Control method and device for wheel hub lock to prevent abnormal locking

Abstract

The present invention relates to the technical field of wheel hub lock control, and discloses a control method and device for a wheel hub lock that prevents abnormal locking, wherein the method comprises: starting and initializing a main control module, multiple Hall sensors, a position detection module, a main power supply module and a backup power supply module, collecting the initial state data of the wheel hub lock of a first position detection switch and a second position detection switch; performing a difference comparison between the first Hall sensor and the second Hall sensor to obtain wheel hub motion state information; generating user operation record data; switching to the backup power supply module when a communication abnormality is detected to obtain an emergency control trigger signal; controlling the actuator drive to obtain self-locking state data; integrating and updating the vehicle state information in the APP in real time. The method improves the reliability of wheel hub rotation state detection, thereby improving the safety and reliability of the wheel hub lock in practical applications.

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 actual application. 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 the failure of the locking function. At the same time, when the user unlocks the wheel through the APP, 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 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 achieve accurate perception of the lock pin position and motor status. In addition, the user authority management mechanism is not perfect and cannot 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 detecting the rotation state of the wheel hub, thereby improving the safety and reliability of the wheel hub lock in practical applications.

[0004] In a first aspect, the present invention provides a method for controlling a hub lock that prevents abnormal locking, the method comprising:

[0005] 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 pin switch lock position signal of the second position detection switch, and obtain the initial state data of the wheel hub lock;

[0006] 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;

[0007] 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;

[0008] 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;

[0009] 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;

[0010] The self-locking state data, the wheel hub motion state information and the user operation record data are integrated and 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.

[0011] In a second aspect, the present invention provides a control device for a hub lock that prevents abnormal locking, the control device for the hub lock that prevents abnormal locking comprising:

[0012] An acquisition module 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;

[0013] a switching module, configured to compare a first rotation state signal of the first Hall sensor with a second rotation state signal of the second Hall sensor according to the initial state data of the wheel hub lock, and switch to a normal Hall sensor when the difference exceeds a preset threshold value to obtain wheel hub motion state information;

[0014] The 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 the user operation record data;

[0015] a monitoring module, configured 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 anomaly is detected to obtain an emergency control trigger signal;

[0016] a control module, configured to control an actuator to drive the lock pin to move to a preset locking position detected by the second position detection switch based on the emergency control trigger signal and the motor position signal of the first position detection switch, and obtain self-locking state data;

[0017] The update module is used to integrate the self-locking status data, the wheel hub motion status 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 status information in the APP in real time.

[0018] In the technical solution provided by the present invention, 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; 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 status and communication anomalies, and features are extracted through multi-layer neural networks and attention mechanisms to achieve more accurate abnormal state judgment; a complete user authority management mechanism is established, multiple verifications are performed on user identities, and detailed operation logs are recorded to effectively prevent illegal operations; real-time monitoring and dynamic display of 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.

[0019] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the present invention. The purposes and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0020] 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

[0021] Figure 1 Schematic diagram of an embodiment of a method for controlling a hub lock to prevent abnormal locking according to an embodiment of the present invention;

[0022] Figure 2 Schematic diagram of an embodiment of a control device for a hub lock that prevents abnormal locking in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0024] The terms "including," "having," and any variations thereof, as used in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to the process, method, product, or device.

[0025] To facilitate understanding of this embodiment, a control method for a hub lock that prevents abnormal locking disclosed in an embodiment of the present invention is first described in detail. Figure 1 As shown, this method includes the following steps:

[0026] 101. Start and initialize the main control module, multiple Hall sensors, position detection modules, main power module, and backup power module, collect the motor stop position signal of the first position detection switch and the lock pin switch lock position signal of the second position detection switch, and obtain the initial state data of the wheel hub lock;

[0027] It is understandable that the execution subject of the present invention may be a control device for the wheel hub lock that prevents abnormal locking, or may be 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.

[0028] Specifically, during startup and initialization of the main control module, multiple Hall sensors, position detection module, main power module, and backup power module, electrical connection testing is performed to confirm the proper connectivity between each module and obtain module connection status data. Based on this module connection status data, the main and backup power modules are connected in parallel to obtain power supply parameters, ensuring a stable power supply to the system under all circumstances. In particular, in the event of a main power module failure or power shortage, the backup power module can provide timely power support. Based on the power supply parameters, the signal acquisition frequency of each position detection module is set to obtain position detection sampling parameters. These parameters are used to adjust the signal acquisition process of the first and second position detection switches, ensuring sufficient accuracy and real-time performance for subsequent precise control of the wheel hub lock position. 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 converted from analog to digital to obtain a digital value of the motor position. Digital position signals are easier to store, transmit, and process, enabling further calculations and verification within the control algorithm. During this process, the position detection module can promptly detect the actual stop position of the motor. Simultaneously, position detection sampling parameters are input into the second position detection switch to acquire the lock position signal from the lock pin switch. The lock pin switch signal is converted to a digital value to obtain the lock pin position data. This lock pin position data reflects the actual motion state of the lock pin, thereby determining whether the wheel hub lock has been successfully locked or unlocked. The motor position digital value is combined with the lock pin position digital value to comprehensively analyze the operation of the motor and lock pin to ensure the safety and reliability of the entire locking process. The motor and lock pin position digital values are then verified and abnormal data points are removed. The purpose of data verification is to identify errors or abnormal signals, such as those caused by sensor failure or signal interference. These abnormal data points can cause misjudgments or erroneous operations in the subsequent control logic and need to be removed. The verified data is considered valid position data, reflecting the actual motion state of the wheel hub lock and lock pin. The valid position data is packaged and processed according to a preset data format to generate a position data stream. This position data stream is a continuous data format that reflects the motion state of the wheel hub lock in real time and can be easily transmitted via the communication module to a higher-level control system or mobile application for display and monitoring. By generating a position data stream, the system's operating status becomes more transparent and traceable, improving the overall system maintainability and user experience. The position data stream is fused with module connection status data, power supply parameters, and position detection sampling parameters to obtain the initial state data of the hub lock.

