A vehicle remote control method and system based on a vehicle networking platform

Abstract

The application discloses a kind of vehicle remote control method and system based on vehicle networking platform, it is related to vehicle control technical field.The application includes: S1, real-time acquisition vehicle operation data, and carry out data preprocessing;S2, time sequence consistency analysis is carried out to vehicle operation data, comprehensive evaluation data synchronism and timeliness, whether current vehicle operation data is credible is judged;S3, analysis vehicle motion trend, the influence of communication time delay is compensated, and the real running state of vehicle is estimated;S4, whether vehicle is in stable stationary state is judged, record continuous static stability time, and the safety margin of remote control execution is judged, whether remote control instruction is allowed to execute is determined.The problem that the real running state of vehicle is not accurately judged, and the safety of remote control execution is influenced due to the difference of vehicle state data acquisition cycle, communication delay and data update lag in the process of remote control is solved.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, specifically to a method and system for remote vehicle control based on a vehicle networking platform. Background Technology

[0002] With the development of vehicle-to-everything (V2X) technology, vehicles are gradually gaining the ability to be remotely monitored and controlled via network platforms. V2X platforms can collect and manage vehicle operating status in real time and support various remote control services, thereby improving vehicle management efficiency and user convenience. In practical applications, vehicle operating status data is typically uploaded to the V2X platform via a communication network through the onboard control unit. The platform analyzes the acquired vehicle operating status data and executes corresponding remote control operations. As the level of vehicle intelligence and networking continues to improve, V2X platforms are increasingly widely used in vehicle operation monitoring, remote control, and vehicle operation safety management.

[0003] For example, Chinese patent CN115113555A discloses a method, device, and vehicle for remote vehicle control. The method includes: when receiving operation success information sent by an execution device in the vehicle, detecting whether the operation success information can be successfully sent to the mobile terminal that initiated the control request; when the operation success information cannot be successfully sent to the mobile terminal, generating a recovery operation request; sending the recovery operation request to the execution device, and the execution device performing the corresponding operation according to the recovery operation request to restore the vehicle to its state before the operation, thereby restoring the vehicle to its state before control and solving the problem that the mobile terminal cannot receive the control result normally due to factors such as unstable mobile networks.

[0004] For example, Chinese patent CN115237100A discloses a remote control system for rail vehicles, used to remotely control circuit breakers in the electrical system of rail vehicles. The remote control system includes a remote control unit and a remote execution unit. The remote execution unit controls the operating state of the circuit breaker according to execution commands and feeds back the circuit breaker's status signal to the remote control unit. The remote control unit receives remote control commands from the rail vehicle control system, issues execution commands to the remote execution unit according to the remote control commands, and generates circuit breaker status information based on the circuit breaker's status signal and feeds it back to the rail vehicle control system. This invention enables remote control of circuit breakers in the electrical system of rail vehicles, thereby improving the efficiency of emergency response to rail vehicle faults, enabling remote monitoring of circuit breakers by the vehicle control system, reducing the difficulty of daily operation and maintenance, and improving the reliability of the vehicle's electrical system.

[0005] However, in vehicle-to-everything (V2X) remote control scenarios, when the platform issues remote control commands to the vehicle, such as remote locking, remote speed limiting, and remote OTA upgrades, the vehicle may be in various dynamic operating states, such as the vehicle being driven, the driver operating the system, or the vehicle system being started. Due to issues such as differences in the vehicle status data acquired by the V2X platform, communication delays, and data cache update lags, the vehicle status obtained by the platform may deviate from the vehicle's actual current state. When remote control commands are executed under unsuitable conditions, it may cause vehicle control anomalies, such as triggering vehicle locking while driving or starting the vehicle during an OTA upgrade, thus affecting the security and reliability of the remote control system.

[0006] Therefore, in order to address the above problems, there is an urgent need for a vehicle remote control method and system based on a vehicle networking platform. Summary of the Invention

[0007] Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a vehicle remote control method and system based on a vehicle networking platform. This solves the problem that the vehicle networking platform's inaccurate judgment of the vehicle's actual operating status during remote control is caused by differences in vehicle status data acquisition cycles, communication delays, and data update lags, which in turn affect the security of remote control execution.

[0008] Technical solution To achieve the above objectives, the present invention provides the following technical solution: a vehicle remote control method based on a vehicle networking platform, comprising the following steps: S1, real-time acquisition of vehicle operation data and data preprocessing of the vehicle operation data; S2, time-series consistency analysis of the vehicle operation data based on a sliding time window, and comprehensive evaluation of data synchronization and timeliness, and determination of the reliability of the current vehicle operation data based on the evaluation results; S3, analysis of vehicle motion trends based on reliable vehicle operation data, compensation for communication delay impact using vehicle motion trends, estimation of the current actual vehicle operation state, and generation of a vehicle actual operation state dataset; S4, determination of whether the vehicle is in a stable stationary state based on the vehicle actual operation state dataset, recording the continuous static stability time, analyzing vehicle deceleration characteristics, and determining the safety margin for remote control execution by combining the continuous static stability time and communication delay, determining whether the remote control command is allowed to be executed based on the safety margin, and triggering the remote control operation.

[0009] Furthermore, the specific process of real-time vehicle operation data acquisition and data preprocessing is as follows: Vehicle operation data is acquired in real-time via the vehicle's CAN bus and connected to the vehicle network control unit, and uploaded to the vehicle network platform. This vehicle operation data includes: vehicle speed, accelerator pedal opening, motor speed, gear status, and ignition status. The sampling time on the vehicle side and the reception time on the vehicle network platform are recorded simultaneously. Time alignment processing is performed on the vehicle operation data, and outliers are identified and removed based on the vehicle's physical operating range. Moving average filtering is used to smooth the vehicle operation data, and minimum-maximum normalization is applied. A vehicle remote control database is established to store the raw and preprocessed vehicle operation data.

[0010] Furthermore, the specific process of performing time-series consistency analysis on vehicle operation data based on a sliding time window and comprehensively evaluating data synchronization and timeliness is as follows: Vehicle operation data within the sliding time window is read, and data sequences corresponding to various types of vehicle operation data are formed according to vehicle speed, accelerator pedal opening, motor speed, gear status, and ignition status; for each data sequence, first-order difference calculations are performed on the sampling time and reception time to obtain the adjacent sampling time interval and adjacent reception time interval, and the median of the sampling time intervals within the sliding time window is taken to obtain the median value of the sampling period; the time interval difference is obtained by subtracting the corresponding sampling time interval from the reception time interval, and the sliding time window is calculated. The median absolute deviation of the intraorbital time interval difference is used to obtain the time jitter value. The time jitter value is divided by the sum of the median value and the smallest positive number of the sampling period to obtain the synchronization deviation value. The difference between the current time and the most recent reception time of various vehicle operation data is calculated to obtain the data lag. The data lag is divided by the sum of the median value and the smallest positive number of the sampling period to obtain the lag value. The synchronization deviation value and the lag value are added together and the opposite is taken to obtain the exponential decay input value of various vehicle operation data. The exponential decay input values ​​of all types of vehicle operation data are summed and divided by the total number of vehicle operation data types to obtain the average decay. The average decay is then subjected to natural exponential calculation to obtain the data reliability value.

