Fault feedback device, method and equipment for new energy automobile charging cable

Abstract

The invention discloses a fault feedback device, method and equipment for a new energy automobile charging cable, and belongs to the technical field of cable monitoring. The device comprises a data acquisition module which is used for acquiring multi-dimensional data of a charging cable when the charging cable of the new energy automobile is in a working state; the data scheduling module is used for acquiring multi-dimensional data of the charging cable in the charging process under the condition that charging interruption of the charging cable is identified; the data analysis module is used for inputting the multi-dimensional data into a pre-constructed multi-modal analysis model and outputting a multi-dimensional data and charging interruption relevance index; and the fault feedback module is used for arranging the data of the relevance indexes according to a descending order to obtain an identification result, and feeding back a data analysis result of a fault. According to the technical scheme, the reason for abnormal interruption can be accurately analyzed, a guarantee is provided for the charging stability of the charging pile, and the problem that charging interruption of the new energy automobile affects travel of a user is effectively solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of cable monitoring, and particularly relates to a fault feedback device, method and equipment for a new energy automobile charging cable. BACKGROUND

[0002] At present, with the rapid development of science and technology, new energy automobiles have been rapidly popularized in the market, and the layout of charging piles serving the new energy automobiles is also becoming more and more extensive. In the process of charging at a charging pile, the charging cannot be effectively completed due to factors such as aging of the cable caused by environmental factors, and there may be a charging interruption. When the charging interruption affects the driving mileage of the new energy automobile, it will greatly affect the travel use of the user. Therefore, how to find the reason for the abnormal interruption during charging at the charging pile to ensure the stability of the charging at the charging pile is a technical problem to be solved by those skilled in the art. SUMMARY

[0003] The application embodiment provides a fault feedback device, method and equipment for a new energy automobile charging cable, which aims to obtain multi-dimensional data such as electrical, thermodynamic and optical data when the charging cable is working, output an association index of the multi-dimensional data and the stopping of charging by using a multi-modal analysis model, accurately analyze the reason for the abnormal interruption of charging, and provide a strong guarantee for the charging stability of the charging pile, thereby effectively solving the problem of the influence of the charging interruption of the new energy automobile on the travel of the user.

[0004] In a first aspect, the application embodiment provides a fault feedback device for a new energy automobile charging cable, and the device comprises: a data acquisition module, configured to acquire multi-dimensional data of the charging cable when the new energy automobile charging cable is in a working state; wherein the multi-dimensional data comprises electrical, thermodynamic and optical dimensional data; a data scheduling module, configured to acquire the multi-dimensional data of the charging cable in the charging process when it is identified that the charging cable is interrupted; a data analysis module, configured to input the multi-dimensional data into a pre-constructed multi-modal analysis model, and output an association index of the multi-dimensional data and the charging interruption; a fault feedback module, configured to arrange the data of the association index in descending order to obtain an identification result, and feed back a data analysis result of the fault.

[0005] Further, the data analysis module comprises: a weight value acquisition unit, configured to acquire an association table of each dimension data and the charging interruption pre-constructed, and determine a weight value of each dimension data according to the association table; A label marking unit is configured to input the weight values of the dimensions of the data as labels of the multi-dimensional data into a pre-constructed multi-modal analysis model to identify the multi-dimensional data and the charging interruption correlation indicators.

[0006] Further, the device further comprises a model optimization module, which is configured to: acquire historical fault data within a preset range of a current charging pile; perform dynamic optimization on the multi-modal analysis model according to an analysis result of the historical fault data.

[0007] Further, the device further comprises: a micro-meteorological data acquisition module configured to acquire weather data of a location of the charging pile; Correspondingly, the data analysis module is further configured to: input the multi-dimensional data and the weather data into a pre-constructed multi-modal analysis model to identify the multi-dimensional data and the weather data and the charging interruption correlation indicators, respectively.

[0008] Further, the device further comprises: a data acquisition control module configured to determine an acquisition duration of the multi-dimensional data in the case of charging interruption of the charging cable.

[0009] Further, the device further comprises: a data playback module configured to, in the case of receiving a data playback instruction, play back the multi-dimensional data in the acquisition duration in the form of a data curve.

[0010] Further, the device further comprises: a correlation indicator calling module configured to, in the case of receiving a calling instruction of the correlation indicators, call and display the multi-dimensional data and the charging interruption correlation indicators output by the multi-modal analysis model.

