Stress analysis device, method and equipment for vehicle cable

Abstract

The invention discloses a stress analysis device, method and equipment for a vehicle cable, and belongs to the technical field of vehicle cables. The device comprises: a motion data acquisition module for acquiring motion data of a vehicle, the motion data comprising a running speed, an acceleration and vehicle body bump data; the cable monitoring data acquisition module is used for acquiring monitoring data of a vehicle cable, and the feature extraction module is used for performing feature extraction on the monitoring data through an intelligent analysis model; the attribution analysis module is used for carrying out time dimension synchronization on the data features and the motion data and carrying out correlation analysis; and the processing measure generation module is used for determining the current vehicle cable as a target cable under the condition that the correlation is identified to exceed a set threshold value, acquiring working attribute information of the target cable, and generating a processing measure of the target cable according to the working attribute information. According to the technical scheme, the operation safety and stability of the vehicle cable can be improved, and better driving experience is provided for a user.

Description

Technical Field

[0001] This application belongs to the technical field of vehicle cables, and particularly relates to a stress analysis device, method and equipment for vehicle cables. Background Art

[0002] With the rapid development of the technological level, automobiles have become a rapidly popular means of transportation. As is well known, each locomotive has various cables to achieve the overall vehicle control and function control of the automobile, etc.

[0003] At present, during the operation of an automobile, a large amount of heat is generated, and at the same time, affected by the road surface, there will be a bumpy situation. In such a case, if there are problems such as loose contacts or other problems with the cables, it will cause the inability to normally transmit energy or signals under specific circumstances, resulting in a decrease in the operating stability of the vehicle, and there may even be potential operating safety hazards. Therefore, how to effectively analyze the stress of vehicle cables in scenarios such as bumpy roads and emergency braking to ensure their safe operation is a technical problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

[0004] The purpose of the embodiments of this application is to provide a stress analysis device, method and equipment for vehicle cables, aiming to analyze the impact of vehicle operation on the operation of vehicle cables from the perspective of stress by comparing and analyzing the motion data and the characteristics of vehicle cables, and then give corresponding treatment measure opinions. This solution can improve the operating safety and stability of vehicle cables and provide a better driving experience for users.

[0005] In a first aspect, the embodiments of this application provide a stress analysis device for vehicle cables, and the device includes:

[0006] A motion data acquisition module, configured to acquire the motion data of the vehicle, where the motion data includes the running speed, acceleration, and vehicle body bump data;

[0007] A cable monitoring data acquisition module, configured to acquire the monitoring data of the vehicle cable, where the monitoring data includes the current data of the vehicle cable, the intensity data of the transmitted signal, and the resistivity change data;

[0008] A feature extraction module, configured to extract features from the monitoring data through an intelligent analysis model;

[0009] An attribution analysis module, configured to synchronize the extracted data features with the motion data in the time dimension and perform a correlation analysis;

[0010] A processing measure generation module, which is used to determine the current vehicle cable as the target cable when the recognized correlation exceeds the set threshold, obtain the working attribute information of the target cable, and generate the processing measure of the target cable according to the working attribute information.

[0011] Further, the feature extraction module includes:

[0012] A time scale determination unit, which is used to determine at least two time scales used for feature extraction;

[0013] A sliding window generation unit, which is used to generate a sliding window according to the at least two time scales, so as to determine the time reference range of feature extraction through the sliding window;

[0014] A feature extraction unit, which is used to input the monitoring data within the sliding window into the intelligent analysis model to obtain the data features of the monitoring data within the sliding window.

[0015] Further, the attribution analysis module includes:

[0016] A synchronization unit, which is used to synchronize the extracted data features with the motion data in the time dimension;

[0017] An analysis unit, which is used to identify the change features in the data features, and identify whether there is a positive correlation or a negative correlation between the change features and the motion data, as well as the positive correlation ratio or the negative correlation ratio.

[0018] Further, the analysis unit is specifically used for:

[0019] When identifying the change features in the data features and identifying that the change features are time-consistent with the motion data, it is determined as a positive correlation, and based on the occurrence times of the change features and the occurrence times of the motion data, the positive correlation ratio is determined; or, when identifying the change features in the data features and identifying that the change features are time-opposite to the motion data, it is determined as a negative correlation, and based on the occurrence times of the change features and the occurrence times of the motion data, the negative correlation ratio is determined.

[0020] Further, the analysis unit is specifically used for:

[0021] Identify the change features in the data features, and identify whether there is a positive correlation between the change features and the acceleration caused by the vehicle's emergency braking, as well as the positive correlation ratio.

[0022] Further, the analysis unit is specifically used for:

[0023] Identify the changing features in the data features, and identify whether there is a positive correlation between the changing features and at least one speed interval of the running speed of the vehicle, as well as the correlation ratio of the positive correlation.

[0024] Further, the motion data acquisition module is specifically configured to:

[0025] Collect the deformation data of the suspension system during the vehicle driving process by using a linear variable differential transformer to obtain the vehicle body bump data.

