Control method and device for hub lock capable of preventing abnormal locking

Abstract

The invention relates to the technical field of hub lock control, and discloses an abnormal locking prevention hub lock control method and device.The method comprises the steps that starting initialization is conducted on a main control module, a plurality of Hall sensors, a position detection module, a main power source module and a standby power source module; hub lock initial state data of the first position detection switch and the second position detection switch are collected; performing difference value comparison on the first Hall sensor and the second Hall sensor to obtain hub motion state information; generating user operation record data; when abnormal communication is detected, switching to the standby power supply module to obtain an emergency control trigger signal; controlling the actuating mechanism to drive to obtain self-locking state data; according to the method, the detection reliability of the rotating state of the hub is improved, and then the safety and reliability of the hub lock in practical application are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wheel hub lock control, and in particular to a control method and device for a wheel hub lock that prevents abnormal locking. Background Art

[0002] Traditional wheel hub locks mainly rely on a single Hall sensor to detect the rotation status of the wheel hub, and use a single power supply. This design has the problem of insufficient reliability in practical applications. When the Hall sensor fails or is interfered with by external factors, the wheel hub lock may not be able to accurately determine the rotation status of the wheel hub, resulting in failure of the locking function. At the same time, when the user unlocks the wheel hub through the APP operation, if the communication line is maliciously cut off, the traditional wheel hub lock may cause the lock body to be in an abnormal state due to the lack of backup power supply and emergency protection mechanism, posing a hidden danger to vehicle safety. In addition, the control system of the existing wheel hub lock lacks precise control of position detection and status monitoring, making it difficult to accurately perceive the lock pin position and motor status, and the user authority management mechanism is not perfect, and it is impossible to effectively prevent illegal unlocking operations. Summary of the invention

[0003] The present invention provides a control method and device for a wheel hub lock that prevents abnormal locking, which are used to improve the reliability of wheel hub rotation state detection, thereby improving the safety and reliability of the wheel hub lock in practical applications.

[0004] In a first aspect, the present invention provides a control method for a wheel hub lock that prevents abnormal locking, the control method for a wheel hub lock that prevents abnormal locking comprising: The main control module, multiple Hall sensors, position detection modules, main power supply module and backup power supply module are started and initialized, and the motor stop position signal of the first position detection switch and the lock position signal of the lock pin switch of the second position detection switch are collected to obtain the initial state data of the wheel hub lock; According to the initial state data of the wheel hub lock, a first rotation state signal of the first Hall sensor is compared with a second rotation state signal of the second Hall sensor, and when the difference exceeds a preset threshold, the normal Hall sensor is switched to obtain the wheel hub motion state information; Input the user identity information into the authority management module for authentication matching, generate an unlock authorization instruction containing the user identity identifier, operation type identifier and timestamp, and obtain the user operation record data; Based on the wheel hub motion state information, the communication link state and the power supply state of the main power module are monitored, and when a communication abnormality is detected, the backup power module is switched to obtain an emergency control trigger signal; According to the emergency control trigger signal and the motor position signal of the first position detection switch, the actuator is controlled to drive the lock pin to move to the preset locking position detected by the second position detection switch to obtain self-locking state data; The self-locking state data, the wheel hub motion state information and the user operation record data are integrated, sent to the APP of the mobile terminal through the communication module for display, and the vehicle state information in the APP is updated in real time.

[0005] In a second aspect, the present invention provides a control device for a wheel hub lock that prevents abnormal locking, the control device for a wheel hub lock that prevents abnormal locking comprises: The acquisition module is used to start and initialize the main control module, multiple Hall sensors, the position detection module, the main power module and the backup power module, collect the motor stop position signal of the first position detection switch and the lock position signal of the lock pin switch of the second position detection switch, and obtain the initial state data of the wheel hub lock; a switching module, for performing a difference comparison between a first rotation state signal of the first Hall sensor and a second rotation state signal of the second Hall sensor according to the initial state data of the wheel hub lock, and switching to a normal Hall sensor when the difference exceeds a preset threshold value to obtain wheel hub motion state information; A matching module is used to input the user identity information into the authority management module for authentication matching, generate an unlock authorization instruction containing the user identity identifier, operation type identifier and timestamp, and obtain user operation record data; A monitoring module, used to monitor the communication link status and the power supply status of the main power module based on the wheel hub motion status information, and switch to the backup power module when a communication abnormality is detected to obtain an emergency control trigger signal; a control module, configured to control the actuator to drive the lock pin to move to a preset locking position detected by the second position detection switch according to the emergency control trigger signal and the motor position signal of the first position detection switch, and obtain self-locking state data; The update module is used to integrate the self-locking state data, the wheel hub motion state information and the user operation record data, send them to the APP of the mobile terminal through the communication module for display, and update the vehicle state information in the APP in real time.

[0006] In the technical solution provided by the present invention, dual Hall sensors are set to detect the rotation state of the wheel hub and an automatic switching mechanism is implemented. When one of the Hall sensors fails, the system automatically switches to the normally working Hall sensor, which significantly improves the reliability of the wheel hub rotation state detection; a main and standby power supply module design is adopted, and when an abnormal situation such as the communication line being cut is detected, it can automatically switch to the standby power supply module for power supply, and execute the self-locking program to effectively prevent illegal unlocking; a first position detection switch and a second position detection switch are set to detect the motor position and the lock pin position respectively, so as to realize accurate monitoring of the lock body state and improve the accuracy of locking control; a deep learning model is introduced to intelligently identify the power supply state and communication anomalies, and features are extracted through multi-layer neural networks and attention mechanisms to realize more accurate abnormal state judgment; a complete user authority management mechanism is established, multiple verifications are performed on the user identity, and detailed operation logs are recorded to effectively prevent illegal operations; real-time monitoring and dynamic display of the hub lock state information are realized, and the complete system operation status is presented through the mobile terminal APP, which is convenient for timely discovery and handling of abnormal situations.

[0007] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0008] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic diagram of an embodiment of a method for controlling a hub lock for preventing abnormal locking in an embodiment of the present invention; Figure 2 Schematic diagram of an embodiment of a control device for a wheel hub lock that prevents abnormal locking in an embodiment of the present invention. DETAILED DESCRIPTION

[0010] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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.

[0011] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device end including a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or device ends.

[0012] To facilitate understanding of this embodiment, a control method for a hub lock for preventing abnormal locking disclosed in an embodiment of the present invention is first described in detail. Figure 1 As shown, the method comprises the following steps: 101. Start and initialize a main control module, multiple Hall sensors, a position detection module, a main power module, and a backup power module, collect a motor stop position signal of a first position detection switch and a lock position signal of a lock pin switch of a second position detection switch, and obtain initial state data of a wheel hub lock; It is understandable that the execution subject of the present invention may be a control device of the wheel hub lock for preventing abnormal locking, or a terminal or a server, which is not limited here. The embodiment of the present invention is described by taking the server as the execution subject as an example.

[0013] Specifically, when starting and initializing the main control module, multiple Hall sensors, position detection module, main power module and backup power module, electrical connection detection is used to confirm whether the connection between each module is normal, and module connection status data is obtained. According to the module connection status data, the main power module and the backup power module are configured in parallel to obtain power supply parameters to ensure that the system has a stable power supply under any circumstances, especially when the main power module fails or the power supply is insufficient, the backup power module can provide power support in time. Based on the power supply parameters, the signal acquisition frequency of each position detection module is set to obtain position detection sampling parameters, which are used to adjust the signal acquisition process of the first position detection switch and the second position detection switch to ensure that the collected signals have sufficient accuracy and real-time performance, so as to accurately control the wheel hub lock position in the future. Based on the position detection sampling parameters, the motor stop position signal output by the first position detection switch is collected. The collected motor stop position signal is an analog quantity, which is converted into a digital quantity to obtain the digital quantity of the motor position. The digitized position signal is easier to store, transmit and process, and can be further operated and verified by the control algorithm. In this process, the position detection module can detect the actual stop position of the motor in time. At the same time, the position detection sampling parameter is input into the second position detection switch to collect the lock position signal of the lock pin switch. The signal of the lock pin switch is converted into a digital quantity to obtain the digital quantity of the lock pin position. The lock pin position data can reflect the actual movement state of the lock pin, so as to determine whether the wheel hub lock has been successfully locked or unlocked. The motor position digital quantity is combined with the lock pin position digital quantity to comprehensively analyze the operation of the motor and the lock pin to ensure the safety and reliability of the entire locking process. The motor position digital quantity and the lock pin position digital quantity are data verified and abnormal data points are eliminated. The purpose of data verification is to find existing errors or abnormal signals, such as abnormal data points caused by sensor failure, signal interference and other factors. These abnormal points will cause misjudgment or erroneous operation in the subsequent control logic and need to be eliminated. The data after verification is valid position data, reflecting the actual movement state of the current wheel hub lock and lock pin. The valid position data is packaged and processed according to the preset data format to generate a position data stream. The position data stream is a continuous data format that can reflect the movement state of the wheel hub lock in real time and is easy to transmit to the upper control system or mobile application through the communication module for display and monitoring. By generating the position data stream, the system's operating status is made more transparent and traceable, improving the maintainability of the overall system and the user's operating experience. The position data stream is fused with the module connection status data, power supply parameters, and position detection sampling parameters to obtain the initial status data of the hub lock.

