Vehicle data reporting method, electronic device and vehicle

Abstract

Embodiments of the present application provide a vehicle data reporting method, an electronic device and a vehicle. The method comprises: obtaining a plurality of groups of first vehicle data collected in a current sampling period; each group of first vehicle data comprising vehicle signals of a plurality of signal types; each signal type corresponding to a signal change characteristic; each signal change characteristic being associated with a preprocessing operation; preprocessing vehicle signals of the same signal type collected in the current sampling period using the preprocessing operation corresponding to the signal change characteristic of the signal type to obtain first reporting data corresponding to the signal type; and reporting the first reporting data corresponding to all signal types in the current sampling period to a target reporting platform. The present application improves the reliability and stability of vehicle reporting data.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a vehicle data reporting method, electronic device, and vehicle. Background Technology

[0002] Currently, some vehicles are required to report vehicle data (such as vehicle speed, battery status, and airbag signals) to national and local monitoring platforms. However, existing vehicle data reporting technologies often only collect the last value within a single sampling period as the reported data, and the data processing strategy is simple, lacking multi-dimensional verification. This makes it difficult to guarantee the reliability and stability of data under complex driving environments, resulting in significant fluctuations in the quality of the reported data. Summary of the Invention

[0003] This application provides a vehicle data reporting method, electronic device, and vehicle, aiming to improve the technical problem in the prior art that it is difficult to guarantee the reliability and stability of vehicle data reporting under complex driving environments.

[0004] A method for reporting vehicle data includes: Acquire multiple sets of first vehicle data collected within the current sampling period; each set of first vehicle data includes vehicle signals of various signal types; each signal type corresponds to a signal change feature; each signal change feature is associated with a preprocessing operation; For vehicle signals of the same signal type collected within the current sampling period, preprocessing is performed using a preprocessing operation corresponding to the signal change characteristics of the signal type to obtain the first reported data corresponding to the signal type. The first reported data corresponding to all signal types in the current sampling period is reported to the target reporting platform.

[0005] In this embodiment, for the same signal type, multiple vehicle signals will be collected multiple times within the same sampling period. Then, for vehicle signals with different signal change characteristics, adaptive preprocessing will be performed through different preprocessing operations. The preprocessing object is multiple vehicle signals of the same signal type collected within the current sampling period. In this way, compared with the scheme of collecting the last value in only one sampling period as the reported data, noise interference or signal omission in a single sampling is effectively avoided, and the reliability and stability of the vehicle reported data are improved from the source.

[0006] Furthermore, the preprocessing of vehicle signals of the same signal type collected within the current sampling period, using a preprocessing operation corresponding to the signal change characteristics of the signal type, includes: Acquire vehicle signals of the same signal type collected within the current sampling period, and acquire signal change characteristics related to the signal type; When the signal change characteristic is a steady-state change characteristic, the preprocessing operation corresponding to the steady-state change characteristic is determined as the first processing operation, and the first processing operation is executed; the first processing operation includes: Valid signals are determined from all vehicle signals of this signal type collected within the current sampling period, and the median of all valid signals is determined as the first reported data corresponding to the signal type.

[0007] In this embodiment, when the signal change characteristics of a vehicle signal of a certain signal type are steady-state change characteristics, several valid signals can be determined from all vehicle signals of that signal type collected in the current sampling period. Then, based on the median of the determined valid signals, the median is determined as the first reported data corresponding to the signal type. This makes the first reported data have good anti-outlier interference effect, fit the actual distribution of vehicle signals, stably reflect the central trend of vehicle signals, and adapt to the characteristics of vehicle signals with steady-state change characteristics.

[0008] Further, determining the valid signal from all vehicle signals of this signal type collected within the current sampling period includes: The signal fluctuation coefficient is determined based on all vehicle signals of this signal type collected within the current sampling period; the signal fluctuation coefficient is determined based on the maximum signal value, minimum signal value, and average signal value of the current sampling period. The effective quantity is determined based on the signal fluctuation coefficient, and the effective quantity of effective signals is selected from all vehicle signals.

[0009] In this embodiment, after determining the signal fluctuation coefficient based on all vehicle signals of the signal type collected within the current sampling period, and determining the effective number corresponding to the signal fluctuation coefficient, a number of effective signals are selected from all vehicle signals of the signal type collected within the current sampling period. Then, all the selected effective signals are sorted according to their size, and the median of all effective signals is taken as the final first reported data. This can effectively filter out occasional extreme interference values, better reflect the real state of vehicle signals with steady-state change characteristics, and can adaptively achieve smoothing of instantaneous interference.

[0010] Furthermore, the preprocessing of vehicle signals of the same signal type collected within the current sampling period, using a preprocessing operation corresponding to the signal change characteristics of the signal type, includes: Acquire vehicle signals of the same signal type collected within the current sampling period, and acquire signal change characteristics related to the signal type; When the signal change characteristic is a dynamic change characteristic, the preprocessing operation corresponding to the dynamic change characteristic is determined as the second processing operation, and the second processing operation is executed; the second processing operation includes: A target interpolation algorithm is determined based on the signal change rate associated with the signal type. Based on the target interpolation algorithm, signal estimation is performed on all vehicle signals of the signal type collected from the current sampling period to obtain the first reported data corresponding to the signal type.

[0011] In this embodiment, when the signal change characteristics of a vehicle signal of a certain signal type are dynamic, a target interpolation algorithm can be determined based on the signal change rate associated with that signal type. Based on the target interpolation algorithm, signal estimation is performed on all vehicle signals of that signal type collected from the current sampling period to obtain the first reported data corresponding to the signal type. In this way, the timeliness and accuracy of the first reported data are effectively improved, and the instantaneous change trend of vehicle signals with dynamic change characteristics under the dynamic operating state of the vehicle can be captured in real time and accurately and reported in a timely manner.

[0012] Furthermore, the preprocessing of vehicle signals of the same signal type collected within the current sampling period, using a preprocessing operation corresponding to the signal change characteristics of the signal type, includes: Acquire vehicle signals of the same signal type collected within the current sampling period, and acquire signal change characteristics related to the signal type; When the signal change characteristic is a fluctuating change characteristic, the preprocessing operation corresponding to the fluctuating change characteristic is determined as the third processing operation, and the third processing operation is executed; the third processing operation includes: determining a change threshold according to the signal type of the vehicle signal, obtaining the signal difference between the vehicle signal of the signal type collected in the current sampling period and the historical reported data of the signal type in the previous sampling period, and determining the first reported data of the signal type according to the signal difference and the change threshold.