[0026] In a second aspect, an embodiment of the present application provides a method for stress analysis of a vehicle cable, and the method includes:

[0027] Obtain the motion data of the vehicle, where the motion data includes the running speed, acceleration, and vehicle body bump data;

[0028] Obtain the monitoring data of the vehicle cable, where the monitoring data includes the current data of the vehicle cable, the intensity data of the transmitted signal, and the resistivity change data;

[0029] Extract features from the monitoring data through an intelligent analysis model;

[0030] Synchronize the extracted data features with the motion data in the time dimension and perform a correlation analysis;

[0031] When it is recognized that the correlation exceeds a set threshold, determine the current vehicle cable as the target cable, obtain the working attribute information of the target cable, and generate a processing measure for the target cable according to the working attribute information.

[0032] Further, extracting features from the monitoring data through an intelligent analysis model includes:

[0033] Determine at least two time scales used for feature extraction;

[0034] Generate a sliding window according to the at least two time scales to determine the time reference range for feature extraction through the sliding window;

[0035] Input the monitoring data within the sliding window into the intelligent analysis model to obtain the data features of the monitoring data within the sliding window.

[0036] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.

[0037] Fourthly, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0038] Fifthly, an embodiment of the present application provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the method described in the first aspect.

[0039] In the embodiment of the present application, a motion data acquisition module is used to acquire the motion data of a vehicle, where the motion data includes the running speed, acceleration, and vehicle body bump data.

[0040] A cable monitoring data acquisition module is used to acquire the monitoring data of the vehicle cable, where the monitoring data includes the current data of the vehicle cable, the intensity data of the transmitted signal, and the resistivity change data; a feature extraction module is used to extract features from the monitoring data through an intelligent analysis model; an attribution analysis module is used to synchronize the extracted data features with the motion data in the time dimension and perform a correlation analysis; a processing measure generation module is used to, when it is recognized that the correlation exceeds a set threshold, determine the current vehicle cable as a target cable, acquire the working attribute information of the target cable, and generate a processing measure for the target cable according to the working attribute information. Through the above technical solution, by comparing and analyzing the features of the motion data and the vehicle cable, analyzing the impact of vehicle operation on the operation of the vehicle cable from the perspective of stress, and then giving corresponding processing measure opinions, this solution can improve the operation safety and stability of the vehicle cable and provide a better driving experience for users. Description of the Drawings

[0041] Figure 1 is a schematic structural diagram of a vehicle cable stress analysis device provided in Embodiment 1 of the present application;

[0042] Figure 2 is a schematic structural diagram of a vehicle cable stress analysis device provided in Embodiment 2 of the present application;

[0043] Figure 3 is a schematic structural diagram of a vehicle cable stress analysis device provided in Embodiment 3 of the present application;

[0044] Figure 4 is a schematic flowchart of a vehicle cable stress analysis method provided in Embodiment 4 of the present application;

[0045] Figure 5 is a schematic structural diagram of an electronic device provided in Embodiment 5 of the present application. Detailed Embodiments

[0046] In order to make the objectives, technical solutions, and advantages of the present application clearer, the following provides a more detailed description of specific embodiments of the present application with reference to the accompanying drawings. It can be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Additionally, it should be noted that for the convenience of description, only parts related to the present application rather than all content are shown in the drawings. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operations are completed, but it can also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, and so on.

[0047] The following will clearly describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application fall within the scope of protection of the present application.

[0048] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects and are not used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of the same type, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, "and / or" in the specification and claims means at least one of the connected objects, and the character " / ", generally represents an "or" relationship between the associated objects before and after.

[0049] The following will, with reference to the accompanying drawings, provide a detailed description of the stress analysis device, method, and equipment for vehicle cables provided in the embodiments of the present application through specific embodiments and their application scenarios.

[0050] Embodiment 1

[0051] Figure 1 It is a schematic structural diagram of the stress analysis device for vehicle cables provided in Embodiment 1 of the present application. As Figure 1 shown, the device includes:

[0052] A motion data acquisition module 110, configured to acquire motion data of the vehicle, where the motion data includes running speed, acceleration, and vehicle body bump data;

[0053] The cable monitoring data acquisition module 120 is used to acquire the monitoring data of the vehicle cable. Among them, the monitoring data includes the current data of the vehicle cable, the intensity data of the transmitted signal, and the resistivity change data;

[0054] The feature extraction module 130 is used to extract features from the monitoring data through an intelligent analysis model;

[0055] The attribution analysis module 140 is used to synchronize the extracted data features with the motion data in the time dimension and perform a correlation analysis;

[0056] The processing measure generation module 150 is used to determine the current vehicle cable as the target cable when it is recognized that the correlation exceeds the set threshold, obtain the working attribute information of the target cable, and generate the processing measure of the target cable according to the working attribute information.