[0011] Furthermore, the specific process for determining the reliability of the current vehicle operation data based on the evaluation results is as follows: the data reliability value is compared with the reliability threshold; when the data reliability value is greater than or equal to the reliability threshold, the current vehicle operation data is marked as reliable data and enters the vehicle real state evaluation process; when the data reliability value is less than the reliability threshold, the current vehicle operation data is marked as unreliable data, and only vehicle operation data is continuously received; the data reliability value and the reliability mark are written into the vehicle remote control database.

[0012] Furthermore, the specific process of analyzing vehicle motion trends based on reliable vehicle operation data is as follows: read the vehicle operation data marked as reliable data and execute the vehicle real state assessment process; calculate the difference between the current time and the sampling time of the vehicle operation data to obtain the communication delay; calculate the vehicle acceleration sequence based on the vehicle speed sequence within the sliding time window through first-order difference, and calculate the vehicle acceleration rate of change sequence based on the vehicle acceleration sequence through first-order difference; take the median of the vehicle acceleration sequence and the vehicle acceleration rate of change sequence respectively to obtain the vehicle trend acceleration and the vehicle trend spurt.

[0013] Furthermore, the specific process of compensating for the impact of communication delay by utilizing vehicle motion trends, estimating the current actual vehicle operating state, and generating a dataset of the actual vehicle operating state is as follows: multiply the current communication delay by the vehicle trend acceleration to obtain the first-order change compensation amount; multiply the square of the current communication delay by the vehicle trend jump and divide by two to obtain the second-order change compensation amount; add the current motor speed, the first-order change compensation amount, and the second-order change compensation amount to obtain the estimated value of the actual motor speed; combine the estimated value of the actual motor speed with the vehicle operating data at the corresponding sampling time to construct and output the dataset of the actual vehicle operating state.

[0014] Furthermore, the specific process of determining whether a vehicle is in a stable stationary state based on the vehicle's actual operating state dataset and recording the continuous static stability time is as follows: Read the vehicle's actual operating state dataset within the sliding time window. If the vehicle speed is less than the speed static stability threshold, the accelerator pedal opening is less than the pedal static stability threshold, the gear is in parking or neutral, the ignition is off, the estimated actual motor speed is less than the motor static stability threshold, and the rate of change of the estimated actual motor speed is less than the change threshold, then the vehicle is determined to have entered a static stability state. Record the start time of the static stability state and continuously accumulate it to obtain the vehicle's continuous static stability time. If the conditions are not met at any moment within the sliding time window, record the start time of a new static stability state.

[0015] Further, the specific process of analyzing vehicle deceleration characteristics and determining the safety margin for remote control execution by combining continuous static stability time and communication delay is as follows: Calculate the vehicle acceleration sequence within the sliding time window, filter out negative vehicle acceleration values, and take the median to obtain the median value of negative vehicle deceleration; based on the actual motor speed estimation sequence within the sliding time window, calculate the motor angular acceleration sequence through first-order difference, filter out negative motor angular acceleration values, and take the median to obtain the median value of motor angular deceleration; divide the absolute value of the current vehicle speed by the sum of the absolute value of the median value of negative vehicle deceleration and the smallest positive number to obtain the first remaining time; divide the absolute value of the current actual motor speed estimation by the sum of the absolute value of the median value of motor angular deceleration and the smallest positive number to obtain the second remaining time; take the larger value between the first and second remaining times as the remaining stable time; subtract the remaining stable time from the continuous static stability time of the vehicle, and subtract the current communication delay to obtain the remote control executable margin value.

[0016] Furthermore, the specific process for determining whether a remote control command is allowed to be executed based on the safety margin and triggering the remote control operation is as follows: When the remote control executeability margin value is greater than zero, it is determined that the current vehicle state meets the safe execution conditions for remote control, and the execution of the remote control command is allowed; when the remote control executeability margin value is less than or equal to zero, it is determined that the current vehicle state does not meet the safe execution conditions for remote control, and the execution of the remote control command is refused; when the execution of the remote control command is allowed, the remote control command issued by the vehicle network platform is mapped into a control message that the vehicle controller can recognize, and sent to the vehicle controller for execution through the vehicle network control unit. After the execution is completed, the vehicle controller returns the execution result and uploads it to the vehicle network platform, and records the remote control command execution log in the vehicle remote control database.

[0017] The second aspect of this invention provides a vehicle remote control system based on a vehicle-to-everything (V2X) platform, comprising: a vehicle operation data acquisition and processing module for real-time acquisition of vehicle operation data and data preprocessing; a vehicle operation data reliability assessment module for performing time-series consistency analysis on vehicle operation data based on a sliding time window, comprehensively assessing data synchronization and timeliness, and determining whether the current vehicle operation data is reliable based on the assessment results; a vehicle real state dynamic estimation module for analyzing vehicle motion trends based on reliable vehicle operation data, compensating for the impact of communication delay using vehicle motion trends, estimating the current vehicle real operating state, and generating a vehicle real operating state dataset; and a remote control safety execution determination module for determining whether the vehicle is in a stable stationary state based on the vehicle real operating state dataset, recording continuous static stability time, analyzing vehicle deceleration characteristics, and determining the safety margin for remote control execution by combining continuous static stability time and communication delay, determining whether remote control commands are allowed to be executed based on the safety margin, and triggering remote control operations.

[0018] Beneficial effects The present invention has the following beneficial effects: (1) This invention performs time sequence consistency analysis on the sampling time interval and receiving time interval of vehicle operation data based on a sliding time window, and comprehensively evaluates the data synchronization and timeliness to make a reliability judgment on the vehicle operation data, thereby reducing the problem of state data distortion caused by differences in collection cycle, communication delay and data update lag, and improving the reliability of the vehicle network platform to obtain vehicle operation status data.

[0019] (2) Based on reliable vehicle operation data, this invention estimates the current operating status of the vehicle by analyzing the vehicle movement trend and combining it with communication delay for dynamic compensation, thereby reducing the impact of communication delay on the judgment of vehicle status and improving the accuracy of the platform side in identifying the true operating status of the vehicle.

[0020] (3) The present invention determines whether a vehicle is in a stable stationary state based on a dataset of the vehicle’s actual operating state and records the continuous static and stable time, thereby avoiding the execution of remote control commands when the vehicle is still in a dynamic or transitional state, and improving the safety of remote control operations.