[0014] 102. According to the initial state data of the wheel hub lock, a first rotation state signal of the first Hall sensor is compared with a second rotation state signal of the second Hall sensor, and when the difference exceeds a preset threshold, the normal Hall sensor is switched to obtain the wheel hub motion state information; Specifically, the module connection state data in the initial state data of the wheel hub lock is parsed, and the position detection sampling parameters of the first Hall sensor and the second Hall sensor are extracted therefrom to ensure that the frequency and accuracy of signal acquisition can meet the actual needs of the system. Based on the position detection sampling parameters, the first rotation state signal collected by the first Hall sensor is converted into a digital quantity and signal filtering is performed. The digital quantity conversion converts the analog signal into a digital signal that can be directly processed by the system, and the signal filtering is to remove the noise in the signal to ensure that the collected data is more accurate. After processing, the first Hall signal value is obtained. At the same time, the second rotation state signal collected by the second Hall sensor is converted into a digital quantity and signal filtered according to the same position detection sampling parameters to obtain the second Hall signal value. The difference calculation is performed on the first Hall signal value and the second Hall signal value to obtain the difference calculation result. Through the difference calculation, the consistency of the two Hall sensors during the movement of the wheel hub is judged. The result of the difference calculation is compared with the preset difference threshold to obtain the Hall signal state data. If the output difference of the two Hall sensors is within the threshold range, it indicates that both are working normally and the data is reliable. Otherwise, it indicates that one of the sensors is abnormal or faulty. According to the Hall signal status data, the Hall sensors whose difference is less than the preset difference threshold are marked as normal Hall sensors, and the corresponding Hall switching mark is generated. Through the Hall switching mark, the signal acquisition channel of the main control module is dynamically switched to ensure that the rotation state signal is collected from the normal Hall sensor to obtain a valid Hall signal. The effective Hall signal is analyzed with the motor position digital quantity and the lock pin position digital quantity to obtain the wheel hub state parameter. The role of the timing correlation analysis is to comprehensively judge the actual operation state of the wheel hub lock through the time relationship between different signals. The motor position digital quantity, the lock pin position digital quantity and the effective Hall signal cooperate with each other to reflect the whole process of the wheel hub movement, ensuring that each step in the locking and unlocking process is carried out according to the expected logic, thereby improving the safety and reliability of the system. On this basis, the wheel hub state parameters, Hall switching marks and effective Hall signals are integrated to generate the final wheel hub movement state information. Data from different sources are processed uniformly to form a complete data set so that the system can better control and manage the state of the wheel hub lock.

[0015] The first Hall signal value is divided into sampling windows, each window contains 100 sampling points, and the sampling sequence of the first signal is obtained. The sampling window division method helps the system to effectively segment the signal so as to better identify the characteristics and changes in the signal. At the same time, the second Hall signal value is segmented according to the same sampling window size to obtain the second signal sampling sequence. The difference between each sampling point in the first signal sampling sequence and the sampling point value at the corresponding position in the second signal sampling sequence is calculated to obtain a difference point sequence. Based on all the sampling points in the difference point sequence, the root mean square value is calculated as the difference measurement value of the signal to obtain the difference statistical parameter. The root mean square value is a statistical indicator used to measure the degree of change and volatility of the signal, and is suitable for detecting the similarity or difference between two signals. The standard deviation is calculated according to the difference statistical parameter, and the standard deviation is compared with the pre-set difference standard threshold to obtain the judgment result of the Hall state. The standard deviation reflects the fluctuation amplitude of the signal difference. If the standard deviation significantly exceeds the set threshold, it indicates that there is an obvious inconsistency between the two Hall sensors, thereby indicating that there is a certain fault or abnormality. Through this step, the signal status in each sampling window is evaluated in real time, so as to timely discover and respond to potential faults. The Hall status judgment results are processed by cumulative counting of the time window. When the judgment results of 5 consecutive sampling windows exceed the set threshold, an abnormal confirmation flag is generated to prevent misjudgment due to accidental noise or short-term signal interference, ensuring that the system will only confirm it as an abnormality when a persistent problem is clearly detected. The abnormal confirmation flag is combined with the difference statistical parameters to generate Hall signal status data.

[0016] 103. Input the user identity information into the authority management module for authentication matching, generate an unlock authorization instruction including the user identity identifier, operation type identifier and timestamp, and obtain user operation record data; Specifically, the user inputs the identity information into the authority management module through the APP of the mobile terminal, and the system preprocesses the user identity information and generates user preprocessing data. The user preprocessing process includes standardization of data format, verification of information and elimination of redundant information to ensure the consistency and integrity of user data for subsequent comparison and verification. The preprocessed user data is compared with the information in the authorized user database, and through permission verification analysis, it is determined whether the currently input user has a matching record in the database to obtain the user matching result. The user matching result is input into the identity identification generation unit in the authority management module, and the identity authentication data is generated according to the preset coding rules. The preset coding rules ensure that the generated identity authentication data is unique and traceable to prevent unauthorized users from impersonating others to perform illegal operations. Based on the identity authentication data, the user's current operation type is identified and classified to generate operation identification data. The operation type includes different operations such as unlocking, locking, and emergency opening. The purpose of classification is to clarify the specific behavior of the user and take corresponding control measures according to different types. The current system time information is added to the operation identification data to generate an operation timestamp. The function of the timestamp is to record the specific time when the operation occurs, which is convenient for subsequent auditing and analysis. Combine the operation timestamp with the identity authentication data to obtain the authorization basic data, including the user's identity, operation type and specific time information. Generate the unlock control instruction for the wheel hub lock based on the authorization basic data. The unlock control instruction is a specific operation command for the vehicle's wheel hub lock. Its generation needs to be based on the user's identity, operation type and system authority verification to ensure that only authorized users can perform the corresponding operation. While generating the unlock control instruction, the user identity, operation time and operation type of this operation are recorded to obtain the operation log data. The operation log data, identity authentication data and unlock control instruction are associated and integrated to obtain the user operation record data.

[0017] 104. Monitor the communication link status and the power supply status of the main power module based on the wheel hub motion status information, and switch to the backup power module when a communication abnormality is detected to obtain an emergency control trigger signal; Specifically, the hub state parameters and effective Hall signals in the hub motion state information are analyzed and extracted in real time to obtain the real-time operation state data of the hub. Through continuous analysis of the Hall sensor data, the actual movement of the hub and the operation state of each component can be reflected. Based on the real-time operation state data, the signal quality of the communication link between the communication module and the mobile terminal APP is detected to obtain the communication link state data. The signal strength value in the communication link state data is a key indicator for evaluating the communication quality of the system. By comparing the signal strength value with the preset signal strength threshold, the working state of the current communication line is judged, especially in judging whether the communication line is cut or whether the communication quality is significantly reduced. The communication anomaly detection results obtained in this process help the system identify potential communication interruption risks in the shortest time. At the same time, the real-time power supply state of the main power module is detected, including the collection and analysis of voltage, current and communication power supply parameters to obtain power supply state data. The power supply state of the main power supply is crucial to the normal operation of the hub lock. Through real-time detection of these power supply parameters, power supply anomalies such as low voltage and unstable current can be discovered in time. The power supply status data is correlated with the communication anomaly detection results to comprehensively judge the current working status of the system. When a communication anomaly is detected, the power supply switching instruction is triggered to obtain the power supply switching instruction, and the switching switch action between the main power module and the backup power module is controlled based on the instruction to realize the automatic switching of the power supply and obtain the power supply switching status data. During the power switching process, the precise control of the switching switch is the key to ensure the smooth transition of the system. Especially in the face of an emergency situation where the main power supply cannot supply power, the timely switching of the backup power supply can ensure the continuous operation of the hub lock system and avoid system failures or safety hazards caused by power interruption. After the power switching is completed, the power switching status data is confirmed to ensure the success of the power switching process and the stability of the power supply. The system working status at the time of switching is recorded to obtain the switching confirmation data. The switching confirmation data reflects the various status indicators of the system during and after the power switching process, ensuring that the power supply switching is smooth and the backup power supply can work normally. On this basis, the switching confirmation data is integrated with the communication anomaly detection results to generate an emergency control trigger signal. The emergency control trigger signal is a comprehensive instruction used to prompt the system to enter an emergency state and take corresponding emergency measures according to the current working state.

[0018] The power supply status data and communication anomaly detection results are input to the input layer of the anomaly detection deep neural network, which contains 64 neurons. The ReLU activation function is used to extract features from the input data. The use of the ReLU activation function can effectively solve the problem of negative values ​​in the data, making the network more computationally efficient, and obtaining the initial feature data. The initial feature data is processed by the first convolution layer, which contains 32 3×3 convolution kernels with a step size of 1, which is used to extract local features in the input data. The convolution layer can find specific patterns or abnormal local features by scanning local areas in the power supply status and communication anomaly data. BatchNormalization is used for feature normalization in the convolution operation to reduce the variance of the data and accelerate the convergence speed of the network, and a first-level feature map is obtained to retain the key features in the power supply and communication status and ensure the stability of the data. The first-level feature map is input to the attention mechanism layer, which calculates the correlation weight matrix between features by using a self-attention structure. The self-attention mechanism can assign different weights to the relationship between feature data, thereby highlighting the key features in the abnormal state. In this way, features related to power supply and communication anomalies can be more accurately identified, and attention feature data can be obtained. It effectively reflects the potential correlation between the power supply status and the communication status, so that the system can make better judgments when facing complex anomalies. The attention feature data is processed by the second convolution layer, which contains 64 3×3 convolution kernels, with a step size of 2. The LeakyReLU activation function is used to maintain the network's sensitivity to negative features, and a secondary feature map is obtained. The increase in convolution kernels and the adjustment of step size make the feature extraction process more in-depth while maintaining the sparsity of feature data. The use of the LeakyReLU activation function helps to alleviate the problem of neuron "death", making the network more sensitive to abnormal conditions and better able to extract complex features. The secondary feature map is input into the fully connected layer, which contains 128 neurons. By connecting all feature maps together, a complete deep feature vector is obtained. In order to prevent overfitting, the Dropout mechanism is used with a Dropout rate of 0.5, which means that half of the neurons are randomly inactivated during training, thereby increasing the generalization ability of the model, so that the model still has strong predictive ability when facing new data. The deep feature vector is input into the bidirectional LSTM layer for time series analysis. The bidirectional LSTM layer contains 64 hidden units. Through forward and reverse time series information processing, it can capture the time series correlation characteristics between power supply status and communication anomalies. The LSTM network is suitable for processing sequence data and can learn and capture dependencies within long time intervals. Therefore, it can effectively analyze the change trend between power supply status and communication signals and obtain a time series correlation vector, which contains the temporal dynamic relationship between power supply and communication anomalies.The time series association vector is input to the decision layer, which contains two fully connected layers, using ReLU and Sigmoid activation functions to process data respectively. The first fully connected layer uses the ReLU activation function to extract high-order features in the time series association vector; the second fully connected layer uses the Sigmoid activation function to output the probability threshold of power switching. When the power switching probability threshold exceeds 0.85, it indicates that there is a significant abnormality in power supply or communication, and the system will trigger the power switching instruction to ensure that the system can respond to potential power problems in a timely manner. Based on the generated power switching instruction, a specific switching control sequence is generated through the output layer. The control sequence is used to control the switching action between the main power module and the backup power module to ensure seamless power switching. In order to ensure the stability of power switching, power supply parameters such as voltage and current are collected during the switching process to obtain power switching status data.