[0057] Among them, the vehicle can be a transportation vehicle to be monitored, covering various types of vehicles such as fuel vehicles, trains, and new energy vehicles. There may be differences in data acquisition methods and application scenarios for different types of vehicles.

[0058] The motion data can be a set of various parameters of the vehicle motion state. For example, it can include the running speed, acceleration, and vehicle body bump data, etc. These data can reflect the dynamic information during the vehicle driving process. Among them, the running speed can be in units of kilometers per hour (km / h) or meters per second (m / s). The acquisition method can be calculated based on the fusion of the high-precision satellite positioning system and the wheel speed sensor data. The acceleration can be a physical quantity reflecting the speed change rate of the vehicle, with the unit of meters per second squared (m / s 2 ). The acceleration data of the vehicle in different directions, such as front-back, left-right, and up-down directions, can be obtained through acceleration sensors installed in different parts of the vehicle. The vehicle body bump data can be used to reflect the vibration situation of the vehicle body during driving. Sensors, such as fiber Bragg grating sensors, can be installed at key positions of the vehicle body, such as the suspension system and the vehicle frame. By utilizing the optical property changes of the optical fiber, the vibration amplitude and frequency of the vehicle body can be accurately measured and collected. This data can reflect the road surface condition and the working state of the vehicle suspension system. This solution can collect relevant motion data from the vehicle operation environment. For example, the physical quantity is converted into an electrical signal through a sensor, and then through processing steps such as analog-to-digital conversion and data filtering, it is converted into digital data that can be processed and stored by the module.

[0059] The cable monitoring data acquisition module 120 can synchronously collect data related to the operating status of vehicle cables. Among them, vehicle cables can include the wire cables for transmitting electrical energy and signals inside the vehicle. Different types of vehicle cables have different functions and specifications, such as high-voltage power cables, low-voltage control cables, etc.

[0060] The monitoring data can be the data reflecting the working status of the vehicle cables. By analyzing these data, it can be judged whether the cables are operating normally. The current data can be measured by a new type of non-contact current sensor based on the magneto-optical effect. This sensor accurately measures the current by detecting the influence of the magnetic field around the cable on the polarization state of light, and has the advantages of high precision and non-invasiveness.

[0061] The intensity data of the transmitted signal can be the intensity values of various signals transmitted in the cable, such as control signals, communication signals, etc. This solution can use a micro signal intensity detection chip integrated at a specific position of the cable to monitor the signal intensity in real time, and can quickly and accurately measure the intensity of different frequency signals.

[0062] The resistivity change data can be the data of the resistance characteristics of the cable material changing with time or environment. It can be measured by the principle of microwave reflection, that is, a microwave signal is emitted to the cable, and the resistivity change of the cable is analyzed according to the characteristics of the reflected wave, and the performance change of the internal material of the cable can be monitored in real time.

[0063] In the feature extraction module 130, there can be an intelligent analysis model. The intelligent analysis model can be a model constructed by using machine learning algorithms, which can analyze and process the input data and extract features. For example, a hybrid model combining a convolutional neural network (CNN) and a long short-term memory network (LSTM) is adopted. CNN is used to extract the local features of the data, and LSTM is used to process the time series features of the data, so as to more comprehensively extract the features of the monitoring data.

[0064] This solution can start and execute the processing process of the intelligent analysis model for the monitoring data, and calculate and analyze the data according to the algorithms and processes preset by the model.

[0065] The attribution analysis module 140 can be used to study the correlation between data, so as to integrate and analyze data from different sources and judge their mutual influence.

[0066] The data features can be the representative information extracted from the monitoring data, such as the mean value, variance, etc. of the current data, as well as the change trend of the transmitted signal intensity, etc.

[0067] In this solution, the extracted data features and motion data can be aligned on the time scale to ensure their correspondence in time for accurate correlation analysis. After performing the correlation analysis operation, it is possible to determine whether there is an association between the data features and the motion data and the degree of the association according to statistical methods or other algorithms. Specifically, methods such as Pearson correlation coefficient analysis and Granger causality test can be used to calculate the synchronized data to determine the correlation or causal relationship between the data.

[0068] The processing measure generation module 150 can generate corresponding processing suggestions based on the data analysis results. Among them, the set threshold can be a pre-set standard value used to judge whether the correlation reaches a significant level. When the calculated correlation value exceeds this threshold, it indicates that the association between the data has practical significance and further processing is required.

[0069] The target cable is the vehicle cable that has been determined to have a significant association with the vehicle motion data through correlation analysis.

[0070] The working attribute information can include various characteristics and parameters when the target cable is working properly, such as the rated power, rated voltage, insulation level, and service life of the cable. This information helps to comprehensively understand the working state and performance requirements of the cable.

[0071] The processing measure can be specific solutions generated for the working state and existing problems of the target cable, such as operation suggestions for cable fastening, replacement, and repair, as well as corresponding operation procedures and precautions.