[0013] In this embodiment, when the signal change characteristics of a vehicle signal of a certain signal type are fluctuating, a change threshold can be determined based on the signal type. The signal difference between the vehicle signal of that signal type collected in the current sampling period and the historical reported data of the same signal type in the previous sampling period can be obtained. The first reported data of the signal type is determined based on the signal difference and the change threshold. In this way, data garbage generated by normal fluctuations in vehicle signals can be effectively filtered out, avoiding frequent reporting of small fluctuations that have no practical significance, greatly reducing the frequency of invalid reporting, and reducing the waste of communication bandwidth and background processing resources. At the same time, by preset a change threshold that matches the signal type, reporting is only triggered when the signal fluctuation exceeds the normal range. This can more accurately capture the real abnormal changes of key signals, ensuring that the first reported data only contains abnormal fluctuation information with practical diagnostic value, and significantly improving the effectiveness and relevance of data reporting.

[0014] Further, the step of reporting the first reporting data corresponding to all signal types in the current sampling period to the target reporting platform includes: Obtain all associated signals corresponding to the key type within the current sampling period; the key type is some or all of all the signal types. When the absolute value of the deviation between all the associated signals corresponding to the same key type and the first reported data is less than or equal to a preset deviation threshold, the first reported data corresponding to the key type is reported to the target reporting platform.

[0015] In this embodiment, before the first reported data corresponding to the key type is reported, it is calibrated by the associated signal. Only when the absolute value of the consistency deviation value corresponding to multiple independent signal sources (the first reported data and one or more associated signals) is within the change threshold, the first reported data is finally determined to be reliable and allowed to be reported, so as to avoid false values ​​in the first reported data corresponding to the key type.

[0016] Furthermore, after acquiring multiple sets of first vehicle data collected within the current sampling period, the process includes: When a vehicle is detected to be in a dormant state, the key signals collected in the current sampling period are stored as valid data frames in a preset memory. The key signals refer to vehicle signals whose signal types match the target type. When the vehicle is detected to switch from hibernation to the initial stage of wake-up, the last valid data frame stored in the preset memory is determined as the second reported data, and the second reported data is reported to the target reporting platform. When the vehicle is detected to switch from the initial wake-up period to the confidence recovery period, second vehicle data is collected in real time, and third reporting data is determined based on the second vehicle data, the valid data frame, the first weight corresponding to the valid data frame, and the second weight corresponding to the second vehicle data, and the third reporting data is reported to the target reporting platform; during the confidence recovery period, the first weight decreases over time.

[0017] In this embodiment, a sleep-wake-up continuation mechanism is introduced. When the vehicle enters the initial wake-up phase, before the valid vehicle signal is fully ready, the last valid data frame stored in the sleep state is retrieved from the preset memory as the second reported data. After the vehicle enters the confidence recovery period, the historical valid data frames are dynamically mixed with the new second vehicle data to determine the third reported data. During the reporting of the third reported data, the first weight corresponding to the valid data frame is gradually reduced. This avoids data interruption or invalid data in the initial wake-up phase, and also achieves a smooth transition from the historical valid data frames to the real-time second vehicle data, ensuring the continuity of the reported data.

[0018] Further, the step of reporting the first reporting data corresponding to all signal types in the current sampling period to the target reporting platform includes: All first reported data corresponding to the same signal type in the current sampling period are input into the preset rule engine, so that the preset rule engine retrieves the real-time vehicle information associated with the signal type according to the verification rules, and verifies all first reported data corresponding to the signal type through the real-time vehicle information to obtain the verification result. When the verification result indicates that the first reported data conforms to the verification rules, the first reported data is reported to the target reporting platform.

[0019] In this embodiment, a lightweight and configurable preset rule engine is introduced. The preset rule engine retrieves real-time vehicle information associated with the signal type according to the verification rules, and then uses the real-time vehicle information to verify all first reported data corresponding to the same signal type. Only when the verification result indicates that the first reported data conforms to the verification rules is the first reported data reported to the target reporting platform. This embodiment realizes rule verification of the preprocessed first reported data through the preset rule engine, completes the initial cleaning of the first reported data, and reduces the processing pressure on the target reporting platform.

[0020] An electronic device includes a processor and a memory, wherein, Memory, used to store computer programs; The processor is used to execute the program stored in the memory to implement the above-mentioned vehicle data reporting method.

[0021] A vehicle that includes the aforementioned electronic equipment. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of a vehicle data reporting method provided in an embodiment of this application; Figure 2 This is a flowchart illustrating step S20 of the vehicle data reporting method provided in the first embodiment of this application; Figure 3 This is a flowchart illustrating step S20 of the vehicle data reporting method provided in the second embodiment of this application; Figure 4 This is a flowchart illustrating step S20 of the vehicle data reporting method provided in the third embodiment of this application; Figure 5 This is a flowchart illustrating step S30 of the vehicle data reporting method provided in the first embodiment of this application; Figure 6 This is a flowchart of a vehicle data reporting method provided in another embodiment of this application; Figure 7 This is a flowchart illustrating step S30 of the vehicle data reporting method provided in the second embodiment of this application; Figure 8 This is a schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0024] To make the technical problems, technical solutions, and beneficial effects solved by this application clearer, the following detailed description is provided in conjunction with embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] In one embodiment, please refer to Figure 1This application provides a vehicle data reporting method, which is applied to, for example... Figure 8 The electronic device includes the following steps S10-S30: S10. Acquire multiple sets of first vehicle data collected within the current sampling period; each set of first vehicle data includes vehicle signals of multiple signal types; each signal type corresponds to a signal change feature; each signal change feature is associated with a preprocessing operation; wherein, the period duration of the sampling period can be set according to requirements, such as 1 second; the current sampling period refers to a sampling period to which the current time point belongs. In this embodiment, multiple types of vehicle signals (e.g., vehicle speed is a vehicle signal of one signal type, battery voltage is also a vehicle signal of one signal type, etc.) will be collected within the current sampling period, and each type of vehicle signal will be collected multiple times. Therefore, multiple sets of first vehicle data will be collected multiple times within the current sampling period, and each set of first vehicle data contains vehicle signals of multiple signal types.

[0027] Specifically, after the vehicle is powered on and enters normal operation, the vehicle's electronic control units can continuously collect first vehicle data at regular intervals. Specifically, the vehicle signals collected by each electronic control unit can be transmitted to the electronic equipment via the CAN (Controller Area Network) bus, so that the electronic equipment can continuously acquire first vehicle data. In this way, multiple sets of first vehicle data collected in the current sampling period form a data window.

