Oil leakage detection method and device of clamp motor and vehicle

Abstract

The invention discloses an oil leakage detection method and device for a clamp motor and a vehicle. The oil leakage detection method for the clamp motor comprises the following steps: acquiring first current data of the clamp motor; and performing oil leakage detection based on the first current data of the clamp motor to obtain an oil leakage detection result of the clamp motor. The embodiment of the invention can provide a technical scheme for carrying out oil leakage detection on the clamp motor based on the deep learning model.

Description

Technical Field

[0001] The present application belongs to the field of vehicle technology, and specifically relates to a method, device and vehicle for detecting oil leakage of a clamp motor, as well as a method for handling oil leakage faults of a clamp motor, a device for handling oil leakage faults of a clamp motor, a system for handling oil leakage faults of a clamp motor, a computer-readable storage medium and a computer program product. Background Art

[0002] The caliper motor is a component used for parking brakes on vehicles. Oil leakage in the caliper motor can affect the vehicle's braking performance. Therefore, oil leak detection is necessary during vehicle operation. While some deep learning models can assist with vehicle operating status monitoring, no technical solutions have been applied to oil leak detection in caliper motors. Summary of the Invention

[0003] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a method, device, and vehicle for detecting oil leakage in a clamp motor, as well as a method, device, and system for troubleshooting oil leakage in a clamp motor, a computer-readable storage medium, and a computer program product. These methods provide a technical solution for detecting oil leakage in a clamp motor based on a deep learning model.

[0004] In a first aspect, an embodiment of the present application provides a method for detecting oil leakage of a clamp motor, comprising:

[0005] Acquire first current data of the clamp motor, where the first current data is real-time current data of the clamp motor continuously collected within a preset time period;

[0006] An oil leakage detection is performed based on the first current data of the clamp motor to obtain an oil leakage detection result of the clamp motor.

[0007] In some embodiments, performing oil leakage detection based on the first current data of the clamp motor to obtain an oil leakage detection result of the clamp motor includes:

[0008] Based on the first current data of the clamp motor, an oil leakage detection is performed using a pre-trained clamp motor oil leakage detection model to obtain an oil leakage detection result of the clamp motor. In some embodiments, the clamp motor oil leakage detection model is trained using the following method:

[0009] Get historical current data of the clamp motor;

[0010] Extracting samples based on historical current data of the clamp motor to obtain a current data sample set, the current data sample set including a plurality of current data samples, the current data samples including current data of the clamp motor continuously collected within a preset time period;

[0011] The clamp motor oil leakage detection model is trained based on the current data sample set.

[0012] In some embodiments, before training the clamp motor oil leakage detection model based on the current data sample set, the method further includes:

[0013] The current data of the clamp motor continuously collected within a preset time period is normalized.

[0014] In some embodiments, further comprising:

[0015] Determine the evaluation function, hyperparameters, and optimizer for the clamp motor oil leakage detection model;

[0016] During the training process of the clamp motor oil leakage detection model, the clamp motor oil leakage detection model is optimized based on the evaluation function, hyperparameters and optimizer.

[0017] In some embodiments, the current data samples include sample labels, which include an oil leakage condition label and a normal condition label. The number of first current data samples including the oil leakage condition label is the same as the number of second current data samples including the normal condition label.

[0018] In some embodiments, the clamp motor oil leakage detection model is a multi-layer perceptron neural network model using a logistic regression algorithm.

[0019] In some embodiments, the multi-layer perceptron neural network model includes:

[0020] Input layer, which contains 32N neurons;

[0021] The middle layer contains 64N neurons;

[0022] Output layer, which contains 2N neurons;

[0023] Wherein, N is a positive integer.

[0024] In some embodiments, the multi-layer perceptron neural network model uses a sigmoid function as an activation function.

[0025] In some embodiments, obtaining the first current data of the clamp motor includes:

[0026] After receiving the wake-up signal, the real-time current data of the clamp motor is collected and stored at a preset interval;

[0027] If the accumulated acquisition time reaches the preset time, the real-time current data of the clamp motor continuously acquired within the preset time is extracted as the first current data of the clamp motor.

[0028] In some embodiments, it further includes:

[0029] The real-time current data of the clamp motor continuously collected within a preset time period is normalized.

[0030] In some embodiments, the above method is executed on an electronic control unit of an electronic parking brake system.

[0031] In some embodiments, it further includes:

[0032] A data message is sent to the vehicle power network, the data message including the oil leakage detection result of the clamp motor.

[0033] In a second aspect, an embodiment of the present application provides a method for handling an oil leakage fault of a clamp motor, comprising:

[0034] Obtaining an oil leakage detection result of the clamp motor, where the oil leakage detection result is obtained according to the method of the first aspect;

[0035] Perform oil leakage fault processing based on the oil leakage detection results of the clamp motor.

[0036] In some embodiments, the above-mentioned obtaining of the oil leakage detection result of the clamp motor includes:

[0037] The train control and management system obtains the oil leakage detection result of the clamp motor from the vehicle power network.

[0038] In some embodiments, the oil leakage fault processing based on the oil leakage detection result of the clamp motor includes:

[0039] Generate and display alarm information based on the oil leakage detection results of the clamp motor;

[0040] Generate a return control signal based on the alarm information;

[0041] The installation vehicle of the clamp motor is controlled based on the return-to-depot control signal to perform a return-to-depot inspection.

[0042] In some embodiments, obtaining the oil leakage detection result of the clamp motor further includes:

[0043] The operation and maintenance platform obtains the oil leakage detection result of the clamp motor from the vehicle power network.

[0044] In some embodiments, the oil leakage fault processing based on the oil leakage detection result of the clamp motor includes:

[0045] Send a warning message about the oil leakage fault of the clamp motor.

[0046] In a third aspect, an embodiment of the present application provides an oil leakage detection device for a clamp motor, comprising:

[0047] A first data acquisition module is used to acquire first current data of the clamp motor, where the first current data is real-time current data of the clamp motor continuously collected within a preset time period;

[0048] The oil leakage detection module is used to perform oil leakage detection based on the first current data of the clamp motor to obtain an oil leakage detection result of the clamp motor.