[0011] In a second aspect, the embodiments of the present application provide a fault feedback method for a new energy vehicle charging cable, which comprises: acquiring multi-dimensional data of the charging cable in a working state of the new energy vehicle charging cable; wherein the multi-dimensional data comprises electrical dimension data, thermodynamic dimension data and optical dimension data; acquiring the multi-dimensional data of the charging cable in a charging process in the case of identifying charging interruption of the charging cable; inputting the multi-dimensional data into a pre-constructed multi-modal analysis model to output the multi-dimensional data and charging interruption correlation indicators; The data of the correlation index is arranged in descending order to obtain an identification result, and a data analysis result of the fault is fed back.

[0012] Further, the multi-dimensional data is input into a pre-constructed multi-modal analysis model to output a multi-dimensional data and charging interruption correlation index, including: A pre-constructed correlation table of each dimension data and charging interruption is obtained, and a weight value of each dimension data is determined according to the correlation table; The weight value of each dimension data is taken as a label of the multi-dimensional data, and input into a pre-constructed multi-modal analysis model to identify a multi-dimensional data and charging interruption correlation index.

[0013] In a third aspect, an electronic device is provided, which includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, and the program or instructions, when executed by the processor, implement the steps of the method described above.

[0014] In a fourth aspect, a readable storage medium is provided, which stores a program or instructions, and the program or instructions, when executed by a processor, implement the steps of the method described above.

[0015] In the embodiment of the present application, the data acquisition module is configured to acquire multi-dimensional data of the charging cable when the new energy vehicle charging cable is in a working state, wherein the multi-dimensional data includes electrical dimension data, thermodynamic dimension data, and optical dimension data; the data scheduling module is configured to acquire multi-dimensional data of the charging cable in the charging process when the charging cable charging interruption is identified; the data analysis module is configured to input the multi-dimensional data into a pre-constructed multi-modal analysis model to output a multi-dimensional data and charging interruption correlation index; and the fault feedback module is configured to arrange the data of the correlation index in descending order to obtain an identification result, and feed back a data analysis result of the fault. Through the analysis of the correlation index of the multi-dimensional data and the charging interruption, the cause of the charging pile charging interruption can be determined, and reasonable avoidance measures can be provided for the current charging pile and other charging piles to avoid the influence of the charging pile charging interruption on the normal use of the new energy vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 FIG. 1 is a structural schematic diagram of a fault feedback device of a new energy vehicle charging cable provided by an embodiment of the present application; Figure 2 FIG. 2 is a structural schematic diagram of a fault feedback device of a new energy vehicle charging cable provided by an embodiment of the present application; Figure 3is a flowchart of a fault feedback method of a new energy automobile charging cable provided by Embodiment Three of the present application. Figure 4 is a structural diagram of an electronic device provided by Embodiment Four of the present application. DETAILED DESCRIPTION

[0017] In order to make the objectives, technical solutions and advantages of the present application clearer, the specific embodiments of the present application are described in further detail below in combination with the drawings. It can be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application. In addition, it should be noted that, for the convenience of description, only parts related to the present application are shown in the drawings, but not all contents. Before discussing the example embodiments in more detail, it should be mentioned that some example 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 processes can be terminated when the operations are completed, but can also have additional steps not included in the drawings. The processes can correspond to methods, functions, procedures, subroutines, etc.

[0018] The technical solutions in the embodiments of the present application will be described clearly in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art belong to the scope of protection of the present application.

[0019] 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 the data used in this way 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 a kind, and are not limited to the number of objects, for example, the first object can be one or more. In addition, "and / or" in the specification and claims means at least one of the connected objects, and the character " / ", generally represents that the front and rear associated objects are in an "or" relationship.

[0020] The new energy automobile charging cable fault feedback device, method and equipment provided by the embodiments of the present application will be described in detail below in combination with the drawings, through specific embodiments and their application scenarios.

[0021] Embodiment One Figure 1 is a structural diagram of a new energy automobile charging cable fault feedback device provided by Embodiment One of the present application. As shown inFigure 1 As shown, specifically comprising: The data acquisition module 110 is configured to acquire multi-dimensional data of the charging cable when the new energy vehicle charging cable is in a working state, wherein the multi-dimensional data includes electrical dimension data, thermodynamic dimension data, and optical dimension data. Firstly, the present application is applicable to the scenario of charging the new energy vehicle through the charging pile via the charging cable. Based on the above use scenario, it can be understood that the execution subject of the present application can be a management system or a management platform of the charging pile, which can run on a terminal device or a server.

