A central control system control method for intelligent management of electric low-speed vehicles

Abstract

The invention discloses a central control system control method for intelligent management of electric low-speed vehicles, which relates to the technical field of intelligent control of electric vehicles. The method comprises S1, dynamically adjusting a cache strategy for data storage based on vibration data collected in real time by a vehicle driving state detection module, S2, when starting data transmission, processing the data in blocks to reduce the amount of data transmitted at a single time, S3, verifying each data read and write operation by a redundant verification technology, S4, pausing the data read and write operation and recording the current state when high vibration intensity is detected, and S5, after restoring the normal state, resuming the unfinished data read and write operation according to the recorded state; the central control system control method for intelligent management of electric low-speed vehicles solves the problem of data storage read and write errors caused by vibration during vehicle driving.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle intelligent control, and in particular to a central control system control method for intelligent management of electric low-speed vehicles. Background Art

[0002] In existing electric low-speed vehicles, intelligent management systems have gradually become the core part of vehicle control, aiming to achieve real-time monitoring of vehicle status, data analysis, and remote control through an integrated central control system. In this way, drivers or managers can obtain key data such as the battery status, driving speed, mileage, etc. of the vehicle in a timely manner during the operation of the vehicle, and can even control the vehicle through remote commands, such as locking, unlocking, and starting the vehicle. These functions significantly improve the safety, convenience, and management efficiency of electric low-speed vehicles.

[0003] At present, common central control systems can effectively collect and process various parameters in vehicle operation through advanced sensor networks, data acquisition modules and wireless communication technologies, and store them in on-board storage devices for subsequent data analysis and optimization. However, since electric low-speed vehicles usually travel on complex road conditions such as urban and rural roads, vehicles will inevitably encounter uneven road conditions and body vibrations during driving. Such vibrations may have an adverse effect on the data reading and writing process of the on-board storage device, leading to storage errors or data loss, affecting the stability of the system and the safety of the vehicle. Summary of the invention

[0004] The purpose of the present invention is to provide a central control system control method for intelligent management of electric low-speed vehicles, so as to solve the problem in the prior art that since electric low-speed vehicles usually travel in complex road conditions such as urban and rural roads, the vehicles will inevitably encounter uneven road conditions, body vibrations, etc. during driving, and such vibrations may have an adverse effect on the data reading and writing process of the on-board storage device, thereby causing storage errors or data loss, affecting the stability of the system and the safety of the vehicle.

[0005] To achieve the above object, the present invention provides the following technical solution: a central control system control method for intelligent management of electric low-speed vehicles, the method comprising:

[0006] S1, dynamically adjust the data storage cache strategy based on the vibration data collected in real time by the vehicle driving status detection module;

[0007] S2. When starting data transmission, the data is divided into blocks to reduce the amount of data transmitted in a single time. Specifically, the data to be transmitted is first divided into multiple blocks. The initial block size is determined according to the network bandwidth and transmission conditions. The sliding window mechanism is used to continuously send multiple data blocks without waiting for confirmation. The window size is dynamically adjusted according to the transmission delay and network load. The specific formula is:

[0008] ,

[0009] in, Indicates the transmission The size of the data block, Indicates data blocks, Indicates the size of the sliding window;

[0010] S3, verify each data read and write operation through redundancy verification technology;

[0011] Specifically: Divide the data into information bits , and use the generated matrix Calculate the corresponding Hamming code As a redundant check code, during each data read and write operation, the check bit is detected to determine whether the data is wrong. If an error is detected, the error correction capability of the Hamming code is used to automatically correct it. The specific formula is:

[0012] ,

[0013] in, represents the generated Hamming code, represents the generator matrix, represents a data bit matrix;

[0014] S4, when high vibration intensity is detected, suspend data reading and writing operations and record the current status;

[0015] S5. After returning to the normal state, resume the unfinished data read and write operations according to the recorded state;

[0016] The step S1 comprises:

[0017] Collect vibration data in real time and input it into the autoregressive integrated moving average model to predict vibration intensity;

[0018] Adjust the cache strategy in advance based on the predicted future vibration intensity. If the vibration is predicted to intensify, reduce the cache capacity. If the vibration is predicted to decrease, increase the cache capacity.