[0049] In a fourth aspect, an embodiment of the present application provides a device for handling oil leakage faults of a clamp motor, comprising:

[0050] a second data acquisition module, configured to acquire an oil leakage detection result of the clamp motor, the oil leakage detection result being obtained by performing oil leakage detection on the clamp motor according to the oil leakage detection device of the third aspect;

[0051] The oil leakage fault processing module is used to perform oil leakage fault processing based on the oil leakage detection result of the clamp motor.

[0052] In a fifth aspect, an embodiment of the present application provides an oil leakage fault handling system for a clamp motor, characterized in that it includes the oil leakage detection device of the third aspect and the oil leakage fault handling device of the fourth aspect.

[0053] In a sixth aspect, an embodiment of the present application provides an electronic device, comprising: a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the above-mentioned program or instructions are executed by the processor, the oil leakage detection method for the clamp motor as described in the first aspect is implemented; or, when the above-mentioned program or instructions are executed by the processor, the steps of the oil leakage fault handling method for the clamp motor as described in the second aspect are implemented.

[0054] In the seventh aspect, an embodiment of the present application provides a vehicle, comprising the electronic device of the sixth aspect, wherein the memory of the electronic device stores programs or instructions that can be run on a processor, and when the above-mentioned programs or instructions are executed by the processor, the steps of the oil leakage detection method for the clamp motor as described in the first aspect are implemented.

[0055] In an eighth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the method for detecting oil leakage of the clamp motor as described in the first aspect is implemented; or, when the program or instruction is executed by a processor, the steps of the method for handling oil leakage fault of the clamp motor as described in the second aspect are implemented.

[0056] In the ninth aspect, an embodiment of the present application provides a computer program product, which, when executed by a vehicle's processor, implements the clamp motor oil leakage detection method as described in the first aspect; or, when the program or instruction is executed by the processor, implements the steps of the clamp motor oil leakage fault handling method as described in the second aspect.

[0057] The technical solution provided in this application obtains first current data of the clamp motor, which is real-time current data of the clamp motor continuously collected over a preset period of time, and then performs oil leakage detection based on the first current data of the clamp motor to obtain an oil leakage detection result for the clamp motor. Because the above technical solution performs oil leakage detection on the clamp motor based on the current data of the clamp motor continuously collected over a preset period of time during the oil leakage detection process, compared to using current data at a specific moment, it can more accurately determine whether the clamp motor is leaking, thereby helping to prevent equipment damage, performance degradation, and potential safety risks caused by oil leakage, thereby improving the safety and convenience of the clamp motor oil leakage detection operation.

[0058] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become obvious from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0060] Figure 1 This is a flow chart of a method for detecting oil leakage of a clamp motor according to an embodiment of the present application;

[0061] Figure 2 This is a schematic diagram of a process for training a clamp motor oil leakage detection model in an embodiment of the present application;

[0062] Figure 3 for Figure 1 A schematic diagram of a specific execution flow of step 101 of the illustrated embodiment;

[0063] Figure 4 This is a schematic diagram of a clamp motor oil leakage detection process in an embodiment of the present application;

[0064] Figure 5 This is a flow chart of a method for handling an oil leakage fault of a clamp motor according to an embodiment of the present application;

[0065] Figure 6 for Figure 5 A schematic diagram of a specific execution flow of step 502 in the illustrated embodiment;

[0066] Figure 7 This is a structural diagram of an oil leakage detection device for a clamp motor in an example of this application;

[0067] Figure 8 This is a structural diagram of a device for handling oil leakage faults of a clamp motor in an example of this application;

[0068] Figure 9 This is a schematic structural diagram of an electronic device according to an embodiment of the present application;

[0069] Figure 10 This is a structural diagram of a vehicle in an embodiment of the present application. DETAILED DESCRIPTION

[0070] The following describes embodiments of the present application in more detail with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are for illustrative purposes only and are not intended to limit the scope of protection of the present application.

[0071] It should be understood that the various steps described in the method embodiments of the present application can be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present application is not limited in this respect.

[0072] In some related technologies, some deep learning models can be used to detect the operating status of vehicles. For example, long short-term memory networks can be used to detect current anomalies in vehicle motors. However, although deep learning has made remarkable achievements in vehicle status monitoring, there is no technical solution for oil leakage detection in clamp motors using deep learning models.

[0073] In addition, since the clamp motor is a component used for parking brakes on vehicles, the stability and reliability of its operating state are directly related to the safety of the vehicle and the smoothness of daily use. Oil leakage is one of the common faults of the clamp motor. If it is not discovered and handled in time, it will not only cause waste of resources, but may also cause more serious equipment damage or even safety accidents.

[0074] Therefore, an embodiment of the present application provides a technical solution. During the process of oil leakage detection of the clamp motor, first current data of the clamp motor is obtained. The first current data is real-time current data of the clamp motor continuously collected over a preset time period. Then, based on the first current data of the clamp motor, an oil leakage detection is performed to obtain an oil leakage detection result of the clamp motor. Since the above technical solution performs oil leakage detection on the clamp motor based on the current data of the clamp motor continuously collected over a preset time period during the oil leakage detection process, compared to using current data at a specific moment, it can more accurately determine whether the clamp motor has an oil leakage, thereby helping to prevent equipment damage, performance degradation, and potential safety risks caused by oil leakage, thereby improving the safety and convenience of the clamp motor oil leakage detection operation.

[0075] Figure 1 FIG. 1 is a flow chart of a method for detecting oil leakage of a clamp motor according to an embodiment of the present application. The method for detecting oil leakage of a clamp motor can be implemented by a corresponding oil leakage detection device for the clamp motor, such as Figure 1 As shown, the method includes the following steps:

[0076] Step 101: Acquire first current data of the clamp motor, where the first current data is real-time current data of the clamp motor continuously collected within a preset time period;

[0077] In the technical solution provided in the embodiment of the present application, the clamp motor is an electric motor on the vehicle for driving the brake clamp, which drives the clamp movement by converting electrical energy into mechanical energy, thereby achieving the clamping and release of the brake disc to achieve the purpose of braking; the first current data is composed of the current data of multiple clamp motors at different time points, and these current data maintain continuity in time. Specifically, it includes the clamp motor current data collected at fixed time intervals from the current moment to before the preset time length. For example, if the preset time length is 1s, and the clamp motor current data is collected every 10ms, then the clamp motor current data can be collected 100 times within this 1s, then the first current data will include the current data of the clamp motor collected at the current time and the current data of the clamp motor collected 99 times before.