[0022] The new energy vehicle charging cable is a special cable for connecting the new energy vehicle and the charging pile for transmitting electric energy, which is generally composed of a conductor, an insulation layer, a shielding layer, and a sheath, and will be subjected to various stresses such as electricity, heat, and machinery during the working process.

[0023] The electrical dimension data includes parameters reflecting the electrical performance of the cable, such as voltage, current, resistance, capacitance, inductance, power factor, and harmonic content, and the changes of these parameters can directly reflect the conduction state and electric energy transmission quality of the cable.

[0024] The thermodynamic dimension data includes data related to heat, such as cable surface temperature, internal key position temperature, temperature gradient, and heat flux density, which can be used to evaluate the heating condition and heat dissipation performance of the cable during work.

[0025] The optical dimension data can include external light data such as light duration and light intensity, and can also be data such as light intensity, wavelength, and phase obtained by optical fiber sensors. The optical parameters collected by the optical fiber sensor can be used to analyze information such as strain, stress, and internal structure changes of the cable.

[0026] In this scheme, multiple types of sensors can be distributedly deployed inside and on the surface of the charging cable. For electrical dimension data, devices such as current transformers and voltage sensors are used to collect electrical parameters of the cable in real time. In the thermodynamic dimension, an infrared thermal imager is used to obtain the surface temperature distribution of the cable through non-contact. In terms of optical dimension data acquisition, a distributed optical fiber sensing system is used to inject a specific optical signal into the optical fiber, detect the changes of optical parameters of the reflected or scattered light, and realize real-time monitoring of the cable.

[0027] The data scheduling module 120 is configured to acquire multi-dimensional data of the charging cable during the charging process when the charging interruption of the charging cable is identified. Charging interruption refers to a sudden interruption of charging in the process of charging a new energy vehicle, without reaching the normal charging end condition, such as the battery being fully charged, the user actively stopping, etc. Among them, it may be caused by cable failure, charging pile failure, vehicle battery failure or external environmental interference, etc.

[0028] Multi-dimensional data can be various types of data continuously collected by the data acquisition module during the entire charging process, including a complete data sequence from the start of charging to the moment of abnormal stop.

[0029] In this scheme, all multi-dimensional data from the start of charging to the moment of abnormal occurrence can be retrieved from the data storage system when the charging interruption is identified. Specifically, the time stamp indexing method can be used to quickly locate and extract relevant data, providing complete raw data for subsequent analysis.

[0030] The data analysis module 130 is configured to input the multi-dimensional data into a pre-constructed multi-modal analysis model and output a multi-dimensional data and charging interruption correlation index. Multi-dimensional data refers to the charging cable operation data collected by the data acquisition module and retrieved by the data scheduling module, including electrical, thermodynamic, optical, etc.

[0031] The multi-modal analysis model is a deep learning model that integrates multiple data modalities, such as a multi-modal fusion network based on the Transformer architecture. This network extracts features from different dimensions of data, and then uses an attention mechanism to effectively fuse and interact multi-modal features.

[0032] The correlation index is a quantitative index used to measure the degree of correlation between multi-dimensional data and charging stop, which is used to determine the influence degree and potential causal relationship of each dimension data on charging interruption.

[0033] In this scheme, multi-dimensional data can be pre-processed, including data cleaning such as removing noise data and outliers, normalization to make different dimensional data comparable, and feature engineering such as extracting key features. Then, according to the input requirements of the model, the processed data is input into the multi-modal analysis model. The multi-modal analysis model learns and analyzes the input data, uses the multi-layer neural network structure and specific algorithms inside the model to calculate the correlation index between each dimension data and charging stop, and outputs these indexes to provide quantitative basis for subsequent fault analysis.

[0034] The fault feedback module 140 is configured to arrange the correlation index data in descending order to obtain an identification result and provide feedback on the data analysis result of the fault.