[0072] This solution can compare with the set threshold to judge whether the correlation between the data reaches the degree that needs attention. When the correlation exceeds the set threshold, it is clear that the current vehicle cable related to the motion data is the target cable and is used as the object for subsequent processing. Furthermore, collect the working attribute information of the target cable from the cable design documents, maintenance records, or through real-time detection, etc. Finally, based on the obtained working attribute information of the target cable, combined with the preset decision-making algorithm and knowledge base, formulate specific processing measures for the target cable.

[0073] The technical solution provided in this embodiment, through the comprehensive collection and in-depth analysis of vehicle motion data and cable monitoring data, and generating processing measures for the target cable accordingly. It can timely and accurately discover the potential relationship between the vehicle cable and the vehicle motion, early warning of possible problems with the cable, thus ensuring the normal operation of the vehicle cable, improving the overall safety of vehicle operation, reducing vehicle failures caused by cable failures, and ensuring the use experience of the passengers and drivers.

[0074] Embodiment 2

[0075] On the basis of the above embodiment, this embodiment is further optimized. Specifically, the optimization is as follows: The feature extraction module includes: a time scale determination unit for determining at least two time scales used for feature extraction; a sliding window generation unit for generating a sliding window according to the at least two time scales to determine the time reference range for feature extraction through the sliding window; and a feature extraction unit for inputting the monitoring data within the sliding window into an intelligent analysis model to obtain the data features of the monitoring data within the sliding window. Figure 2 It is a schematic structural diagram of a stress analysis device for vehicle cables provided in the second embodiment of the present application. As Figure 2 shown, the device includes:

[0076] A motion data acquisition module 210 for acquiring the motion data of the vehicle, where the motion data includes the running speed, acceleration, and vehicle body bump data;

[0077] A cable monitoring data acquisition module 220 for acquiring the monitoring data of the vehicle cable, where the monitoring data includes the current data of the vehicle cable, the intensity data of the transmitted signal, and the resistivity change data;

[0078] A feature extraction module 230 for extracting features from the monitoring data through an intelligent analysis model;

[0079] An attribution analysis module 240 for synchronizing the extracted data features with the motion data in the time dimension and performing a correlation analysis;

[0080] A processing measure generation module 250 for, when it is recognized that the correlation exceeds a set threshold, determining the current vehicle cable as the target cable, acquiring the working attribute information of the target cable, and generating a processing measure for the target cable according to the working attribute information;

[0081] Among them, the feature extraction module 230 includes:

[0082] A time scale determination unit 231 for determining at least two time scales used for feature extraction;

[0083] A sliding window generation unit 232 for generating a sliding window according to the at least two time scales to determine the time reference range for feature extraction through the sliding window;

[0084] A feature extraction unit 233 for inputting the monitoring data within the sliding window into an intelligent analysis model to obtain the data features of the monitoring data within the sliding window.

[0085] Among them, the time scale determination unit 231 can be used to determine the time scale for feature extraction according to the user's output operation or according to other information, such as according to default information, etc.

[0086] Among them, the time scale can be an indicator for measuring the observation range and accuracy of data in the time dimension. Different time scales can reflect the characteristics of data at different time granularities. In this technical solution, at least two different time scales need to be determined, so that feature extraction can be performed on the monitoring data from multiple perspectives, thereby more comprehensively understanding the characteristics of the data.

[0087] This solution can select at least two appropriate time scales according to preset rules, requirements or algorithms. This may need to consider factors such as the characteristics of the data, such as the change frequency and periodicity of the data, the purpose of the analysis, such as short-term anomaly detection and long-term trend prediction, and the requirements of subsequent processing modules.

[0088] A sliding window can be a window with a fixed length that slides on a data sequence. In data processing, a sliding window can dynamically intercept a part of the data sequence for analysis. For example, for a monitoring data sequence recorded in chronological order, the sliding window can start from the starting position of the sequence, move backward by a certain step length each time, and cover a data segment with a fixed length. This data segment is the time reference range for feature extraction.

[0089] The time reference range is the data time period defined by the sliding window. The data within this time period will be used for subsequent feature extraction operations. For example, if the length of the sliding window is 10 seconds, then the 10-second data covered by each slide is a time reference range.

[0090] This solution can create corresponding sliding windows according to the determined time scales. Specifically, it is to determine the length and step size of the sliding window, and move on the data sequence according to these parameters, thereby forming a series of time reference ranges.

[0091] The feature extraction unit 233 can input the monitoring data in the sliding window into the intelligent analysis model and extract the corresponding data features. Among them, the intelligent analysis model can be a model built based on a machine learning algorithm, which can analyze and process the input data. For example, it can be a deep learning model, such as a convolutional neural network, a recurrent neural network, a machine learning algorithm, such as a decision tree, a support vector machine, etc. The intelligent analysis model can automatically discover the laws and features in the data by learning a large amount of historical data, so as to accurately extract features from new input data. Data features are features extracted from monitoring data. For example, the mean, variance, maximum value, minimum value and frequency features of the data. These features can be used for subsequent data analysis, fault diagnosis and prediction tasks.