[0028] Understandably, signal variation characteristics refer to the performance of different types of vehicle signals as they change over time during vehicle operation. Signal variation characteristics include, but are not limited to, steady-state variation characteristics, dynamic variation characteristics, and fluctuation variation characteristics.

[0029] S20. For vehicle signals of the same signal type collected within the current sampling period, preprocessing is performed using a preprocessing operation corresponding to the signal change characteristics of the signal type to obtain the first reported data corresponding to the signal type. In this application, different preprocessing operations are required for differentiated processing of vehicle signals with different signal change characteristics.

[0030] As an example, please refer to Figure 2 In step S20, the preprocessing of vehicle signals of the same signal type collected within the current sampling period using a preprocessing operation corresponding to the signal change characteristics of the signal type includes: S201, acquire vehicle signals of the same signal type collected within the current sampling period, and acquire the signal change characteristics associated with the signal type; that is, after determining the signal type of the vehicle signals, since each signal type is pre-associated with a signal change characteristic, after acquiring all vehicle signals of the same signal type collected within the current sampling period, the signal change characteristics associated with that signal type can be directly acquired. Signal change characteristics include steady-state change characteristics, which refer to the smooth change characteristics of the vehicle signal within the sampling period. For example, when the signal type of the vehicle signal is the current battery level or battery temperature value, since both the current battery level and battery temperature value have the characteristic of smooth change within the sampling period, the signal change characteristics associated with the current battery level and battery temperature value are steady-state change characteristics.

[0031] S202, when the signal change characteristic is a steady-state change characteristic, determine the preprocessing operation corresponding to the steady-state change characteristic as the first processing operation, and execute the first processing operation; the first processing operation includes: determining the valid signal from all vehicle signals of the signal type collected in the current sampling period, and determining the median of all valid signals as the first reporting data corresponding to the signal type.

[0032] In this embodiment, when the signal change characteristics of a vehicle signal of a certain signal type are steady-state change characteristics, a first processing operation can be performed on all vehicle signals of that signal type collected within the current sampling period to obtain the first reported data corresponding to that signal type. Specifically, several valid signals can be determined from all vehicle signals of that signal type collected within the current sampling period. Then, based on the median of the determined valid signals, the median is determined as the first reported data corresponding to the signal type. This ensures that the first reported data has good anti-outlier interference effect, closely matches the actual distribution of vehicle signals, stably reflects the central trend of vehicle signals, and adapts to the characteristics of vehicle signals with steady-state change characteristics.

[0033] As an example, in step S202, determining the valid signal from all vehicle signals of this signal type collected within the current sampling period includes: The signal fluctuation coefficient is determined based on all vehicle signals of this signal type collected within the current sampling period. The signal fluctuation coefficient is determined based on the maximum signal value, minimum signal value, and average signal value of the current sampling period. The maximum signal value refers to the maximum value among all vehicle signals of this signal type in the current sampling period; the minimum signal value refers to the minimum value among all vehicle signals of this signal type in the current sampling period; and the average signal value refers to the average value of all vehicle signals of this signal type in the current sampling period. The signal fluctuation coefficient is used to characterize the fluctuation amplitude of the vehicle signal of this signal type in the current sampling period. In some embodiments, the signal fluctuation coefficient is the difference between the maximum and minimum signal values ​​of the current sampling period, divided by the average signal value.

[0034] The effective number is determined based on the signal fluctuation coefficient, and the effective number of valid signals is selected from all vehicle signals. Specifically, in this application, different signal fluctuation ranges are set, and each signal fluctuation range is associated with a different number of samples. In this step, the signal fluctuation coefficient is compared with different signal fluctuation ranges, and the number of samples corresponding to the signal fluctuation range to which the signal fluctuation coefficient belongs can be determined as the effective number. For example, suppose the signal fluctuation range includes three levels: low fluctuation range (e.g., less than or equal to 3%), medium fluctuation range (e.g., greater than 3% and less than or equal to 8%), and high fluctuation range (e.g., greater than 8%). The number of samples corresponding to the low fluctuation range is the first number (e.g., 3), the number of samples corresponding to the medium fluctuation range is the second number (e.g., 5), and the number of samples corresponding to the high fluctuation range is the third number (e.g., 7), wherein the second number is greater than the first number and less than the third number. At this point, if the signal fluctuation coefficient is in the low fluctuation range, the effective quantity is the first quantity corresponding to the low fluctuation range; for example, the signal fluctuation coefficient of a vehicle signal with a battery remaining charge is 2%, which belongs to the low fluctuation range "less than or equal to 3%", and the effective quantity is the first quantity 3 corresponding to the low fluctuation range. If the signal fluctuation coefficient is in the medium fluctuation range, the effective quantity is the second quantity corresponding to the medium fluctuation range, to balance the filtering effect and real-time performance; for example, the signal fluctuation coefficient of a vehicle signal with an engine coolant temperature is 7%, which belongs to the medium fluctuation range "greater than 3% and less than or equal to 8%", and the effective quantity is the second quantity 5 corresponding to the medium fluctuation range. If the signal fluctuation coefficient is in the high fluctuation range, the effective quantity is the third quantity corresponding to the high fluctuation range, to strengthen the filtering of extreme values; for example, the signal fluctuation coefficient of a vehicle signal with an air conditioning compressor pressure is 10%, which belongs to the medium fluctuation range "greater than 8%", and the effective quantity is the third quantity 7 corresponding to the high fluctuation range.

[0035] In this embodiment, after determining the signal fluctuation coefficient based on all vehicle signals of the signal type collected within the current sampling period, and determining the effective number corresponding to the signal fluctuation coefficient, a number of effective signals are selected from all vehicle signals of the signal type collected within the current sampling period. Then, all selected effective signals are sorted by size, and the median of all effective signals is taken as the final first reported data. For example, if the effective number of vehicle signals of the battery temperature signal type is 5, the effective data selected from all vehicle signals of this signal type collected within the current sampling period are: 35℃, 36℃, 35℃, 37℃, and 34℃. After sorting, the median of 35℃ is taken as the first reported data corresponding to this signal type. The first reported data determined by the above method of this application can effectively filter out occasional extreme interference values ​​(such as 40℃), better reflecting the real state of vehicle signals with steady-state variation characteristics, and can adaptively achieve smoothing of instantaneous interference.