[0078] Step 102: Perform oil leakage detection based on the first current data of the clamp motor to obtain an oil leakage detection result of the clamp motor.

[0079] After the first current data of the clamp motor is acquired in step 101 , an oil leakage detection is performed based on the first current data to obtain a detection result of the clamp motor.

[0080] As described above, the oil leakage detection of the clamp motor is performed based on the current data of the clamp motor continuously collected within a preset time period. Compared with using the current data at a certain moment, it is possible to more accurately determine whether there is oil leakage in the clamp motor, thereby helping to prevent equipment damage, performance degradation and potential safety risks caused by oil leakage, thereby improving the safety and convenience of the clamp motor oil leakage detection operation.

[0081] In some embodiments, in the process of performing oil leakage detection based on the first current data of the clamp motor and obtaining the oil leakage detection result of the clamp motor, a pre-trained clamp motor oil leakage detection model needs to be introduced. Specifically, based on the acquired first current data of the clamp motor, the clamp motor is subjected to oil leakage detection through a pre-trained clamp motor oil leakage detection model to obtain the above-mentioned clamp motor oil leakage detection result. The above-mentioned pre-trained clamp motor oil leakage detection model can be a deep learning model, which can automatically perform oil leakage detection on the above-mentioned clamp motor according to the input first current data of the clamp motor after pre-training. It can realize oil leakage detection without the need for additional physical components, thereby improving the accuracy of the clamp motor oil leakage detection. Usually, before testing the data, it is necessary to first train a corresponding model for specific data features and detection targets. This process involves using historical data or labeled sample sets, and continuously adjusting and optimizing the parameters and structure of the model through machine learning or deep learning algorithms, so that it can learn the rules and features in the data, so that the model has the ability to extract valuable information from unknown data, and then in the subsequent detection process, it can accurately judge the status of the data or predict future trends. Figure 2 This is a flow chart of a training model for detecting oil leakage of a clamp motor according to an embodiment of the present application. Figure 2 As shown, the following steps are included:

[0082] Step 201: Obtain historical current data of the clamp motor;

[0083] The historical current data in this step is the current data generated by the clamp motor during the historical period, which includes the current data of the clamp motor in various working states under normal working conditions and the current data in various working states under oil leakage conditions. After these current data are collected, they are stored in the corresponding database for subsequent analysis and reference.

[0084] Step 202: extracting samples based on historical current data of the clamp motor to obtain a current data sample set, where the current data sample set includes a plurality of current data samples, and the current data samples include current data of the clamp motor continuously collected within a preset time period;

[0085] This step is to extract samples of the historical current data of the clamp motor after obtaining it in step 201 to obtain a current data sample set, and the current data sample set includes multiple current data samples, and the current data samples include the current data of the clamp motor continuously collected within a preset time length, wherein the data sample set is the training data of the clamp motor oil leakage detection model.

[0086] In an embodiment of the present application, when extracting samples of the historical current data of the clamp motor obtained above, it is necessary to first segment the historical current data of the clamp motor to obtain multiple separate current data samples, and these current data samples are continuous in time. Such segmentation can ensure that each current data sample can fully reflect the operating status of the clamp motor within a period of time, which is convenient for subsequent data analysis; then the above current data samples are classified and processed, and at the same time, each current data sample needs to be labeled with a corresponding sample label. Such classification and labeling will help subsequent data management and model training. For example, if a current data sample is the current data of each working state under normal working conditions, it will be labeled as normal; if a current data sample is the current data of each working state under oil leakage conditions, it will be labeled as oil leakage.

[0087] Step 203: Training a clamp motor oil leakage detection model based on the current data sample set.

[0088] This step is based on step 202 and uses the extracted current data sample set to train the pre-built clamp motor oil leakage detection model.

[0089] In an embodiment of the present application, in order to ensure that the model can smoothly and effectively train the clamp motor oil leakage detection model, it is necessary to normalize the current data of the clamp motor continuously collected within a preset time period before training the clamp motor oil leakage detection model based on the current data sample set. Normalization is a commonly used technique in data preprocessing, which scales the data to a specific smaller interval, for example, the interval [0,1] or the interval [-1,1]. The data can be normalized by selecting a suitable normalization method based on factors such as the distribution of the data, the correlation between features, and the stability of the model. The above-mentioned normalization methods include: linear normalization, zero-mean normalization, nonlinear normalization, and mean-variance normalization. In this application, by normalizing the current data, the dimensional influence of the current data can be eliminated, the model training process can be optimized, the model performance can be improved, and the model can be helped to better adapt to actual application scenarios.

[0090] In addition, before training the clamp motor oil leakage detection model based on the above-mentioned current data sample set, it is also necessary to determine the key elements such as the evaluation function, hyperparameters and optimizer required for training the clamp motor oil leakage detection model. The evaluation function is used to measure the degree of deviation between the model prediction value and the actual target value. The selection depends on the type of specific task. For example, when processing regression tasks, the mean square error function can be selected to effectively reflect the square mean of the difference between the predicted value and the target value. When processing classification tasks, the cross loss function can be selected to better measure the uncertainty of the model's category prediction and improve the accuracy of classification. Since detecting whether the clamp motor is leaking oil is a classification task, the binary cross-entropy loss function is selected in the embodiment shown in this application. Loss, BCE) is used to evaluate the clamp motor oil leakage detection model; the hyperparameters are set before model training and are continuously optimized during the model training process, and are mainly used to control the training process and structure of the model, which include learning rate, batch size, number of iterations, regularization parameters, etc., and different values of these hyperparameters will lead to significant differences in the performance and generalization ability of the model. In the embodiment shown in this application, when training the clamp motor oil leakage detection model, the learning rate is initially set to 0.01, the number of model cycle training times is 10, the number of iterations for each training is 500, the batch size is 128, and the regularization parameter L2 is added, and the data format of the input data is <batch size, time length>, where the time length is set is set to 50, that is, the current data of 50 clamp motors collected is one input data; the optimizer is used to update the weights and biases of the model and adjust the hyperparameters of the model during the training process to minimize the value of the loss function, thereby improving the accuracy and performance of the model. It is necessary to select a suitable optimizer based on the structure of the model, the amount of data, the objective function and the training time. For example, for large-scale data sets and complex models, the Adam optimizer is usually selected. Since the clamp motor oil leakage detection model in the embodiment shown in the application is a deep learning model and needs to be trained based on a large amount of current data of the clamp motor, when training the above-mentioned clamp motor oil leakage detection model, the Adam optimizer is selected to update the weights, biases, etc. of the model.