[0021] (4) This invention analyzes the vehicle's deceleration characteristics and combines continuous static stability time and communication delay to assess the safety margin of remote control execution. This allows for a comprehensive judgment of control conditions before remote control execution, reducing the risk of triggering remote control commands under unsuitable conditions and improving the overall stability and reliability of remote control operation.

[0022] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0023] Figure 1 This is a flowchart of a vehicle remote control method based on a vehicle networking platform; Figure 2 This is a structural diagram of a vehicle remote control system based on a vehicle networking platform; Figure 3 A bar chart showing the executable margin values ​​for remote control; Figure 4 This is a schematic diagram for determining the executable margin value for remote control. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. As those skilled in the art will understand, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Please see Figures 1-4 This invention provides a technical solution: a method for remote vehicle control based on a vehicle networking platform, such as... Figure 1 As shown, the process includes the following steps: S1, real-time acquisition of vehicle operation data and data preprocessing; S2, time-series consistency analysis of vehicle operation data based on a sliding time window, and comprehensive evaluation of data synchronization and timeliness, determining the reliability of the current vehicle operation data based on the evaluation results; S3, analysis of vehicle motion trends based on reliable vehicle operation data, compensation for communication latency effects using vehicle motion trends, estimation of the current actual vehicle operation state, and generation of a vehicle actual operation state dataset; S4, determination of whether the vehicle is in a stable stationary state based on the vehicle actual operation state dataset, recording the continuous static stability time, analyzing vehicle deceleration characteristics, and determining the safety margin for remote control execution by combining the continuous static stability time and communication latency, determining whether the remote control command is allowed to be executed based on the safety margin, and triggering the remote control operation.

[0026] Specifically, the real-time acquisition and preprocessing of vehicle operation data involves the following steps: Vehicle operation data is acquired in real-time via the vehicle's CAN bus and the vehicle-to-everything (V2X) control unit, and uploaded to the V2X platform. This data includes vehicle speed, accelerator pedal opening, motor speed, gear position, and ignition status. The vehicle's CAN bus reads the corresponding vehicle operation status signals from the vehicle controller. The V2X control unit packages the acquired data and transmits it to the V2X platform via a wireless communication network, which can be a cellular mobile communication network or a dedicated V2X communication network. The sampling time and reception time of the vehicle operation data on the vehicle side and the V2X platform side are recorded simultaneously. The vehicle-side sampling time is timestamped by the vehicle control system during data acquisition, while the V2X platform reception time is recorded by the platform server upon data arrival, creating a dual-timestamp data record for subsequent timing consistency analysis. Vehicle operation data undergoes time alignment processing, and outliers are identified and removed based on the vehicle's physical operating range. Time alignment is used to reorder various types of vehicle operation data into a unified time series based on the vehicle-side sampling time, ensuring that different types of vehicle operation data correspond on the same time axis, thus guaranteeing the temporal consistency of subsequent data analysis. The preferred time reference for time alignment is the vehicle-side sampling time as the unified time axis, and different types of vehicle operation data are resampled or interpolated according to a preset time alignment step. The time alignment step is preferably consistent with the typical sampling period of the vehicle's CAN bus, ideally between 10ms and 100ms, to ensure the correspondence between different data sequences on the unified time axis. The vehicle's physical operating range is preset during initialization based on the vehicle's powertrain and overall vehicle control parameters. For example, the vehicle speed range is 0 to the vehicle's rated maximum speed, the accelerator pedal opening range is 0 to 100%, the motor speed range is 0 to the motor's rated maximum speed, the gear status is limited to the legal gear set defined by the vehicle control system, and the ignition status is limited to on and off states. Data exceeding the corresponding physical range is identified as outlier and removed. A moving average filter is used to smooth vehicle operation data, and min-maximum normalization is performed on the data. The moving average filter calculates the mean of continuously sampled vehicle operation data based on a preset length of moving time window, preferably 3 to 10 sampling periods. By taking the average value of the data within the moving time window as the current filter output value, the smoothing effect is ensured while avoiding the loss of dynamic features due to over-smoothing, and the impact of instantaneous noise on the judgment of vehicle operation status is reduced. The min-maximum normalization process linearly maps various types of vehicle operation data according to the minimum and maximum values ​​within the corresponding moving time window, so that the processed data are uniformly mapped to a standard interval, so that different types of vehicle operation data can be uniformly scaled in subsequent analysis.A vehicle remote control database is established to store raw and preprocessed vehicle operation data. The vehicle operation data is stored sequentially according to timestamps, and vehicle identification information, data type identification, and corresponding timestamp information are recorded, thus forming a data foundation record that can be used for subsequent vehicle status assessment and remote control determination.

[0027] In this implementation plan, by collecting vehicle operation data in real time and combining it with preprocessing operations such as time alignment, outlier identification, moving average filtering, and minimum-maximum normalization, the temporal consistency and data quality of vehicle operation data can be improved, and the impact of noise interference and abnormal data on subsequent analysis can be reduced. At the same time, by establishing a vehicle remote control database to uniformly store and manage raw and preprocessed data, a stable and reliable data foundation can be provided for the assessment of the actual operating status of vehicles and the determination of remote control safety, thereby improving the accuracy and reliability of the vehicle network remote control system.