[0036] As an example, please refer to Figure 3 In step S20, the preprocessing of vehicle signals of the same signal type collected within the current sampling period using a preprocessing operation corresponding to the signal change characteristics of the signal type includes: S201: Acquire vehicle signals of the same signal type collected within the current sampling period, and acquire the signal change characteristics associated with the signal type. That is, after determining the signal type of the vehicle signals, since each signal type is pre-associated with a signal change characteristic, after acquiring all vehicle signals of the same signal type collected within the current sampling period, the signal change characteristics associated with that signal type can be directly acquired. Signal change characteristics include dynamic change characteristics, which refer to the rapid changes in vehicle signals within the sampling period, accurately reflecting real-time trends rather than static values. For example, when the vehicle signal type is vehicle speed or acceleration (such as lateral or longitudinal acceleration), since both vehicle speed and acceleration exhibit rapid changes within the sampling period, the signal change characteristics associated with vehicle speed and acceleration are dynamic change characteristics.

[0037] S203, when the signal change characteristic is a dynamic change characteristic, the preprocessing operation corresponding to the dynamic change characteristic is determined as a second processing operation, and the second processing operation is executed; the second processing operation includes: determining a target interpolation algorithm based on the signal change rate associated with the signal type, and performing signal estimation on all vehicle signals of that signal type collected from the current sampling period according to the target interpolation algorithm to obtain first reported data corresponding to the signal type. That is, when the signal change characteristic of a vehicle signal of a certain signal type is a dynamic change characteristic, the second processing operation can be used to preprocess all vehicle signals of that signal type collected from the current sampling period to obtain first reported data corresponding to that signal type.

[0038] Specifically, the following second processing operation can be used to achieve estimation by adaptively determining the target interpolation algorithm, avoiding data jumps caused by single-point acquisition failures. First, the signal change rate of the vehicle signal of this signal type in the current sampling period can be determined. If the signal change rate is less than or equal to a preset change rate threshold (for example, taking a vehicle signal of acceleration type as an example, the preset change rate threshold is 0.5 m / s³; when the signal change rate, i.e., the acceleration change rate, is ≤ 0.5 m / s³, the current vehicle is in a uniform acceleration scenario), then the target interpolation algorithm is a linear interpolation algorithm, using a linear interpolation algorithm to ensure real-time performance and computational lightweighting. If the rate of change of the signal is greater than the preset rate of change threshold (for example, taking a vehicle signal of acceleration as an example, the preset acceleration threshold is 0.5 m / s³, and when the rate of change of the signal, that is, the rate of change of acceleration > 0.5 m / s³, the current vehicle is in a nonlinear scenario of rapid acceleration), then the target interpolation algorithm is a quadratic polynomial interpolation algorithm (the formula corresponding to the quadratic polynomial interpolation algorithm is: y = ax² + bx + c). Then, based on the vehicle signal fitting coefficients (a, b, c) of the latest 3 valid sampling points, the first reported data corresponding to the next sampling point can be estimated by using the quadratic polynomial interpolation algorithm, so that the first reported data determined by this method can more accurately characterize the curvature change of the vehicle signal. In this embodiment, taking vehicle signals of speed as an example, the sampling period is 1 second. The vehicle speed is 30 km / h at t1=0.3s and 36 km / h at t2=0.7s. The calculated speed is a=(36-30) / (0.7-0.3)=15 km / h / s. If the current time t=1.0s (the end of the current sampling period, i.e., the time when vehicle data is reported), the first reported data corresponding to the vehicle speed is estimated to be v=36+15×(1.0-0.7)=40.5km / h through a linear interpolation algorithm (formula: v=v0+aΔt). This result retains the acceleration trend and avoids the lag caused by prior art, which often only takes the vehicle signal corresponding to the last sample (e.g., 36 km / h at t2=0.7s) as the reported data, thus more closely reflecting the actual vehicle motion state.

[0039] In this embodiment, when the signal change characteristics of a vehicle signal of a certain signal type are dynamic, a target interpolation algorithm can be determined based on the signal change rate associated with that signal type. Based on the target interpolation algorithm, signal estimation is performed on all vehicle signals of that signal type collected from the current sampling period to obtain the first reported data corresponding to the signal type. In this way, the timeliness and accuracy of the first reported data are effectively improved, and the instantaneous change trend of vehicle signals with dynamic change characteristics under the dynamic operating state of the vehicle can be captured in real time and accurately and reported in a timely manner.

[0040] As an example, please refer to Figure 4In step S20, the preprocessing of vehicle signals of the same signal type collected within the current sampling period using a preprocessing operation corresponding to the signal change characteristics of the signal type includes: S201: Acquire vehicle signals of the same signal type collected within the current sampling period, and acquire the signal change characteristics associated with that signal type. That is, after determining the signal type of the vehicle signals, since each signal type is pre-associated with a signal change characteristic, after acquiring all vehicle signals of the same signal type collected within the current sampling period, the signal change characteristics associated with that signal type can be directly acquired. Signal change characteristics include fluctuation change characteristics, which refer to the characteristic that the vehicle signal has high-frequency, small fluctuations within the sampling period, but the fluctuation amplitude does not exceed the normal operating range (frequent reporting of such vehicle signals with fluctuation change characteristics will generate data garbage due to their small and high-frequency fluctuations). For example, when the signal type of the vehicle signal is cell voltage (including low cell voltage and high cell voltage), since cell voltage has the characteristic of changing rapidly within the sampling period, the signal change characteristic associated with cell voltage is a fluctuation change characteristic.

[0041] S204, when the signal change characteristic is a fluctuating change characteristic, the preprocessing operation corresponding to the fluctuating change characteristic is determined as a third processing operation, and the third processing operation is executed; the third processing operation includes: determining a change threshold according to the signal type of the vehicle signal, obtaining the signal difference between the vehicle signal of the signal type collected in the current sampling period and the historical reported data of the signal type in the previous sampling period, and determining the first reported data of the signal type according to the signal difference and the change threshold. In this embodiment, each signal type is associated with a change threshold, and different change thresholds can be the same or different. For example, the change threshold for low cell voltage is 0.8% of the preset voltage baseline, and the change threshold for high cell voltage is 0.3% of the preset voltage baseline; wherein, the preset voltage baseline refers to the voltage reference value that can be used as a reference reference when the cell is in a stable working / resting state. Voltage changes that deviate from the preset voltage baseline will be judged as the real voltage fluctuation of the cell (such as charging and discharging, fault, aging, etc.), which is the core reference indicator for the battery management system (BMS) to monitor the cell status. The historical data corresponding to this signal type refers to the data reported to the target reporting platform in the previous sampling period that corresponds to this signal type, i.e., the last reported data corresponding to this signal type. For example, for a low-voltage cell with a preset voltage baseline of 12.2V, the corresponding change threshold is 0.0976V (12.2 × 0.8%), while for a high-voltage cell with a preset voltage baseline of 298V, the change threshold is 0.894V (298 × 0.3%).