[0091] In the above embodiment, after determining the evaluation function, hyperparameters, and optimizer required for training the clamp motor oil leakage detection model, the clamp motor oil leakage detection model can be optimized based on the determined evaluation function, hyperparameters, and optimizer during the formal training of the clamp motor oil leakage detection model. For example, the binary cross entropy loss function is used to quantify the model's ability to predict clamp motor oil leakage; by adjusting hyperparameters such as the learning rate of 0.01, the batch size of 128, and the number of iterations per training of 500, the relationship between the model's training efficiency and the model's generalization ability is balanced; and the optimizer Adam is used to update the model's weights, bias, and other model parameters. By optimizing the clamp motor oil leakage detection model based on the above hyperparameters, the optimized clamp motor oil leakage detection model can more accurately predict the clamp motor oil leakage in actual applications, providing a strong guarantee for the safe operation of the equipment.

[0092] In some embodiments, the above-mentioned current data samples also include sample labels, which include oil leakage condition labels and normal condition labels, and the number of first current data samples including oil leakage condition labels is the same as the number of second current data samples including normal condition labels. This setting can maintain data balance and fairness of the analysis structure, which helps to avoid model performance deviation caused by data deviation in subsequent training and testing of the clamp motor oil leakage detection model, thereby more accurately evaluating the model's recognition ability and generalization performance for different working conditions.

[0093] In an embodiment of the present application, the above-mentioned clamp motor oil leakage detection model is a multi-layer perceptron neural network model using logistic regression, and the model deeply analyzes and processes the input current data sample set by constructing a multi-layer neural network, and utilizes the classification mechanism of logistic regression, relying on the pytorch deep learning framework to build and optimize the model, thereby realizing the analysis and classification of current data.

[0094] In some embodiments, the above-mentioned multi-layer perceptron neural network model is a three-layer neural network architecture with a clear structure and powerful functions. The model consists of an input layer, an intermediate layer, and an output layer, which are interconnected in a fully connected manner to ensure effective transmission and processing of information.

[0095] Specifically, the input layer contains 32N neurons, where N is a positive integer. These neurons are primarily responsible for receiving current data from the input current data sample set. Each neuron in the input layer corresponds to a feature or dimension of the input current data, enabling the entire network to process multi-dimensional input current data. After preprocessing, these input current data are fed into the network for further processing and learning. In the embodiment shown in this application, the value of N can be 1, i.e., the input layer contains 32 neurons.

[0096] The middle layer contains 64N neurons, where N is a positive integer. These neurons extract the intrinsic features and patterns of the current data by performing complex nonlinear transformations on the current data transmitted from the input layer. The number of neurons in the middle layer is greater than that in the input layer, indicating that the network in this layer can learn more complex and abstract feature representations than the original input current data. This allows the network to capture patterns in the data that are not easily observed directly, thus helping to solve complex problems. In the embodiment shown in this application, the value of N can be 1, that is, the middle layer contains 64 neurons.

[0097] The output layer contains 2N neurons, where N is a positive integer. Its primary function is to make a final prediction based on the features of the current data extracted by the intermediate layer. For classification tasks, the number of neurons in the output layer typically corresponds to the number of classification categories. For example, two neurons may represent two possible outcomes for a binary classification problem. In the embodiment shown in this application, the value of N can be 1, meaning that the output layer contains two neurons.

[0098] The entire multi-layer perceptron neural network receives current data through the input layer, then uses the intermediate layer to extract and convert features, and finally the output layer gives the model prediction results, realizing a complex mapping relationship from input to output, thereby improving the accuracy and efficiency of oil leakage prediction for the clamp motor.

[0099] Typically, in classification tasks, an activation function is applied after the last layer of the neural network model. This introduces nonlinear factors into the model, improving the model's expressiveness. It also provides strong support for the model's training and prediction processes by controlling the output range, assisting in decision-making and optimization, and improving model performance. Depending on the specific type of classification task, you can choose an activation function that matches the constructed neural network model. For example, for a binary classification task, you might use a sigmoid activation function, which maps the model's output to the [0,1] interval, indicating the probability that the model's output belongs to a certain category. For a multi-classification task, you might use a softmax activation function, which converts the model's output into a probability distribution, where the sum of the probabilities that the model's output belongs to each category is 1. In an embodiment of the present application, because the above-mentioned detection of oil leakage of the clamp motor is a binary classification task, the above-mentioned multi-layer perceptron neural network model applies the sigmoid activation function to process the output result of the model, and judges whether the clamp motor is leaking oil based on the processed result. For example, after the clamp motor oil leakage detection model passes through the sigmoid activation function, the output value range is in the range of [0,1]. If the output value is less than 0.5, the working condition of the clamp motor is regarded as an oil leakage condition; if the output value is greater than or equal to 0.5, the working condition of the clamp motor is regarded as a normal condition.

[0100] Specifically, Figure 3 for Figure 1 The specific execution flow diagram of step 101 of the embodiment shown in the figure is as follows: Figure 3 As shown, the following steps are included:

[0101] Step 301: After receiving the wake-up signal, collect and store the real-time current data of the clamp motor at a preset interval;

[0102] The wake-up signal in the embodiment of the present application refers to the signal for waking up the electronic control unit in the electronic parking brake system of the vehicle. The above-mentioned electronic control unit (ECU), also known as the vehicle's driving computer, mainly uses data collection and exchange of various sensors and buses to determine the vehicle status and the driver's intention, and controls the vehicle through actuators. The wake-up methods of the vehicle ECU mainly include hard-line wake-up, CAN bus wake-up, external signal wake-up, timer wake-up, communication wake-up or external interrupt wake-up. The preset time can be set according to actual needs. For example, if the preset time is set to 10ms, the real-time current data of the clamp motor will be collected every 10ms, and the collected current data will be stored.

