processor 701, communication interface 703).

[0188] The processor 701 may include any one or more processors such as a CPU, a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0189] The memory 704 may include a volatile memory, such as a random access memory (RAM). The processor 701 may also include a non-volatile memory, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid state drive (SSD).

[0190] The communication interface 703 uses a transceiver module such as, but not limited to, a network interface card or a transceiver to implement communication between the computer device and other devices or a communication network.

[0191] The memory 704 stores executable program codes, and the processor 701 executes the executable program codes to respectively implement the functions of the vehicle data reporting device or the CPU core in the aforementioned method embodiment. That is, the memory 704 stores the program codes for executing the aforementioned vehicle data reporting method.

[0192] On the other hand, a computer-readable storage medium is provided, wherein at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by a processor to implement the vehicle data reporting method provided in the above-mentioned method embodiments.

[0193] On the other hand, a computer program product is provided. The computer program product includes a computer program or instructions. When the computer program or instructions are executed by a processor, the vehicle data reporting method described above is implemented.

[0194] On the other hand, a chip system is provided, comprising at least one processor and at least one interface circuit, wherein the at least one interface circuit is used to perform transceiver functions and send instructions to the at least one processor. When the at least one processor executes the instructions, the at least one processor executes to implement the vehicle data reporting method as described above.

[0195] The method steps in this embodiment can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and storage medium can be located in an ASIC. In addition, the ASIC can be located in a computer device. Of course, the processor and storage medium can also exist in a computer device as discrete components.

[0196] In the above embodiments, they can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is performed in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user device or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer program or instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, a hard disk, or a tape; it can also be an optical medium, such as a digital video disc (DVD); it can also be a semiconductor medium, such as a solid state drive (SSD). The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A vehicle data reporting method, characterized in that: The method comprises: Acquiring a dynamic sensor signal and position information of a vehicle, wherein the dynamic sensor signal is a signal collected by a sensor in the vehicle; Triggering reporting of vehicle data when the dynamic sensor signal meets a time sequence condition and the location information meets a spatial sequence condition, wherein the time sequence condition is a configured standard for determining whether the dynamic sensor signal meets the trigger condition, and the spatial sequence condition is a configured standard for determining whether the location information meets the trigger condition; The vehicle data is reported.

2. The method according to claim 1, characterized in that The method further comprises: performing data analysis on the dynamic sensor signal based on a sliding time window; determining that the dynamic sensor signal satisfies the time series condition when the dynamic sensor signal within the sliding time window is greater than or equal to a monitoring signal threshold; or, Acquire a normal distribution of historical dynamic sensor signals; obtain an abnormal signal threshold based on the normal distribution; and determine that the dynamic sensor signal meets the time series condition when the dynamic sensor signal is greater than or equal to the abnormal signal threshold.

3. The method according to claim 2, characterized in that The dynamic sensor signal within the sliding time window is greater than or equal to the monitoring signal threshold, including: The signal values ​​of the dynamic sensor signal within the sliding time window in consecutive first time periods are all greater than or equal to the monitoring signal threshold; and / or, The signal mean value of the dynamic sensor signal within the sliding time window is greater than or equal to the monitoring signal threshold, and the signal mean value refers to the average value of the signal value of the dynamic sensor signal within the sliding time window.

4. The method according to claim 2, characterized in that The monitoring signal threshold and the abnormal signal threshold are both dynamically adjusted, and the adjustment of the monitoring signal threshold and the abnormal signal threshold is associated with the location and scene of the vehicle.

5. The method according to claim 1, wherein The method further comprises: Analyzing the location information to obtain the scene in which the vehicle is located; if the scene is a risk scene, the location information satisfies the spatial sequence condition; or, The position information is analyzed to obtain a position parameter of the vehicle's position; when the position parameter is greater than or equal to a position parameter threshold, the position information meets the spatial sequence condition.

6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: After the report is triggered, data processing is performed on the dynamic sensor signal and the position information to obtain the vehicle data.

7. The method according to claim 6, characterized in that The data processing includes at least one of the following: filtering invalid signals in the dynamic sensor signal and the position information; Comparing the dynamic sensor signal of the current cycle with the dynamic sensor signal of the previous cycle, comparing the position information of the current cycle with the position information of the previous cycle, and filtering out a difference signal from the dynamic sensor signal of the current cycle and the position information; performing signal sampling on the dynamic sensor signal and the position information; Alternatively, a timestamp and / or positioning coordinates are added to the dynamic sensor signal and the position information.

8. The method according to any one of claims 1 to 5, characterized in that Reporting the vehicle data includes: When reporting the vehicle data, different transmission channels are selected to report the vehicle data according to attribute parameters of the vehicle data or the network status of the vehicle.

9. The method according to claim 8, characterized in that The selecting different transmission channels to report the vehicle data according to the attribute parameters of the vehicle data includes: According to the urgency of the vehicle data, transmission channels with different transmission speeds are selected to report the vehicle data.