[0042] In this embodiment, when the signal change characteristics of a vehicle signal of a certain signal type are fluctuating, a change threshold can be determined based on the signal type. The signal difference between the vehicle signal of that signal type collected in the current sampling period and the historical reported data of the same signal type in the previous sampling period can be obtained. The first reported data of the signal type is determined based on the signal difference and the change threshold. In this way, data garbage generated by normal fluctuations in vehicle signals can be effectively filtered out, avoiding frequent reporting of small fluctuations that have no practical significance, greatly reducing the frequency of invalid reporting, and reducing the waste of communication bandwidth and background processing resources. At the same time, by preset a change threshold that matches the signal type, reporting is only triggered when the signal fluctuation exceeds the normal range. This can more accurately capture the real abnormal changes of key signals, ensuring that the first reported data only contains abnormal fluctuation information with practical diagnostic value, and significantly improving the effectiveness and relevance of data reporting.

[0043] As an example, in step S204, the first reported data for determining the signal type based on the signal difference and the change threshold includes: When the signal difference corresponding to all vehicle signals of the signal type within the current sampling period is less than the change threshold, the historically reported data is determined as the first reported data; If at least one of the signal differences corresponding to all vehicle signals of the signal type within the current sampling period is greater than or equal to the change threshold, the vehicle signal corresponding to the signal difference greater than or equal to the change threshold is determined as the first reported data.

[0044] In this embodiment, the first newly acquired vehicle signal of this signal type in the current sampling period is compared with the historical reported data from the previous sampling period. If the signal difference between the first vehicle signal and the historical reported data does not exceed the change threshold, the historical reported data is used. The next vehicle signal of this signal type is then compared with the historical reported data. If the signal difference between the next vehicle signal and the historical reported data still does not exceed the change threshold, the historical reported data is used again, and so on. If the signal difference between the last vehicle signal of this signal type in the current sampling period and the historical reported data still does not exceed the change threshold, it is determined as the first reported data corresponding to this signal type in the current sampling period. Conversely, if the signal difference corresponding to one of the vehicle signals of this signal type exceeds the change threshold, the vehicle signal corresponding to that signal difference is determined as the first reported data.

[0045] In this embodiment, the first reported data of vehicle signals with fluctuating characteristics is differentiated by the signal difference and the change threshold. This effectively avoids the problems of "misjudgment caused by small fluctuations in the system corresponding to high cell voltage" or "missed judgment caused by excessive change threshold in the system corresponding to low cell voltage". This embodiment significantly reduces invalid data reporting while ensuring the system's ability to sensitively detect real anomalies (such as a sudden drop in low cell voltage to 10V or a sudden drop in high cell voltage to 290V).

[0046] S30. Report the first reporting data corresponding to all signal types in the current sampling period to the target reporting platform. The target reporting platform can be a national or local monitoring platform that the vehicle needs to report data to, or it can be a cloud-based platform.

[0047] In this embodiment, for the same signal type, multiple vehicle signals will be collected multiple times within the same sampling period. Then, for vehicle signals with different signal change characteristics, adaptive preprocessing will be performed through different preprocessing operations. The preprocessing object is multiple vehicle signals of the same signal type collected within the current sampling period. In this way, compared with the scheme of collecting the last value in only one sampling period as the reported data, noise interference or signal omission in a single sampling is effectively avoided, and the reliability and stability of the vehicle reported data are improved from the source.

[0048] In one example, please refer to Figure 5 Step S30, namely, reporting the first reporting data corresponding to all signal types in the current sampling period to the target reporting platform, includes: S301, acquire all associated signals corresponding to the key type within the current sampling period; the key type is some or all of all the signal types. S302, when the absolute value of the deviation between all the associated signals corresponding to the same key type and the first reported data is less than or equal to a preset deviation threshold, the first reported data corresponding to the key type is reported to the target reporting platform.

[0049] In this embodiment, the key type refers to a key signal type selected from all signal types, such as vehicle speed (which can be used to determine whether a vehicle accident has occurred), battery level, and airbag status. For each key type of vehicle signal, cross-validation can be performed using associated signals to evaluate the data and intercept erroneous data reporting at the source. Associated signals refer to vehicle signals that are related to and corroborate the key type of vehicle signal. For example, if the key type is vehicle speed transmitted via the CAN bus, the associated signals for this vehicle speed include GPS displacement speed and the target vehicle speed calculated based on wheel speed. In this case, it is necessary to obtain two deviation values ​​between the GPS displacement speed and the target vehicle speed and the first reported data corresponding to the wheel speed. The absolute values ​​of the two deviation values ​​are compared with preset deviation thresholds. If the absolute values ​​of both deviation values ​​are less than or equal to the preset deviation thresholds, the first reported data corresponding to the wheel speed can be reported. For example, the first reported data determined based on the vehicle speed transmitted via the CAN bus is 50 km / h; the GPS displacement speed is 49.5 km / h; and the target vehicle speed calculated based on the wheel speed is 50.2 km / h. If the preset deviation threshold is 1.5 km / h, then the absolute values ​​of both deviation values ​​are less than the preset deviation threshold, and the first reported data corresponding to the vehicle speed will be reported to the target reporting platform.

[0050] Understandably, after obtaining the deviation values ​​between all the associated signals corresponding to the same key type and the first reported data, if the absolute value of at least one of the deviation values ​​is greater than a preset deviation threshold, it indicates that the first reported data has a significant contradiction. In this case, a data invalidation mechanism needs to be triggered to mark the first reported data as invalid and prevent it from being reported to the target reporting platform, thereby avoiding the reporting of invalid data or false signals. In this embodiment, before reporting the first reported data corresponding to the key type, it is calibrated by the associated signals. Only when the absolute value of the consistency deviation value corresponding to multiple independent signal sources (the first reported data and one or more associated signals) is within the change threshold is the first reported data finally determined to be reliable and allowed to be reported, so as to avoid false values ​​in the first reported data corresponding to the key type.