[0029] 102. Based on the initial state data of the wheel hub lock, perform 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. When the difference exceeds a preset threshold, switch to the normal Hall sensor to obtain wheel hub motion state information.

[0030] Specifically, the module connection status data in the wheel hub lock's initial status data is parsed, and the position detection sampling parameters for the first and second Hall sensors are extracted from this data to ensure that the frequency and accuracy of signal acquisition meet the system's actual requirements. Based on the position detection sampling parameters, the first rotational status signal acquired by the first Hall sensor is digitally converted and filtered. Digital conversion converts the analog signal into a digital signal that the system can directly process, while signal filtering removes noise from the signal to ensure more accurate acquired data. This processing results in a first Hall signal value. Simultaneously, the second rotational status signal acquired by the second Hall sensor is digitally converted and filtered using the same position detection sampling parameters to obtain a second Hall signal value. A difference calculation is performed between the first and second Hall signal values to obtain a difference calculation result. This difference calculation determines the consistency of the two Hall sensors during wheel hub motion. The difference calculation result is compared with a preset difference threshold to obtain Hall signal status data. If the difference in the output of the two Hall sensors is within the threshold range, both are operating properly and the data is reliable. Otherwise, one of the sensors is abnormal or faulty. Based on the Hall signal status data, Hall sensors with a difference value less than a preset difference threshold are marked as normal, and a corresponding Hall switch flag is generated. The Hall switch flag dynamically switches the signal acquisition channel of the main control module to ensure that the rotation status signal is collected from the normal Hall sensors and a valid Hall signal is obtained. The valid Hall signal is then time-correlated with the digital values of the motor position and the lock pin position to obtain the wheel hub status parameters. This time-correlation analysis allows the system to comprehensively determine the actual operating status of the wheel hub lock by analyzing the temporal relationships between different signals. The digital values of the motor position, the lock pin position, and the valid Hall signal work together to reflect the entire wheel hub movement process, ensuring that each step in the locking and unlocking process proceeds according to the expected logic, thereby improving system safety and reliability. Based on this, the wheel hub status parameters, Hall switch flag, and valid Hall signal are integrated to generate the final wheel hub movement status information. Data from different sources is uniformly processed to form a complete data set, enabling the system to better control and manage the wheel hub lock status.

[0031] The first Hall effect signal is divided into sampling windows, with each window containing 100 sampling points, to obtain a sampling sequence for the first signal. This sampling window division facilitates effective segmented signal analysis, enabling better identification of signal features and variations. Simultaneously, the second Hall effect signal is segmented using the same sampling window size to obtain a second signal sampling sequence. The difference between each sampling point in the first signal sampling sequence and the corresponding sampling point in the second signal sampling sequence is calculated to obtain a difference point sequence. The root mean square (RMS) value (RMS) of all sampling points in the difference point sequence is calculated as a measure of signal difference, generating a statistical parameter for the difference. The RMS value is a statistical metric used to measure the degree of signal variation and volatility and is suitable for detecting similarities or differences between two signals. The standard deviation is calculated based on the statistical parameter for the difference and compared with a pre-set standard threshold for the difference to determine the Hall effect state. The standard deviation reflects the fluctuation amplitude of the signal difference. If the standard deviation significantly exceeds the set threshold, it indicates a significant inconsistency between the two Hall effect sensors, indicating a fault or abnormality. This step evaluates the signal status within each sampling window in real time, enabling timely detection and response to potential faults. The Hall effect status judgment results are cumulatively counted over the time window. When the judgment results for five consecutive sampling windows exceed the set threshold, an abnormality confirmation flag is generated. This prevents misjudgments due to occasional noise or short-term signal interference, ensuring that the system only confirms an abnormality when a persistent problem is clearly detected. The abnormality confirmation flag is combined with the difference statistical parameters and encoded to generate Hall effect signal status data.

[0032] 103. Input the user identity information into the authority management module for authentication and matching, generate an unlock authorization instruction including the user identity identifier, operation type identifier and timestamp, and obtain user operation record data;

[0033] Specifically, a user enters their identity information into the permissions management module via a mobile app. The system then preprocesses the user's identity information and generates preprocessed user data. This preprocessing process includes standardizing the data format, verifying the information, and eliminating redundant information to ensure the consistency and integrity of the user data, facilitating subsequent comparison and verification. The preprocessed user data is then compared with the information in the authorized user database. Through permission verification analysis, it is determined whether there is a matching record for the currently entered user in the database, generating a user match result. The user match result is then input into the identity identification generation unit within the permissions management module, which generates authentication data according to pre-set encoding rules. These pre-set encoding rules ensure the uniqueness and traceability of the generated authentication data, preventing unauthorized users from impersonating others and performing illegal operations. Based on this authentication data, the user's current operation type is identified and classified, generating operation identification data. Operation types include unlocking, locking, and emergency opening. The purpose of these classifications is to clarify the user's specific behavior and implement corresponding control measures based on the type. The current system time information is added to the operation identification data to generate an operation timestamp. The timestamp records the specific time of the operation, facilitating subsequent auditing and analysis. The operation timestamp is combined with the authentication data to generate the authorization base data, which includes the user's identity, operation type, and specific time information. Based on the authorization base data, an unlock control instruction for the wheel hub lock is generated. The unlock control instruction is a specific operation command for the vehicle's wheel hub lock. Its generation requires verification based on the user's identity, operation type, and system permissions to ensure that only authorized users can perform the corresponding operation. When the unlock control instruction is generated, the user identity, operation time, and operation type of this operation are recorded to generate operation log data. The operation log data, authentication data, and unlock control instruction are correlated and integrated to obtain user operation record data.