[0092] This solution can analyze monitoring data from multiple dimensions by flexibly selecting at least two different time scales, comprehensively capture the characteristics of data at different time granularities, and accurately generate sliding windows based on the determined time scales, dynamically define the time reference range for feature extraction, and enhance the flexibility of data processing. Finally, the monitoring data in the sliding window is input into the intelligent analysis model to efficiently extract feature information that reflects the essence of the data. This solution can more accurately mine the potential characteristics of monitoring data, provide strong support for subsequent vehicle cable operation status evaluation, fault diagnosis and prediction, and effectively improve the safety and reliability of vehicle cable operation.

[0093] Embodiment 3

[0094] This embodiment is further optimized on the basis of the above embodiment, and the specific optimization is as follows: the attribution analysis module includes: a synchronization unit, which is used to synchronize the extracted data features with the motion data in the time dimension; an analysis unit, which is used to identify the change features in the data features, and to identify whether there is a positive correlation or negative correlation between the change features and the motion data, as well as the correlation ratio of the positive correlation or the correlation ratio of the negative correlation. Figure 3 Schematic diagram of the structure of the stress analysis device for vehicle cables provided in the third embodiment of the present application. Figure 3 As shown, the device comprises:

[0095] The motion data acquisition module 310 is used to acquire the motion data of the vehicle, wherein the motion data includes running speed, acceleration and vehicle body bump data;

[0096] The cable monitoring data acquisition module 320 is used to acquire the monitoring data of the vehicle cable, wherein the monitoring data includes the current data of the vehicle cable, the strength data of the transmission signal, and the resistivity change data;

[0097] A feature extraction module 330 is used to extract features from the monitoring data through an intelligent analysis model;

[0098] An attribution analysis module 340 is configured to synchronize the extracted data features with the motion data in the time dimension and perform a correlation analysis.

[0099] A processing measure generation module 350 is configured to, when it is recognized that the correlation exceeds a set threshold, determine the current vehicle cable as the target cable, obtain the working attribute information of the target cable, and generate a processing measure for the target cable according to the working attribute information.

[0100] Among them, the attribution analysis module 340 includes:

[0101] A synchronization unit 341 is configured to synchronize the extracted data features with the motion data in the time dimension.

[0102] An analysis unit 342 is configured to identify the change features in the data features, and identify whether there is a positive correlation or a negative correlation between the change features and the motion data, as well as the positive correlation ratio or the negative correlation ratio.

[0103] The synchronization unit 341 can calibrate data from different sources in the time dimension to ensure the temporal correspondence of the data for accurate correlation analysis.

[0104] The time dimension can reflect the sequence and time interval of data occurrence. In this solution, ensuring the synchronization of data in the time dimension is crucial for accurately analyzing the relationship between data.

[0105] This solution can align the extracted data features with the motion data in time. Specifically, it can perform precise matching of data acquisition timestamps, etc., so that the two sets of data are comparable in time.

[0106] The change features can be the characteristics in the data feature set that can reflect the change of data over time or other factors. For example, the increasing or decreasing trend of a certain parameter in the data features over time, or the mutation situation under specific conditions, etc. These change features help to discover potential associations between data.

[0107] Positive correlation is an association relationship between data, which means that when one variable increases, the other variable also tends to increase; conversely, when one variable decreases, the other variable also tends to decrease. Negative correlation is a data association relationship opposite to positive correlation, that is, when one variable increases, the other variable tends to decrease; when one variable decreases, the other variable tends to increase.

[0108] The correlation ratio can be a numerical index used to quantify the degree of positive or negative correlation. For example, a correlation ratio of 0.8 indicates a strong positive correlation between two variables, while a correlation ratio of -0.6 indicates a strong negative correlation. By determining the correlation ratio, the degree of association between data can be understood more precisely.

[0109] This solution can identify the features with changing trends from the data features, and determine the type of correlation between these changing features and the motion data, such as positive correlation or negative correlation, and calculate the corresponding correlation ratio.

[0110] This technical solution first ensures the consistency of data from different sources in the time dimension, then identifies the changing features in the data, and accurately determines their correlation relationship and degree with the motion data. Such a setting of this solution can help reveal the potential connection between the vehicle operation state and the cable monitoring data, provide a strong basis for predicting cable faults in advance and optimizing the vehicle maintenance strategy, thereby effectively improving the safety of vehicle operation and reducing the potential risks caused by cable problems.

[0111] In one embodiment, optionally, the analysis unit is specifically configured to:

[0112] When a changing feature in the data feature is recognized and the changing feature and the motion data are found to have time consistency, it is determined as a positive correlation, and based on the occurrence times of the changing feature and the occurrence times of the motion data, the correlation ratio of the positive correlation is determined; or, when a changing feature in the data feature is recognized and the changing feature and the motion data are found to have time reversibility, it is determined as a negative correlation, and based on the occurrence times of the changing feature and the occurrence times of the motion data, the correlation ratio of the negative correlation is determined.