[0028] Specifically, the process of performing time-series consistency analysis on vehicle operation data based on a sliding time window and comprehensively evaluating data synchronization and timeliness is as follows: Vehicle operation data within the sliding time window is read, and data sequences corresponding to various vehicle operation data are formed according to vehicle speed, accelerator pedal opening, motor speed, gear status, and ignition status. The sliding time window is a fixed-length data interval continuously selected in chronological order. The length of the sliding time window is determined based on the typical sampling period of the vehicle operation data, preferably 3s to 10s. In the process of collecting vehicle operation data in the Internet of Vehicles (IoV), the vehicle CAN bus sampling period is typically 10ms to 100ms. Therefore, at least 30 continuous sampling points can be covered within the 3s to 10s time range, ensuring sufficient sample size for statistical calculations to improve the stability of the median value and time jitter value calculations. Simultaneously, the sliding time window length should not be too large to avoid the introduction of historical state data over an excessively long time span, which could affect the current time-series consistency evaluation. For each data sequence, first-order differences are calculated for both the sampling and reception times to obtain adjacent sampling and reception time intervals. The median of the sampling time intervals within the sliding time window is then used to obtain the median value of the sampling period. The first-order difference is used to calculate the adjacent time difference between consecutive sampling timestamps and reception timestamps, thus obtaining the time interval sequence of the data sampling period and the platform reception period. By taking the median of all sampling time intervals within the sliding time window as the median value of the sampling period, the impact of occasional sampling jitter and abnormal timestamps on the sampling period estimation results can be reduced. The time interval difference is obtained by subtracting the corresponding sampling time interval from the reception time interval. The median absolute deviation of the time interval difference within the sliding time window is calculated to obtain the time jitter value. The time interval difference characterizes the degree of deviation between the platform reception rhythm and the vehicle sampling rhythm, while the median absolute deviation measures the stability of the deviation within the sliding time window, thereby reflecting the time jitter level of the communication link in the current time period. The synchronization deviation value is obtained by dividing the time jitter value by the sum of the median value of the sampling period and the smallest positive number. The smallest positive number is used to avoid the situation where the denominator is zero and the value is unstable when the median value of the sampling period is close to zero, thus ensuring the numerical stability of the calculation process. The synchronization deviation value is used to characterize the degree of synchronization between the vehicle data sampling rhythm and the platform receiving rhythm. The larger the value, the worse the data synchronization.The difference between the current time and the most recent reception time of various vehicle operation data is calculated to obtain the data lag. The lag is then divided by the sum of the median value and the smallest positive number in the sampling period to obtain the lag value. Here, the current time is the current system time of the vehicle network platform, and the most recent reception time is the timestamp of the latest vehicle operation data received by the platform. The time difference reflects the freshness of the current data. The lag value characterizes the degree of lag in updating vehicle operation data on the platform side; a larger value indicates a more delayed update. The synchronization deviation value is added to the lag value, and the inverse is taken to obtain the exponential decay input value for various vehicle operation data. The exponential decay input value comprehensively reflects the reliability of vehicle operation data in both synchronization and timeliness dimensions. When synchronization deviation and lag increase, the corresponding exponential decay input value decreases, thus reducing the data reliability assessment result. The average decay is obtained by summing the exponential decay input values ​​of all types of vehicle operation data and dividing by the total number of vehicle operation data types. The total number of vehicle operation data types represents the number of vehicle operation data categories participating in the credibility assessment, used to balance the impact of different types of vehicle operation data. The average decay is then subjected to a natural exponential operation to obtain the data credibility value. The natural exponential function maps the average decay to a non-negative credibility index. The data credibility value uses a mapping method of "negative sum and natural exponential function," mapping the combined deviation formed by the synchronization deviation value and the time lag value to a data credibility index within the interval (0,1). Because the natural exponential function has monotonically decreasing and continuously smooth characteristics, the credibility value decays rapidly when the deviation increases, while the credibility change is relatively stable when the deviation is small. This ensures the monotonicity, numerical stability, and robustness to abnormal deviations of the credibility assessment results, and allows for comprehensive evaluation of different types of vehicle operation data under a unified scale.

[0029] The specific formula for the data credibility value is as follows: ; In the formula, This indicates the data reliability value, which comprehensively assesses the overall reliability of vehicle operation data by measuring the degree of time jitter in vehicle data and the freshness of the current data. This represents the total number of data types used in vehicle operation, and is used to average the credibility contribution of different data types. This represents the data type index for vehicle operation, used to traverse various types of vehicle operation data; Indicates the receiving time interval, used to reflect the timing of data reception by the platform; Indicates the sampling time interval, used to reflect the actual sampling period of vehicle data; This represents the median value of the sampling period, which is used as a reference value for a stable sampling period of vehicle data. This indicates the current time, specifically the current system time of the vehicle-to-everything (V2X) platform. This indicates the most recent time the vehicle operation data was received, and is used to calculate the freshness of the data; This represents the time jitter value, used to measure whether the data arrival rhythm is stable and whether there is significant jitter in the communication link; This represents a very small positive number, used to prevent numerical instability caused by a denominator of zero or too small, thus ensuring the stability of the calculation process. A value of [value missing] is preferred. arrive .

[0030] In this implementation scheme, by comparing and analyzing the sampling rhythm of vehicle operation data with the platform's receiving rhythm within a sliding time window, and combining this with the data update timeliness, the synchronization and timeliness of vehicle operation data are comprehensively evaluated. This effectively identifies data timing anomalies caused by communication delays, data cache update lags, or sampling jitter, thereby improving the accuracy of vehicle operation data credibility judgment. At the same time, by introducing robust statistical methods such as median and median absolute deviation, the calculation stability is enhanced, making the data credibility assessment process more robust to abnormal timestamps and occasional jitter, providing a reliable data foundation for subsequent vehicle real-state assessment and remote control safety determination.

[0031] Specifically, the process for determining the reliability of current vehicle operation data based on the evaluation results is as follows: The data reliability value is compared with a reliability threshold. The data reliability value is a reliability index calculated based on the synchronization deviation and time lag of vehicle operation data within a sliding time window, reflecting the overall reliability of the current vehicle operation data in terms of time synchronization and timely data updates. The reliability threshold can be set based on the statistical characteristics of historical operation data, the stability level of the communication link, and security requirements. When the data reliability value is greater than or equal to the reliability threshold, the current vehicle operation data is marked as reliable data and enters the vehicle real-state evaluation process. This process estimates and analyzes the vehicle's current actual operating state based on reliable data to avoid unreliable data from participating in subsequent state judgment processes. When the data reliability value is less than the reliability threshold, the current vehicle operation data is marked as unreliable data, and only vehicle operation data continues to be received. In this case, the vehicle network platform continues to receive subsequent vehicle operation data uploaded by the vehicle and re-executes the data reliability evaluation within a new sliding time window to ensure that only data meeting the reliability criteria enters the subsequent vehicle state evaluation stage. Data credibility values ​​and credibility tags are written into the vehicle remote control database to facilitate subsequent vehicle status analysis, remote control execution records, and operation log traceability.

[0032] In this implementation plan, by comparing the data credibility value with the credibility threshold, the reliability of vehicle operation data is automatically determined. Only data that meets the requirements of time sequence consistency and timeliness enters the subsequent vehicle status assessment process, thereby avoiding unreliable data caused by communication delays, data jitter, or cache lag from participating in remote control decisions, and improving the accuracy of vehicle operation status judgment and the safety and reliability of system operation.