[0019] 105. According to the emergency control trigger signal and the motor position signal of the first position detection switch, the actuator is controlled to drive the lock pin to move to the preset locking position detected by the second position detection switch to obtain self-locking state data; Specifically, based on the emergency control trigger signal, the communication abnormality mark and the power switching state are extracted to obtain the emergency processing parameters, which reflect the current emergency state of the system and ensure that the control logic can be adjusted accordingly according to the actual situation. At the same time, according to the preset position detection sampling parameters, the motor position signal output by the first position detection switch is sampled to obtain the motor sampling data, and the current position information of the motor is provided so that the system can accurately judge the state of the motor. The motor drive control instructions are generated according to the emergency processing parameters and the motor sampling data to control the motor start in the actuator. The generated motor drive control instructions are transmitted to the actuator to obtain the motor start parameters, which include the motor start time, start current and initial speed, etc., to ensure that the motor can start smoothly according to the set requirements. The motor start parameters are input into the motion control unit in the main control module, and the motion control unit obtains the lock pin displacement data by calculating the displacement required for the lock pin to move from the current position to the preset locking position. The lock pin position feedback signal output by the second position detection switch is collected in real time, and the feedback signal is compared with the lock pin displacement data to obtain the motion parameters of the lock pin. The motion parameters of the lock pin reflect the actual state of the lock pin during the movement process. These parameters are used to adjust the control instructions of the motor in real time to ensure that the lock pin moves along the predetermined path to the preset locking position. Based on the motion parameters of the lock pin, the motor in the actuator is controlled, including the speed and direction of the motor. By accurately adjusting these parameters, the lock pin is driven to move toward the preset locking position to obtain motion control data. The motion control data is continuously updated throughout the movement process to ensure that the state of the motor and the movement of the lock pin are always under control. During the movement of the lock pin, the motion control data and the real-time position of the lock pin detected by the second position detection switch are closed-loop controlled until the lock pin reaches the preset locking position. The role of closed-loop control is to monitor the position change of the lock pin in real time and adjust the control instructions according to the feedback information to ensure that the lock pin can accurately reach the target position and avoid deviation caused by external interference or system error. Through closed-loop control, the movement of the lock pin is always under precise control to achieve safe and reliable locking operation. When the lock pin reaches the preset locking position, the locking position data is recorded, indicating that the lock pin has been successfully locked. At the same time, the motor operation status and the change of the lock pin position during the entire self-locking process are recorded and stored to obtain the self-locking status data. The motor operation status includes information such as the motor speed, current, and direction during the entire locking process. These data reflect the actual working conditions of the motor when performing the locking task. The lock pin position change data records the movement trajectory and time point of the lock pin from the initial position to the locking position.

[0020] 106. Integrate the self-locking status data, wheel hub motion status information and user operation record data, send them to the APP of the mobile terminal through the communication module for display, and update the vehicle status information in the APP in real time.

[0021] Specifically, the locking position data, the motor running state and the position information of the lock pin are extracted from the self-locking state data, and the information is classified and sorted to obtain the lock body state information. The lock body state information can reflect the key state parameters in the locking process, ensuring that the user can understand the current state of the lock body and the specific situation of the locking. At the same time, the wheel hub state parameters, the Hall switch mark and the effective Hall signal are extracted from the wheel hub motion state information, and the information is classified and processed to obtain the motion monitoring information. The motion monitoring information is used to describe the dynamic motion state of the vehicle wheel hub, helping the user to understand the vehicle's motion at the current moment and the working state of the sensor. At the same time, the user operation record data is processed to extract the user identity, operation type mark and operation timestamp, and the authority verification and operation trajectory are extracted through this information to obtain the user interaction information. The user interaction information can reflect each operation behavior of the user on the vehicle, record the operation time, user identity and operation type, and ensure the safety of the vehicle and the traceability of the user behavior. The lock body state information, motion monitoring information and user interaction information are merged according to the time series to obtain the state integration data. The merging of time series enables data to be presented in a continuous form, providing a complete trajectory of vehicle state changes. The state integration data covers the mechanical state of the vehicle, motion information, and all key data of user operations. These information are interrelated to form a panoramic vehicle state image. The state integration data is sent to the data receiving unit in the mobile terminal APP through the communication module according to a specific data transmission protocol to obtain the terminal receiving data. The use of the data transmission protocol ensures the integrity and security of the data during transmission to prevent data loss or malicious tampering. After the terminal receives the data, the display interface in the mobile terminal APP is updated with the vehicle state information based on the received data to obtain the interface display data. The interface display data includes the real-time status of the vehicle, such as the locking status of the lock body, the running status of the motor, the movement information of the wheel hub, and the user operation record. According to the ambient light intensity information in the interface display data, the brightness of the display screen corresponding to the APP is dynamically adjusted. The brightness adjustment mechanism automatically adjusts the brightness of the screen according to the changes in the ambient light to ensure that users can clearly view the vehicle status information under different light conditions, avoiding invisible information or visual fatigue caused by too strong or too weak light. During the entire process, the system continuously updates the vehicle status information on the mobile terminal APP in real time, so that users can see the latest vehicle status every time they check, ensuring the timeliness and accuracy of the information.

[0022] In the embodiment of the present invention, dual Hall sensors are provided to detect the rotation state of the wheel hub and an automatic switching mechanism is implemented. When one of the Hall sensors fails, the system automatically switches to the normally working Hall sensor, which significantly improves the reliability of the detection of the rotation state of the wheel hub. The main and standby power supply modules are designed. When abnormal conditions such as the communication line being cut are detected, the system can automatically switch to the standby power supply module for power supply and execute the self-locking program to effectively prevent illegal unlocking. The first position detection switch and the second position detection switch are provided to detect the motor position and the lock pin position respectively, so as to realize accurate monitoring of the lock body state and improve the accuracy of locking control. A deep learning model is introduced to intelligently identify the power supply state and communication anomalies, and features are extracted through multi-layer neural networks and attention mechanisms to realize more accurate abnormal state judgment. A complete user authority management mechanism is established to perform multiple verifications on the user identity and record detailed operation logs to effectively prevent illegal operations. Real-time monitoring and dynamic display of the wheel hub lock state information are realized, and the complete system operation status is presented through the mobile terminal APP, which is convenient for timely discovery and handling of abnormal conditions.

[0023] In a specific embodiment, the process of executing step 101 may specifically include the following steps: Conduct electrical connection detection on the main control module and multiple Hall sensors, position detection modules, main power supply module and backup power supply module to obtain module connection status data, and configure the main power supply module and backup power supply module in parallel according to the module connection status data to obtain power supply parameters; The signal acquisition frequencies of the first position detection switch and the second position detection switch are set based on the power supply parameters to obtain the position detection sampling parameters, and the motor stop position signal output by the first position detection switch is collected according to the position detection sampling parameters, and the motor stop position signal is converted into a digital quantity to obtain the digital quantity of the motor position; Inputting the position detection sampling parameter into the second position detection switch, collecting the lock pin switch lock position signal output by the second position detection switch, and performing digital quantity conversion to obtain the lock pin position digital quantity; Perform data verification and remove abnormal data points on the digital quantity of the motor position and the digital quantity of the lock pin position to obtain valid position data, and package the valid position data according to the preset data format to generate a position data stream; The position data stream is fused with the module connection status data, power supply parameters and position detection sampling parameters to obtain the initial status data of the wheel hub lock.