[0019] The specific formula is:

[0020] ,

[0021] in, Indicates vibration data, Indicates time, represents a constant term, represents the coefficient of the autoregressive part, Represents historical shock data, represents the coefficient of the moving average part, Indicates time The random error term, Indicates the error value at a past time.

[0022] Preferably, step S4 comprises:

[0023] Use PID controller to monitor the vibration intensity of the vehicle in real time and set a safe vibration threshold;

[0024] When the vibration intensity exceeds the threshold, the PID controller suspends the read and write operations to avoid system failure;

[0025] The specific formula is:

[0026] ,

[0027] in, Indicates the control output, Indicates the error between the set vibration threshold and the actual vibration value. represents the proportional, integral and differential coefficients of the PID controller, Indicates time, Represents the cumulative integral of the error.

[0028] Preferably, step S5 comprises:

[0029] After returning to normal state, the unfinished read and write tasks during the previous pause are identified and divided into several subtasks;

[0030] Use a dynamic programming algorithm to restore unfinished tasks in sequence, starting with the most important tasks, and gradually restore to the state before the system interruption. Each step of recovery is based on the optimal solution of the previous state.

[0031] The specific formula is:

[0032] ,

[0033] in, Indicated in The recovery state of the step, Indicates -1 and - The optimal solution for step 2, Indicates The operation recovery profit function at step , Indicates -2 related recovery benefits.

[0034] Preferably, the step S1 further comprises:

[0035] Collect vibration data and calculate vibration intensity;

[0036] When the vibration intensity I exceeds the set threshold T, the cache size and write frequency are adjusted, where I>T means that the vibration intensity is too large and needs to be adjusted;

[0037] Reduce the frequency of cache writes to reduce vibration effects;

[0038] After restoration of stability, the cache write frequency is gradually increased, where I is the currently detected vibration intensity and T is the preset threshold.

[0039] Preferably, the step S1 further comprises:

[0040] The LMS algorithm is used to adaptively filter the vibration data collected in real time. Based on the filtered data, the cache strategy is dynamically adjusted to adapt to vibration changes. The LM algorithm formula is:

[0041] ,

[0042] in, Indicates The weight coefficient in the iteration, represents the learning rate, Indicates the current error, Indicates the input vibration data, Indicates the number of iterations.

[0043] Preferably, the step S2 comprises:

[0044] After dividing the data into blocks, each data block is compressed and dimensionally reduced by wavelet transform. The specific formula is:

[0045] ,

[0046] in, represents the original data signal, Indicates The basis functions of the wavelet transform are represents the wavelet coefficients of the corresponding level and position, Indicates time, represents the scale parameter, Represents the translation parameter.

[0047] Preferably, the step S4 further comprises:

[0048] While suspending the read and write operations, the PID controller records the timestamp and vibration intensity information of the current data operation and uses the information in subsequent data recovery operations.

[0049] Preferably, the step S5 further comprises:

[0050] Profit function in recovery operation and The value is calculated based on the completion of each data reading and writing task, the importance of the data, and the intensity of external shocks.

[0051] Preferably, the step S2 further comprises:

[0052] The size of the data block is dynamically adjusted according to the current network bandwidth. When the network bandwidth increases, the transmission size of the data block is automatically increased. The size of the sliding window is adjusted according to the real-time network packet loss rate. When the packet loss rate is detected to be increased, the sliding window size is automatically reduced.

[0053] It can be seen from the above technical solution that the present invention has the following beneficial effects:

[0054] The central control system control method for intelligent management of electric low-speed vehicles dynamically adjusts the cache strategy of data storage based on the vibration data collected in real time by the vehicle driving state detection module, blocks the data when starting data transmission to reduce the amount of data transmitted at a single time, verifies each data reading and writing operation by redundant verification technology, suspends the data reading and writing operation and records the current state when high vibration intensity is detected, and after the normal state is restored, the unfinished data reading and writing operation is restored according to the recorded state, which can effectively improve the reliability of data storage, ensure the accurate recording of key data during vehicle operation and the normal operation of the system, further improve the stability and safety of the central control system, and solve the problem in the prior art that electric low-speed vehicles usually travel in complex road conditions such as urban and rural roads, and the vehicles will inevitably encounter uneven road conditions, body vibrations, etc. during driving, and such vibrations may have adverse effects on the data reading and writing process of the vehicle-mounted storage device, thereby causing storage errors or data loss, affecting the stability of the system and the safety of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] like Figure 1 As shown, a central control system control method for intelligent management of electric low-speed vehicles is characterized in that the method includes:

[0058] S1, dynamically adjust the data storage cache strategy based on the vibration data collected in real time by the vehicle driving status detection module;

[0059] S2. When starting data transmission, the data is divided into blocks to reduce the amount of data transmitted in a single time. Specifically, the data to be transmitted is first divided into multiple blocks. The initial block size is determined according to the network bandwidth and transmission conditions. The sliding window mechanism is used to continuously send multiple data blocks without waiting for confirmation. The window size is dynamically adjusted according to the transmission delay and network load. The specific formula is:

[0060] ,

[0061] in, Indicates the transmission The size of the data block, Indicates data blocks, Indicates the size of the sliding window;

[0062] S3, verify each data read and write operation through redundancy verification technology;

[0063] Specifically: Divide the data into information bits , and use the generated matrix Calculate the corresponding Hamming code As a redundant check code, during each data read and write operation, the check bit is detected to determine whether the data is wrong. If an error is detected, the error correction capability of the Hamming code is used to automatically correct it. The specific formula is:

[0064] ,

[0065] in, represents the generated Hamming code, represents the generator matrix, represents a data bit matrix;

[0066] S4, when high vibration intensity is detected, suspend data reading and writing operations and record the current status;

[0067] S5. After returning to the normal state, the unfinished data read and write operations are resumed according to the recorded state.

[0068] In this embodiment, first, during the driving process of the vehicle, the driving state detection module collects the vehicle vibration data in real time through the sensor, and the vibration data obtained is stored in a dynamically adjusted cache set. This dynamically adjusted cache mechanism can automatically adjust the cache size according to the different working conditions of the vehicle to meet the needs of efficient data storage and processing.

[0069] During data transmission, the system first divides the data into blocks to reduce the size of the unit data block, thereby effectively reducing the amount of data transmitted at a time. According to the network delay and network load, the system dynamically adjusts the size of the transmission window to optimize the transmission efficiency. Specifically, the formula is used Calculate the size of each data block, where Indicates the transmission The size of the data block, Indicates data blocks, Indicates the size of the sliding window to ensure the efficiency of data partitioning and window adjustment.

[0070] Through the redundancy check technology, each data read and write operation is checked. During each read and write operation, the system will plan the data bit as the information bit. , and using the generator matrix Calculate the redundant code The redundant code and the information bit together form the verification data to ensure that the data written each time can be transmitted accurately. The verification process can detect errors in the data transmission process. If an error is found, the system will use the redundant code to calculate the error value. , and automatically correct it to ensure the integrity and accuracy of the data. When anomalies such as data errors or network failures are detected, the system will immediately suspend the current data reading and writing operations and record the current status to avoid continuing to transmit erroneous data. After the system returns to normal, the system re-executes the unfinished operations based on the previously recorded status to ensure data integrity and consistency. The beneficial effect of the control method of the central control system is that it greatly improves the efficiency and reliability of data transmission through dynamic cache adjustment and block data transmission. Especially in the case of heavy network load or large transmission delay, the blocking situation in data transmission can be effectively reduced by dynamically adjusting the sliding window size. In addition, through redundant check technology, the system can automatically detect and correct errors in the data reading and writing process, further improving the stability of the system and data security. The method also provides an exception handling mechanism. When data transmission anomalies or system failures are detected, the operation can be automatically suspended to avoid data damage, and the unfinished operation can be resumed after the failure is recovered to ensure the fault tolerance and data consistency of the system.

[0071] Step S1 includes:

[0072] Collect vibration data in real time and input it into the autoregressive integrated moving average model to predict vibration intensity;

[0073] Adjust the cache strategy in advance based on the predicted future vibration intensity. If the vibration is predicted to intensify, reduce the cache capacity. If the vibration is predicted to decrease, increase the cache capacity.

[0074] The specific formula is:

[0075] ,

[0076] in, Indicates vibration data, Indicates time, represents a constant term, represents the coefficient of the autoregressive part, Represents historical shock data, represents the coefficient of the moving average part, Indicates time The random error term, Indicates the error value at a past time.