[0033] Specifically, the process of analyzing vehicle movement trends based on trusted vehicle operation data is as follows: Vehicle operation data marked as trusted data is read, and a vehicle real-state assessment process is executed. The vehicle operation data marked as trusted data consists of data records that meet the trusted threshold conditions after data trustedness assessment. By using only trusted data in subsequent calculations, interference from communication anomalies and data jitter on the vehicle state assessment results can be avoided. The difference between the current time and the vehicle operation data sampling time is calculated to obtain the communication latency. The current time is the current system time of the vehicle network platform, and the vehicle operation data sampling time is the timestamp generated by the vehicle side when collecting the corresponding data. By calculating the time difference between the two, the communication and transmission delay between vehicle data collection and platform processing can be obtained. Based on the vehicle velocity sequence within a sliding time window, a vehicle acceleration sequence is calculated using first-order differencing. The vehicle velocity sequence is arranged chronologically, and first-order differencing is used to calculate the rate of change between two adjacent velocity samples, thus revealing the vehicle's acceleration changes within adjacent sampling periods. A vehicle acceleration rate of change sequence is also calculated based on the vehicle acceleration sequence using first-order differencing. This rate of change characterizes the trend of vehicle acceleration, i.e., the speed of change in vehicle power, further reflecting the characteristics of vehicle motion state changes. The median is taken from both the vehicle acceleration sequence and the vehicle acceleration rate of change sequence to obtain the vehicle trend acceleration and vehicle trend judder. Using the median statistical method reduces the impact of abnormal acceleration data and instantaneous fluctuations on the calculation results, thus obtaining stable statistical characteristics that reflect the overall vehicle motion trend. The vehicle trend acceleration characterizes the overall acceleration or deceleration trend of the vehicle within the current time period, and the vehicle trend judder characterizes the stability of the vehicle acceleration trend, providing a motion trend basis for subsequent communication delay compensation and vehicle real-state estimation. Since the vehicle operation data has been normalized, the influence of different physical dimensions on the statistical results has been eliminated. This allows the vehicle trend acceleration and vehicle trend jerk to reflect the trend of vehicle motion change on a unified scale, improving the stability and comparability of subsequent communication delay compensation and vehicle real state estimation processes.

[0034] In this implementation scheme, by extracting vehicle speed change characteristics based on reliable vehicle operation data and analyzing vehicle motion trends in conjunction with communication latency, the acceleration or deceleration pattern of the vehicle within the current time period can be effectively characterized. At the same time, by adopting the median statistical method, the robustness to abnormal data and instantaneous fluctuations is enhanced, thereby improving the stability and accuracy of vehicle motion trend identification and providing a reliable basis for subsequent estimation of the actual vehicle operating status and remote control safety determination.

[0035] Specifically, the process of compensating for the impact of communication delay by utilizing vehicle motion trends, estimating the current actual vehicle operating state, and generating a dataset of the actual vehicle operating state is as follows: The current communication delay is multiplied by the vehicle trend acceleration to obtain a first-order change compensation amount. The vehicle trend acceleration is the overall vehicle acceleration trend value obtained by statistically analyzing the vehicle acceleration sequence within a sliding time window, used to characterize the average acceleration or deceleration change level of the vehicle in the current time period. By multiplying the vehicle trend acceleration by the communication delay, the impact of vehicle speed changes on the current state during the communication delay period can be estimated. The square of the current communication delay is multiplied by the vehicle trend jump and then divided by two to obtain a second-order change compensation amount. The vehicle trend jump is the statistical result of the vehicle acceleration change rate sequence, used to characterize the speed of vehicle acceleration change. By introducing the square term of the communication delay, second-order compensation can be performed on the vehicle acceleration change trend, thereby improving the state estimation accuracy when vehicle dynamics change rapidly. The current motor speed, first-order compensation value, and second-order compensation value are added together to obtain the estimated true motor speed. The current motor speed is the motor speed data acquired from the vehicle's CAN bus. By superimposing the first-order and second-order compensation values ​​obtained based on communication delay estimation, the lag in motor speed observation caused by communication delay can be corrected, resulting in a motor speed estimation result that more closely approximates the vehicle's current true state. In the electric drive system, there is a fixed transmission ratio relationship between vehicle speed and motor speed; therefore, the trend of vehicle speed change can be used to estimate the trend of motor speed change. The estimated true motor speed is combined with vehicle operating data from the corresponding sampling time to construct and output a dataset of the vehicle's true operating state. This dataset includes vehicle speed, accelerator pedal opening, estimated motor speed, gear status, ignition status, and corresponding timestamps. By combining the corrected estimated motor speed with other vehicle operating data, a data record reflecting the vehicle's current true operating state can be formed, which is used for subsequent vehicle static stability determination and remote control safety execution analysis.

[0036] The specific formula for estimating the actual motor speed is as follows: ; In the formula, This represents the estimated value of the actual motor speed. By utilizing vehicle motion trend information, the motor speed at the sampling time is compensated for in the time dimension with first-order and second-order trend compensation. This corrects the state lag caused by the time interval between data sampling and platform reception, enabling the platform to be closer to the current actual operating state of the vehicle. It indicates the motor speed and reflects the original motor speed data; It represents the vehicle's trend acceleration, used to describe the reference trend of changes in vehicle speed; This indicates the current time, specifically the current system time of the vehicle-to-everything (V2X) platform. This indicates the sampling time, representing the data sampling timestamp corresponding to the time when the current data is collected from the vehicle side; This represents the trend jump of a vehicle, used to describe the second-order trend characteristics of changes in vehicle motion.

[0037] In this implementation scheme, by using vehicle motion trends to compensate for state lag caused by communication delays, and by combining vehicle acceleration and acceleration change trends to dynamically correct motor speed, the impact of communication delays on vehicle state perception can be effectively reduced. This makes the vehicle state obtained by the platform side closer to the vehicle's current actual operating state, thereby improving the accuracy of vehicle state estimation and providing a more reliable state basis for remote control safety judgment.

[0038] Specifically, the process of determining whether a vehicle is in a stable stationary state based on the vehicle's real operating status dataset and recording the continuous static stability time is as follows: read the vehicle's real operating status dataset within the sliding time window. The vehicle's real operating status dataset is a set of vehicle status data obtained after communication delay compensation, which is used to reflect the vehicle's current real operating state. If the vehicle speed is less than the speed static stability threshold, the accelerator pedal opening is less than the pedal static stability threshold, the gear is in park or neutral, the ignition is off, the estimated actual motor speed is less than the motor static stability threshold, and the rate of change of the estimated actual motor speed is less than the change threshold, then the vehicle is determined to have entered a static stable state. The speed static stability threshold limits the range within which the vehicle speed approaches a stationary state and is set based on the vehicle's low-speed operation identification standard. The pedal static stability threshold determines whether the driver has applied acceleration and is set based on the normal fluctuation range of the vehicle's accelerator pedal zero-position sensor. The motor static stability threshold determines whether the powertrain is in a stopped or near-stopped state and is set based on the motor speed range during vehicle powertrain idling and shutdown. The change threshold limits the allowable range of the motor speed change rate and is set based on the normal fluctuation range of the motor speed in a stable state, thus avoiding misjudgment of a static stable state when there is still a significant trend of change in the vehicle's powertrain. The starting time of the static stable state is recorded and continuously accumulated to obtain the continuous static stable time of the vehicle. The starting time of the static stable state is the point in time when the vehicle first simultaneously meets the above-mentioned static stability judgment conditions. By accumulating the time when the static stability conditions are met consecutively, the duration of the vehicle maintaining a stable stationary state in the current stage can be obtained. If the conditions are not met at any time within the sliding time window, a new starting time of the static stable state is recorded. That is, when any condition changes and no longer meets the judgment conditions, the current static stable time accumulation process is terminated, and the new point in time when the static stability conditions are met is taken as the new starting time of the static stable state, thereby ensuring that the continuous static stable time can accurately reflect the duration of the vehicle's stable stationary state.