10. The method according to any one of claims 1 to 5, characterized in that Reporting the vehicle data includes: In the case that the vehicle data contains sensitive data, the vehicle data is encrypted and then reported.

11. A vehicle data reporting system, characterized in that: The system includes: a signal collector, a processor and a communication component, wherein the signal collector is communicatively connected to the communication component via the processor; The signal collector is used to obtain dynamic sensor signals and position information of the vehicle, wherein the dynamic sensor signals are signals collected by sensors in the vehicle; the processor being configured to trigger reporting of vehicle data when the dynamic sensor signal satisfies a time sequence condition and the position information satisfies a space sequence condition, wherein the time sequence condition is a configured standard for determining whether the dynamic sensor signal satisfies the trigger condition, and the space sequence condition is a configured standard for determining whether the position information satisfies the trigger condition; The communication component is used to report the vehicle data.

12. The system according to claim 11, wherein: The processor is further configured to perform data analysis on the dynamic sensor signal based on a sliding time window; and determine that the dynamic sensor signal satisfies the time series condition if the dynamic sensor signal within the sliding time window is greater than or equal to a monitoring signal threshold; or, The processor is further configured to obtain a normal distribution of historical dynamic sensor signals; obtain an abnormal signal threshold based on the normal distribution; and determine that the dynamic sensor signal satisfies the time series condition when the dynamic sensor signal is greater than or equal to the abnormal signal threshold.

13. The system according to claim 12, wherein: The processor is further configured to determine that the dynamic sensor signal within the sliding time window satisfies the time series condition if the signal values ​​of the dynamic sensor signal within a continuous first time period are all greater than or equal to the monitoring signal threshold; and / or, The processor is further configured to determine that the dynamic sensor signal satisfies the time series condition when a signal mean of the dynamic sensor signal within the sliding time window is greater than or equal to a monitoring signal threshold, wherein the signal mean refers to an average value of the signal values ​​of the dynamic sensor signal within the sliding time window.

14. The system according to claim 12, wherein: The monitoring signal threshold and the abnormal signal threshold are both dynamically adjusted, and the adjustment of the monitoring signal threshold and the abnormal signal threshold is associated with the location and scene of the vehicle.

15. The system according to claim 11, wherein: The processor is further configured to analyze the position information to obtain a scene in which the vehicle is located; if the scene is a risk scene, the position information satisfies the spatial sequence condition; or, The processor is further configured to analyze the position information to obtain a position parameter of the vehicle's position; when the position parameter is greater than or equal to a position parameter threshold, the position information satisfies the spatial sequence condition.

16. The system according to any one of claims 11 to 15, characterized in that The processor is further configured to perform data processing on the dynamic sensor signal and the position information to obtain the vehicle data after triggering the report.

17. The system according to claim 16, wherein: The data processing includes at least one of the following: filtering invalid signals in the dynamic sensor signal and the position information; Comparing the dynamic sensor signal of the current cycle with the dynamic sensor signal of the previous cycle, comparing the position information of the current cycle with the position information of the previous cycle, and filtering out a difference signal from the dynamic sensor signal of the current cycle and the position information; performing signal sampling on the dynamic sensor signal and the position information; Alternatively, a timestamp and / or positioning coordinates are added to the dynamic sensor signal and the position information.

18. The system according to any one of claims 11 to 15, characterized in that The communication component is further configured to select different transmission channels to report the vehicle data according to attribute parameters of the vehicle data or the network status of the vehicle when reporting the vehicle data.

19. The system according to claim 18, wherein: The communication component is further configured to select a transmission channel with different transmission speeds to report the vehicle data according to the urgency of the vehicle data.

20. The system according to any one of claims 11 to 15, characterized in that The communication component is further configured to encrypt the vehicle data and then report it when the vehicle data contains sensitive data.

21. A vehicle data reporting device, characterized in that: The device comprises: An acquisition module, configured to acquire a dynamic sensor signal and position information of a vehicle, wherein the dynamic sensor signal is a signal collected by a sensor in the vehicle; a calculation module, configured to trigger reporting of vehicle data when the dynamic sensor signal satisfies a time sequence condition and the position information satisfies a spatial sequence condition, wherein the time sequence condition is a configured standard for determining whether the dynamic sensor signal satisfies the trigger condition, and the spatial sequence condition is a configured standard for determining whether the position information satisfies the trigger condition; The reporting module is used to report the vehicle data.

22. A vehicle, characterized in that: The vehicle includes: a processor and a memory, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor to implement the vehicle data reporting method according to any one of claims 1 to 10.

23. A computer-readable storage medium, characterized in that The computer-readable storage medium stores at least one computer program, and the at least one computer program is loaded and executed by the processor to implement the vehicle data reporting method according to any one of claims 1 to 10.

24. A computer program product, characterized in that The computer program product includes a computer program or instructions. When the computer program or instructions are executed by a processor, the vehicle data reporting method according to any one of claims 1 to 10 is implemented.

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