[0034] 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;

[0035] Specifically, the wheel hub status parameters and valid Hall effect signals contained in the wheel hub motion status information are analyzed and extracted in real time to obtain real-time operating status data. Continuous analysis of Hall effect sensor data reflects the actual wheel hub motion and the operating status of each component. Based on this real-time operating status data, the communication link between the communication module and the mobile terminal app is tested for signal quality, generating communication link status data. The signal strength value in this communication link status data is a key indicator for evaluating system communication quality. By comparing this signal strength value with a preset signal strength threshold, the current operating status of the communication line is determined, particularly to determine whether the communication line has been severed or if communication quality has significantly degraded. The communication anomaly detection results obtained in this process help the system identify potential communication interruption risks in the shortest possible time. Simultaneously, the real-time power supply status of the main power module is monitored, including the collection and analysis of voltage, current, and communication power supply parameters, generating power supply status data. The main power supply status is crucial for the proper operation of the wheel hub lock. Real-time monitoring of these power supply parameters allows for the timely detection of power supply anomalies such as low voltage and unstable current. Power supply status data is correlated with communication anomaly detection results to comprehensively determine the system's current operating status. When a communication anomaly is detected, a power switch command is triggered, generating a power supply switching instruction. Based on this instruction, the switch between the main and backup power modules is controlled to automatically switch power and generate power supply switching status data. During the power switchover process, precise control of the switch is crucial for ensuring a smooth system transition. Especially in emergency situations where the main power supply fails, timely switching of the backup power source ensures the continued operation of the hub lock system and prevents system failures or safety hazards caused by power outages. After the power switchover is complete, the power switchover status data is verified to ensure the success of the power switchover process and the stability of the power supply. The system operating status at the time of the switchover is recorded to generate switchover confirmation data. This switchover confirmation data reflects various system status indicators during and after the power switchover, ensuring a smooth power switchover and the proper functioning of the backup power source. Based on this, the switchover 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 that prompts the system to enter an emergency state and initiate appropriate emergency measures based on the current operating status.

[0036] The power supply status data and communication anomaly detection results are fed into the input layer of the anomaly detection deep neural network. This layer, consisting of 64 neurons, uses the ReLU activation function to extract features from the input data. The ReLU activation function effectively addresses negative values in the data, making the network more computationally efficient and generating initial feature data. This initial feature data is then processed by the first convolutional layer, which consists of 32 3×3 convolution kernels with a stride of 1. This layer extracts local features from the input data. By scanning local regions within the power supply status and communication anomaly data, the convolutional layer can detect specific patterns or local features that indicate an anomaly. BatchNormalization is used during the convolution operation to normalize the features, reducing data variance and accelerating network convergence. This generates a first-level feature map that retains key features of the power supply and communication status, ensuring data stability. The first-level feature map is then fed into the attention mechanism layer, which uses a self-attention structure to calculate a correlation weight matrix between features. The self-attention mechanism assigns different weights to relationships between feature data, highlighting key features of the anomaly. This allows for more accurate identification of features related to power supply and communication anomalies, generating attention feature data. This effectively reflects the potential correlation between power supply and communication conditions, enabling the system to better judge complex anomalies. The attention feature data is processed by the second convolutional layer, which consists of 64 3×3 convolution kernels with a stride of 2. The LeakyReLU activation function is used to maintain the network's sensitivity to negative features, resulting in a second-level feature map. The addition of convolution kernels and the adjustment of the stride enable deeper feature extraction while maintaining the sparsity of feature data. The use of the LeakyReLU activation function helps mitigate the problem of neuron "death," making the network more sensitive to anomalies and better able to extract complex features. The second-level feature map is input to a fully connected layer with 128 neurons. By concatenating all feature maps, a complete deep feature vector is generated. To prevent overfitting, a dropout mechanism with a dropout rate of 0.5 is used. This randomly drops half of the neurons during training, improving the model's generalization and maintaining strong predictive power when faced with new data. The deep feature vector is input into a bidirectional LSTM layer for time series analysis. The bidirectional LSTM layer contains 64 hidden units. By processing forward and reverse time series information, it can capture the temporal correlation characteristics between power supply status and communication anomalies. LSTM networks are suitable for processing sequential data and can learn and capture dependencies over long time intervals. Therefore, they can effectively analyze the changing trends between power supply status and communication signals, generating a time series correlation vector that contains the temporal dynamic relationship between power supply and communication anomalies.The time series correlation vector is input to the decision layer, which consists of two fully connected layers, using ReLU and Sigmoid activation functions, respectively, to process the data. The first fully connected layer uses the ReLU activation function to extract high-order features from the time series correlation vector; the second fully connected layer uses the Sigmoid activation function to output a power switching probability threshold. When this power switching probability threshold exceeds 0.85, indicating a significant anomaly in power supply or communication, the system triggers a power switching instruction, ensuring timely response to potential power supply issues. Based on the generated power switching instruction, the output layer generates a specific switching control sequence. This control sequence is used to control the switching operation between the main power module and the backup power module, ensuring seamless power supply switching. To ensure the stability of the power switching process, power supply parameters such as voltage and current are collected during the switching process to generate power switching status data.

[0037] 105. According to the emergency control trigger signal and the motor position signal of the first position detection switch, control the actuator 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;