[0039] In this implementation plan, by combining multiple conditions to jointly determine the characteristics of vehicle speed, pedal opening, gear status, ignition status, and motor speed changes, and by dynamically recording continuous static stability time, it is possible to accurately identify whether the vehicle is in a stable stationary state. At the same time, by setting a judgment threshold that matches the vehicle's operating characteristics, it is possible to effectively avoid misjudgments caused by short-term fluctuations or abnormal data, thereby providing a reliable basis for determining the vehicle status for remote control operations and improving the safety and accuracy of remote control execution.

[0040] Specifically, the process of analyzing vehicle deceleration characteristics and determining the safety margin of remote control execution by combining continuous static steady-state time and communication delay is as follows: The vehicle acceleration sequence within the sliding time window is calculated, and negative vehicle acceleration values ​​are selected and the median is taken to obtain the median value of negative vehicle deceleration. The vehicle acceleration sequence is calculated from the vehicle velocity sequence using first-order difference. Negative vehicle acceleration values ​​indicate that the vehicle is in a deceleration state. By selecting negative acceleration values ​​and taking the median, a robust statistic reflecting the overall deceleration trend of the vehicle can be obtained, thereby reducing the impact of abnormal fluctuations and individual abnormal sampling points on the deceleration estimation results. Based on the actual motor speed estimation value sequence within the sliding time window, the motor angular acceleration sequence is calculated using first-order difference. Negative motor angular acceleration values ​​are selected and the median is taken to obtain the median value of motor angular deceleration. The motor angular acceleration sequence reflects the rate of change of motor speed. By selecting negative angular acceleration values, the power change characteristics of the motor during the deceleration phase can be extracted, thus obtaining the median value of motor angular deceleration reflecting the motor power attenuation trend, thereby characterizing the overall deceleration capability of the power system. The first remaining time is obtained by dividing the absolute value of the current vehicle speed by the sum of the absolute value of the median of the vehicle's negative deceleration and its smallest positive value. This first remaining time estimates the time required for the vehicle to decay from its current speed to near a standstill under the current deceleration trend. The smallest positive value is used to avoid the denominator being zero and the value being unstable when the median of the vehicle's negative deceleration is close to zero. The second remaining time is obtained by dividing the absolute value of the current estimated actual motor speed by the sum of the absolute value of the median of the motor's angular deceleration and its smallest positive value. This second remaining time estimates the time required for the powertrain system to decay from its current motor speed to near a stop under the current deceleration trend. By simultaneously considering the vehicle speed decay time and the motor speed decay time, the time required for the entire vehicle's powertrain system to fully stabilize can be comprehensively reflected. The larger value between the first and second remaining times is taken as the remaining stabilization time. By selecting the larger value, the estimation result ensures that it covers the slowest decelerating parts of the vehicle's motion system and powertrain system, thus obtaining a more conservative and safer stabilization time estimate. The remote control execution margin value is obtained by subtracting the remaining settling time from the vehicle's continuous static settling time and subtracting the current communication delay. This value is used to characterize the safe time margin that the vehicle has to perform remote control operations in the current state.

[0041] The specific formula for the remote control executable margin value is as follows: ; In the formula, This represents the remote control execution margin value. It is determined by comparing the vehicle's continuous static stability time with the remaining motion time that the vehicle may generate under the current speed and motor speed conditions, and by adjusting for communication latency, to determine whether the vehicle has sufficient stability time to safely execute remote control commands. This indicates the continuous static stability time of a vehicle, representing the length of time from when the static stability conditions are met to the current moment when the vehicle maintains a static stability state. Indicates vehicle speed, used to reflect the vehicle's current state of motion; This represents the median value of the vehicle's negative deceleration, used to characterize the stable characteristics of vehicle speed reduction; This represents the estimated actual motor speed, used to reflect the current operating status of the power system; This represents the median value of the motor's angular deceleration, used to characterize the trend of motor speed decay; It represents communication latency and is used to adjust the time safety margin for remote control execution decisions; This represents a very small positive number, used to prevent numerical instability caused by a denominator of zero or too small, thus ensuring the stability of the calculation process. A value of [value missing] is preferred. arrive .

[0042] In this embodiment, Table 1 is a data table of remote control executable margin values. The smallest positive number is... The table details the vehicle speed, median negative deceleration, estimated actual motor speed, median motor angular deceleration, continuous static stability time, communication latency, and remote control actuarial margin at five specific times. Specifically, time 1 corresponds to a vehicle speed of 0.3, a median negative deceleration of 1.2, an estimated actual motor speed of 120, a median motor angular deceleration of 240, a continuous static stability time of 3.0, a communication latency of 0.10, and a remote control actuarial margin of [value missing]. Time 2 corresponds to a vehicle speed of 0.5, a median negative deceleration of 1.1, an estimated actual motor speed of 180, a median motor angular deceleration of 220, a continuous static stability time of 2.5, a communication latency of 0.12, and a remote control actuarial margin of 1.5618. Time 3 corresponds to a vehicle speed of 0.8, a median negative deceleration of 1.0, an estimated actual motor speed of 220, and a median motor angular deceleration of [value missing]. The values ​​are as follows: 200, vehicle continuous static stability time is 1.5, communication delay is 0.10, and remote control executable margin is 0.3000; the vehicle speed at time 4 is 1.2, the median negative deceleration of the vehicle is 1.0, the estimated actual motor speed is 260, the median angular deceleration of the motor is 180, the vehicle continuous static stability time is 1.0, the communication delay is 0.12, and the remote control executable margin is -0.5644; the vehicle speed at time 5 is 0.6, the median negative deceleration of the vehicle is 0.9, the estimated actual motor speed is 200, the median angular deceleration of the motor is 150, the vehicle continuous static stability time is 1.0, the communication delay is 0.67, and the remote control executable margin is -1.0033.

[0043] Table 1 Remote Control Execution Margin Data Table

[0044] like Figure 3 The chart shown is a bar chart of remote control executable margin values. The horizontal axis represents different time numbers, and the vertical axis represents the remote control executable margin value calculated at the corresponding time. It intuitively shows the changes in the safety margin for remote control execution under the vehicle status at each time. A bar value greater than zero indicates that the vehicle currently has a safety margin for remote control execution, while a bar value less than or equal to zero indicates that the vehicle currently does not have a safety margin for remote control execution.