[0051] As an example, please refer to Figure 6 After step S10, that is, after acquiring multiple sets of first vehicle data collected within the current sampling period, the process includes: S40, when the vehicle is detected to be in a sleep state, the key signals collected within the current sampling period are stored as valid data frames in a preset memory. The key signals refer to vehicle signals whose signal type matches the target type. The target type is set according to requirements; for example, it can refer to the signal type corresponding to a vehicle signal that cannot be immediately obtained after the vehicle enters the initial wake-up phase. The target type can refer to signal types such as airbag status, cell voltage, current, and battery temperature. Understandably, when the vehicle is detected to be entering a sleep state within a preset time, it is determined that the vehicle is in a sleep state. At this time, the electronic equipment will not immediately stop working, but will write the key signals (not all vehicle data) into the preset memory to achieve differentiated persistent storage before sleep. The preset memory is preferably a non-volatile memory. Understandably, in some embodiments, it is necessary to first perform integrity verification on the key signals (which can be performed through cyclic redundancy check) to ensure their integrity before writing them into the non-volatile memory. Understandably, after the vehicle enters a sleep state after a preset time of sleep state, the electronic equipment will stop reporting data.

[0052] S50, when the vehicle is detected to switch from sleep state to the initial wake-up state, the last valid data frame stored in the preset memory is determined as the second reporting data, and the second reporting data is reported to the target reporting platform; wherein, when the vehicle is in sleep state, the electronic equipment will stop reporting data, and when the vehicle switches from sleep state to the initial wake-up state (that is, when it has just entered the wake-up state), the electronic control units may not be fully ready. At this time, the electronic equipment cannot obtain valid vehicle signals (including key signals) from the electronic control units through the CAN bus. Therefore, at this time, the electronic equipment will preferentially select the last valid data frame stored in the vehicle in the sleep state from the preset memory as the second reporting data to avoid reporting invalid values ​​or the interruption of reporting data. For example, regarding critical signals like vehicle mileage, when a vehicle switches from sleep mode to the initial wake-up phase, since the vehicle is not in a driving state, no new mileage is added, and therefore no new valid vehicle mileage can be obtained. In this case, the vehicle data reporting method in this embodiment determines the last stored frame in the preset memory corresponding to the valid data frame of the vehicle mileage as the second reported data, instead of using the default value (0xFFFFFFFF) as the second reported data. This avoids abnormal jumps in the second reported data received by the target reporting platform. That is, in this application, if valid second vehicle data is not yet obtained, historical valid data frames will be directly reported, completely preventing invalid values ​​from being reported. Understandably, after the electronic device receives the first (or a preset number) valid critical signals collected by the electronic control unit, it can confirm the switch from the initial wake-up phase to the confidence recovery period.

[0053] S60, when the vehicle is detected to switch from the initial wake-up period to the confidence recovery period, second vehicle data is collected in real time, and third reporting data is determined based on the second vehicle data, the valid data frame, the first weight corresponding to the valid data frame, and the second weight corresponding to the second vehicle data, and the third reporting data is reported to the target reporting platform; during the confidence recovery period, the first weight decreases over time. Each set of second vehicle data includes vehicle signals of multiple signal types.

[0054] In other words, after entering the confidence recovery period from the initial wake-up phase, a confidence smooth transition mechanism is introduced. As the vehicle operates and each electronic control unit becomes ready, valid vehicle signals (second vehicle data) are continuously collected. The system dynamically mixes historically stored valid data frames with newly acquired second vehicle data to determine the third reported data. During this process, the first weight corresponding to the valid data frames is gradually reduced until all valid data frames are replaced by the real-time acquired second vehicle data. In this way, a smooth transition of the third reported data can be achieved through dynamic data mixing, avoiding the cloud receiving invalid third reported data during the confidence recovery period and ensuring the continuity of reported data. Understandably, at the end of the confidence recovery period, the third reported data reported by the electronic equipment will be determined entirely based on the second vehicle data.

[0055] Understandably, after the vehicle enters the initial wake-up phase, the electronic control unit (ECU) needs more than 10 seconds to complete initialization before it can operate normally. However, the recovery time of the ECU is not uniform; some are faster and some are slower. Therefore, during the period from the vehicle entering the initial wake-up phase to the end of the confidence recovery period, the time it takes for the ECU to capture real-time vehicle signals (vehicle signals in the second vehicle data) is also inconsistent. In some embodiments, the confidence recovery period is confirmed to have ended when at least one vehicle signal of all signal types in the second vehicle data is acquired in real time. In other embodiments, a basic recovery time can be set. When the duration after the vehicle enters the initial wake-up phase is longer than the basic recovery time, and at least one vehicle signal of all signal types in the second vehicle data is acquired in real time, the confidence recovery period is confirmed to have ended. The basic recovery time is determined based on the recovery time of each ECU; for example, the maximum value of the recovery time corresponding to all ECUs can be determined as the basic recovery time.

[0056] During the confidence recovery period, the third reported data of a certain signal type can be determined according to the following formula: A = B × w + C × (1 - w), where A is the third reported data of that signal type; B is the valid data frame of that signal type; C is the vehicle signal in the second vehicle data corresponding to that signal type; w is the first weight; w = 1 – t / T, where t is the duration after entering the initial wake-up phase, and T is the basic recovery duration corresponding to the confidence recovery period. For example, when the basic recovery duration corresponding to the recovery period is T = 10s, the first weight w = 70% in the 3rd second after entering the initial wake-up phase. In this embodiment, the mechanism of linearly decreasing the first weight over time until it fully transitions to the real-time data stream of the second vehicle data avoids the long-term use of stored valid data frames, ensuring the final timeliness and accuracy of the reported data, and also avoids data fluctuations on the target reporting platform, reducing the risk of misjudgment.

[0057] In this embodiment, a sleep-wake-up continuation mechanism is introduced. When the vehicle enters the initial wake-up phase, before the valid vehicle signal is fully ready, the last valid data frame stored in the sleep state is retrieved from the preset memory as the second reported data. After the vehicle enters the confidence recovery period, the historical valid data frames are dynamically mixed with the new second vehicle data to determine the third reported data. During the reporting of the third reported data, the first weight corresponding to the valid data frame is gradually reduced. This avoids data interruption or invalid data in the initial wake-up phase, and also achieves a smooth transition from the historical valid data frames to the real-time second vehicle data, ensuring the continuity of the reported data.