[0024] Specifically, electrical connection detection is performed between the main control module and multiple Hall sensors, position detection modules, main power modules, and backup power modules to confirm whether each module is correctly connected and in normal working condition. By performing electrical detection on these modules, module connection status data is obtained to determine whether there is a fault or abnormality in the electrical connection of each module. Assume that the module connection status data is ,in Contains the status of each module connection. If the module connection is normal, the corresponding status value is 1, if it is abnormal, it is 0. By detecting the connection status of all modules, a comprehensive connection status matrix is ​​formed. ,in Indicates After confirming that each module is connected normally, the main power module and the backup power module are configured in parallel according to the module connection status data to ensure the stability and reliability of the system power supply. The purpose of the parallel configuration of the main power module and the backup power module is to achieve power supply redundancy to prevent the system from stopping working when the main power fails. Assume that the supply voltage of the main power is , the supply voltage of the backup power supply is , then the total voltage after parallel connection is It is expressed as: ; This selection mechanism ensures that the system's power supply voltage is the lowest of the two, thus ensuring stable system operation. Then by current superposition we get: ; in and The currents of the main power supply and the backup power supply are respectively. Based on these voltage and current parameters, the power supply parameters are obtained , which is expressed as: ; Based on the power supply parameters, the signal acquisition frequency of the first position detection switch and the second position detection switch is set. The signal acquisition frequency should be set to ensure that the collected data can meet the real-time requirements of the control and effectively reduce the interference of noise to ensure the accuracy of the data. Assume that the acquisition frequency is According to the power supply parameters set up: ; in is a proportional coefficient related to the system configuration, which determines the responsiveness of the acquisition frequency to the change of the power supply. The output signal of the first position detection switch is acquired according to the sampling frequency parameter to obtain the motor stop position signal. The acquired position signal is an analog signal, which is converted into a digital signal for subsequent processing and calculation. Assume that the analog signal is , then the motor position digital quantity obtained by the analog-to-digital converter (ADC) It is expressed as: ; This digital amount It can accurately reflect the current stop position of the motor and is an important basis for controlling the motor action. After completing the acquisition and digital processing of the motor position signal, the lock pin switch lock position signal of the second position detection switch is collected. Similar to the processing of the motor position signal, the sampling parameters are input to the second position detection switch and its output signal is collected. The lock pin position signal is converted into a digital quantity to obtain the lock pin position digital quantity , which is expressed as: ; in It is the analog position signal of the lock pin. The digital value of the motor position and lock pin position digital These are important data to describe the state of the actuator. In order to ensure the accuracy of system control, the collected digital values ​​of the motor position and the lock pin position are verified and abnormal data points are eliminated. The verification process uses the standard mean and standard deviation method to eliminate abnormal data points. Assume that the collected data set is , then the mean and standard deviation They are: ; ; For any data point , if it satisfies , then the data point is considered to be abnormal data and should be removed. In this way, valid position data is obtained and the abnormal points caused by noise and interference are removed. The valid position data is packaged according to the preset data format to generate a position data stream. Assume that the valid position data is , then the location data stream is represented as an ordered data set, where each data point contains a location, a timestamp, and a state identifier. The expression is: ; in is the acquisition timestamp, is a status identifier used to describe the validity and source of the data point. The position data stream is integrated with the module connection status data, power supply parameters and position detection sampling parameters to obtain the initial state data of the hub lock. Data from different sources are integrated in a unified format to generate a state description containing comprehensive information. Assume that the initial state data is , then it is expressed as: ; The initial status data includes the system's module connection status, power supply status, sampling frequency, and hub lock position status information.

[0025] In a specific embodiment, the process of executing step 102 may specifically include the following steps: Parsing the module connection state data in the initial state data of the wheel hub lock, and extracting the position detection sampling parameters of the first Hall sensor and the second Hall sensor; Performing digital quantity conversion and signal filtering on a first rotation state signal collected by the first Hall sensor according to the position detection sampling parameter to obtain a first Hall signal value, and performing digital quantity conversion and signal filtering on a second rotation state signal collected by the second Hall sensor according to the position detection sampling parameter to obtain a second Hall signal value; Performing a difference calculation on the first Hall signal value and the second Hall signal value to obtain a difference calculation result, and comparing the difference calculation result with a preset difference threshold to obtain Hall signal state data; According to the Hall signal state data, the Hall sensor whose mark difference is less than the preset difference threshold is a normal Hall sensor, and a Hall switch mark is obtained; Based on the Hall switch flag, the signal acquisition channel of the main control module is switched to the normal Hall sensor, the rotation state signal output by the normal Hall sensor is collected, and a valid Hall signal is obtained; The effective Hall signal is analyzed in time series correlation with the motor position digital quantity and the lock pin position digital quantity to obtain the wheel hub status parameters. The wheel hub status parameters, Hall switch flags and effective Hall signals are integrated to obtain the wheel hub motion status information.

[0026] Specifically, the module connection status data is parsed from the initial status data of the wheel hub lock, and the position detection sampling parameters of the first Hall sensor and the second Hall sensor are extracted. Assume that the position detection sampling parameters are , represents the sampling frequency of the Hall sensor, which is used to ensure that the signal acquisition meets the real-time requirements while reducing the impact of noise on data quality. Based on the extracted position detection sampling parameters, the rotation state signal collected by the first Hall sensor is converted into a digital quantity and filtered to obtain the first Hall signal value. Assume that the original analog signal of the first Hall sensor is , the digital value of the first Hall signal is obtained by performing digital conversion through the analog-to-digital converter (ADC) ; The converted digital signal is filtered. Assuming that a low-pass filter is used to eliminate high-frequency noise, the filtered signal is expressed as: ; in, Represents the value of the first Hall signal after filtering, is the time constant of the filter, which controls the smoothness of the filtering effect. Similarly, according to the same position detection sampling parameters, the rotation state signal of the second Hall sensor is converted into a digital quantity and filtered. Assume that the original analog signal of the second Hall sensor is , the digital value of the second Hall signal is obtained through the analog-to-digital converter : ; After the filter processing, the second Hall signal value is obtained : ; Through the above process, the digital signal values ​​of the first and second Hall sensors after filtering are obtained, that is, and The difference between the first and second Hall signal values ​​is calculated to determine the consistency of the two sensors in their working states. The difference calculation formula is expressed as: ; in, is the difference between the first and second Hall signals. By calculating the difference, it is determined whether there is a significant difference between the two sensors, so as to evaluate their working status. The difference calculation result is compared with the preset difference threshold. Compare to get the Hall signal status data. , it is considered that the working states of the two Hall sensors are consistent and the signals are reliable; if , it indicates that the sensor is abnormal and needs to be processed. According to the Hall signal status data, the Hall sensor with a difference value less than the preset threshold is a normal Hall sensor, and the Hall switch mark is obtained. The Hall switch mark is used to indicate which sensor can be used as the current normal signal source for subsequent wheel hub motion status monitoring. Assume that the Hall switch mark is ,when When it is a normal signal, set ,when When it is a normal signal, set Based on the Hall switch flag, the signal acquisition channel of the main control module is switched to the normal Hall sensor to collect the rotation state signal output by the sensor and obtain the effective Hall signal. Assume that the effective Hall signal is , which depends on the Hall switch flag: ; By selecting a valid Hall signal, it is ensured that the accurate wheel hub rotation status signal can still be collected in the event of a sensor failure. The valid Hall signal is analyzed in time sequence with the motor position digital quantity and the lock pin position digital quantity to obtain the wheel hub status parameters. Assume that the motor position digital quantity is , the digital value of the lock pin position is , then the hub state parameter By combining these three sets of data in the time dimension, we get the following form: ; Wheel status parameters Contains the data of the currently valid Hall signal, motor position, and lock pin position. This information reflects the working status of the wheel hub lock at the current moment. The wheel hub state parameters, Hall switch flags, and valid Hall signals are integrated to obtain the wheel hub motion state information. Assume that the wheel hub motion state information is , then it is expressed as: ; The wheel hub motion status information is combined with the wheel hub motion parameters, the identification of the current normal sensor and the effective Hall signal, so that the system can fully grasp the motion dynamics of the wheel hub lock in the subsequent control process.

[0027] In a specific embodiment, the execution step performs a difference calculation on the first Hall signal value and the second Hall signal value to obtain a difference calculation result, and compares the difference calculation result with a preset difference threshold value to obtain the Hall signal state data. The process may specifically include the following steps: The first Hall signal value is divided into sampling windows, each window contains 100 sampling points, to obtain a first signal sampling sequence, and the second Hall signal value is segmented according to the sampling window size to obtain a second signal sampling sequence; Subtract the sampling point value at the corresponding position of the second signal sampling sequence from each sampling point in the first signal sampling sequence to obtain a difference point sequence, and calculate the root mean square value based on all sampling points of the difference point sequence as the signal difference measurement value to obtain the difference statistical parameter; The standard deviation is calculated based on the difference statistical parameters, and compared with the pre-set difference standard threshold to obtain the Hall state judgment result; The Hall state judgment results are cumulatively counted in a time window. When the judgment results of five consecutive windows exceed the threshold, an abnormal confirmation flag is obtained. The abnormal confirmation flag is combined and encoded with the difference statistical parameters to generate Hall signal state data.

[0028] Specifically, the signal data obtained from the first Hall sensor is divided into sampling windows. Assume that the value of the first Hall signal is , perform discrete sampling and divide the sampling windows into 100 sampling points each. Suppose the signal sequence after sampling is ,in is the total number of sampling points collected. The sequence is divided into a sampling window according to every 100 sampling points to obtain the first signal sampling sequence ,in Indicates A sampling window, Each window contains 100 sampling points. At the same time, the second Hall signal value The second Hall signal is segmented according to the same sampling window size as the first signal to ensure the alignment and synchronization of the two signals. The second signal sampling sequence is obtained , each window also contains 100 sampling points, ,in Indicates that the second Hall signal is The values ​​of sampling points are subtracted from each sampling point in the first signal sampling sequence and the sampling point value at the corresponding position in the second signal sampling sequence to obtain a difference point sequence. Let the difference point sequence be ,in , each difference point Calculated by the following formula: ; Difference point sequence The signal difference between the two Hall sensors in the same time window is the key data for analyzing the consistency of their working status. Based on all sampling points in the difference point sequence, the root mean square value (RMS) is calculated as a measure of the signal difference. The formula for calculating the root mean square value is: ; in, Indicates The signal difference measurement value of a sampling window can effectively quantify the overall difference between the two Hall signals in this time period. If the outputs of the two Hall sensors are consistent, will be very small; on the contrary, if there is a large deviation between the two signals, The value of will increase accordingly. Calculate the standard deviation based on the difference statistical parameters to evaluate the fluctuation of the signal. The standard deviation of the difference point sequence within a sampling window is , and its calculation formula is: ; in, For the The mean of the difference point sequence in the window is expressed as: ; By calculating the standard deviation , analyze the volatility of the difference data and determine whether the difference between the signals is occasional noise or a systematic problem. The difference with the pre-set standard threshold Compare to get the Hall status judgment result. , it indicates that the difference between the two Hall sensors exceeds the normal range, there is a sensor failure or signal abnormality; otherwise, it is considered that the outputs of the two sensors are consistent and the system state is normal. Suppose the Hall state judgment result is , which is expressed as: ; in, Indicates an abnormality. Indicates normal. The Hall status judgment result is counted cumulatively in the time window to determine whether there is a persistent abnormality. When the judgment results of 5 consecutive sampling windows are all abnormal, that is, If the error continues for 5 times, the system generates an abnormal confirmation flag, indicating that the difference between the Hall sensors has reached a level that requires emergency treatment. , which is expressed as: ; in, Indicates that there is an abnormal confirmation and further processing is required; Indicates that no continuous abnormality is detected. The abnormality confirmation flag is combined with the difference statistical parameter to generate the final Hall signal state data. Assume that the Hall signal state data is , which is marked by an abnormal confirmation and statistical parameters of differences (such as RMS value and standard deviation ) together, expressed as: ; The working status of the Hall sensor is recorded through a combined encoding method.