[0045] like Figure 4 The diagram illustrates the determination of the remote control executable margin value. The horizontal axis represents the sum of the vehicle's remaining stabilization time and communication delay, the vertical axis represents the vehicle's continuous static stabilization time, and the straight line represents the safe execution judgment boundary. When the vehicle's continuous static stabilization time is greater than the sum of the remaining stabilization time and communication delay, the data point is located above the judgment boundary line, corresponding to a remote control executable margin value greater than zero, and the remote control command is allowed to be executed. When the vehicle's continuous static stabilization time is less than or equal to the sum of the remaining stabilization time and communication delay, the data point is located below the judgment boundary line, corresponding to a remote control executable margin value less than or equal to zero, and the remote control command is rejected. (Refer to Table 1 and...) Figure 4 It can be seen that the data points corresponding to times 1, 2, and 3 are located above the decision boundary line, indicating that the vehicle's continuous static stability time is significantly greater than the sum of the vehicle's remaining stability time and communication delay. The remote control execution margin is greater than zero, the vehicle is stable, and the remote control command can be executed safely. However, the data points at times 4 and 5 are located below the decision boundary line, indicating that the vehicle's continuous static stability time is insufficient to cover the vehicle's remaining stability time and communication delay. The remote control execution margin is less than zero, the vehicle still has a potential motion trend, and therefore the remote control operation is rejected.

[0046] In this implementation plan, by jointly analyzing the deceleration characteristics of vehicle speed and motor speed, and combining the vehicle's continuous static stability time with communication delay to comprehensively evaluate the vehicle's stability margin, it is possible to more accurately determine the safe time margin for the vehicle to perform remote control operations in the current state. At the same time, by adopting the median statistical method to enhance the robustness to abnormal fluctuations, the reliability and stability of the safety judgment for remote control execution are improved.

[0047] Specifically, the process of determining whether a remote control command is allowed to be executed based on the safety margin and triggering the remote control operation is as follows: When the remote control executable margin value is greater than zero, it is determined that the current vehicle state meets the safe execution conditions for remote control, and the execution of the remote control command is allowed. The remote control executable margin value characterizes the safe time margin that the vehicle has to execute remote control operations in its current state. When the remote control executable margin value is greater than zero, it indicates that the vehicle's continuous static stability time can cover the impact of vehicle deceleration stabilization time and communication latency; therefore, the vehicle state can be considered to meet the safe conditions for remote control execution. When the remote control executable margin value is less than or equal to zero, it is determined that the current vehicle state does not meet the safe execution conditions for remote control, and the execution of the remote control command is rejected. In this case, only vehicle operation data continues to be received and the vehicle state is reassessed to avoid executing remote control operations while the vehicle is still moving and the power system is not yet stable, thereby reducing the safety risks caused by false triggering of remote control. When remote control commands are permitted, the remote control commands issued by the vehicle networking platform are mapped into control messages recognizable by the vehicle controller and sent to the vehicle controller for execution via the vehicle networking control unit. These remote control commands include, but are not limited to, remote locking, remote unlocking, remote speed limiting, remote start, and remote software upgrade commands. The vehicle networking control unit converts the platform-issued control commands into control messages recognizable by the vehicle's internal control network and sends them to the vehicle controller for execution via the vehicle's CAN bus. After the remote control command is executed, the vehicle controller returns the execution result and uploads it to the vehicle networking platform. A remote control command execution log is also recorded in the vehicle remote control database. This log includes vehicle identification information, remote control command type, command issuance time, execution result status, and corresponding timestamp information, used to record and trace the remote control operation process, thereby improving the traceability and safety management capabilities of the operation.

[0048] In this implementation plan, the execution conditions of remote control commands are determined based on the remote control executable margin value, so that remote control operations are only allowed to be executed when the vehicle status meets the safety conditions. This effectively avoids triggering remote control operations when the vehicle is still in motion or the power system is unstable, thereby improving the safety and reliability of the remote control execution process. At the same time, recording remote control execution logs enhances the traceability and management capabilities of the operation.

[0049] Reference Figure 2As shown, the second aspect of the present invention provides a vehicle remote control system based on a vehicle networking platform, applied to the aforementioned vehicle remote control method based on a vehicle networking platform, comprising: a vehicle operation data acquisition and processing module, used to acquire vehicle operation data in real time and perform data preprocessing on the vehicle operation data; a vehicle operation data reliability assessment module, used to perform time sequence consistency analysis on vehicle operation data based on a sliding time window, and comprehensively assess data synchronization and timeliness, and determine whether the current vehicle operation data is reliable based on the assessment results; a vehicle real state dynamic estimation module, used to analyze vehicle motion trends based on reliable vehicle operation data, use vehicle motion trends to compensate for the impact of communication delay, estimate the current vehicle real operating state, and generate a vehicle real operating state dataset; and a remote control safety execution determination module, used to determine whether the vehicle is in a stable stationary state based on the vehicle real operating state dataset, record continuous static stability time, analyze vehicle deceleration characteristics, and determine the safety margin for remote control execution by combining continuous static stability time and communication delay, determine whether the remote control command is allowed to be executed based on the safety margin, and trigger the remote control operation.

[0050] In this implementation plan, by constructing functional modules such as vehicle operation data acquisition and processing, data reliability assessment, dynamic estimation of vehicle's true state, and remote control safety execution determination, the reliability of vehicle operation data, the true operating state of the vehicle, and the conditions for remote control execution are analyzed and determined step by step. This ensures that remote control operations are only executed when the vehicle's state meets safety conditions, thereby effectively reducing the impact of communication delays and data inconsistencies on remote control decisions and improving the safety, accuracy, and reliability of the vehicle network remote control system.