[0103] Step 302: If the accumulated acquisition time reaches the preset time, the real-time current data of the clamp motor continuously acquired within the preset time is extracted as the first current data of the clamp motor.

[0104] This step is based on step 301, and the accumulated collection time is compared with the preset time. If the accumulated collection time reaches the preset time, the real-time current data of the clamp motor continuously collected within the preset time is extracted as the first current data of the clamp motor. The preset time can be set according to actual needs. For example, if the preset time is set to 2s, the real-time current data of the clamp motor collected 200 times is extracted as the first current data of the clamp motor.

[0105] In an embodiment of the present application, before the real-time current data of the clamp motor is input into the above-mentioned pre-trained model, the real-time current data of the above-mentioned clamp motor needs to be normalized, for example, the above-mentioned real-time current data is scaled to the interval [0, 1], so as to ensure that the pre-trained clamp motor oil leakage detection model can recognize the real-time current data of the clamp motor continuously collected within a preset time length, and thus effectively determine whether the above-mentioned clamp motor has an oil leakage.

[0106] In the embodiments of this application, Figure 1-Figure 3The embodiments shown are all executed on the electronic control unit of the electronic parking brake system. The above-mentioned electronic parking brake system (EPB) is a parking brake control motor that directly controls the rear wheel brake caliper to achieve parking braking. Its main components include three parts: the parking brake switch, the electronic control unit and the parking brake actuator motor. The parking brake switch is used to control the opening and closing of the EPB.

[0107] In some embodiments, during the process of vehicle operation monitoring and maintenance, detailed data messages are sent to the vehicle power network periodically or in real time. These data messages contain operating status information of key components of the vehicle. In an embodiment of the present application, the above-mentioned data messages also include the oil leakage detection results of the clamp motor. The oil leakage detection results of the clamp motor are encapsulated in the data message so that the vehicle power network system can quickly receive, parse and make corresponding processing or maintenance instructions, thereby ensuring the safe operation and efficient maintenance of the vehicle.

[0108] In the embodiment of the present application, when the clamp motor oil leakage detection method in the above embodiment of the application is used to detect the oil leakage of the clamp motor on the vehicle, the current state of the electronic control unit in the vehicle's electronic parking brake system must also be considered. If the electronic control unit in the vehicle's electronic parking brake system is in an awakened state, the oil leakage detection of the clamp motor on the vehicle is allowed; if the electronic control unit in the vehicle's electronic parking brake system is in a dormant state, the oil leakage detection of the clamp motor on the vehicle is suspended. The specific oil leakage detection process is as follows: Figure 4 As shown, the following steps are included:

[0109] Step 401: Start oil leakage detection of the clamp motor;

[0110] In this step, the oil leakage detection of the clamp motor can be started in the following two ways: one is to automatically trigger the oil leakage detection function when the vehicle is started; the other is that the administrator starts the oil leakage detection of the clamp motor through the control button.

[0111] Step 402: wake up the electronic control unit in the electronic parking brake system;

[0112] Based on step 401, a wake-up signal is used to wake up the electronic control unit in the vehicle's electronic parking brake system, enabling the caliper motor to operate and generate corresponding current data. A series of wake-up signals are generated when the vehicle is started or when the administrator presses a button. These wake-up signals include: inserting and turning the key, pressing the start button, opening the door, unlocking the vehicle, charging the vehicle, and braking.

[0113] Step 403: collecting real-time current data of the clamp motor at a preset time interval;

[0114] After the electronic control unit in the vehicle's electronic parking brake system is awakened in step 402 , the real-time current data of the clamp motor is collected according to a preset time, for example, the real-time current data of the clamp motor is collected every 10 ms.

[0115] Step 404: storing the collected real-time current data of the clamp motor;

[0116] This step is based on step 403 and stores the collected real-time current data of the clamp motor.

[0117] Step 405: Determine whether the accumulated collection time has reached the preset time;

[0118] Specifically, based on the preset time for collecting the real-time current data of the clamp motor, the cumulative collection time is determined, and it is judged whether the cumulative collection time reaches the preset time. If the above collection time reaches the preset time, step 406 is executed, otherwise step 409 is executed.

[0119] Step 406: extracting the real-time current data of the clamp motor continuously collected within a preset time period;

[0120] After it is determined in the above step 405 that the accumulated acquisition time reaches the preset time, the real-time current data of the clamp motor continuously acquired within the preset time is extracted.

[0121] Step 407: Determine whether the clamp motor is leaking oil;

[0122] This step determines whether the clamp motor is leaking oil based on the real-time current data continuously collected over a preset period of time extracted in step 406 and the stored pre-trained clamp motor oil leakage detection model. If so, step 408 is executed; otherwise, step 409 is executed.

[0123] Step 408: Sending data message to the vehicle power network;

[0124] After determining that the clamp motor is leaking oil in step 407 , the oil leakage detection result of the clamp motor is encapsulated into a data message, and the data message is sent to the vehicle power network.

[0125] Step 409: determine whether the electronic control unit in the electronic parking brake system is in sleep mode;

[0126] After determining in the above step 405 that the cumulative collection time has not reached the preset time, or after determining in step 407 that the clamp motor is not leaking oil, and after sending a data message including the clamp motor oil leakage detection result to the vehicle power network, determine whether the electronic control unit in the electronic parking brake system is dormant. If the electronic control unit in the electronic parking brake system is dormant, the oil leakage detection of the clamp motor is automatically terminated, otherwise step 403 is executed.

[0127] In the embodiment shown in the present application, the clamp motor oil leakage detection method described above can be used to detect the clamp motor on the vehicle and obtain the clamp motor oil leakage detection result. Based on the clamp motor oil leakage detection result, the clamp motor with oil leakage fault can be processed, thereby ensuring the stable operation of the clamp motor and the overall safety performance of the vehicle. Figure 5 FIG. 1 is a flow chart of a method for handling an oil leakage fault of a clamp motor according to an embodiment of the present application. Figure 5 As shown, the following steps are included:

[0128] Step 501: Obtain an oil leakage detection result of the clamp motor, where the oil leakage detection result is obtained according to an oil leakage detection method for the clamp motor;

[0129] This step is to obtain the oil leakage detection result of the clamp motor based on the collected real-time current data of the clamp motor and the clamp motor oil leakage detection method in the above-mentioned application embodiment.