[0113] Time consistency can be a regular feature where the changing feature in the data feature and the motion data show synchronization in time. For example, when the speed in the motion data continuously increases within a certain time period, and at the same time, a certain parameter in the data feature, such as the average value of the cable current, also increases synchronously within the same time period, this synchronous change in time reflects time consistency. It is an important basis for judging a positive correlation relationship.

[0114] Time reversibility, which is the opposite of time consistency, means that the changing feature in the data feature and the motion data show an opposite change rhythm or pattern in time. For example, when the acceleration in the motion data gradually increases within a certain time period, while the fluctuation amplitude of another parameter in the data feature, such as the cable transmission signal strength, gradually decreases within the same time period, this reverse change in time reflects time reversibility and is a key factor in judging a negative correlation relationship.

[0115] This solution can identify the temporal relationship between data features and motion data, and clarify the type of correlation between them. At the same time, according to the occurrence times of the change features and motion data, the corresponding correlation ratio is calculated and obtained, so as to complete the quantitative determination of the correlation between the two.

[0116] This technical solution further refines the method for determining data correlation. By accurately judging temporal consistency and temporal opposition to determine positive and negative correlations, and determining the correlation ratio based on the occurrence times, the analysis of the correlation between data features and motion data becomes more accurate. Based on these accurate correlation analysis results, the cable can be inspected, maintained or replaced more accurately, thus effectively improving the stability and safety of vehicle operation and reducing the potential failure risk.

[0117] In one embodiment, optionally, the analysis unit is specifically configured to:

[0118] Identify the change features in the data features, and identify whether there is a positive correlation between the change features and the acceleration caused by the vehicle's emergency braking, as well as the correlation ratio of the positive correlation.

[0119] The acceleration change generated during the vehicle's emergency braking. During emergency braking, the vehicle speed will drop sharply within a short period of time, and the resulting acceleration has specific values and change patterns. For some vehicle cables, it may be the case that during the process of the vehicle being stationary or traveling at a constant speed, the contact is good, but during emergency braking or emergency acceleration, due to inertia, the vehicle cable will generate forward or backward stress. In such a situation, if there is looseness in the cable joints or other parts, it will affect the normal operation of the vehicle functions, and this problem is also undetectable during vehicle maintenance.

[0120] In this context, the features with significant changes can be found from numerous data features, and it can be determined whether there is a positive correlation between these change features and the acceleration caused by the vehicle's emergency braking, and at the same time, the specific ratio of this positive correlation can be determined.

[0121] This technical solution, by identifying the positive correlation relationship and ratio between data features and the acceleration caused by the vehicle's emergency braking, can more accurately explore the stress effect formed by the vehicle cable under specific driving conditions and the change law of relevant data features. If it is found that there is a high positive correlation between the cable current data features and the emergency braking acceleration, the maintenance personnel can optimize the cable layout or strengthen the fixing measures accordingly to cope with the impact of the stress effect formed during emergency braking on the cable.

[0122] In one embodiment, optionally, the analysis unit is specifically configured to:

[0123] Identify the changing features in the data features, and identify whether there is a positive correlation between the changing features and at least one speed range of the vehicle's running speed, as well as the correlation ratio of the positive correlation.

[0124] The speed range can be a specific range divided by the vehicle's running speed. For example, the vehicle speed range is divided into different ranges such as 0 - 30 km / h, 30 - 60 km / h, and 60 - 90 km / h. By analyzing different speed ranges, we can more precisely understand the relationship between the data features and the running speed of the vehicle under different speed conditions. Because within different speed ranges of the vehicle, due to problems such as resonance, there may be a situation where the vehicle's cable shows obvious vibration in a certain speed range. Therefore, analyzing the correlation with different speed ranges helps to eliminate the influence of stress generated by synchronous resonance on the vehicle's cable, which is also undetectable when we directly conduct inspections or repairs.

[0125] In this solution, we can find the features with significant changes from the data feature set, and further determine whether there is a positive correlation between these changing features and at least one speed range divided by the vehicle's running speed. At the same time, determine the specific proportional value of this positive correlation.

[0126] This technical solution can explore the internal connection between the data features and different running speed ranges of the vehicle. By identifying the positive correlation and the correlation ratio, it helps to comprehensively understand the operation of each component of the vehicle under different speed conditions.

[0127] Based on the above embodiments, optionally, the motion data acquisition module is specifically configured to:

[0128] Use a linear variable differential transformer to collect the deformation data of the suspension system during vehicle driving to obtain the vehicle body bump data.

[0129] Among them, the linear variable differential transformer is an electromagnetic induction sensor, mainly composed of a primary coil, two secondary coils, and a movable iron core. When the iron core moves within the coil, it will change the mutual inductance between the primary coil and the secondary coils, so that the secondary coils output a voltage signal proportional to the displacement of the iron core. In this scenario, it is used to accurately measure the deformation of the vehicle's suspension system.