[0077] In this embodiment, the system collects the vibration data of the vehicle in real time through sensors, and inputs these data into the autoregressive moving average model to predict the future vibration intensity. The model combines historical vibration data and error terms, and calculates the predicted value through model parameters. This predicted value can reflect the trend of vibration intensity changes in the future, thereby providing a basis for the dynamic adjustment of the cache set. When the predicted vibration intensity increases, the system will actively reduce the capacity of the cache to improve the data transmission speed and efficiency and avoid cache overload; when the predicted vibration intensity decreases, the system can increase the cache capacity to smoothly handle the possible instantaneous data surge and ensure the safe storage and transmission of vibration data. Through this prediction mechanism, the system can realize adaptive cache adjustment according to actual working conditions, thereby further optimizing data management and storage. This embodiment introduces a vibration intensity prediction model, so that the system can make cache adjustments in advance according to future vibration change trends, which effectively improves the efficiency of data storage and processing. By reducing the cache capacity, the system load can be reduced during high-intensity vibrations, avoiding data congestion or cache overflow; when the vibration is reduced, increasing the cache capacity can provide more buffer space and reduce the risk of data loss. This prediction mechanism not only improves the flexibility and adaptability of the system, but also improves the data processing efficiency of the vehicle under complex working conditions, ensuring the stability of the system under various road conditions. At the same time, this implementation method predicts vibration intensity through an autoregressive moving average model, avoiding the traditional experience-based cache adjustment method, greatly improving the scientificity and accuracy of cache adjustment.

[0078] Step S4 includes:

[0079] Use PID controller to monitor the vibration intensity of the vehicle in real time and set a safe vibration threshold;

[0080] When the vibration intensity exceeds the threshold, the PID controller suspends the read and write operations to avoid system failure;

[0081] The specific formula is:

[0082] ,

[0083] in, Indicates the control output, Indicates the error between the set vibration threshold and the actual vibration value. represents the proportional, integral and differential coefficients of the PID controller, Indicates time, Represents the cumulative integral of the error.

[0084] In this embodiment, the PID controller is used to monitor the vibration intensity of the electric low-speed vehicle in real time. When the vehicle is driving, the vibration detection module continuously collects real-time vibration data and compares it with the preset vibration safety threshold to obtain the error value. The PID controller processes the error value to adjust the control output of the data read and write operation. PID controller control strategy includes three parts: proportional control, integral control and differential control. Among them, proportional control is based on the current error Determines the output regulation amount, and integral control is based on the accumulation of error over time , helping to eliminate small errors that exist for a long time. The differential control responds to possible rapid vibration changes in advance according to the error change rate. When the vibration intensity exceeds the set threshold, the PID controller will control the output signal according to the , immediately suspend the system's read and write operations to prevent data corruption or system failure due to excessive vibration. Once the vibration returns to a safe range, the system can restart data read and write operations according to the recovery mechanism. By using the PID controller for vibration monitoring and adjustment, this implementation can effectively respond to vehicle vibration changes under complex road conditions. The PID controller combines proportional, integral, and differential control strategies to enable the system to quickly respond to instantaneous changes in vibration while also reducing the long-term impact of cumulative errors on system operations. When the vibration intensity exceeds the safety threshold, the system can immediately suspend read and write operations to avoid data corruption or storage failures due to severe vibration, further improving the stability and safety of the system. In addition, the parameters of the PID controller It can be flexibly adjusted according to the actual use of the vehicle to ensure that the system can achieve the best control effect under different working conditions. This method not only improves the anti-interference ability of the central control system, but also improves the security and reliability of data reading and writing, especially under harsh road conditions or frequent vibrations. It can greatly reduce the risk of data loss or equipment damage.

[0085] Step S5 includes:

[0086] After returning to normal state, the unfinished read and write tasks during the previous pause are identified and divided into several subtasks;

[0087] Use a dynamic programming algorithm to restore unfinished tasks in sequence, starting with the most important tasks, and gradually restore to the state before the system interruption. Each step of recovery is based on the optimal solution of the previous state.

[0088] The specific formula is:

[0089] ,

[0090] in, Indicated in The recovery state of the step, Indicates -1 and - The optimal solution for step 2, Indicates The operation recovery profit function at step , Indicates -2 related recovery benefits.