[0130] Step 502: Perform oil leakage fault processing based on the oil leakage detection result of the clamp motor.

[0131] After the oil leakage detection result of the clamp motor is obtained in step 501, the oil leakage fault of the clamp motor is processed according to the oil leakage detection result.

[0132] As described above, timely handling of the oil leakage of the clamp motor through the method for handling the oil leakage of the clamp motor can effectively stop the oil from continuing to leak and prevent it from causing pollution or damage to other parts of the vehicle, thereby avoiding failures such as vehicle dragging and complete damage to the clamp motor, thereby extending the service life of the motor, reducing maintenance costs, and ensuring the overall performance and reliability of the vehicle.

[0133] In some embodiments, the train control and management system can obtain the oil leakage detection results of the clamp motor from the vehicle power network. Specifically, the train control and management system can obtain data messages from the vehicle power network, and the data messages include the oil leakage detection results of the clamp motor. After parsing the data messages, the oil leakage detection results of the clamp can be obtained. The Train Control and Management System (TCMS) is mainly used to implement locomotive characteristic control, logic control, fault monitoring and self-diagnosis, and transmit information to the microcomputer display screen on the driver's console to intuitively reflect the real-time status of the locomotive. It includes a main control device and two display units. The main CPU adopts a redundant design and is equipped with two sets of control links, one for the main control link and the other for the hot standby control link. When the main control link fails, it will automatically cut off and the standby control link will immediately and automatically start working.

[0134] Specifically, Figure 6 for Figure 5 The specific execution flow diagram of step 502 of the embodiment shown is as follows: Figure 6 As shown, the following steps are included:

[0135] Step 601: Generate and display an alarm message based on the oil leakage detection result of the clamp motor;

[0136] After obtaining the clamp motor oil leakage detection results in step 501, the train control and management system generates corresponding alarm information based on the clamp motor oil leakage detection results. This alarm information is sent to the driver controller and signaling system, and displayed on the driver controller's display screen. The driver controller is a control device that primarily controls the locomotive's running direction, power, traction, and speed.

[0137] Step 602: Generate a return-to-stock control signal based on the alarm information;

[0138] This step is to generate corresponding return-to-depot control information based on the alarm information after the alarm information is generated in step 601.

[0139] Step 603: Control the installation vehicle of the clamp motor to return to the warehouse for inspection based on the return control information.

[0140] This step is after the return-to-depot control information is generated in step 602. According to the above return-to-depot control information, the vehicle system or the driver controls the vehicle on which the clamp motor is installed to return to the depot for inspection. Specifically, after receiving the return-to-depot control signal, the vehicle on which the clamp motor is installed will first ensure that the current operating trajectory is completed, that is, the established task is completed. After that, the vehicle will automatically plan the route and safely return to the base for inspection. Alternatively, the vehicle is operated by the driver. When the driver sees the prompt of the above return-to-depot control signal, he will first control the vehicle on which the clamp motor is installed to complete the current operating trajectory, and then manually control the vehicle to return to the base for inspection.

[0141] In some embodiments, the operation and maintenance platform can also obtain the oil leakage detection results of the clamp motor from the vehicle power network. Specifically, the operation and maintenance platform first obtains a data message including the oil leakage inspection results of the clamp motor from the vehicle power network, and then parses the above data message to obtain the oil leakage inspection results of the clamp motor, and based on the oil leakage inspection results of the clamp motor, it displays which clamp motor has the oil leakage problem.

[0142] In some embodiments, after the operation and maintenance platform obtains the clamp motor oil leakage inspection results from the vehicle power network, it generates a fault prompt based on the clamp motor oil leakage inspection results and sends the fault prompt information to the operation and maintenance personnel. After receiving the fault prompt information, the operation and maintenance personnel will perform troubleshooting on the clamp motor according to the fault prompt information. Based on the detection results, the following two fault handling strategies are set: if the clamp motor with the alarm is found to have an oil leakage problem, the clamp motor is replaced and the vehicle is restored to normal operation; if the clamp motor with the alarm is not found to have an oil leakage problem, further detection of the cause of the current abnormality is required, and the clamp motor is replaced, after which the vehicle is restored to normal operation.

[0143] With the above Figure 1 Corresponding to the provided method for detecting oil leakage of the clamp motor, the embodiments of the present application provide a corresponding device. Figure 7 This is a schematic diagram of the structure of an oil leakage detection device for a clamp motor in an example of this application, such as Figure 7 As shown, the oil leakage detection device of the clamp motor includes a first data acquisition module 11 and an oil leakage detection module 12, wherein the first data acquisition module 11 is used to obtain the first current data of the clamp motor, and the first current data is the real-time current data of the clamp motor continuously collected within a preset time period; the oil leakage detection module 12 is used to perform oil leakage detection based on the first current data of the clamp motor to obtain the oil leakage detection result of the clamp motor.

[0144] In some embodiments, the oil leakage detection based on the first current data of the clamp motor is performed to obtain the oil leakage detection result of the clamp motor, including:

[0145] Based on the first current data of the clamp motor, oil leakage detection is performed using a pre-trained clamp motor oil leakage detection model to obtain an oil leakage detection result of the clamp motor.

[0146] In some embodiments, the clamp motor oil leakage detection model is trained by the following method:

[0147] Get historical current data of the clamp motor;

[0148] Extracting samples based on historical current data of the clamp motor to obtain a current data sample set, the current data sample set including a plurality of current data samples, the current data samples including current data of the clamp motor continuously collected within a preset time period;

[0149] The clamp motor oil leakage detection model is trained based on the current data sample set.

[0150] In some embodiments, before training the clamp motor oil leakage detection model based on the current data sample set, the method further includes:

[0151] Normalizing the current data of the clamp motor continuously collected within a preset time period;

[0152] In some embodiments, it further includes:

[0153] Determine the evaluation function, hyperparameters, and optimizer for the clamp motor oil leakage detection model;

[0154] During the training process of the clamp motor oil leakage detection model, the clamp motor oil leakage detection model is optimized based on the evaluation function, hyperparameters and optimizer.