[0038] Specifically, based on the emergency control trigger signal, the communication anomaly indicator and power switching status are extracted to obtain emergency processing parameters. These parameters reflect the system's current emergency state and ensure that the control logic can be adjusted accordingly. Simultaneously, the motor position signal output by the first position detection switch is sampled according to preset position detection sampling parameters to obtain motor sampling data, providing information about the motor's current position, enabling the system to accurately determine the motor's status. Based on the emergency processing parameters and the motor sampling data, motor drive control instructions are generated to control the motor startup in the actuator. The generated motor drive control instructions are transmitted to the actuator to obtain motor startup parameters, including the motor startup time, startup current, and initial speed, ensuring that the motor starts smoothly according to the specified requirements. The motor startup parameters are input into the motion control unit in the main control module. The motion control unit calculates the displacement required for the locking pin to move from its current position to the preset locking position, generating locking pin displacement data. The locking pin position feedback signal output by the second position detection switch is collected in real time and compared with the locking pin displacement data to obtain the locking pin's motion parameters. The lock pin's motion parameters reflect its actual state during movement. These parameters are used to adjust the motor control commands in real time to ensure that the lock pin moves along the predetermined path to the preset locking position. Based on the lock pin's motion parameters, the motor in the actuator is controlled, including its speed and direction. By precisely adjusting these parameters, the lock pin is driven toward the preset locking position, generating motion control data. This motion control data is continuously updated throughout the movement process, ensuring that the motor's state and the lock pin's movement remain under control. During the lock pin's movement, closed-loop control is performed on the motion control data and the real-time position of the lock pin detected by the second position detection switch until the lock pin reaches the preset locking position. Closed-loop control monitors the lock pin's position changes in real time and adjusts the control commands based on this feedback to ensure that the lock pin accurately reaches the target position, avoiding deviations caused by external interference or system errors. This closed-loop control ensures that the lock pin's movement is always under precise control, ensuring 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. The motor's operating status and the changes in the lock pin's position throughout the self-locking process are recorded and stored to generate self-locking status data. The motor's operating status includes information such as speed, current, and direction of rotation throughout the locking process, reflecting the motor's actual operating conditions during the locking task. The lock pin's position change data records the lock pin's trajectory and time points from its initial position to the locked position.

[0039] 106. Integrate the self-locking status data, wheel hub motion status information and user operation record data, send them to the mobile terminal APP through the communication module for display, and update the vehicle status information in the APP in real time.

[0040] Specifically, lock position data, motor operating status, and lock pin position information are extracted from the self-locking status data and classified to obtain lock status information. This lock status information reflects key state parameters during the locking process, ensuring that users can understand the current state of the lock and the specific locking situation. Simultaneously, wheel hub status parameters, Hall effect switching flags, and valid Hall effect signals are extracted from the wheel hub motion status information and classified to obtain motion monitoring information. Motion monitoring information describes the dynamic motion of the vehicle's wheel hub, helping users understand the vehicle's current motion and sensor operating status. Simultaneously, user operation log data is processed to extract user identity, operation type, and operation timestamps. This information is used to verify permissions and extract operation traces, generating user interaction information. User interaction information reflects every user operation on the vehicle, recording the time, user identity, and operation type, ensuring vehicle safety and traceability of user behavior. The lock status information, motion monitoring information, and user interaction information are merged in time series to generate state integration data. The merging of time series enables data to be presented in a continuous format, providing a complete trajectory of vehicle status changes. The integrated status data encompasses all key data related to the vehicle's mechanical status, motion information, and user operations. This information is interconnected to form a comprehensive picture of the vehicle's status. The communication module transmits this integrated status data to the data receiving unit in the mobile terminal app according to a specific data transmission protocol, resulting in terminal-received data. This data transmission protocol ensures data integrity and security during transmission, preventing data loss or malicious tampering. After receiving the data, the terminal updates the vehicle status display interface in the mobile terminal app based on this data, generating interface display data. This interface display data includes the vehicle's real-time status, such as the lock status of the lock, the operating status of the motor, wheel movement information, and user operation records. Based on the ambient light intensity information in the interface display data, the brightness of the corresponding app display is dynamically adjusted. This brightness adjustment mechanism automatically adjusts the screen brightness based on changes in ambient light, ensuring that users can clearly view vehicle status information in varying lighting conditions, avoiding information loss or visual fatigue caused by excessive or insufficient 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.

[0041] In an embodiment of the present invention, dual Hall sensors are set up 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 functioning Hall sensor, significantly improving the reliability of the wheel hub rotation status detection. A main and standby power supply module design is adopted. 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 a 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 achieve 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 status and communication anomalies, and features are extracted through multi-layer neural networks and attention mechanisms to achieve more accurate abnormal state judgment. A complete user authority management mechanism is established, multiple verifications are performed on user identities, and detailed operation logs are recorded to effectively prevent illegal operations. Real-time monitoring and dynamic display of wheel hub lock status information are achieved, and the complete system operation status is presented through the mobile terminal APP, which is convenient for timely discovery and handling of abnormal situations.

[0042] In a specific embodiment, the process of executing step 101 may specifically include the following steps:

[0043] Perform electrical connection detection on the main control module and multiple Hall sensors, position detection module, main power module and backup power module to obtain module connection status data, and configure the main power module and backup power module in parallel according to the module connection status data to obtain power supply parameters;

[0044] 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;

[0045] 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;

[0046] 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 then package the valid position data according to the preset data format to generate a position data stream;

[0047] The position data stream is fused with the module connection status data, power supply parameters and position detection sampling parameters to obtain the initial state data of the wheel hub lock.

[0048] Specifically, the electrical connection test 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 tests on these modules, the 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 the After confirming that all modules are properly connected, the main power module and the backup power module are connected 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 backup power supply voltage is , then the total voltage after parallel connection is Expressed as:

[0049] ;

[0050] This selection mechanism ensures that the system's power supply voltage is the lowest of the two, thus ensuring stable system operation. Then we can get by current superposition:

[0051] ;

[0052] in and The current of the main power supply and the backup power supply are obtained according to these voltage and current parameters. , which is expressed as:

[0053] ;

[0054] 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 control requirements and effectively reduce noise interference to ensure data accuracy. Assume that the acquisition frequency is According to the power supply parameters set up:

[0055] ;

[0056] 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 analog-to-digital converter (ADC) Expressed as:

[0057] ;

[0058] This digital amount The ability to accurately reflect the current stop position of the motor is an important basis for controlling the motor's movement. 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 value of the lock pin position through digital quantity conversion. , which is expressed as:

[0059] ;

[0060] 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 all important data describing 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:

[0061] ;

[0062] ;

[0063] For any data point , if it satisfies , then the data point is considered to be abnormal data and should be eliminated. In this way, valid position data is obtained and the abnormal points caused by noise and interference are eliminated. 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 status identifier. is expressed as:

[0064] ;

[0065] 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. The 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:

[0066] ;

[0067] The initial status data includes the system's module connection status, power supply status, sampling frequency, and hub lock position status information.