[0058] As an example, please refer to Figure 7 In step S30, namely, reporting the first reporting data corresponding to all signal types in the current sampling period to the target reporting platform, the following steps are included: S303, all first reported data corresponding to the same signal type in the current sampling period are input into the preset rule engine. The preset rule engine retrieves real-time vehicle information associated with the signal type according to verification rules, and verifies all first reported data corresponding to the signal type using the real-time vehicle information to obtain a verification result. The preset rule engine is embedded in the vehicle and is lightweight and configurable according to verification rules. The preset rule engine can perform rule verification on the pre-processed reported data (including first reported data, second reported data, and third reported data) to perform initial data cleaning and reduce the processing pressure on the target reporting platform. The verification rules can be defined according to actual needs. For example, they can be defined as follows: when a preset condition (e.g., "signal A value exceeds the limit and signal B state is invalid") is met, a verification result indicating that the first reported data conforms to the verification rule is obtained; when the preset condition is not met, a verification result indicating that the first reported data does not conform to the verification rule is obtained. The preset conditions may include one or more conditions.

[0059] The verification rules can be issued by the target reporting platform (such as the cloud). The preset rule engine supports dynamically receiving verification rules from the cloud via HTTPS and can update and deprecate these rules. The preset rule engine uses flexible deployment methods to dynamically adapt to the iterative needs of the reported data, supporting differentiated data verification strategies for different regions and vehicle models through different verification rules. Specifically, the cloud issues new verification rules containing version numbers and activation conditions via HTTPS. After the new verification rules pass vehicle-side signature verification and condition verification, the preset rule engine will update its local verification rules according to the new rules and activate them; otherwise, it will use the existing local verification rules. Different verification rules can be issued and configured for different vehicle models and regions. When issuing verification rules, the cloud can carry version numbers, activation conditions, and also a difference identifier. The difference identifier indicates the content updated in this verification rule update; for example, the difference identifier may indicate that the current verification rule only updates the "battery voltage exceeding the limit threshold," rather than the entire rule set. At this time, the preset rule engine supports "incremental parsing", that is, only the content of this update represented by the difference identifier is loaded to cover the part of the old verification rules that corresponds to the content of this update, while keeping the other parts unchanged, so as to minimize the resource consumption on the vehicle side.

[0060] Vehicle-side verification refers to the security, integrity, and legality checks performed on vehicles after new verification rules are issued by the cloud via HTTPS. For example, vehicles may use cyclic redundancy check (CR) to verify the integrity of the new rules, ensuring that the downloaded rules are complete and preventing tampering, forgery, or receiving of corrupted or invalid rules during transmission. Conditional verification, on the other hand, involves the vehicle verifying whether its current status and attributes meet the conditions for effectiveness specified in the cloud-issued verification rules. These conditions may include one or more of the following: vehicle model compatibility, region compatibility, version compatibility, and hardware / software compatibility. For instance, a verification rule might include the condition that it only applies to vehicles with a specific model and version A1.1.

[0061] S304, when the verification result indicates that the first reported data conforms to the verification rule, the first reported data is reported to the target reporting platform. Understandably, when the verification result indicates that the first reported data conforms to the verification rule, the first reported data is reported to the target reporting platform. Simultaneously, preset actions can also be executed. These preset actions may include: marking the reported data as suspicious, triggering a diagnostic fault code (for example, if the preset condition in the verification rule, "airbag deployment is detected and the reported data corresponding to the vehicle speed is greater than 5," is met, then since airbag deployment indicates the vehicle cannot move, the reported data with a speed greater than 5 is clearly incorrect and should be filtered out at the vehicle end; therefore, this reported data needs to be discarded and a fault code triggered), displaying an alarm on the instrument panel, or other operations.

[0062] In this embodiment, a lightweight and configurable preset rule engine is introduced. The preset rule engine retrieves real-time vehicle information associated with the signal type according to the verification rules, and then uses the real-time vehicle information to verify all first reported data corresponding to the same signal type. Only when the verification result indicates that the first reported data conforms to the verification rules is the first reported data reported to the target reporting platform. This embodiment realizes rule verification of the preprocessed first reported data through the preset rule engine, completes the initial cleaning of the first reported data, and reduces the processing pressure on the target reporting platform.

[0063] Understandably, in some embodiments, in conjunction with the above embodiments, the first reported data will only be reported to the target reporting platform when the absolute value of the deviation between all the associated signals corresponding to the same key type and the first reported data is less than or equal to a preset deviation threshold, and when the above verification result indicates that the first reported data conforms to the verification rule.

[0064] In some embodiments, when the verification result indicates that the first reported data does not conform to the verification rule, the reported data (including the first reported data, the second reported data, and the third reported data) is directly discarded without being reported, and the reason for discarding is recorded to avoid data garbage consuming bandwidth and causing cloud misjudgment. For example, if the reported data is 295V, the preset condition in the verification rule "battery voltage < 280V is abnormal" is not met. Therefore, the verification result indicates that the first reported data does not conform to the verification rule, and the reported data needs to be discarded and filtered out without being reported.

[0065] In some embodiments, when the verification result indicates that the first reported data conforms to the verification rules, if conforming to the verification rules means that the vehicle is currently experiencing a serious fault, a fault event will be triggered immediately and reported to the manufacturer's platform corresponding to the vehicle. At the same time, a preset after-sales process will be automatically started, such as generating a repair work order, realizing an automated link of "data anomaly - fault reporting - repair work order dispatch", which greatly shortens the fault handling cycle and fully explores the value of data.

[0066] This application enables end-to-end protection of vehicle data from "preprocessing to interruption resumption to after-sales service," significantly reducing the reporting of invalid and junk data, improving data accuracy, continuity, and compliance, and effectively ensuring the high quality of reported national standard data. Simultaneously, addressing the limitations of vehicle data processing rules, which are often hard-coded and cannot support dynamic updates from the cloud, and lack of linkage mechanisms with after-sales service, this application resolves the problems of difficult rule iteration, bandwidth consumption by large amounts of "data junk," and lengthy cycles from data anomalies to repair response. By introducing a lightweight, configurable, pre-defined rule engine in collaboration with the cloud, it achieves remote dynamic updates of data processing strategies and automatic fault reporting and work order dispatch, improving the efficiency of data value mining and the timeliness of security supervision. The cloud can distribute and update data processing verification rules, allowing the system to flexibly adapt to standard iterations and new requirements without requiring over-the-air (OTA) upgrades for the entire vehicle. Furthermore, this application establishes automated linkage with the after-sales service system. When a serious fault is detected, it can automatically trigger after-sales processes such as repair work orders, greatly shortening the cycle from data anomaly detection to repair response, fully mining data value, and realizing true data-driven service.