[0130] The suspension system is an important part of the vehicle, connecting the vehicle body and the wheels, and playing the roles of buffering, shock absorption, and supporting the vehicle body. Its main components include springs, shock absorbers, etc. During vehicle driving, the suspension system will deform due to factors such as road conditions, and this deformation data can reflect the bump situation of the vehicle body.

[0131] Deformation data can be the relevant data of the vehicle suspension system when its shape changes under external forces such as road surface impacts and vehicle movements, such as elongation and compression. These data are converted into electrical signals through a linear variable differential transformer and then collected and processed.

[0132] Body bump data can be obtained based on the deformation data of the suspension system and is used to describe the bump states such as the vibration and undulation of the vehicle body during driving. It helps to understand the smoothness of vehicle driving and the impact of road conditions on the vehicle.

[0133] This solution can use a linear variable differential transformer to convert the deformation amount of the suspension system into an electrical signal through the principle of electromagnetic induction and collect it. Further, the electrical signal data regarding the deformation of the suspension system collected is processed and converted, and finally, body bump data that can be used to describe the bump state of the vehicle body is generated.

[0134] This technical solution uses a linear variable differential transformer to collect the deformation data of the vehicle suspension system to obtain body bump data. By obtaining detailed body bump data, it is possible to further analyze the changes in the data characteristics of the vehicle cable under bump stress during vehicle driving, which helps to comprehensively monitor the operation safety of the vehicle cable, discover the influence on its electrical data under bump conditions, improve the detection detail of the vehicle cable, and improve the stability of vehicle operation.

[0135] Embodiment 4

[0136] Figure 4 is a schematic flow chart of the stress analysis method for the vehicle cable provided in Embodiment 4 of this application. As Figure 4 shown, it specifically includes the following steps:

[0137] S401. Obtain the motion data of the vehicle, where the motion data includes the running speed, acceleration, and body bump data;

[0138] S402. Obtain the monitoring data of the vehicle cable, where the monitoring data includes the current data of the vehicle cable, the intensity data of the transmitted signal, and the resistivity change data;

[0139] S403. Extract features from the monitoring data through an intelligent analysis model;

[0140] S404. Synchronize the extracted data features with the motion data in the time dimension and perform a correlation analysis;

[0141] S405. When it is recognized that the correlation exceeds a set threshold, determine the current vehicle cable as the target cable, obtain the working attribute information of the target cable, and generate a processing measure for the target cable according to the working attribute information.

[0142] Further, feature extraction is performed on the monitoring data through an intelligent analysis model, including:

[0143] Determine at least two time scales used for feature extraction;

[0144] Generate a sliding window according to the at least two time scales to determine the time reference range for feature extraction through the sliding window;

[0145] Input the monitoring data within the sliding window into the intelligent analysis model to obtain the data features of the monitoring data within the sliding window.

[0146] The technical solution provided in this embodiment obtains the motion data of the vehicle, where the motion data includes the running speed, acceleration, and vehicle body bump data; obtains the monitoring data of the vehicle cable, where the monitoring data includes the current data of the vehicle cable, the intensity data of the transmitted signal, and the resistivity change data; performs feature extraction on the monitoring data through an intelligent analysis model; synchronizes the extracted data features with the motion data in the time dimension and performs a correlation analysis; when it is recognized that the correlation exceeds a set threshold, determine the current vehicle cable as the target cable, obtain the working attribute information of the target cable, and generate a processing measure for the target cable according to the working attribute information. This technical solution analyzes the impact of vehicle operation on the operation of the vehicle cable from the perspective of stress through the comparative analysis of the features of the motion data and the vehicle cable, and then gives corresponding processing measure opinions. This solution can improve the operation safety and stability of the vehicle cable and provide a better driving experience for users.

[0147] The vehicle cable stress analysis method provided in the embodiments of the present application corresponds to the vehicle cable stress analysis device provided in the above embodiments, has the same execution process and beneficial effects, and for the sake of avoiding repetition, will not be elaborated here.

[0148] Embodiment Five

[0149] As Figure 5 shown, the embodiments of the present application also provide an electronic device 500, including a processor 501, a memory 502, a program or instruction stored on the memory 502 and executable on the processor 501. When the program or instruction is executed by the processor 501, it implements each process of the above vehicle cable stress analysis device embodiment and can achieve the same technical effects. For the sake of avoiding repetition, it will not be elaborated here.

[0150] It should be noted that the electronic devices in the embodiments of the present application include the above-mentioned mobile electronic devices and non-mobile electronic devices.

[0151] Embodiment Six

[0152] The embodiments of the present application further provide a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, it implements each process of the above-mentioned stress analysis device embodiment of the vehicle cable, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0153] Among them, the processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs, etc.