[0068] In a specific embodiment, the process of executing step 102 may specifically include the following steps:

[0069] Parsing the module connection status data in the wheel hub lock initial status data to extract position detection sampling parameters of the first Hall sensor and the second Hall sensor;

[0070] 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;

[0071] 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;

[0072] According to the Hall signal status data, the Hall sensor whose mark difference is less than the preset difference threshold is a normal Hall sensor, and a Hall switching flag is obtained;

[0073] 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;

[0074] The effective Hall signal is analyzed in time series with the digital quantity of the motor position and the digital quantity of the lock pin position to obtain the wheel hub status parameters. The wheel hub status parameters, Hall switch flags and effective Hall signals are then integrated to obtain the wheel hub motion status information.

[0075] Specifically, the module connection status data is parsed from the initial state 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 signal acquisition meets 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 , digital value of the first Hall signal is obtained by performing digital conversion through analog-to-digital converter (ADC)

[0076] ;

[0077] 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:

[0078] ;

[0079] in, Indicates 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 , get the digital value of the second Hall signal through the analog-to-digital converter :

[0080] ;

[0081] After filter processing, the second Hall signal value is obtained :

[0082] ;

[0083] 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:

[0084] ;

[0085] in, is the difference between the first and second Hall signals. By calculating the difference, we can determine whether there is a significant difference between the two sensors, and thus evaluate their working status. The difference calculation result is compared with the preset difference threshold. Compare to get the Hall signal status data. , then 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 flag is obtained. The Hall switch flag 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 flag 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 it and obtain the effective Hall signal. Assume that the effective Hall signal is , which depends on the Hall switch flag:

[0086] ;

[0087] By selecting a valid Hall signal, we can ensure that even in the case of sensor failure, we can still collect accurate wheel hub rotation status signals. We can analyze the timing correlation between the valid Hall signal and 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:

[0088] ;

[0089] Hub status parameters Contains the current valid Hall signal, motor position and lock pin position data, which reflects the working status of the hub lock at the current moment. The hub state parameters, Hall switch flags and valid Hall signals are integrated to obtain the hub motion state information. Assume that the hub motion state information is , then it is expressed as:

[0090] ;

[0091] The wheel hub motion status information is combined with the wheel hub motion parameters, the current normal sensor identification 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.

[0092] In a specific embodiment, the step of 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 the Hall signal state data may specifically include the following steps:

[0093] 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;

[0094] Subtracting the sampling point value at the corresponding position in 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 in the difference point sequence as a signal difference measurement value to obtain a difference statistical parameter;

[0095] 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;

[0096] 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 with the difference statistical parameter for encoding to generate Hall signal state data.

[0097] 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. Let the signal sequence after sampling be ,in The total number of sampling points collected is divided into a sampling window according to every 100 sampling points, and the first signal sampling sequence is obtained. ,in Indicates the Sampling windows, 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 value of each sampling point in the first signal sampling sequence is subtracted from 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:

[0098] ;

[0099] Difference point sequence The difference in signals between two Hall effect sensors within the same time window is key data for analyzing the consistency of their operating states. The root mean square (RMS) value is calculated based on all sampling points in the difference point sequence as a measure of signal difference. The formula for calculating the RMS value is:

[0100] ;

[0101] in, Indicates the 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, The value of 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:

[0102] ;

[0103] in, For the The mean of the difference point sequence within a window is expressed as:

[0104] ;

[0105] 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 from 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 status is normal. Suppose the Hall status judgment result is , which is expressed as:

[0106] ;

[0107] in, Indicates an abnormality. Indicates normal. Accumulate the time window count of the Hall status judgment result to determine whether there is a persistent abnormality. When the judgment results of 5 consecutive sampling windows are all abnormal, that is, If it continues for 5 times, the system will generate an abnormal confirmation flag, indicating that the difference between the Hall sensors has reached a level that requires emergency treatment. , which is expressed as:

[0108] ;

[0109] 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 status data. Assume that the Hall signal status data is , which is confirmed by the exception flag and statistical parameters of differences (such as RMS and standard deviation ) together, expressed as:

[0110] ;

[0111] The working status of the Hall sensor is recorded through a combination of coding methods.

[0112] In a specific embodiment, the process of executing step 103 may specifically include the following steps:

[0113] Input the user identity information in the mobile terminal APP 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;

[0114] The user matching result is input into the identity identification generation unit of the authority management module, and the identity authentication data is generated according to the preset coding rules. The type of the current operation is identified and classified according to the identity authentication data to generate operation identification data;

[0115] Add the system current 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;

[0116] Generate an unlocking control instruction for the wheel hub lock based on 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;

[0117] The operation log data, identity authentication data and unlocking control instructions are associated and integrated to obtain user operation record data.

[0118] Specifically, the identity information entered by the user in the mobile terminal APP is passed to the authority management module for pre-processing of the user information. Assume that the user identity information is , which contains the user's unique identity (such as username 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 purpose of this preprocessing process is to improve the comparability of user data and reduce matching failures caused by non-standard input. The user preprocessed data is compared with the information in the authorized user database. The information in the authorized user database is represented as , which contains the identity information of registered users in the system and the corresponding permission information. Perform permission verification analysis with the data in the database to determine whether the user has the operation permission and ensure that the input identity information is legal and valid. The comparison process is expressed by the following formula:

[0119] ;

[0120] 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 authentication data based on the user matching result. Let the authentication data be , which is generated based on the preset encoding rules, encoding the user's unique identity and matching results. For example, if the user's identity is If the match is successful, the authentication data is generated as follows:

[0121] ;

[0122] 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 non-tampering of the authentication. Based on the authentication data, the type of the operation is identified and classified to generate the 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 authentication data and the user's operation request, the corresponding operation identification data is determined and generated:

[0123] ;

[0124] in, Represents a function that classifies operations based on authentication data and user requests. After generating the operation identification data, the operation identification data is added to the system's current time information to generate the operation timestamp. Assume that the system's current time is , then the operation timestamp is expressed as , which is generated by combining the operation identification data and time information:

[0125] ;

[0126] The operation timestamp records the specific type and time of the user's operation. Combine to obtain authorization basic data :

[0127] ;

[0128] The authorization basic data includes user 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. Let the unlocking control instruction be 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:

[0129] ;

[0130] in, Represents the control instruction generation function, which verifies the permissions based on the authorization basic data and generates the corresponding operation instructions. 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 :

[0131] ;

[0132] Operation log data is used to record the detailed information of each user operation, ensuring that the system can trace 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 :

[0133] ;

[0134] User operation record data includes all important information related to the current operation, such as the user's operation log, authentication information, and unlock control instructions.