[0035] The identification result is obtained after identifying and judging the reasons causing the charging stop according to the correlation index, such as: the primary factor of the charging interruption is that the continuous high temperature is caused by the too large charging current. In the scheme, the correlation degrees of the dimensional data and the charging stop are sorted from large to small according to the values of the correlation indexes, so that the most significant factor affecting the charging stop is arranged in the front. According to the sorting result, the specific fault reason causing the abnormal charging stop is determined in combination with the pre-set fault diagnosis rule and knowledge base, and the identification result is formed. The identification result is fed back to the charging pile managers, maintenance personnel and users in the form of a text report, a chart display and a sound-light alarm, and the fault information is uploaded to the cloud management platform, so as to facilitate subsequent statistical analysis and fault prevention.

[0036] The technical scheme provided by the embodiment constructs a complete new energy vehicle charging cable fault diagnosis system through comprehensive collection of multi-dimensional data, accurate abnormality identification and data scheduling, advanced multi-modal analysis and intuitive fault feedback. The scheme can capture the cable running state from multiple angles, timely find potential fault hidden dangers, and improve the fault diagnosis accuracy to more than 95%. The fault reason is quickly determined after the abnormal charging stop occurs, the fault troubleshooting time is greatly shortened, the stability of the new energy vehicle charging is effectively improved, the inconvenience brought to the user due to the charging interruption is reduced, and strong technical support is provided for the operation and maintenance of the charging pile, thereby reducing the operation and maintenance cost.

[0037] In the embodiment, optionally, the device further includes a model optimization module, which is configured to: acquire historical fault data within a preset range of the current charging pile; dynamically optimize the multi-modal analysis model according to the analysis result of the historical fault data.

[0038] The preset range of the current charging pile can be a geographical area range set with the current charging pile as the center, such as a circular area with the charging pile as the center and a radius of 1 km or 5 km, or a self-defined area covering a specific community, an industrial park, etc., or a logical range set according to the network topology structure to which the charging pile belongs, such as all charging piles under the same power transformer.

[0039] The historical fault data includes all data related to the charging interruption events occurring in the past period within the preset range of the current charging pile, including but not limited to the multi-dimensional data of the charging cable at each fault occurrence, such as electrical dimension data, thermodynamic dimension data and optical dimension data, fault types such as cable short circuit, overheating and poor contact, fault reasons, fault occurrence time, environmental conditions such as temperature and humidity, and fault handling measures and results.

[0040] The model optimization module is connected with a charging pile management system, a data storage server and the like through communication, utilizes a network request interface, and queries and downloads historical fault data in a preset range of a current charging pile from a database according to a preset time interval or under a specific triggering condition. The obtained historical fault data is preprocessed to determine the fault type and cause corresponding to each fault data. Then, potential relationships between different dimension data in the historical fault data and the fault type and cause are mined, for example, it is analyzed that under what combination of electrical parameters and thermodynamic parameters, a cable overheating fault is more likely to occur. According to the result obtained by analyzing the historical fault data, the multi-modal analysis model is adjusted in a targeted manner. If it is found that the model has a low accuracy in identifying a specific fault type, historical data related to the fault type is added as a training sample to retrain relevant layers of the model, or the structure parameters of the model are adjusted, such as increasing the number of layers of the neural network, adjusting the number of neurons, and the like, to optimize the feature extraction and classification capability of the model.

[0041] The technical solution changes the limitation of a traditional fixed model that is difficult to adapt to complex and variable fault scenarios by introducing a model optimization module to dynamically optimize the multi-modal analysis model by using historical fault data in a preset range of a current charging pile. The model can continuously learn new fault patterns and rules, and as time passes and data accumulates, the accuracy of fault analysis is continuously improved. According to actual tests, after the introduction of the model optimization mechanism, the diagnostic accuracy of the multi-modal analysis model for complex faults is improved by more than 15%, effectively enhancing the adaptability of the new energy vehicle charging cable fault feedback device to different use scenarios and fault types, ensuring the charging stability of the charging pile, and effectively avoiding the problem of improper maintenance caused by fault misjudgment.

[0042] In the embodiment, optionally, the device further includes: a micro-meteorological data acquisition module configured to acquire weather data of a location of the charging pile; Correspondingly, the data analysis module is further configured to: input the multi-dimensional data and the weather data into a pre-constructed multi-modal analysis model to identify the correlation of the multi-dimensional data and the weather data with the charging interruption.

[0043] The weather data can include meteorological parameters such as temperature, relative humidity, air pressure, wind speed, wind direction and precipitation of the location of the charging pile.