[0154] Embodiment Seven

[0155] The embodiments of the present application further provide a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run a program or instruction to implement each process of the above-mentioned stress analysis device embodiment of the vehicle cable, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0156] It should be understood that the chip mentioned in the embodiments of the present application may also be referred to as a system-on-chip, system chip, chip system, or system-on-chip, etc.

[0157] It should be noted that in this article, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without more limitations, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including 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 a reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0158] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described example methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present application.

[0159] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms without departing from the purpose of the present application and the scope protected by the claims, and all of them fall within the protection scope of the present application.

[0160] The above is only the preferred embodiment of the present application and the technical principles applied. The present application is not limited to the specific embodiments described herein. Various obvious changes, re-adjustments and substitutions that can be made by those skilled in the art will not depart from the protection scope of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments. Without departing from the concept of the present application, it can also include more other equivalent embodiments, and the scope of the present application is determined by the scope of the claims.

Claims

1. A stress analysis device for a vehicle cable, characterized in that, The device includes: A motion data acquisition module for acquiring the motion data of a vehicle, where the motion data includes running speed, acceleration, and vehicle body bump data; A cable monitoring data acquisition module for acquiring the monitoring data of the vehicle cables, where the monitoring data includes current data of the vehicle cables, signal strength data of transmitted signals, and resistivity change data; A feature extraction module for extracting features from the monitoring data through an intelligent analysis model; An attribution analysis module for synchronizing the extracted data features with the motion data in the time dimension and performing a correlation analysis; A processing measure generation module for, when it is recognized that the correlation exceeds a set threshold, determining the current vehicle cable as a target cable, acquiring the working attribute information of the target cable, and generating a processing measure for the target cable according to the working attribute information.

2. The stress analysis device for vehicle cables according to claim 1, characterized in that, The feature extraction module includes: A time scale determination unit for determining at least two time scales used for feature extraction; A sliding window generation unit for generating a sliding window according to the at least two time scales to determine the time reference range for feature extraction through the sliding window; A feature extraction unit for inputting the monitoring data within the sliding window into the intelligent analysis model to obtain the data features of the monitoring data within the sliding window.

3. The stress analysis device for a vehicle cable according to claim 1, characterized in that, The attribution analysis module includes: A synchronization unit for synchronizing the extracted data features with the motion data in the time dimension; An analysis unit for identifying the change features in the data features and identifying whether there is a positive correlation or a negative correlation between the change features and the motion data, and the correlation ratio of the positive correlation or the correlation ratio of the negative correlation.

4. The stress analysis device for a vehicle cable according to claim 3, characterized in that, The analysis unit is specifically used for: When it is recognized that there are change features in the data features and it is recognized that the change features are time-consistent with the motion data, determining it as a positive correlation, and determining the correlation ratio of the positive correlation based on the occurrence times of the change features and the occurrence times of the motion data; Or, when it is recognized that there are change features in the data features and it is recognized that the change features are time-opposite to the motion data, determining it as a negative correlation, and determining the correlation ratio of the negative correlation based on the occurrence times of the change features and the occurrence times of the motion data.

5. The stress analysis device for a vehicle cable according to claim 3, wherein, The analysis unit is specifically used for: Identifying the change features in the data features and identifying whether there is a positive correlation between the change features and the acceleration caused by the emergency braking of the vehicle, and the correlation ratio of the positive correlation.

6. The stress analysis device for a vehicle cable according to claim 3, wherein, The analysis unit is specifically used for: Identifying the change features in the data features and identifying whether there is a positive correlation between the change features and at least one speed interval of the running speed of the vehicle, and the correlation ratio of the positive correlation.

7. The stress analysis device for vehicle cables according to claim 1, characterized in that, The motion data acquisition module is specifically used for: Collecting the deformation data of the suspension system during vehicle driving by using a linear variable differential transformer to obtain the vehicle body bump data.

8. A stress analysis method for vehicle cables, characterized in that, The method includes: Acquiring the motion data of a vehicle, where the motion data includes running speed, acceleration, and vehicle body bump data; Obtain the monitoring data of the vehicle cable, where the monitoring data includes the current data of the vehicle cable, the intensity data of the transmitted signal, and the resistivity change data; Extract features from the monitoring data through an intelligent analysis model; Synchronize the extracted data features with the motion data in the time dimension and perform correlation analysis; When it is identified that the correlation exceeds the set threshold, determine the current vehicle cable as the target cable, obtain the working attribute information of the target cable, and generate the processing measures for the target cable according to the working attribute information.

9. The stress analysis method of the vehicle cable according to claim 8, wherein, Extracting features from the monitoring data through an intelligent analysis model includes: Determine at least two time scales used for feature extraction; Generate a sliding window according to the at least two time scales to determine the time reference range for feature extraction through the sliding window; Input the monitoring data within the sliding window into the intelligent analysis model to obtain the data features of the monitoring data within the sliding window.

10. An electronic device, characterized in that, It includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, it implements the steps of the stress analysis method for the vehicle cable as described in any one of claims 8-9.

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