[0135] In a specific embodiment, the process of executing step 104 may specifically include the following steps:

[0136] Perform real-time analysis and status extraction on the hub status parameters and effective Hall signals in the hub motion status information to obtain real-time operating status data. Based on the real-time operating status data, perform signal quality detection on the communication link between the communication module and the mobile terminal's APP to obtain communication link status data.

[0137] 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 and obtain the communication anomaly detection result. In addition, the real-time power supply status of the main power module is detected, including the collection and analysis of voltage, current and communication power supply parameters to obtain power supply status data.

[0138] The power supply status data is correlated with the communication anomaly detection results 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 power supply switching status data.

[0139] 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. The switching confirmation data is integrated with the communication anomaly detection result to generate an emergency control trigger signal.

[0140] 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 represents the actual rotation state of the current wheel hub. By analyzing these data, the real-time running state data of the wheel hub lock is obtained, which is recorded as . Based on real-time operating status data , perform signal quality detection on the communication link between the communication module and the mobile terminal APP 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 interruption risk. To determine whether there is an abnormality in the communication line, the signal strength value With the preset signal strength threshold Compare. 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 results Obtained by the following formula:

[0141] ;

[0142] 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. Expressed as:

[0143] ;

[0144] Voltage Indicates the current power supply potential and 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. ) triggers 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:

[0145] ;

[0146] in, Indicates that power switching 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. The data includes the voltage and current changes recorded during the switching process, as well as the action status of the switch. The power switching process is designed to ensure that the system still has a stable power source in the event of communication anomalies, so as to avoid system downtime caused by power failure. Confirm the switching result to ensure the success of the switching operation and record the system working status at the time of switching. , which contains the power status at the time of switching and other key operating parameters of the system. By confirming the data, it is verified whether the power switch is successful and whether there are any abnormalities during the switching process. The switching confirmation data is represented as:

[0147] ;

[0148] in, Indicates the power status during 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 the emergency. The emergency control trigger signal is expressed as:

[0149] ;

[0150] The trigger signal contains the communication anomaly detection results and switching confirmation data, which is the core basis for the system to make decisions in emergency situations.

[0151] In a specific embodiment, the execution step associates and analyzes the power supply status data with the communication anomaly detection result, triggers a power supply switching instruction when a communication anomaly is detected, obtains a 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:

[0152] The power supply status data and communication anomaly detection results are input into the input layer of the anomaly detection deep neural network. The input layer contains 64 neurons and uses the ReLU activation function for feature extraction to obtain initial feature data.

[0153] 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 for feature normalization to obtain a first-level feature map.

[0154] 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.

[0155] The attention feature data is processed by the second convolutional layer, which contains 64 3×3 convolution kernels with a stride of 2 and uses the LeakyReLU activation function to obtain the secondary feature map;

[0156] The secondary feature map is input into the fully connected layer, which contains 128 neurons and uses random inactivation with a dropout rate of 0.5 to prevent overfitting, to obtain a deep feature vector;

[0157] The deep feature vector is subjected to time series analysis through a bidirectional LSTM layer. The bidirectional LSTM layer contains 64 hidden units, extracting the time series correlation features of power supply status and communication anomalies to obtain a time series correlation vector.

[0158] The time series correlation vector is input into the decision layer. The decision layer consists of two fully connected layers, using ReLU and Sigmoid activation functions respectively, and outputs the power switching probability threshold. When the power switching probability threshold exceeds 0.85, the power switching instruction is triggered;

[0159] 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.

[0160] 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, The power supply for communication 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 implements nonlinear feature extraction through the ReLU activation function. The ReLU activation function is defined as:

[0161] ;

[0162] The ReLU activation function has good nonlinear and sparse characteristics, which can effectively alleviate the gradient disappearance 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 convolution layer. The first convolution layer contains 32 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 by the following convolution formula:

[0163] ;

[0164] 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, which uses a self-attention structure to extract the key features of the abnormal state by calculating the correlation weight matrix between features. Assume that 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:

[0165] ;

[0166] in, and are the query vector and key vector of the feature, respectively, Obtained through linear transformation. Weight matrix Used to measure the relationship between features to extract more representative attention feature data The attention feature data is processed by the second convolutional layer, which contains 64 layers of size The convolution kernel is 2, with a stride of 2, to extract high-level features and reduce feature dimensions. LeakyReLU is used as the activation function, which is defined as:

[0167] ;

[0168] in LeakyReLU is a small positive number, usually 0.01. LeakyReLU allows a small amount 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 to 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 with a Dropout rate of 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:

[0169] ;

[0170] in and are the weights and biases of the fully connected layer, respectively. The deep feature vector is input to 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 power supply status and communication anomalies. LSTM (Long Short-Term Memory) networks are 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 backward directions respectively. Let the forward hidden state be , the backward hidden state is , then the time series correlation vector for:

[0171] ;

[0172] 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 consists of 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. :

[0173] ;

[0174] in, and are weight 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. When the power supply is abnormal, 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 Expressed as:

[0175] ;

[0176] The power switching status data contains all key power parameters during the switching process, as well as specific instructions for switching control, and is used to record and verify the success or failure of the power switching.

[0177] In a specific embodiment, the process of executing step 105 may specifically include the following steps:

[0178] 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 motor sampling data;

[0179] Generate motor drive control instructions based on emergency processing parameters and motor sampling data, control the actuator to start the motor, obtain motor starting parameters, and input the motor starting parameters into the motion control unit of the main control module to calculate the displacement of the lock pin from the current position to the preset locking position to obtain lock pin displacement data;

[0180] 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. The motor speed and direction of the actuator are controlled based on the lock pin motion parameters to drive the lock pin to a preset locking position and obtain motion control data.