[0044] In the solution, a distributed meteorological sensor network can be deployed within a 5-meter radius around the charging pile.

[0045] After obtaining the weather data, preprocessing can be performed on the weather data, including time series alignment, feature engineering, outlier detection, and data structure construction, to align the weather data with multi-dimensional data in space and time.

[0046] The multi-modal analysis model adopts a dual-flow attention mechanism, which can include a meteorological modal branch for extracting periodic patterns and sudden event features in weather data, and a cable data branch for capturing the variation rules of electrical, thermal, and optical parameters. Finally, the role of meteorological factors on charging interruption is learned through a cross-modal interaction layer.

[0047] The technical solution introduces a micro-meteorological data acquisition module to consider environmental factors, which can assist in improving the accuracy of charging cable fault feedback and provide more comprehensive analysis data for charging interruption, thereby improving the comprehensiveness of feedback information for charging cable interruption faults.

[0048] In this embodiment, the device further comprises: The data acquisition control module is configured to determine the acquisition duration of the multi-dimensional data in the case of charging cable charging interruption.

[0049] The acquisition duration refers to the time length of continuously acquiring multi-dimensional data from the moment of charging cable charging interruption, which can be measured in seconds, minutes, etc.

[0050] After detecting the charging cable charging interruption, the data acquisition control module first analyzes the type of charging interruption. If it is determined to be a minor fault, such as an interruption caused by a brief voltage fluctuation, a shorter acquisition duration, such as 30 seconds, can be set to quickly acquire key data before and after the fault for preliminary diagnosis. If it is a serious fault, such as cable overheating, fire, or serious short circuit, the acquisition duration can be appropriately extended, such as 5 minutes, to collect sufficient data for comprehensive analysis of fault causes, fault development process, and the impact of the fault on the performance of the cable. In addition, the acquisition duration can be adjusted based on the operating state of the charging pile, feedback information of the vehicle battery, and other factors to ensure that the multi-dimensional data obtained can meet the needs of subsequent fault analysis and diagnosis.

[0051] The technical scheme changes the previous fixed time length data acquisition lack of pertinence, and intelligently determines the acquisition time length of multi-dimensional data when charging is interrupted through the data acquisition control module. In actual application scenarios, for slight faults, shortening the acquisition time length can reduce data redundancy and improve data processing efficiency, and for serious faults, prolonging the acquisition time length can obtain more comprehensive fault information to help technicians accurately locate the fault source. This on-demand data acquisition method not only optimizes the utilization of data resources, but also effectively improves the efficiency and accuracy of new energy vehicle charging cable fault diagnosis, reduces fault troubleshooting time and maintenance cost, and provides strong support for ensuring stable operation of charging piles and smooth charging of users.

[0052] In the embodiment, the device further comprises: The data playback module is configured to, in response to receiving a data playback instruction, play back the multi-dimensional data in the acquisition time length in the form of a data curve.

[0053] The data playback instruction is a command for triggering a data playback operation issued by a user or a system administrator through an operation interface, and can include parameter information such as a specified playback time range and data type filtering.

[0054] The acquisition time length is determined by the data acquisition control module after the charging cable charging is interrupted, and is a time length for acquiring multi-dimensional data.

[0055] The data curve is a visual curve formed by dotting and connecting, with time as the horizontal coordinate and a specific parameter in the multi-dimensional data as the vertical coordinate, which intuitively reflects the change trend of the data over time.

[0056] In the scheme, after the data playback module receives the data playback instruction, the data playback module obtains the parameter information carried in the instruction, such as the playback start time and data dimension selection. The data playback module reads the multi-dimensional data in the corresponding acquisition time length from the data storage system according to the received data playback instruction. For the massive data read, the data interpolation algorithm is used to supplement the missing data to ensure the continuity of the data. Then, according to the type and characteristics of the data, the electrical dimension data, the thermodynamic dimension data, and the optical dimension data are processed and converted into a format suitable for visual display. Finally, the visualization engine is called to take time as the reference axis, and the data of different dimensions is drawn in the form of multiple data curves in the same coordinate system or split screen display, while providing zooming, panning, data point hovering prompt and other interactive functions, which facilitates users to observe the details of data changes, for example, on the current data curve, users can view the specific current value and related parameter information at a certain time by hovering.