[0029] In a specific embodiment, the process of executing step 103 may specifically include the following steps: Input the user identity information in the APP of the mobile terminal into the authority management module for user information preprocessing to obtain user preprocessing data, and perform authorized user database comparison and authority verification analysis on the user preprocessing data to obtain user matching results; The user matching result is input into the identity identification generation unit of the authority management module, identity authentication data is generated according to the preset coding rules, and the type of the operation is identified and classified according to the identity authentication data to generate operation identification data; Add the current system time information to the operation identification data to generate an operation timestamp, and combine it with the identity verification data to obtain the authorization basic data; Generate an unlocking control instruction for the wheel hub lock according to the authorization basic data, and record the user identity, operation time, and operation type of this operation based on the unlocking control instruction to obtain operation log data; The operation log data, identity authentication data and unlocking control instructions are associated and integrated to obtain user operation record data.

[0030] Specifically, the identity information entered by the user in the mobile terminal APP is passed to the authority management module for preprocessing of the user information. Assume that the user identity information is , which contains the user's unique identity (such as user name or user ID) and other verification information (such as password or fingerprint data). Preprocess the user's identity information, including formatting user input, removing spaces, converting to standard encoding, etc., to ensure that the input data is consistent with the data format in the database, and obtain user preprocessed data The preprocessing process aims to improve the comparability of user data and reduce matching failures caused by irregular input. The user preprocessed data is compared with the information in the authorized user database, which is represented by , which contains the identity information of registered users in the system and the corresponding permission information. The data in the database is compared to perform permission verification analysis to determine whether the user has the operation authority and ensure that the input identity information is legal and valid. The comparison process is represented by the following formula: ; in, Indicates the user matching result. 1 indicates a successful match and the user has legal permissions. 0 indicates a failed match or the user has no operation permissions. The identity generation unit of the input permission management module generates unique identity authentication data based on the user matching result. Let the identity authentication data be The generation process is based on the preset encoding rules, encoding the user's unique identity and matching results. For example, if the user's identity is and the match is successful, the authentication data is generated in the following way: ; Among them, hash It is a hash function that combines the user identity and timestamp to generate a unique verification data to ensure the uniqueness and immutability of the identity verification. Based on the identity verification data, the type of this operation is identified and classified to generate operation identification data. Operation identification data Used to describe the type of operation the user wants to perform, such as unlocking, locking, querying status, etc. Based on the information in the identity authentication data and the user's operation request, the corresponding operation identification data is determined and generated: ; in, Represents a function that classifies operations based on authentication data and user requests. After the operation identification data is generated, the operation identification data is added to the current system time information to generate an operation timestamp. Assume that the current system time is , then the operation timestamp is expressed as , which is generated by combining the operation identification data and time information: ; The operation timestamp records the specific type and time of the user's operation. Combine to get authorization basic data : ; The authorization basic data includes user identity authentication information, operation type and time information, forming a complete authorization information set to ensure the legitimacy and traceability of user operations. The unlocking control instruction for the wheel hub lock is generated based on the authorization basic data. Suppose the unlocking control instruction is The generation process is based on the identity information and operation identifier in the authorization basic data, ensuring that only verified users can generate valid unlocking instructions: ; in, Represents the control instruction generation function, which performs permission verification based on the authorization basic data and generates the corresponding operation instruction. The unlock control instruction is then passed to the execution module of the wheel hub lock to control the unlocking process of the lock. At the same time, based on the unlock control instruction, the user identity, operation time and operation type of this operation are recorded to form operation log data : ; Operation log data is used to record detailed information about each operation of the user, ensuring that the system can track the source and specific details of each operation when necessary. , Authentication Data And unlock control instructions Perform association integration to generate user operation record data : ; User operation record data includes the user's operation log, identity authentication information, unlocking control instructions and other important information related to this operation.

[0031] In a specific embodiment, the process of executing step 104 may specifically include the following steps: Perform real-time analysis and state extraction on the wheel hub state parameters and effective Hall signals in the wheel hub motion state information to obtain real-time operation state data, and perform signal quality detection on the communication link between the communication module and the APP of the mobile terminal according to the real-time operation state data to obtain communication link state data; Compare the signal strength value in the communication link status data with the preset signal strength threshold to determine whether the communication line is cut off, obtain the communication anomaly detection result, and detect the real-time power supply status of the main power supply module, including the collection and analysis of voltage, current and communication power supply parameters, to obtain power supply status data; The power supply status data is correlated with the communication anomaly detection result and analyzed. When a communication anomaly is detected, a power supply switching instruction is triggered to obtain a power supply switching instruction. Based on the power supply switching instruction, the switching action of the main power supply module and the backup power supply module is controlled to obtain the power supply switching status data. The power switching result is confirmed on the power switching status data, and the system working status at the switching time is recorded to obtain the switching confirmation data, which is then integrated with the communication anomaly detection result to generate an emergency control trigger signal.

[0032] Specifically, the hub state parameters are analyzed in real time. Assuming that the hub state parameters are , including motor speed, lock pin position and Hall sensor feedback information, and the effective Hall signal is , which indicates the actual rotation state of the current wheel hub movement. By analyzing these data, the real-time operation state data of the wheel hub lock is obtained, which is recorded as . Based on real-time operating status data , the signal quality of the communication link between the communication module and the mobile terminal APP is detected to ensure the effectiveness of remote monitoring and operation. Assume that the signal strength of the communication link is , by extracting and analyzing the data of the communication module in real-time operation, the signal quality of the communication link is obtained. Communication link status data It includes parameters such as signal strength, signal quality (such as signal-to-noise ratio), and link delay. For the signal strength value, use Indicates the stability of the communication link and whether there is a potential risk of interruption. To determine whether there is an abnormality in the communication line, the signal strength value With the preset signal strength threshold If at a certain point in time , signal strength If the signal strength is less than the threshold, there is a communication anomaly or the communication line is cut. Communication anomaly detection result Obtained by the following formula: ; in, Indicates that a communication anomaly is detected. Indicates that the communication is normal. At the same time, the real-time power supply status of the main power module is monitored and the voltage is collected. , Current And communication power supply parameters These parameters are used to describe the operation of the power supply in different working states. Power supply status data It is expressed as: ; Voltage Indicates the current power supply potential, current Indicates the current intensity passing through the circuit, power supply parameters It is used to describe whether the communication module has sufficient power supply to support normal communication. When any parameter of the power supply status is abnormal, it will affect the stability of the entire wheel hub lock system. and power supply status data Perform correlation analysis to fully understand the current communication and power supply status and determine whether there are potential risks in the system. ), trigger the power supply switching command to ensure that the backup power supply can be normally connected when the communication link fails, ensuring the continuous operation of the system. The generation of is expressed by the following formula: ; in, Indicates that a power switching operation is required. Indicates that no switching is required. When it is 1, the switch between the main power module and the backup power module is controlled to operate to ensure that the system switches from the main power supply to the backup power supply. Power supply switching status data The power switching process is designed to ensure that the system still has a stable power source in the event of abnormal communication, so as to avoid system downtime due to power failure. Confirm the switching result to ensure the success of the switching operation and record the system working status during the switching. Suppose the switching confirmation data is , which contains the power supply status at the time of switching and other key operating parameters of the system. By confirming the data, it is verified whether the power supply switching is successful and whether there are any abnormal conditions during the switching process. The switching confirmation data is expressed as: ; in, Indicates the power status when switching. Indicates the operating status of the system at the time of switching. Communication anomaly detection results Integrate and generate emergency control trigger signals The emergency control trigger signal is used to mark whether the current system is in an emergency state and provide necessary control instructions to deal with emergency situations. The emergency control trigger signal is expressed as: ; The trigger signal contains the communication anomaly detection results and switching confirmation data, and is the core basis for the system to make decisions in emergency situations.

[0033] In a specific embodiment, the execution step associates the power supply status data with the communication anomaly detection result for analysis, triggers the power supply switching instruction when the communication anomaly is detected, obtains the power supply switching instruction, and controls the switching action of the main power supply module and the backup power supply module based on the power supply switching instruction. The process of obtaining the power supply switching status data may specifically include the following steps: The power supply status data and communication anomaly detection results are input into the input layer of the anomaly detection deep neural network, where the input layer contains 64 neurons and uses the ReLU activation function for feature extraction to obtain initial feature data; The initial feature data is processed by the first convolutional layer, which contains 32 3×3 convolution kernels with a step size of 1. BatchNormalization is used to normalize the features to obtain a first-level feature map. The first-level feature map is input into the attention mechanism layer. The attention mechanism layer adopts a self-attention structure and extracts the key features of the abnormal state by calculating the correlation weight matrix between features to obtain the attention feature data. The attention feature data is processed through the second convolutional layer, which contains 64 3×3 convolution kernels with a step size of 2 and uses the LeakyReLU activation function to obtain the secondary feature map; The secondary feature map is input into the fully connected layer, which contains 128 neurons. The random dropout with a Dropout rate of 0.5 is used to prevent overfitting and obtain a deep feature vector. The deep feature vector is subjected to time series analysis through a bidirectional LSTM layer, which contains 64 hidden units. The time series correlation features of power supply status and communication anomalies are extracted to obtain a time series correlation vector. The time series correlation vector is input into the decision layer. The decision layer contains two fully connected layers, which use ReLU and Sigmoid activation functions respectively, and output the power switching probability threshold. When the power switching probability threshold exceeds 0.85, the power switching instruction is triggered. Based on the power switching instruction, a switching control sequence is generated through the output layer to control the switching switches of the main power module and the backup power module to perform the switching action, and the voltage and current parameters during the switching process are collected to obtain the power switching status data.