[0091] In this implementation, when the system is restored to normal state, the state monitoring module first identifies the unfinished read and write tasks caused by failures or interruptions. The system divides these tasks into multiple subtasks and determines the optimal recovery order through a dynamic programming algorithm. The dynamic programming algorithm determines how to achieve the optimal recovery strategy in the current state by analyzing the recovery tasks of each step and the task benefits in the previous state. Specifically, each recovery state Both depend on the previous two states , and combined with the benefits of the current task and the task benefits related to the previous two steps , to calculate the optimal recovery path, starting from the most important task, give priority to recovering the operation that has the greatest impact on the overall system, ensure the integrity and accuracy of key data read and write operations, and each step of the recovery operation is dynamically adjusted according to the current system state and the recovery status of the previous step, so as to gradually recover to the working state before the system interruption. This implementation method can effectively identify and recover the unfinished read and write tasks due to system interruption by introducing a dynamic programming algorithm, and ensure the integrity and continuity of data during the system recovery process. The dynamic programming algorithm can find the optimal recovery path according to the current and previous system states, maximize the efficiency and benefits of the recovery operation, and by dividing the unfinished tasks into several subtasks, and recovering them in turn according to the importance of the tasks and the optimal solution for recovery, the system can give priority to recovering the tasks that have a greater impact on the global system, and ensure the integrity and security of key data. This task priority recovery strategy can effectively reduce the risk of data damage or loss, especially in the event of system interruption or failure, to ensure that the system can be restored to normal working state at the fastest speed. In addition, the introduction of the dynamic programming algorithm makes the recovery process highly flexible, can adapt to different task structures and system states, and ensure the optimization and efficiency of the recovery process.

[0092] Step S1 also includes:

[0093] Collect vibration data and calculate vibration intensity;

[0094] When the vibration intensity I exceeds the set threshold T, the cache size and write frequency are adjusted, where I>T means that the vibration intensity is too large and needs to be adjusted;

[0095] Reduce the frequency of cache writes to reduce vibration effects;

[0096] After restoration of stability, the cache write frequency is gradually increased, where I is the currently detected vibration intensity and T is the preset threshold.

[0097] In this embodiment, the system first collects the vibration data of the vehicle in real time through the vibration sensor, and processes these data to calculate the vibration intensity I. The vibration intensity I is the result obtained after the system performs comprehensive processing on the vibration signal, which is used to measure the vibration amplitude of the current vehicle. When the vibration intensity I is detected to exceed the preset threshold value T, it indicates that the vibration intensity is too large, and the system must take countermeasures. At this time, the system will adjust the size of the data cache and reduce the write frequency to reduce the negative impact of vibration on data storage and transmission. By reducing the write frequency, the system can reduce the vibration superposition caused by frequent operations, and avoid further affecting the integrity of the data and the stability of the system. At the same time, the size of the cache is also dynamically adjusted according to the vibration situation to ensure that the data can be stored safely and stably. When the vibration returns to the normal range, the system will gradually increase the write frequency of the cache to restore the system to a normal operating state. Through this dynamic adjustment, the system can effectively deal with the adverse effects of vibration on data transmission and storage, and maintain the efficient operation of the system. The beneficial effect of this embodiment is that by monitoring the vibration intensity and dynamically adjusting the write frequency of the cache, the system can maintain the stability and integrity of the data in a high vibration environment. Reducing the write frequency can effectively reduce the interference of vehicle vibration on data transmission and storage, and avoid storage errors or data damage caused by vibration. In addition, the cache adjustment and write frequency recovery process is carried out gradually, ensuring that after the vibration returns to a stable state, the system can smoothly transition to normal operation to avoid re-vibration problems caused by premature recovery. This design improves the system's anti-interference ability and data security, especially when the vehicle encounters bumps or complex road conditions during driving, which can effectively ensure the normal operation of the system.

[0098] Step S1 also includes:

[0099] The LMS algorithm is used to adaptively filter the vibration data collected in real time. Based on the filtered data, the cache strategy is dynamically adjusted to adapt to vibration changes. The LM algorithm formula is:

[0100] ,

[0101] in, Indicates The weight coefficient in the iteration, represents the learning rate, Indicates the current error, Indicates the input vibration data, Indicates the number of iterations.