[0051] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0052] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. As those skilled in the art will understand, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for remote vehicle control based on a vehicle networking platform, characterized in that, Includes the following steps: S1 collects vehicle operation data in real time and performs data preprocessing on the vehicle operation data; S2, performs time-series consistency analysis on vehicle operation data based on a sliding time window, comprehensively evaluates data synchronization and timeliness, and judges whether the current vehicle operation data is reliable based on the evaluation results; The specific process of performing time-series consistency analysis on vehicle operation data based on a sliding time window and comprehensively evaluating data synchronization and timeliness is as follows: Read the vehicle operation data within the sliding time window, and form data sequences corresponding to various types of vehicle operation data according to vehicle speed, accelerator pedal opening, motor speed, gear status and ignition status; For each data sequence, the sampling time and the receiving time are calculated using first-order difference to obtain the adjacent sampling time interval and the adjacent receiving time interval. The median of the sampling time interval within the sliding time window is then taken to obtain the median value of the sampling period. The receiving time interval is subtracted from the corresponding sampling time interval to obtain the time interval difference. The median absolute deviation of the time interval difference within the sliding time window is calculated to obtain the time jitter value. The time jitter value is divided by the sum of the median value of the sampling period and the smallest positive number to obtain the synchronization deviation value. Calculate the difference between the current time and the most recent time of receiving various vehicle operation data to obtain the data lag. Divide the data lag by the sum of the median value and the smallest positive number in the sampling period to obtain the lag value. Add the synchronization deviation value to the time lag value and take the opposite number to obtain the exponential decay input value of various vehicle operation data; The average decay value is obtained by summing the exponential decay input values ​​of all types of vehicle operation data and dividing by the total number of vehicle operation data types. The average decay value is then calculated using natural exponentiation to obtain the data reliability value. The specific process for determining the reliability of the current vehicle operation data based on the evaluation results is as follows: Compare the data confidence value with the confidence threshold: When the data credibility value is greater than or equal to the credibility threshold, the current vehicle operation data is marked as credible data and enters the vehicle real state assessment process. When the data credibility value is less than the credibility threshold, the current vehicle operation data is marked as untrusted data, and only vehicle operation data is continuously received. Write the data credibility value and credibility tag into the vehicle remote control database; S3 analyzes vehicle motion trends based on reliable vehicle operation data, compensates for the impact of communication latency using vehicle motion trends, estimates the current real operating status of the vehicle, and generates a dataset of the real operating status of the vehicle. The specific process of analyzing vehicle motion trends based on reliable vehicle operation data is as follows: Read vehicle operation data marked as trusted data and execute the vehicle real-state assessment process; calculate the difference between the current time and the vehicle operation data sampling time to obtain the communication latency; Based on the vehicle velocity sequence within the sliding time window, the vehicle acceleration sequence is calculated using first-order difference, and the vehicle acceleration rate of change sequence is calculated using first-order difference based on the vehicle acceleration sequence. The median of the vehicle acceleration sequence and the vehicle acceleration rate of change sequence are taken respectively to obtain the vehicle trend acceleration and vehicle trend jerk. The specific process of compensating for the impact of communication latency by utilizing vehicle motion trends, estimating the current actual vehicle operating state, and generating a dataset of the actual vehicle operating state is as follows: Multiply the current communication delay by the vehicle trend acceleration to obtain the first-order change compensation amount, and multiply the square of the current communication delay by the vehicle trend jump and divide by two to obtain the second-order change compensation amount. Add the current motor speed, the first-order change compensation amount, and the second-order change compensation amount to obtain the estimated value of the actual motor speed; The estimated actual motor speed is combined with the vehicle operation data at the corresponding sampling time to construct and output a dataset of the actual vehicle operation status. S4. Based on the vehicle's real operating status dataset, determine whether the vehicle is in a stable stationary state, record the continuous static stability time, analyze the vehicle's deceleration characteristics, and combine the continuous static stability time with communication delay to determine the safety margin for remote control execution. Based on the safety margin, determine whether the remote control command is allowed to be executed and trigger the remote control operation. The specific process of determining whether a vehicle is in a stable stationary state based on the vehicle's real operating state dataset and recording the continuous static stability time is as follows: Read the vehicle's actual operating status dataset within the sliding time window. If the vehicle speed is less than the speed static stability threshold, the accelerator pedal opening is less than the pedal static stability threshold, the gear is in parking or neutral, the ignition is off, the estimated actual motor speed is less than the motor static stability threshold, and the rate of change of the estimated actual motor speed is less than the change threshold, then the vehicle is determined to have entered a static stability state. Record the start time of the static stability state and continuously accumulate it to obtain the vehicle's continuous static stability time. If the conditions are not met at any time within the sliding time window, record the start time of a new static stability state. The specific process of analyzing vehicle deceleration characteristics and determining the safety margin of remote control execution by combining continuous static stability time and communication delay is as follows: The vehicle acceleration sequence within the sliding time window is calculated, and the negative vehicle acceleration values ​​are selected and the median value of the negative vehicle deceleration is obtained. Based on the actual motor speed estimation sequence within the sliding time window, the motor angular acceleration sequence is calculated through first-order difference, and the negative motor angular acceleration values ​​are selected and the median value of the motor angular deceleration is obtained. The first remaining time is obtained by dividing the absolute value of the current vehicle speed by the sum of the absolute value of the median of the vehicle's negative deceleration and the smallest positive number; the second remaining time is obtained by dividing the absolute value of the current estimated actual motor speed by the sum of the absolute value of the median of the motor's angular deceleration and the smallest positive number. The larger value between the first remaining time and the second remaining time is taken as the remaining stable time; the remaining stable time is subtracted from the vehicle's continuous static stable time, and the current communication delay is also subtracted to obtain the remote control executable margin value; The specific process of determining whether a remote control command is allowed to be executed based on a safety margin and triggering the remote control operation is as follows: When the remote control executable margin value is greater than zero, it is determined that the current vehicle status meets the safe execution conditions for remote control, and the execution of remote control commands is allowed. When the remote control execute margin value is less than or equal to zero, it is determined that the current vehicle status does not meet the safe execution conditions for remote control, and the remote control command is refused to be executed. When remote control commands are permitted, the remote control commands issued by the vehicle networking platform are mapped into control messages that can be recognized by the vehicle controller, and sent to the vehicle controller for execution through the vehicle networking control unit. After execution, the vehicle controller returns the execution result and uploads it to the vehicle networking platform, and records the remote control command execution log in the vehicle remote control database.

2. The vehicle remote control method based on a vehicle networking platform according to claim 1, characterized in that, The specific process of real-time collection of vehicle operation data and data preprocessing of the vehicle operation data is as follows: Vehicle operation data is collected in real time through the vehicle CAN bus and the vehicle network control unit and uploaded to the vehicle network platform. The vehicle operation data includes: vehicle speed, accelerator pedal opening, motor speed, gear status and ignition status. The sampling time of the vehicle operation data on the vehicle side and the receiving time on the vehicle network platform side are recorded simultaneously. The vehicle operation data is time-aligned, and outliers in the data are identified and removed based on the vehicle's physical operating range. The vehicle operation data is smoothed using a moving average filter, and min-max normalization is performed. A remote vehicle control database is established to store the raw and pre-processed vehicle operation data.

3. A vehicle remote control system based on a vehicle networking platform, employing the vehicle remote control method based on a vehicle networking platform as described in any one of claims 1-2, characterized in that, include: The vehicle operation data acquisition and processing module is used to collect vehicle operation data in real time and perform data preprocessing on the vehicle operation data. The vehicle operation data reliability assessment module is used to perform time series consistency analysis on vehicle operation data based on a sliding time window, and comprehensively assess the data synchronization and timeliness, and determine whether the current vehicle operation data is reliable based on the assessment results. The vehicle real state dynamic estimation module is used to analyze the vehicle motion trend based on reliable vehicle operation data, use the vehicle motion trend to compensate for the impact of communication latency, estimate the current vehicle real operating state, and generate a vehicle real operating state dataset. The remote control safety execution determination module is used to determine whether the vehicle is in a stable stationary state based on the vehicle's real operating status dataset, record the continuous static stability time, analyze the vehicle's motion deceleration characteristics, and combine the continuous static stability time with communication delay to determine the safety margin of remote control execution. Based on the safety margin, it determines whether the remote control command is allowed to be executed and triggers the remote control operation.

Images