[0034] Specifically, the power supply status data and the communication anomaly detection results are input into the input layer of the anomaly detection deep neural network. Assume that the power supply status data is ,in is the voltage, is the current, is the power supply for communication; the communication anomaly detection result is , which is a binary variable, indicating whether the communication is normal. These data are input into the input layer, which contains 64 neurons. Each neuron processes the input features and realizes nonlinear feature extraction through the ReLU activation function. The ReLU activation function is defined as: ; The ReLU activation function has good nonlinear and sparse characteristics, which can effectively alleviate the gradient vanishing problem and obtain the initial feature data. , including the deep features of power supply status and communication anomalies. The initial feature data is processed by the first convolutional layer. The first convolutional layer contains 32 layers of size The convolution kernel of is 1, and the step size is 1, which is designed to extract local features from the input features. The convolution layer operation captures the spatial information of the features by sliding the convolution kernel. Suppose the initial feature data is , then the first-level feature map after convolution It is obtained through the following convolution formula: ; in, is the weight of the convolution kernel, is the bias term, and * represents the convolution operation. In order to make the features have a more stable distribution, BatchNormalization is used to normalize the data in the first-level feature map, thereby accelerating the training of the network and improving the stability of the model. The first-level feature map is input into the attention mechanism layer. The attention mechanism layer adopts a self-attention structure and extracts the key features of the abnormal state by calculating the correlation weight matrix between features. Suppose the first-level feature map is , whose dimension is n×d, where is the number of feature points, is the feature dimension. The attention mechanism calculates the similarity between features and obtains the correlation weight matrix through dot product , defined as: ; in, and are the query vector and key vector of the feature, respectively, Obtained through linear transformation. The weight matrix Used to measure the relationship between features, so as to extract more representative attention feature data The attention feature data is processed by the second convolutional layer, which contains 64 convolutional layers of size The convolution kernel with a step size of 2 is used to extract high-level features and reduce feature dimensions. LeakyReLU is used as the activation function, which is defined as: ; in is a small positive number, usually 0.01. LeakyReLU allows a small part of negative values ​​to pass through, thereby alleviating the "neuron death" problem encountered in the ReLU activation function and obtaining a secondary feature map The secondary feature map is input into the fully connected layer, which contains 128 neurons, and maps the features of the convolutional layer to a higher-dimensional feature space. In order to prevent the model from overfitting, the Dropout technology is used, and the Dropout rate is set to 0.5, that is, 50% of the neurons are randomly discarded during each training, so that the model will not be too dependent on a certain part of the features during the training process, and the deep feature vector is obtained. , which is expressed as: ; in and are the weights and biases of the fully connected layer, respectively. The deep feature vector is input into the bidirectional LSTM layer for time series analysis. The bidirectional LSTM layer contains 64 hidden units, which are used to extract the time series correlation features between the power supply status and the communication anomaly. LSTM (Long Short-Term Memory) network is good at processing sequence data and can capture temporal dependencies. The bidirectional LSTM contains two LSTM networks, forward and backward, which capture features from the forward and reverse directions respectively. Let the forward hidden state be , the backward hidden state is , then the time series correlation vector for: ; Through the processing of the bidirectional LSTM layer, the correlation between the current moment and the historical moment is extracted, so as to better understand the relationship between the power supply status and communication anomalies and obtain the time series correlation vector The time series correlation vector is input to the decision layer, which contains two fully connected layers, using ReLU and Sigmoid activation functions respectively. The first fully connected layer extracts high-level features through the ReLU activation function, and the second fully connected layer uses the Sigmoid activation function to output the power switching probability threshold. : ; in, and are weights and bias terms respectively. The Sigmoid activation function limits the output to [0,1], which is used to represent the probability of triggering power switching. , the system will trigger the power switching instruction. After the power switching instruction is generated, the switching control sequence is generated through the output layer to control the switching action between the main power module and the backup power module to ensure that the system can continue to operate when the power supply is abnormal. Suppose the switching control sequence is , which is used to control the specific operation of the power switch and collect voltage and current parameters in real time during the switching process. Assume that the voltage during the switching process is , the current is , then the power switching status data It is expressed as: ; The power switching status data contains all key power parameters during the switching process, as well as specific instructions for switching control, which are used to record and verify the success or failure of the power switching.

[0035] In a specific embodiment, the process of executing step 105 may specifically include the following steps: Based on the emergency control trigger signal, the communication abnormality mark and the power switching state are extracted to obtain the emergency processing parameters, and the motor position signal output by the first position detection switch is sampled according to the position detection sampling parameters to obtain the motor sampling data; Generate motor drive control instructions according to emergency processing parameters and motor sampling data, control the actuator to start the motor, obtain motor start parameters, and input the motor start parameters into the motion control unit of the main control module, calculate the displacement of the lock pin from the current position to the preset locking position, and obtain the lock pin displacement data; The lock pin position feedback signal output by the second position detection switch is collected in real time and compared with the lock pin displacement data to obtain the lock pin motion parameters, and the motor speed and direction of the actuator are controlled based on the lock pin motion parameters to drive the lock pin to move to the preset locking position to obtain motion control data; The motion control data and the real-time position of the lock pin detected by the second position detection switch are closed-loop controlled until the lock pin reaches the preset locking position to obtain the locking position data. The locking position data and the motor operation status and lock pin position changes of the entire self-locking process are recorded and stored to obtain the self-locking status data.

[0036] Specifically, the communication abnormality flag and the power switching state are extracted based on the emergency control trigger signal. Assume that the emergency control trigger signal is , the signal contains a communication abnormality flag and power switching status By extracting and , get detailed information about the current emergency situation. The role of the emergency processing parameters is to determine the specific steps of the emergency operation according to the current system state to ensure the safety and stability of the system in an emergency. Sample the motor position signal output by the first position detection switch according to the position detection sampling parameters. Suppose the position detection sampling parameters are , represents the frequency of signal acquisition, the motor position signal output by the first position detection switch is , the sampled motor position signal is recorded as , which is obtained by the following formula: ; in, According to the sampling frequency The time point for acquisition. By sampling the motor position signal, the current accurate position data of the motor is obtained. The motor drive control instruction is generated according to the emergency processing parameters and the motor sampling data to control the actuator to start the motor. Suppose the motor drive control instruction is , which contains information such as the motor's starting voltage and current. By analyzing the emergency processing parameters and motor sampling data, the motor's starting conditions are determined, thereby generating control instructions. The control actuator starts the motor and obtains the motor's starting parameters , which includes the motor’s starting time, voltage, and initial speed. Assume the motor’s starting voltage is , the start time is The initial speed is , then the motor starting parameters are expressed as: ; These parameters ensure that the motor starts as expected and provides enough drive force to move the locking pin. Input to the motion control unit of the main control module. The motion control unit needs to calculate the displacement of the lock pin from the current position to the preset locking position in order to control the motor action later. Suppose the current position of the lock pin is , the preset locking position is , then the displacement of the locking pin Calculated by the following formula: ; Displacement Determine the distance the motor needs to drive the lock pin to move. By calculating the displacement, the movement of the lock pin is accurately controlled to ensure that it eventually reaches the predetermined locking position. After the displacement of the lock pin is determined, the lock pin position feedback signal output by the second position detection switch is collected in real time to monitor the actual movement of the lock pin. Suppose the lock pin position feedback signal is , the lock pin position data obtained after sampling is recorded as , which is expressed by the following formula: ; Compare the lock pin position feedback signal with the lock pin displacement data to obtain the lock pin motion parameters , which is used to describe whether the actual movement of the lock pin is in line with the expected one. If there is a deviation between the feedback signal and the preset displacement, the movement of the lock pin is corrected by adjusting the motor speed and direction. Motor speed and steering according to the locking pin motion parameters Dynamic adjustment is performed to ensure that the locking pin moves along the predetermined trajectory. Assume the target speed is , then the motor speed control formula is: ; in, is the proportional gain, is the current position of the lock pin, by adjusting The value controls the response speed of the system to ensure that the locking pin can smoothly reach the preset position. The real-time position of the lock pin detected by the second position detection switch Closed-loop control is performed until the lock pin reaches the preset locking position. Closed-loop control adjusts the control instructions through real-time feedback to ensure that the system can accurately execute each step of the operation to avoid the lock pin deviating from the preset position due to external disturbances or error accumulation. Suppose the closed-loop control output is , which is adjusted continuously according to the difference between the current position and the target position until x(t)= Once the lock pin reaches the preset position, record the lock position data at that moment , marking the locking success. After the lock pin reaches the preset locking position, the motor running status, lock pin position change and other information during the entire self-locking process are recorded and stored to obtain complete self-locking status data The self-locking state data includes the voltage, current, and speed of the motor during the entire movement process, as well as the displacement of the lock pin during the movement process and the final locking position. These data are used to verify the performance of the system and perform fault diagnosis. Assume that the motor running state is , the lock pin position changes to , then the self-locking state data is expressed as: ; The recording and storage of this data is of great significance to the safety and reliability of the system, especially when a failure occurs, the root cause of the problem can be found and solved by analyzing this historical data.

[0037] In a specific embodiment, the process of executing step 106 may specifically include the following steps: The locking position data, motor running status and lock pin position information in the self-locking status data are extracted and classified to obtain the lock body status information, and the hub status parameters, Hall switching flags and effective Hall signals in the hub motion status information are classified to obtain the motion monitoring information; According to the user identity, operation type and operation timestamp in the user operation record data, the authority verification and operation trajectory extraction are performed to obtain the user interaction information, and the lock body status information, motion monitoring information and user interaction information are merged according to the time series to obtain the status integration data; The state integration data is sent to the data receiving unit in the APP of the mobile terminal through the communication module according to the data transmission protocol to obtain the terminal receiving data; Based on the data received by the terminal, the vehicle status information is updated on the display interface of the APP of the mobile terminal to obtain the interface display data, and the brightness of the display screen corresponding to the APP is dynamically adjusted according to the ambient light intensity information in the interface display data, and the real-time updated vehicle status information is presented on the APP of the mobile terminal.