[0181] 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.

[0182] Specifically, the communication abnormality flag and power switching status are extracted based on the emergency control trigger signal. Assume that the emergency control trigger signal is , the signal contains the 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. The motor position signal output by the first position detection switch is sampled according to the position detection sampling parameters. Assume that the position detection sampling parameters are , which 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:

[0183] ;

[0184] 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. According to the emergency processing parameters and the motor sampling data, the motor drive control instruction is generated to control the actuator to start the motor. Assume that 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 startup time is , the initial speed is , then the motor starting parameters are expressed as:

[0185] ;

[0186] These parameters ensure that the motor starts as expected and provides sufficient driving force for the locking pin to move. 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. Assume that 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:

[0187] ;

[0188] 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 determining the displacement of the lock pin, 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. Assume that the lock pin position feedback signal is , the lock pin position data obtained through sampling is recorded as , which is expressed by the following formula:

[0189] ;

[0190] 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 meets the expected value. If there is a deviation between the feedback signal and the preset displacement, the movement of the lock pin is corrected by adjusting the speed and direction of the motor. 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:

[0191] ;

[0192] in, is the proportional gain, is the current position of the lock pin, by adjusting The value of controls the response speed of the system to ensure that the locking pin can reach the preset position smoothly. 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 and avoid the lock pin deviating from the preset position due to external disturbances or error accumulation. Let the closed-loop control output be , 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 locking 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, speed of the motor during the entire movement process, as well as the displacement of the lock pin during the movement 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:

[0193] ;

[0194] 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.

[0195] In a specific embodiment, the process of executing step 106 may specifically include the following steps:

[0196] The locking position data, motor operating status, and lock pin position information in the self-locking status data are extracted and classified to obtain the lock body status information. The hub status parameters, Hall switching flags, and valid Hall signals in the hub motion status information are classified and processed to obtain motion monitoring information.

[0197] Based on the user identity, operation type and operation timestamp in the user operation record data, permission verification and operation trajectory extraction are performed to obtain user interaction information. The lock body status information, motion monitoring information and user interaction information are then merged in time series to obtain status integration data.

[0198] Through the communication module, the status integration data is sent to the data receiving unit in the mobile terminal APP according to the data transmission protocol to obtain the terminal reception data;

[0199] Based on the data received by the terminal, the vehicle status information is updated on the display interface of the mobile terminal APP 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 mobile terminal APP.

[0200] 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 operating 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:

[0201] ;

[0202] 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 state parameters , Hall switch mark And the effective Hall signal . Hub status parameters Describes the current motion state 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; valid 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:

[0203] ;

[0204] This information set comprehensively monitors the current motion state of the wheel hub, thus helping the system to accurately track the wheel hub behavior. After sorting out the lock body status information and motion monitoring information, the user operation record data is processed. Assume that the user operation record data is , which contains the user identity , Operation type identifier , and the operation timestamp User identity 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 trace extraction on these data, it is determined whether the user has the permission to perform the operation and generates user interaction information. :

[0205] ;

[0206] The generation of user interaction information ensures that the system accurately records and verifies the permissions of each operation, thereby ensuring the security and traceability of the system. The lock body status information, motion monitoring information and user interaction information are merged according to the time series to obtain the state integration data. Let the state integration data be , which is expressed as:

[0207] ;

[0208] 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 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. Assume that the data transmission protocol is , which includes information such as data encoding format, error detection mechanism and transmission speed. By using appropriate data transmission protocols, it is ensured that data is not lost or tampered with during transmission, so that the terminal receives data in the mobile terminal. :

[0209] ;

[0210] 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 vehicle status information is updated on the display interface of the mobile terminal APP to obtain the interface display data. . Contains the current status of the vehicle, such as the lock 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:

[0211] ;

[0212] in The brightness adjustment coefficient adjusts the display brightness according to the ambient light intensity, ensuring 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, movement status and user operations are achieved, and this information is synchronized to the mobile terminal app in real time through data transmission, allowing users to understand the vehicle status in real time.

[0213] 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 In one embodiment of the present invention, a control device for a hub lock that prevents abnormal locking includes:

[0214] The acquisition module 201 is used to start and initialize the main control module, multiple Hall sensors, position detection module, main power module and backup power module, collect the motor stop position signal of the first position detection switch and the lock pin switch lock position signal of the second position detection switch, and obtain the initial state data of the wheel hub lock;

[0215] A switching module 202 is configured 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 based on 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 wheel hub motion state information;

[0216] 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 identifier, operation type identifier and timestamp, and obtain user operation record data;

[0217] 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;

[0218] The control module 205 is used to control the actuator to drive the lock pin to move to the 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;

[0219] The updating module 206 is used to 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.

[0220] Through the coordinated cooperation of the above components, dual Hall sensors are set up to detect the wheel hub rotation status 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, to achieve accurate monitoring of the lock body status and improve the accuracy of locking control; a deep learning model is introduced to intelligently identify power supply status and communication anomalies, and features are extracted through multi-layer neural networks and attention mechanisms to achieve more accurate abnormal status judgment; a complete user authority management mechanism is established, user identity is multiple-level verified, and detailed operation logs are recorded to effectively prevent illegal operations; real-time monitoring and dynamic display of 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 detection and handling of abnormal situations.

[0221] Those skilled in the art will 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.