[0067] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0068] This application also provides an electronic device 60, please refer to... Figure 8It includes a memory 601 and a processor 602, wherein the memory 601 is used to store computer programs; and the processor 602 is used to execute the programs stored in the memory 601 to implement the vehicle data reporting method described in any embodiment of this application.

[0069] In one embodiment, this application provides a vehicle including the aforementioned electronic device 60.

[0070] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the vehicle data reporting method described in any embodiment of this application.

[0071] In this application, "multiple" refers to two or more.

[0072] The terms “first,” “second,” “third,” “fourth,” etc., used in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0073] In this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, in this application, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0074] Unless otherwise specified, all steps in this application may be performed sequentially or randomly. For example, if the method includes steps A and B, it means that the method may include steps A and B performed sequentially, or it may include steps B and A performed sequentially. For example, if the method may also include step C, it means that step C may be added to the method in any order. For example, the method may include steps A, B, and C, or it may include steps A, C, and B, or it may include steps C, A, and B, etc.

[0075] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for reporting vehicle data, characterized in that, include: Acquire multiple sets of first vehicle data collected within the current sampling period; each set of first vehicle data includes vehicle signals of multiple signal types; each signal type corresponds to a signal change feature. Each signal change characteristic is associated with a preprocessing operation; For vehicle signals of the same signal type collected within the current sampling period, preprocessing is performed using a preprocessing operation corresponding to the signal change characteristics of the signal type to obtain the first reported data corresponding to the signal type. The first reported data corresponding to all signal types in the current sampling period is reported to the target reporting platform.

2. The vehicle data reporting method as described in claim 1, characterized in that, The preprocessing of vehicle signals of the same signal type collected within the current sampling period, using a preprocessing operation corresponding to the signal change characteristics of the signal type, includes: Acquire vehicle signals of the same signal type collected within the current sampling period, and acquire signal change characteristics related to the signal type; When the signal change characteristic is a steady-state change characteristic, the preprocessing operation corresponding to the steady-state change characteristic is determined as the first processing operation, and the first processing operation is executed; the first processing operation includes: Valid signals are determined from all vehicle signals of this signal type collected within the current sampling period, and the median of all valid signals is determined as the first reported data corresponding to the signal type.

3. The vehicle data reporting method as described in claim 2, characterized in that, The process of determining valid signals from all vehicle signals of this signal type collected within the current sampling period includes: The signal fluctuation coefficient is determined based on all vehicle signals of this signal type collected within the current sampling period; the signal fluctuation coefficient is determined based on the maximum signal value, minimum signal value, and average signal value of the current sampling period. The effective quantity is determined based on the signal fluctuation coefficient, and the effective quantity of effective signals is selected from all vehicle signals.

4. The vehicle data reporting method as described in claim 1, characterized in that, The preprocessing of vehicle signals of the same signal type collected within the current sampling period, using a preprocessing operation corresponding to the signal change characteristics of the signal type, includes: Acquire vehicle signals of the same signal type collected within the current sampling period, and acquire signal change characteristics related to the signal type; When the signal change characteristic is a dynamic change characteristic, the preprocessing operation corresponding to the dynamic change characteristic is determined as the second processing operation, and the second processing operation is executed; the second processing operation includes: A target interpolation algorithm is determined based on the signal change rate associated with the signal type. Based on the target interpolation algorithm, signal estimation is performed on all vehicle signals of the signal type collected from the current sampling period to obtain the first reported data corresponding to the signal type.

5. The vehicle data reporting method as described in claim 1, characterized in that, The preprocessing of vehicle signals of the same signal type collected within the current sampling period, using a preprocessing operation corresponding to the signal change characteristics of the signal type, includes: Acquire vehicle signals of the same signal type collected within the current sampling period, and acquire signal change characteristics related to the signal type; When the signal change characteristic is a fluctuating change characteristic, the preprocessing operation corresponding to the fluctuating change characteristic is determined as the third processing operation, and the third processing operation is executed; the third processing operation includes: determining a change threshold according to the signal type of the vehicle signal, obtaining the signal difference between the vehicle signal of the signal type collected in the current sampling period and the historical reported data of the signal type in the previous sampling period, and determining the first reported data of the signal type according to the signal difference and the change threshold.

6. The vehicle data reporting method as described in claim 1, characterized in that, The step of reporting the first reporting data corresponding to all signal types in the current sampling period to the target reporting platform includes: Obtain all associated signals corresponding to the key type within the current sampling period; the key type is some or all of all the signal types. When the absolute value of the deviation between all the associated signals corresponding to the same key type and the first reported data is less than or equal to a preset deviation threshold, the first reported data corresponding to the key type is reported to the target reporting platform.

7. The vehicle data reporting method as described in claim 1, characterized in that, After acquiring multiple sets of first vehicle data collected within the current sampling period, the process includes: When a vehicle is detected to be in a dormant state, the key signals collected in the current sampling period are stored as valid data frames in a preset memory. The key signals refer to vehicle signals whose signal types match the target type. When the vehicle is detected to switch from hibernation to the initial stage of wake-up, the last valid data frame stored in the preset memory is determined as the second reported data, and the second reported data is reported to the target reporting platform. When the vehicle is detected to switch from the initial wake-up period to the confidence recovery period, second vehicle data is collected in real time, and third reporting data is determined based on the second vehicle data, the valid data frame, the first weight corresponding to the valid data frame, and the second weight corresponding to the second vehicle data, and the third reporting data is reported to the target reporting platform; during the confidence recovery period, the first weight decreases over time.

8. The vehicle data reporting method as described in claim 1, characterized in that, The step of reporting the first reporting data corresponding to all signal types in the current sampling period to the target reporting platform includes: All first reported data corresponding to the same signal type in the current sampling period are input into the preset rule engine, so that the preset rule engine retrieves the real-time vehicle information associated with the signal type according to the verification rules, and verifies all first reported data corresponding to the signal type through the real-time vehicle information to obtain the verification result. When the verification result indicates that the first reported data conforms to the verification rules, the first reported data is reported to the target reporting platform.

9. An electronic device, characterized in that, Including processor and memory, among which, Memory, used to store computer programs; A processor is configured to execute a program stored in a memory to implement the vehicle data reporting method as described in any one of claims 1 to 8.

10. A vehicle, characterized in that, Including the electronic device as described in claim 9.

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