[0038] Specifically, the locking position data, motor operation status and lock pin position information in the self-locking state data are extracted and classified. , which contains the locking position data , Motor running status and the lock pin position These data reflect the locking position of the lock body, the operating parameters of the motor (such as voltage, current and speed), and the real-time position of the lock pin during the entire self-locking process. By extracting and classifying these data, the lock body status information is obtained. It is expressed as: ; This information set fully describes the current state of the lock body and provides a basis for subsequent monitoring and status updates. At the same time, the wheel hub motion state information is processed. Assume that the wheel hub motion state information is , which contains the hub status parameters , Hall switch mark And the effective Hall signal . Wheel status parameters Describes the current motion status of the wheel hub, such as wheel speed, acceleration, etc.; Hall switch mark Used to indicate which Hall sensor is currently selected by the system; effective Hall signal It reflects the actual output state of the sensor. By classifying and processing this information, we can obtain motion monitoring information. , which is expressed as: ; This information set comprehensively monitors the current motion state of the wheel hub, which helps the system to accurately track the behavior of the wheel hub. After sorting out the lock body state information and motion monitoring information, the user operation record data is processed. Suppose the user operation record data is , which contains the user identity , Operation type identifier , and the operation timestamp . User ID Used to uniquely identify the user of the current operation and the operation type identifier Indicates the user's operation type (such as unlocking, locking, etc.), and the operation timestamp The specific time of the operation is recorded. By performing permission verification and operation track extraction on these data, it is determined whether the user has the permission to perform the operation and generates user interaction information. : ; The generation of user interaction information ensures that the system accurately records and verifies permissions for each operation, thereby ensuring the security and traceability of the system. The lock state information, motion monitoring information, and user interaction information are merged according to the time series to obtain state integration data. Suppose the state integration data is , which is expressed as: ; By integrating data from different sources, a complete status report is generated, which describes the current status of the system, including the mechanical status of the lock body, the motion status of the wheel hub, and the user operation. Through the communication module, the status integration data is transmitted according to the preset data transmission protocol. The data receiving unit in the APP sent to the mobile terminal. The communication module reliably transmits the real-time data in the system to the user end. Suppose the data transmission protocol is , which contains information such as data encoding format, error detection mechanism and transmission speed. By using a suitable data transmission protocol, it is ensured that data is not lost or tampered with during transmission, so that the terminal receives the data in the mobile terminal. : ; This received data contains all the key status information in the system and is the basis for updating the APP display interface. Based on the terminal receiving data, the display interface in the mobile terminal APP updates the vehicle status information and obtains the interface display data . Contains the current vehicle status, such as locking status, motor running status, wheel hub movement status, and user operation records. This information is presented on the APP in a user-friendly way, allowing users to understand the current status of the vehicle. In addition, according to the ambient light intensity information in the interface display data Dynamically adjust the brightness of the APP display. Assume that the display brightness is , and its adjustment formula is: ; in The brightness adjustment coefficient is used to adjust the display brightness according to the ambient light intensity to ensure that users can clearly view the vehicle status information in the APP under different lighting conditions, providing a better user experience. Through the above operations, comprehensive monitoring and management of the lock body status, motion status and user operations are achieved, and this information is synchronized to the mobile terminal APP in real time through data transmission, so that users can grasp the status of the vehicle in real time.

[0039] The above describes the control method of the wheel hub lock for preventing abnormal locking in the embodiment of the present invention. The following describes the control device of the wheel hub lock for preventing abnormal locking in the embodiment of the present invention. Figure 2 , an embodiment of the control device of the wheel hub lock for preventing abnormal locking in the embodiment of the present invention comprises: The acquisition module 201 is used to start and initialize the main control module, multiple Hall sensors, position detection modules, main power supply module and backup power supply module, collect the motor stop position signal of the first position detection switch and the lock position signal of the lock pin switch of the second position detection switch, and obtain the initial state data of the wheel hub lock; The switching module 202 is used to compare the difference between the first rotation state signal of the first Hall sensor and the second rotation state signal of the second Hall sensor according to the initial state data of the wheel hub lock, and switch to the normal Hall sensor when the difference exceeds a preset threshold to obtain the wheel hub motion state information; Matching module 203, used to input user identity information into the authority management module for authentication matching, generate an unlock authorization instruction including user identity identification, operation type identification and timestamp, and obtain user operation record data; The monitoring module 204 is used to monitor the communication link status and the power supply status of the main power module based on the wheel hub motion status information, and switch to the backup power module when a communication abnormality is detected to obtain an emergency control trigger signal; The control module 205 is used to control the actuator to drive the lock pin to move to a preset locking position detected by the second position detection switch according to the emergency control trigger signal and the motor position signal of the first position detection switch, and obtain self-locking state data; The updating module 206 is used to integrate the self-locking state data, the wheel hub movement state information and the user operation record data, send them to the APP of the mobile terminal through the communication module for display, and update the vehicle state information in the APP in real time.

[0040] Through the coordinated cooperation of the above-mentioned components, dual Hall sensors are set to detect the rotation status of the wheel hub and an automatic switching mechanism is implemented. When one of the Hall sensors fails, the system automatically switches to the normally working Hall sensor, which significantly improves the reliability of the wheel hub rotation status detection; the main and standby power supply module design is adopted. When abnormal conditions such as the communication line being cut are detected, it can automatically switch to the standby power supply module for power supply and execute the self-locking program to effectively prevent illegal unlocking; the first position detection switch and the second position detection switch are set to detect the motor position and the lock pin position respectively, so as to realize accurate monitoring of the lock body state and improve the accuracy of locking control; the deep learning model is introduced to intelligently identify the power supply status and communication anomalies, and the features are extracted through multi-layer neural networks and attention mechanisms to realize more accurate abnormal state judgment; a complete user authority management mechanism is established to perform multiple verifications on the user identity and record detailed operation logs to effectively prevent illegal operations; the real-time monitoring and dynamic display of the wheel hub lock status information are realized, and the complete system operation status is presented through the mobile terminal APP, which is convenient for timely discovery and handling of abnormal situations.

[0041] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0042] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0043] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A control method for a wheel hub lock to prevent abnormal locking, characterized in that: The method comprises: The main control module, multiple Hall sensors, position detection modules, main power supply module and backup power supply module are started and initialized, and the motor stop position signal of the first position detection switch and the lock position signal of the lock pin switch of the second position detection switch are collected to obtain the initial state data of the wheel hub lock; According to the initial state data of the wheel hub lock, a first rotation state signal of the first Hall sensor is compared with a second rotation state signal of the second Hall sensor, and when the difference exceeds a preset threshold, the normal Hall sensor is switched to obtain the wheel hub motion state information; Input the user identity information into the authority management module for authentication matching, generate an unlock authorization instruction containing the user identity identifier, operation type identifier and timestamp, and obtain the user operation record data; Based on the wheel hub motion state information, the communication link state and the power supply state of the main power module are monitored, and when a communication abnormality is detected, the backup power module is switched to obtain an emergency control trigger signal; According to the emergency control trigger signal and the motor position signal of the first position detection switch, the actuator is controlled to drive the lock pin to move to the preset locking position detected by the second position detection switch to obtain self-locking state data; The self-locking state data, the wheel hub motion state information and the user operation record data are integrated, sent to the APP of the mobile terminal through the communication module for display, and the vehicle state information in the APP is updated in real time.

2. The control method of the hub lock for preventing abnormal locking according to claim 1, characterized in that: The main control module, multiple Hall sensors, position detection modules, main power supply module and backup power supply module are started and initialized, a motor stop position signal of the first position detection switch and a lock pin switch lock position signal of the second position detection switch are collected, and the initial state data of the wheel hub lock is obtained, including: Perform electrical connection detection on the main control module and multiple Hall sensors, the position detection module, the main power module and the backup power module to obtain module connection status data, and configure the main power module and the backup power module in parallel according to the module connection status data to obtain power supply parameters; The signal acquisition frequencies of the first position detection switch and the second position detection switch are set based on the power supply parameters to obtain position detection sampling parameters, and the motor stop position signal output by the first position detection switch is collected according to the position detection sampling parameters, and the motor stop position signal is converted into a digital quantity to obtain a digital quantity of the motor position; Inputting the position detection sampling parameter into the second position detection switch, collecting the lock pin switch lock position signal output by the second position detection switch, and performing digital quantity conversion to obtain a lock pin position digital quantity; Performing data verification and eliminating abnormal data points on the motor position digital quantity and the lock pin position digital quantity to obtain valid position data, and packaging the valid position data according to a preset data format to generate a position data stream; The position data stream is fused with the module connection status data, the power supply parameters and the position detection sampling parameters to obtain the initial status data of the wheel hub lock.

3. The control method of the hub lock for preventing abnormal locking according to claim 2, characterized in that: The first rotation state signal of the first Hall sensor is compared with the second rotation state signal of the second Hall sensor according to the initial state data of the wheel hub lock, and when the difference exceeds a preset threshold, the normal Hall sensor is switched to obtain the wheel hub motion state information, including: Parsing the module connection state data in the wheel hub lock initial state data, and extracting the position detection sampling parameters of the first Hall sensor and the second Hall sensor; Performing digital quantity conversion and signal filtering on the first rotation state signal collected by the first Hall sensor according to the position detection sampling parameter to obtain a first Hall signal value, and performing digital quantity conversion and signal filtering on the second rotation state signal collected by the second Hall sensor according to the position detection sampling parameter to obtain a second Hall signal value; Performing a difference calculation on the first Hall signal value and the second Hall signal value to obtain a difference calculation result, and comparing the difference calculation result with a preset difference threshold to obtain Hall signal state data; According to the Hall signal state data, the Hall sensor whose mark difference is less than the preset difference threshold is a normal Hall sensor, and a Hall switch mark is obtained; Switching the signal acquisition channel of the main control module to the normal Hall sensor based on the Hall switch identifier, acquiring the rotation state signal output by the normal Hall sensor, and obtaining a valid Hall signal; The effective Hall signal is analyzed in time series correlation with the motor position digital quantity and the lock pin position digital quantity to obtain the wheel hub state parameter, and the wheel hub state parameter, the Hall switch mark and the effective Hall signal are integrated to obtain the wheel hub motion state information.