[0102] In this embodiment, the system performs adaptive filtering on the vibration data of the vehicle through the LMS algorithm. The LMS algorithm is an adaptive filter algorithm that continuously adjusts the weight coefficient of the filter by minimizing the filtering error. , to adapt to the changes in vibration signals. During system operation, vehicle vibration data Will be input into the LMS filter in real time, and the algorithm calculates the error To update the weight coefficients, thereby optimizing the performance of the filter, each iteration, the algorithm is based on the formula Adjust the weight coefficient, where is the learning rate, which is used to control the speed of weight update. Will speed up convergence, but may cause instability; smaller The stability of the filter will be guaranteed, but the convergence speed is slow. By continuously adjusting the weights, the system can adaptively filter out high-frequency noise or abnormal vibration signals according to real-time vibration data to ensure the accuracy of the data. The filtered vibration data will be used as the basis for dynamically adjusting the cache strategy. When the vibration changes greatly, the system will reduce the cache write frequency to cope with the data instability caused by severe vibration; when the vibration returns to stability, the system will gradually increase the cache write frequency to restore normal data storage and processing operations. By introducing the LMS adaptive filtering algorithm, this implementation method can monitor and filter the vibration data during vehicle driving in real time, reducing the impact of external interference on data acquisition. The LMS algorithm has adaptive characteristics and can automatically adjust the filter weights according to changes in vibration data, so that the system can maintain a high data processing accuracy under different vibration conditions. In addition, combined with the filtered data, the system can dynamically adjust the cache strategy according to the actual vibration situation to reduce the instability of data reading and writing caused by vibration changes. This dynamic adjustment method greatly improves the system's data processing capabilities in complex vibration environments and ensures data reliability and integrity. By combining adaptive algorithms with cache adjustments, the system can effectively reduce the possibility of data errors when vibrations are severe, and gradually resume normal operations after the vibrations recover, ensuring the system's stability and anti-interference capabilities in high-vibration environments.

[0103] Step S2 includes:

[0104] After dividing the data into blocks, each data block is compressed and dimensionally reduced by wavelet transform. The specific formula is:

[0105] ,

[0106] in, represents the original data signal, Indicates The basis functions of the wavelet transform are represents the wavelet coefficients of the corresponding level and position, Indicates time, represents the scale parameter, Represents the translation parameter.

[0107] In this implementation, the transmitted data is first processed in blocks according to a predetermined strategy, and each data block represents a portion of the vibration or sensor data to be processed. Next, the system performs wavelet transform processing on each data block. Wavelet transform is a mathematical tool that can analyze signals in both the time domain and the frequency domain. By performing multi-resolution analysis on the data, wavelet transform can effectively extract the local features of the data. is the original vibration signal, and It is the basis function for wavelet transform, which represents the signal characteristics of different scales and positions. The system calculates the wavelet coefficients To compress and reduce the dimension of data, retain important signal components, and remove redundant or irrelevant noise information. This processing process can significantly reduce the size of the data block, thereby reducing the storage and transmission overhead. After the wavelet transform, the high-frequency part and the low-frequency part of the data block are separated, wherein the high-frequency part usually contains noise or transient signals, which can be appropriately compressed or discarded, while the low-frequency part retains the main characteristic information of the signal. This method can not only effectively compress data, but also ensure that important information is retained in the compressed data block, thereby improving the efficiency and reliability of the system in subsequent transmission and storage. By using wavelet transform to compress and reduce the dimension of the data block, this implementation method can significantly reduce the storage amount and transmission bandwidth requirements of the data, while retaining the key features of the vibration data. This method is particularly suitable for processing a large amount of vibration or sensor data. It ensures the integrity of the information while compressing the data. Compared with traditional compression algorithms, wavelet transform can better capture the local characteristics of the signal, and is particularly suitable for the processing of non-stationary signals. It can not only filter out high-frequency noise, but also reduce data redundancy through dimensionality reduction, and reduce the occupation of network bandwidth and cache space in subsequent transmission. This solution effectively improves the data processing capabilities of the central control system under complex working conditions, allowing the system to operate efficiently even when there are strong vibrations or large amounts of data.