[0155] In some embodiments, the current data samples include sample labels, which include an oil leakage condition label and a normal condition label. The number of first current data samples including the oil leakage condition label is the same as the number of second current data samples including the normal condition label.

[0156] In some embodiments, the clamp motor oil leakage detection model is a multi-layer perceptron neural network model using a logistic regression algorithm.

[0157] In some embodiments, the multi-layer perceptron neural network model includes:

[0158] Input layer, which contains 32N neurons;

[0159] The middle layer contains 64N neurons;

[0160] Output layer, which contains 2N neurons;

[0161] Wherein, N is a positive integer.

[0162] In some embodiments, the multi-layer perceptron neural network model uses a sigmoid function as an activation function.

[0163] In some embodiments, obtaining the first current data of the clamp motor includes:

[0164] After receiving the wake-up signal, the real-time current data of the clamp motor is collected and stored at a preset interval;

[0165] If the accumulated acquisition time reaches the preset time, the real-time current data of the clamp motor continuously acquired within the preset time is extracted as the first current data of the clamp motor.

[0166] In some embodiments, it further includes:

[0167] The real-time current data of the clamp motor continuously collected within a preset time period is normalized.

[0168] In some embodiments, the above method is executed on an electronic control unit of an electronic parking brake system.

[0169] In some embodiments, it further includes:

[0170] A data message is sent to the vehicle power network, the data message including the oil leakage detection result of the clamp motor.

[0171] As described above, the device used to detect oil leakage in the clamp motor can more accurately determine whether there is oil leakage in the clamp motor compared to a device that uses current data at a certain moment. This helps prevent equipment damage, performance degradation, and potential safety risks caused by oil leakage, thereby improving the safety and convenience of the clamp motor oil leakage detection operation.

[0172] With the above Figure 5 Corresponding to the provided method for handling oil leakage fault of the clamp motor, the embodiment of the present application provides a corresponding device. Figure 8 This is a schematic diagram of the structure of a clamp motor oil leakage fault processing device in this application example, as shown in FIG. Figure 8 As shown, the oil leakage fault processing device of the clamp motor includes a second data acquisition module 21 and an oil leakage fault processing module 22, wherein the second data acquisition module 21 is used to obtain the oil leakage detection result of the clamp motor, and the oil leakage detection result is based on Figure 7 The oil leakage detection device of the clamp motor of the illustrated embodiment performs oil leakage detection; the oil leakage fault processing module 22 is used to perform oil leakage fault processing based on the oil leakage detection result of the clamp motor.

[0173] In some embodiments, the above-mentioned obtaining of the oil leakage detection result of the clamp motor includes:

[0174] The train control and management system obtains the oil leakage detection result of the clamp motor from the vehicle power network.

[0175] In some embodiments, the oil leakage fault processing based on the oil leakage detection result of the clamp motor includes:

[0176] Generate and display alarm information based on the oil leakage detection results of the clamp motor;

[0177] Generate a return control signal based on the alarm information;

[0178] The installation vehicle of the clamp motor is controlled based on the return-to-depot control signal to perform a return-to-depot inspection.

[0179] In some embodiments, obtaining the oil leakage detection result of the clamp motor further includes:

[0180] The operation and maintenance platform obtains the oil leakage detection result of the clamp motor from the vehicle power network.

[0181] In some embodiments, the oil leakage fault processing based on the oil leakage detection result of the clamp motor includes:

[0182] Send a warning message about the oil leakage fault of the clamp motor.

[0183] As described above, performing oil leakage fault treatment on the clamp motor based on the oil leakage detection result of the clamp motor by the oil leakage detection device of the clamp motor can effectively stop the oil from continuing to leak and prevent it from causing pollution or damage to other parts of the vehicle, thereby avoiding faults such as vehicle dragging and complete damage to the clamp motor, thereby extending the service life of the motor, reducing maintenance costs, and ensuring the overall performance and reliability of the vehicle.

[0184] The present application also provides a clamp motor oil leakage fault processing system, the clamp motor oil leakage fault processing system includes Figure 7 The oil leakage detection device of the clamp motor and Figure 8 The oil leakage fault processing device of the clamp motor.

[0185] As described above, through the oil leakage fault handling system of the clamp motor, the clamp motor can be detected for oil leakage, and the clamp motor can be handled for oil leakage based on the oil leakage detection result of the clamp motor, which can effectively extend the service life of the clamp motor, reduce maintenance costs, and ensure the overall performance and reliability of the vehicle.

[0186] The present application also provides an electronic device, such as Figure 9 As shown, the electronic device includes: a processor 31 and a memory 32, the memory 31 stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the following is achieved: Figure 1-Figure 4 The steps of the oil leakage detection method of the clamp motor; or, when the program or instruction is executed by the processor, the following is achieved Figure 5-Figure 6 The steps of the method for handling oil leakage failure of the clamp motor.

[0187] The present application also provides a vehicle, such as Figure 10 As shown, the vehicle includes Figure 9 The electronic device 41, wherein the memory of the electronic device stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the following is achieved: Figure 1-Figure 4 The steps of the oil leakage detection method of the clamp motor.

[0188] The present application also provides a computer-readable storage medium, wherein a program or instruction is stored on the computer-readable storage medium, and when the program or instruction is executed by a processor, the following is realized: Figure 1-Figure 4 The steps of the oil leakage detection method of the clamp motor; or, when the program or instruction is executed by the processor, the following is achieved Figure 5-Figure 6 The steps of the method for handling oil leakage failure of the clamp motor.

[0189] The present application also provides a computer program product, which, when executed by a vehicle processor, implements the following Figure 1-Figure 4 The steps of the oil leakage detection method of the clamp motor; or, when the program or instruction is executed by the processor, the following is achieved Figure 5-Figure 6 The steps of the method for handling oil leakage failure of the clamp motor.

[0190] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0191] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of a computer software product plus a necessary general-purpose hardware platform, or of course, by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.) and includes a number of instructions for enabling a terminal or network-side device to execute the methods described in each embodiment of the present application.

[0192] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms of implementation methods without departing from the purpose of this application and the scope of protection of the claims. These implementation methods are all within the protection of this application.