4. The control method of the hub lock for preventing abnormal locking according to claim 3, characterized in that: The performing difference calculation on the first Hall signal value and the second Hall signal value to obtain a difference calculation result, and comparing the difference calculation result with a preset difference threshold to obtain Hall signal state data, includes: Divide the first Hall signal value into sampling windows, each window containing 100 sampling points, to obtain a first signal sampling sequence, and segment the second Hall signal value according to the sampling window size to obtain a second signal sampling sequence; Subtracting the sampling point value at the corresponding position of the second signal sampling sequence from each sampling point in the first signal sampling sequence to obtain a difference point sequence, and calculating the root mean square value based on all sampling points of the difference point sequence as a signal difference measurement value to obtain a difference statistical parameter; Calculate the standard deviation according to the difference statistical parameter, compare it with the preset difference standard threshold value, and obtain the Hall state judgment result; The Hall state judgment result is cumulatively counted in a time window. When the judgment results of five consecutive windows exceed the threshold, an abnormal confirmation mark is obtained, and the abnormal confirmation mark is combined and encoded with the difference statistical parameter to generate Hall signal state data.

5. The control method of the wheel hub lock for preventing abnormal locking according to claim 4, characterized in that: The user identity information is input into the authority management module for authentication matching, an unlocking authorization instruction including a user identity identifier, an operation type identifier and a timestamp is generated, and user operation record data is obtained, including: Input the user identity information in the APP of the mobile terminal into the authority management module to pre-process the user information to obtain user pre-processed data, and perform authorized user database comparison and authority verification analysis on the user pre-processed data to obtain a user matching result; Input the user matching result into the identity identification generation unit of the authority management module, generate identity authentication data according to a preset coding rule, and identify and classify the type of this operation according to the identity authentication data to generate operation identification data; Adding the current time information of the system to the operation identification data generates an operation timestamp, and combining it with the identity verification data to obtain authorization basic data; Generate an unlocking control instruction for the wheel hub lock according to the authorization basic data, and record the user identity, operation time, and operation type of this operation based on the unlocking control instruction to obtain operation log data; The operation log data, the identity verification data and the unlocking control instruction are associated and integrated to obtain user operation record data.

6. The control method of the hub lock for preventing abnormal locking according to claim 5, characterized in that: The monitoring of the communication link status and the power supply status of the main power module based on the wheel hub motion status information, switching to the backup power module when a communication abnormality is detected, and obtaining an emergency control trigger signal includes: Performing real-time analysis and state extraction on the hub state parameters and effective Hall signals in the hub motion state information to obtain real-time operation state data, and performing signal quality detection on the communication link between the communication module and the APP of the mobile terminal according to the real-time operation state data to obtain communication link state data; Compare the signal strength value in the communication link status data with a preset signal strength threshold to determine whether the communication line is cut, obtain a communication anomaly detection result, and detect the real-time power supply status of the main power supply module, including the collection and analysis of voltage, current and communication power supply parameters, to obtain power supply status data; Correlate and analyze the power supply status data with the communication anomaly detection result, trigger a power supply switching instruction when a communication anomaly is detected, obtain a power supply switching instruction, and control the switching action of the main power supply module and the backup power supply module based on the power supply switching instruction to obtain power supply switching status data; The power switching result is confirmed on the power switching state data, and the system working state at the switching time is recorded to obtain switching confirmation data, and the switching confirmation data is integrated with the communication anomaly detection result to generate an emergency control trigger signal.

7. The control method of the wheel hub lock for preventing abnormal locking according to claim 6, characterized in that: The power supply status data is correlated with the communication anomaly detection result and analyzed, a power supply switching instruction is triggered when a communication anomaly is detected, a power supply switching instruction is obtained, and a switching switch action of the main power supply module and the backup power supply module is controlled based on the power supply switching instruction to obtain the power supply switching status data, including: Inputting the power supply status data and the communication anomaly detection result into an input layer of an anomaly detection deep neural network, wherein the input layer comprises 64 neurons, and a ReLU activation function is used for feature extraction to obtain initial feature data; The initial feature data is processed by a first convolutional layer, where the first convolutional layer includes 32 3×3 convolution kernels with a step size of 1, and BatchNormalization is used to perform feature normalization to obtain a primary feature map; The first-level feature map is input into the attention mechanism layer, and the attention mechanism layer adopts a self-attention structure, and extracts the key features of the abnormal state by calculating the correlation weight matrix between the features to obtain the attention feature data; The attention feature data is processed through a second convolutional layer, where the second convolutional layer includes 64 3×3 convolution kernels with a step size of 2 and a LeakyReLU activation function to obtain a secondary feature map; Input the secondary feature map into a fully connected layer, where the fully connected layer contains 128 neurons, and random inactivation with a Dropout rate of 0.5 is used to prevent overfitting, to obtain a deep feature vector; Performing time series analysis on the deep feature vector through a bidirectional LSTM layer, wherein the bidirectional LSTM layer includes 64 hidden units, extracting time series correlation features of power supply status and communication anomaly, and obtaining a time series correlation vector; The time series association vector is input into a decision layer, the decision layer comprises two fully connected layers, respectively using ReLU and Sigmoid activation functions, outputting a power switching probability threshold, and triggering the power switching instruction when the power switching probability threshold exceeds 0.85; Based on the power switching instruction, a switching control sequence is generated through the output layer, the switching switches of the main power module and the backup power module are controlled to perform the switching action, and the voltage and current parameters during the switching process are collected to obtain the power switching status data.

8. The control method of the hub lock for preventing abnormal locking according to claim 7, characterized in that: According to the emergency control trigger signal and the motor position signal of the first position detection switch, the actuator is controlled to drive the lock pin to move to the preset locking position detected by the second position detection switch to obtain the self-locking state data, including: Based on the emergency control trigger signal, the communication abnormality flag and the power switching state are extracted to obtain the emergency processing parameters, and the motor position signal output by the first position detection switch is sampled according to the position detection sampling parameters to obtain the motor sampling data; Generate a motor drive control instruction according to the emergency processing parameter and the motor sampling data, control the actuator to start the motor, obtain the motor start parameter, and input the motor start parameter into the motion control unit of the main control module, calculate the displacement of the lock pin from the current position to the preset locking position, and obtain the lock pin displacement data; The lock pin position feedback signal output by the second position detection switch is collected in real time, and compared with the lock pin displacement data to obtain lock pin motion parameters, and the motor speed and direction of the actuator are controlled based on the lock pin motion parameters to drive the lock pin to move to the preset locking position to obtain motion control data; The motion control data and the real-time position of the lock pin detected by the second position detection switch are closed-loop controlled until the lock pin reaches the preset locking position to obtain the locking position data, and the locking position data and the motor operation status and lock pin position changes of the entire self-locking process are recorded and stored to obtain the self-locking status data.

9. The control method of the hub lock for preventing abnormal locking according to claim 8, characterized in that: The self-locking state data, the wheel hub motion state information and the user operation record data are integrated, sent to the APP of the mobile terminal through the communication module for display, and the vehicle state information in the APP is updated in real time, including: Extracting and classifying the locking position data, motor operation status and lock pin position information in the self-locking status data to obtain lock body status information, and classifying the hub status parameters, Hall switch identifiers and effective Hall signals in the hub motion status information to obtain motion monitoring information; Performing authority verification and operation track extraction according to the user identity identifier, operation type identifier and operation timestamp in the user operation record data to obtain user interaction information, and merging the lock body state information, the motion monitoring information and the user interaction information according to the time series to obtain state integration data; The communication module sends the status integration data to the data receiving unit in the APP of the mobile terminal according to the data transmission protocol to obtain the terminal receiving data; Based on the data received by the terminal, the vehicle status information is updated on the display interface in the APP of the mobile terminal to obtain interface display data, and the brightness of the display screen corresponding to the APP is dynamically adjusted according to the ambient light intensity information in the interface display data, and the real-time updated vehicle status information is presented on the APP of the mobile terminal.

10. A control device for a wheel hub lock for preventing abnormal locking, characterized in that: A control method for a wheel hub lock for preventing abnormal locking according to any one of claims 1 to 9, the device comprising: The acquisition module is used to start and initialize the main control module, multiple Hall sensors, the position detection module, the main power module and the backup power module, collect the motor stop position signal of the first position detection switch and the lock position signal of the lock pin switch of the second position detection switch, and obtain the initial state data of the wheel hub lock; a switching module, for performing a difference comparison between a first rotation state signal of the first Hall sensor and a second rotation state signal of the second Hall sensor according to the initial state data of the wheel hub lock, and switching to a normal Hall sensor when the difference exceeds a preset threshold value to obtain wheel hub motion state information; The matching module is used to input the user identity information into the authority management module for authentication matching, generate an unlock authorization instruction including the user identity identifier, operation type identifier and timestamp, and obtain the user operation record data; A monitoring module, used to monitor the communication link status and the power supply status of the main power module based on the wheel hub motion status information, and switch to the backup power module when a communication abnormality is detected to obtain an emergency control trigger signal; a control module, configured to control the actuator to drive the lock pin to move to a preset locking position detected by the second position detection switch according to the emergency control trigger signal and the motor position signal of the first position detection switch, and obtain self-locking state data; The update module is used to integrate the self-locking state data, the wheel hub motion state information and the user operation record data, send them to the APP of the mobile terminal through the communication module for display, and update the vehicle state information in the APP in real time.

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