[0108] Step S4 also includes:

[0109] While suspending the read and write operations, the PID controller records the timestamp and vibration intensity information of the current data operation, and uses this information in the subsequent data recovery operation. While the PID controller suspends the read and write operations, the system saves the data operation timestamp and the corresponding vibration intensity at the time of interruption through the built-in log recording function. The timestamp marks the exact time point when the system is interrupted, and the vibration intensity reflects the external environment state at that time. When resuming the operation, the system can quickly locate the interrupted task through these recorded timestamps and vibration information, and evaluate the security of data recovery according to the vibration intensity. If the vibration intensity is high, the system can postpone data recovery until the vibration returns to a stable state. In addition, the recording of timestamps can ensure that the system can continue the read and write operations from the correct interruption point to avoid data duplication or loss. By recording the timestamp and vibration intensity information when the operation is suspended, the system can control the data recovery process more accurately to ensure that the unfinished tasks can be continued quickly and accurately after the system is restored. This design avoids the problem of repeated reading and writing or data inconsistency during the recovery process, and can effectively improve the security of data recovery, especially in the case of complex vibration environment, to ensure the stability of data reading and writing.

[0110] Step S5 also includes:

[0111] Profit function in recovery operation and The value is calculated based on the completion of each data reading and writing task, the importance of the data, and the intensity of external shocks.

[0112] Revenue Function The calculation is based on three factors: the completion of the data task The importance of data and external vibration intensity ,Completeness Represents the percentage of each task that has been completed, the importance reflects the priority of the task in the system, and the vibration intensity It is used to judge the safety of restoring the task at present. The profit function can be calculated by the following formula:

[0113] ,

[0114] in, is the weight coefficient, which indicates the impact of vibration intensity on revenue. When it is larger, the system will reduce the benefit value of the task, thereby delaying its recovery order to ensure the integrity and security of the data.

[0115] By introducing completion, data importance, and vibration intensity as key parameters for recovery operations, the system can intelligently determine the priority of task recovery. In particular, when external vibrations are large, the system can choose to postpone the recovery of low-priority or high-risk tasks to ensure the safety of the entire system recovery process and data consistency.

[0116] Step S2 also includes:

[0117] The size of the data block is dynamically adjusted according to the current network bandwidth. When the network bandwidth increases, the transmission size of the data block is automatically increased. The size of the sliding window is adjusted according to the real-time network packet loss rate. When the packet loss rate is detected to be increased, the sliding window size is automatically reduced.

[0118] In this embodiment, the system can monitor the bandwidth of the current network in real time and dynamically adjust the size of the data block according to the change of bandwidth. When the bandwidth increases, the system increases the transmission size of each data block, thereby improving the efficiency of data transmission. On the contrary, when the bandwidth decreases, the system automatically reduces the size of the data block to avoid network congestion. At the same time, the size of the sliding window is also dynamically adjusted according to the network packet loss rate. When the packet loss rate is detected to be increased, the system automatically reduces the size of the sliding window to reduce the network load and the error rate of data transmission. By adjusting the sliding window in real time, the system can ensure the stability and reliability of data transmission while maintaining efficient transmission. This embodiment can maintain the efficiency and stability of data transmission under different network conditions by dynamically adjusting the size of the data block and the size of the sliding window. Especially when the network conditions are unstable or the packet loss rate is high, the system can reduce data loss by reducing the sliding window to ensure accurate data transmission. This method improves the adaptive ability of the central control system under changes in the network environment and ensures the reliability and transmission efficiency during data transmission.

[0119] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A central control system control method for intelligent management of electric low-speed vehicles, characterized in that: The method comprises: S1, dynamically adjust the data storage cache strategy based on the vibration data collected in real time by the vehicle driving status detection module; S2. When starting data transmission, the data is divided into blocks to reduce the amount of data transmitted in a single time. Specifically, the data to be transmitted is first divided into multiple blocks. The initial block size is determined according to the network bandwidth and transmission conditions. The sliding window mechanism is used to continuously send multiple data blocks without waiting for confirmation. The window size is dynamically adjusted according to the transmission delay and network load. The specific formula is: ， in, Indicates the transmission The size of the data block, Indicates data blocks, Indicates the size of the sliding window; S3, verify each data read and write operation through redundancy verification technology; Specifically: Divide the data into information bits , and use the generated matrix Calculate the corresponding Hamming code As a redundant check code, during each data read and write operation, the check bit is detected to determine whether the data is wrong. If an error is detected, the error correction capability of the Hamming code is used to automatically correct it. The specific formula is: ， in, represents the generated Hamming code, represents the generator matrix, represents a data bit matrix; S4, when high vibration intensity is detected, suspend data reading and writing operations and record the current status; S5. After returning to the normal state, resume the unfinished data read and write operations according to the recorded state; The step S1 comprises: Collect vibration data in real time and input it into the autoregressive integrated moving average model to predict vibration intensity; Adjust the cache strategy in advance based on the predicted future vibration intensity. If the vibration is predicted to intensify, reduce the cache capacity; if the vibration is predicted to decrease, increase the cache capacity. The specific formula is: ， in, Indicates vibration data, Indicates time, represents a constant term, represents the coefficient of the autoregressive part, Represents historical shock data, represents the coefficient of the moving average part, Indicates time The random error term, Indicates the error value at a past time.