Claims

1. A method for detecting oil leakage of a clamp motor, characterized in that: include: Acquire first current data of the clamp motor, where the first current data is real-time current data of the clamp motor continuously collected within a preset time period; An oil leakage detection is performed based on the first current data of the clamp motor to obtain an oil leakage detection result of the clamp motor.

2. The method according to claim 1, characterized in that The performing oil leakage detection based on the first current data of the clamp motor to obtain the oil leakage detection result of the clamp motor includes: Based on the first current data of the clamp motor, oil leakage detection is performed using a pre-trained clamp motor oil leakage detection model to obtain an oil leakage detection result of the clamp motor.

3. The method according to claim 2, characterized in that The clamp motor oil leakage detection model is trained by the following method: Acquiring historical current data of the clamp motor; Extracting samples based on historical current data of the clamp motor to obtain a current data sample set, wherein the current data sample set includes a plurality of current data samples, and the current data samples include current data of the clamp motor continuously collected within a preset time period; A clamp motor oil leakage detection model is trained based on the current data sample set.

4. The method according to claim 3, characterized in that Before training the clamp motor oil leakage detection model based on the current data sample set, the method further includes: Normalization is performed on the current data of the clamp motor continuously collected within a preset time period.

5. The method according to claim 4, characterized in that Also includes: Determining an evaluation function, hyperparameters, and optimizer for the clamp motor oil leakage detection model; During the training process of the clamp motor oil leakage detection model, the clamp motor oil leakage detection model is optimized based on the evaluation function, hyperparameters and optimizer.

6. The method according to claim 3, characterized in that The current data samples further include sample labels, which include an oil leakage condition label and a normal condition label. The number of first current data samples including the oil leakage condition label is the same as the number of second current data samples including the normal condition label.

7. The method according to claim 3, characterized in that The clamp motor oil leakage detection model is a multi-layer perceptron neural network model using a logistic regression algorithm.

8. The method according to claim 7, characterized in that The multi-layer perceptron neural network model includes: An input layer, comprising 32N neurons; A middle layer, comprising 64N neurons; An output layer, the output layer comprising 2N neurons; Wherein, N is a positive integer.

9. The method according to claim 7, characterized in that The multi-layer perceptron neural network model uses the sigmoid function as the activation function.

10. The method according to claim 1, characterized in that The obtaining of first current data of the clamp motor includes: After receiving the wake-up signal, the real-time current data of the clamp motor is collected and stored at a preset interval; If the accumulated acquisition time reaches the preset time, the real-time current data of the clamp motor continuously acquired within the preset time is extracted as the first current data of the clamp motor.

11. The method according to claim 10, characterized in that Also includes: Normalization is performed on the real-time current data of the clamp motor continuously collected within the preset time period.

12. The method according to any one of claims 1 to 11, characterized in that: The method is executed on an electronic control unit of an electronic parking brake system.

13. The method according to claim 12, characterized in that Also includes: A data message is sent to a vehicle power network, wherein the data message includes an oil leakage detection result of the clamp motor.

14. A method for handling oil leakage fault of a clamp motor, characterized in that: include: Obtaining an oil leakage detection result of the clamp motor, wherein the oil leakage detection result is obtained according to any one of the methods described in claims 1-13; An oil leakage fault is processed based on the oil leakage detection result of the clamp motor.

15. The method according to claim 14, characterized in that The obtaining of the oil leakage detection result of the clamp motor includes: The train control and management system obtains the oil leakage detection result of the clamp motor from the vehicle power network.

16. The method according to claim 14, characterized in that The oil leakage fault processing based on the oil leakage detection result of the clamp motor includes: generating and displaying an alarm message based on an oil leakage detection result of the clamp motor; generating a return-to-stock control signal based on the alarm information; The installation vehicle of the clamp motor is controlled to return to the warehouse for inspection based on the return control signal.

17. The method according to any one of claims 14 to 16, characterized in that: The obtaining of the oil leakage detection result of the clamp motor further includes: The operation and maintenance platform obtains the oil leakage detection result of the clamp motor from the vehicle power network.

18. The method according to claim 17, characterized in that The oil leakage fault processing based on the oil leakage detection result of the clamp motor includes: Send a warning message about the oil leakage fault of the clamp motor.

19. An oil leakage detection device for a clamp motor, characterized in that: include: a first data acquisition module, configured to acquire first current data of the clamp motor, wherein the first current data is real-time current data of the clamp motor continuously collected within a preset time period; The oil leakage detection module is used to perform oil leakage detection based on the first current data of the clamp motor to obtain an oil leakage detection result of the clamp motor.

20. A device for handling oil leakage fault of a clamp motor, characterized in that: include: a second data acquisition module, configured to acquire an oil leakage detection result of the clamp motor, wherein the oil leakage detection result is obtained by performing oil leakage detection using the oil leakage detection device for the clamp motor according to claim 19; The oil leakage fault processing module is used to perform oil leakage fault processing based on the oil leakage detection result of the clamp motor.

21. A clamp motor oil leakage fault treatment system, characterized in that: It includes the oil leakage detection device according to claim 19 and the oil leakage fault handling device according to claim 20.

22. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, it implements the oil leakage detection method for the clamp motor as described in any one of claims 1 to 13; or, when the program or instruction is executed by the processor, it implements the steps of the oil leakage fault handling method for the clamp motor as described in any one of claims 14 to 18.

23. A vehicle, characterized in that: An electronic device comprising the electronic device of claim 22, wherein the memory of the electronic device stores a program or instruction that can be run on a processor, and when the program or instruction is executed by the processor, the steps of the oil leakage detection method for the clamp motor as described in any one of claims 1 to 13 are implemented.

24. A computer-readable storage medium, characterized in that The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, it implements the oil leakage detection method of the clamp motor as described in any one of claims 1 to 13; or, when the program or instruction is executed by the processor, it implements the steps of the oil leakage fault handling method of the clamp motor as described in any one of claims 14 to 18.

25. A computer program product, characterized in that When the program product is executed by the vehicle's processor, it implements the clamp motor oil leakage detection method as described in any one of claims 1 to 13; or, when the program or instruction is executed by the processor, it implements the steps of the clamp motor oil leakage fault processing method as described in any one of claims 14 to 18.

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