[0222] If the integrated unit is implemented as 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, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0223] 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 above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can 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 method for controlling a hub lock to prevent abnormal locking, characterized in that: The method comprises: The main control module, multiple Hall sensors, position detection module, main power module and backup power module are started and initialized, and the motor stop position signal of the first position detection switch and the lock pin switch lock position signal of the second position detection switch are collected to obtain the initial state data of the wheel hub lock; specifically including: performing electrical connection detection on the main control module and multiple Hall sensors, position detection module, main power module and backup power module to obtain module connection status data, and configuring the main power module and the backup power module in parallel according to the module connection status data to obtain power supply parameters; setting the signal acquisition frequency of the first position detection switch and the second position detection switch based on the power supply parameters to obtain position detection sampling parameters, and The sampling parameters are used to collect the motor stop position signal output by the first position detection switch, and the motor stop position signal is converted into a digital quantity to obtain the motor position digital quantity; the position detection sampling parameters are input into the second position detection switch, and the lock pin switch lock position signal output by the second position detection switch is collected and converted into a digital quantity to obtain the lock pin position digital quantity; data verification and abnormal data points are performed on the motor position digital quantity and the lock pin position digital quantity to obtain valid position data, and the valid position data are packaged and processed according to a preset data format to generate a position data stream; the position data stream is data-fused with the module connection status data, the power supply parameters and the position detection sampling parameters to obtain the wheel hub lock initial state data; 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 and 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 user identity information is input into the authority management module for authentication and matching, and an unlock authorization instruction containing the user identity identifier, operation type identifier and timestamp is generated to obtain user operation record data, 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 rights management module, generate identity authentication data according to the preset coding rules, and identify and classify the type of this operation according to the identity authentication data to generate operation identification data; Adding the system current time information to the operation identification data to generate an operation timestamp, and combining it with the identity verification data to obtain authorization basic data; generating an unlocking control instruction for the wheel hub lock according to the authorization basic data, and recording the user identity, operation time, and operation type of the current 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.

3. The control method of the hub lock for preventing abnormal locking according to claim 2, 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 wheel hub state parameters and effective Hall signals in the wheel 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 based on the real-time operation state data to obtain communication link state data; Comparing the signal strength value in the communication link status data with a preset signal strength threshold to determine whether the communication line is cut, obtaining a communication anomaly detection result, and detecting the real-time power supply status of the main power module, including collecting and analyzing voltage, current, and communication power supply parameters, to obtain power supply status data; Correlating and analyzing the power supply status data with the communication anomaly detection result, triggering a power supply switching instruction when a communication anomaly is detected to obtain a power supply switching instruction, and controlling a 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 moment is recorded to obtain switching confirmation data, which is integrated with the communication anomaly detection result to generate an emergency control trigger signal.

4. The control method of the hub lock for preventing abnormal locking according to claim 3, characterized in that: The correlating analysis of the power supply status data with the communication anomaly detection result, triggering a power supply switching instruction when a communication anomaly is detected to obtain a power supply switching instruction, and controlling the switching action of the main power supply module and the backup power supply module based on the power supply switching instruction to obtain the power supply switching status data includes: Inputting the power supply status data and the communication anomaly detection result into the input layer of the anomaly detection deep neural network, wherein the input layer comprises 64 neurons and uses a ReLU activation function for feature extraction to obtain initial feature data; The initial feature data is processed by a first convolutional layer, where the first convolutional layer contains 32 3×3 convolution kernels with a step size of 1 and uses BatchNormalization to perform feature normalization to obtain a first-level feature map; Inputting the first-level feature map into the attention mechanism layer, the attention mechanism layer adopts a self-attention structure, extracts key features of the abnormal state by calculating the correlation weight matrix between features, and obtains attention feature data; The attention feature data is processed through a second convolutional layer, which contains 64 3×3 convolution kernels with a stride of 2 and a LeakyReLU activation function to obtain a secondary feature map; Input the secondary feature map into a fully connected layer containing 128 neurons, using random inactivation with a dropout rate of 0.5 to prevent overfitting, and obtain a deep feature vector; Performing time series analysis on the deep feature vector through a bidirectional LSTM layer, where the bidirectional LSTM layer includes 64 hidden units, extracting time series correlation features of power supply status and communication anomalies, and obtaining a time series correlation vector; The time series correlation vector is input into a decision layer, which includes two fully connected layers, using ReLU and Sigmoid activation functions respectively, and outputting a 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.

5. The control method of the hub lock for preventing abnormal locking according to claim 4, characterized in that: The step of controlling 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 to obtain self-locking state data includes: Extracting the communication abnormality flag and the power switching state based on the emergency control trigger signal to obtain emergency processing parameters, and sampling the motor position signal output by the first position detection switch according to the position detection sampling parameters to obtain motor sampling data; generating a motor drive control instruction based on the emergency processing parameters and the motor sampling data, controlling the actuator to start the motor, obtaining motor starting parameters, inputting the motor starting parameters into the motion control unit of the main control module, calculating the displacement of the lock pin from the current position to the preset locking position, and obtaining lock pin displacement data; collecting a lock pin position feedback signal output by the second position detection switch in real time and comparing it with the lock pin displacement data to obtain lock pin motion parameters, and controlling the motor speed and direction of the actuator based on the lock pin motion parameters to drive the lock pin to move toward 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.

6. The control method of the hub lock for preventing abnormal locking according to claim 5, 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 mobile terminal APP via 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 operating 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 switching flags, and valid Hall signals in the hub motion status information to obtain motion monitoring information; Performing permission verification and operation trajectory extraction based on 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 status information, the motion monitoring information, and the user interaction information in time series to obtain status integration data; The communication module sends the state integration data to the data receiving unit in the APP of the mobile terminal according to the data transmission protocol to obtain the terminal reception 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 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.

7. A control device for a hub lock that prevents abnormal locking, characterized in that: A method for controlling a hub lock for preventing abnormal locking according to any one of claims 1 to 6, the device comprising: An acquisition module 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; a switching module, configured to compare a first rotation state signal of the first Hall sensor with a second rotation state signal of the second Hall sensor according to the initial state data of the wheel hub lock, and switch 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 containing the user identity identifier, operation type identifier and timestamp, and obtain the user operation record data; a monitoring module, configured 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 anomaly is detected to obtain an emergency control trigger signal; a control module, configured to control an actuator to drive the lock pin to move to a preset locking position detected by the second position detection switch based on 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 status data, the wheel hub motion status 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 status information in the APP in real time.

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