2. A central control system control method for intelligent management of electric low-speed vehicles according to claim 1, characterized in that: The step S4 comprises: Use PID controller to monitor the vibration intensity of the vehicle in real time and set a safe vibration threshold; When the vibration intensity exceeds the threshold, the PID controller suspends the read and write operations to avoid system failure; The specific formula is: ， in, Indicates the control output, Indicates the error between the set vibration threshold and the actual vibration value. represents the proportional, integral and differential coefficients of the PID controller, Indicates time, Represents the cumulative integral of the error.

3. The central control system control method for intelligent management of electric low-speed vehicles according to claim 1, characterized in that: The step S5 comprises: After returning to normal state, the unfinished read and write tasks during the previous pause are identified and divided into several subtasks; Use a dynamic programming algorithm to restore unfinished tasks in sequence, starting with the most important tasks, and gradually restore to the state before the system interruption. Each step of recovery is based on the optimal solution of the previous state. The specific formula is: ， in, Indicated in The recovery state of the step, Indicates -1 and - The optimal solution for step 2, Indicates The operation recovery profit function at step , Indicates -2 related recovery benefits.

4. The method for controlling a central control system for intelligent management of an electric low-speed vehicle according to claim 1, characterized in that: The step S1 further comprises: Collect vibration data and calculate vibration intensity; When the vibration intensity I exceeds the set threshold T, the cache size and write frequency are adjusted, where I>T means that the vibration intensity is too large and needs to be adjusted; Reduce the frequency of cache writes to reduce vibration effects; After restoration of stability, the cache write frequency is gradually increased, where I is the currently detected vibration intensity and T is the preset threshold.

5. The central control system control method for intelligent management of electric low-speed vehicles according to claim 2, characterized in that: The step S1 further comprises: The LMS algorithm is used to adaptively filter the vibration data collected in real time. Based on the filtered data, the cache strategy is dynamically adjusted to adapt to vibration changes. The LM algorithm formula is: ， in, Indicates The weight coefficient in the iteration, represents the learning rate, Indicates the current error, Indicates the input vibration data, Indicates the number of iterations.

6. The method for controlling a central control system for intelligent management of an electric low-speed vehicle according to claim 1, characterized in that: The step S2 comprises: After dividing the data into blocks, each data block is compressed and dimensionally reduced by wavelet transform. The specific formula is: ， in, represents the original data signal, Indicates The basis functions of the wavelet transform are represents the wavelet coefficients of the corresponding level and position, Indicates time, represents the scale parameter, Represents the translation parameter.

7. A central control system control method for intelligent management of electric low-speed vehicles according to claim 2, characterized in that: The step S4 further comprises: While suspending the read and write operations, the PID controller records the timestamp and vibration intensity information of the current data operation and uses the information in subsequent data recovery operations.

8. The method for controlling a central control system for intelligent management of an electric low-speed vehicle according to claim 3, characterized in that: The step S5 further comprises: Profit function in recovery operation and The value is calculated based on the completion of each data reading and writing task, the importance of the data, and the intensity of external shocks.

9. The central control system control method for intelligent management of electric low-speed vehicles according to claim 1, characterized in that: The step S2 further comprises: The size of the data block is dynamically adjusted according to the current network bandwidth. When the network bandwidth increases, the transmission size of the data block is automatically increased. The size of the sliding window is adjusted according to the real-time network packet loss rate. When the packet loss rate is detected to be increased, the sliding window size is automatically reduced.

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