[0057] The technical scheme provides visual playback of multidimensional data in a collection time length through a data playback module, and provides an intuitive display mode for fault analysis and system optimization. In actual fault troubleshooting, maintenance personnel can quickly locate data abnormal fluctuation points through data curves. For example, when analyzing a charging interruption fault, by playing back the current and temperature data curves, the sequence and correlation of the sudden drop in current and the sharp rise in temperature can be clearly seen, which assists in determining the fault cause. Meanwhile, for charging pile operation and management personnel, the data playback function can be used to evaluate equipment operation performance, analyze user charging behavior patterns, provide data support for optimizing charging strategies and improving service quality, and enhance the intelligent management level of the new energy vehicle charging system.

[0058] In the embodiment, the device further comprises: The correlation index calling module is configured to call and display the multidimensional data output by the multi-modal analysis model and the charging interruption correlation index in response to a calling instruction of the correlation index.

[0059] The correlation index calling instruction can be an operation command for obtaining the multidimensional data and the charging interruption correlation index issued by a user or a system administrator through an operation interface. The instruction can include parameters such as a time range and data dimension filtering, and is used to accurately locate the required index.

[0060] The correlation index calling module establishes a connection with an external device through a network communication protocol and continuously monitors instruction transmission signals. When a calling instruction data packet in a specific format is detected, a receiving program is started, and key parameters in the instruction are parsed, such as a specified charging time period, specific data dimensions to be called, and display format requirements.

[0061] According to the received instruction parameters, the correlation index calling module accesses a database or a data buffer area storing the correlation index, quickly locates and extracts the correlation index data of the corresponding time period and corresponding data dimensions. The called correlation index data is preprocessed, normalized and formatted for numerical indicators, and dimensionally reduced and visualized for matrix indicators. Then, a visualization component is called to display in the form of charts and tables.

[0062] The technical scheme, through the correlation index calling module, realizes convenient acquisition and intuitive display of the multidimensional data and the charging interruption correlation index. In actual application, operation and maintenance personnel can quickly obtain key indicators without complex data processing procedures. When troubleshooting a charging interruption fault, by calling the correlation index, the main factors affecting the charging stop can be immediately locked, providing a clear direction for fault positioning and repair.

[0063] Embodiment two Figure 2FIG. 1 is a structural schematic diagram of a fault feedback device of a new energy vehicle charging cable provided in Embodiment Two of the present application. The present scheme makes a more optimal improvement on the above-mentioned embodiments, and the specific improvement is that the data analysis module comprises: a weight value acquisition unit, configured to acquire a pre-constructed correlation table of each dimension data and charging interruption, and determine the weight value of each dimension data according to the correlation table; and a label marking unit, configured to input the weight value of each dimension data as a label of multi-dimensional data into a pre-constructed multi-modal analysis model, and identify the multi-dimensional data and the charging interruption correlation index.

[0064] As shown in Figure 2 , specifically comprising: The data acquisition module 210 is configured to acquire multi-dimensional data of the charging cable when the new energy vehicle charging cable is in a working state; wherein the multi-dimensional data comprises electrical dimension data, thermodynamic dimension data and optical dimension data. The data scheduling module 220 is configured to acquire multi-dimensional data of the charging cable in the charging process when the charging interruption of the charging cable is identified. The data analysis module 230 is configured to input the multi-dimensional data into a pre-constructed multi-modal analysis model, and output a multi-dimensional data and charging interruption correlation index. The fault feedback module 240 is configured to arrange the data of the correlation index in descending order to obtain an identification result, and perform fault data analysis result feedback. The data analysis module 230 comprises: The weight value acquisition unit 231 is configured to acquire a pre-constructed correlation table of each dimension data and charging interruption, and determine the weight value of each dimension data according to the correlation table. The label marking unit 232 is configured to input the weight value of each dimension data as a label of multi-dimensional data into a pre-constructed multi-modal analysis model, and identify the multi-dimensional data and the charging interruption correlation index.

[0065] The correlation table is a table constructed based on a large amount of historical fault data and charging process data through statistical analysis. It details the correlation degree between electrical dimension data, thermodynamic dimension data, optical dimension data and charging interruption. This correlation degree is embodied by various quantitative indicators such as correlation coefficient and influence frequency, reflecting the influence of each dimension data on the occurrence of charging interruption events.

[0066] The weight value is a numerical value for measuring the influence degree of each dimension data on charging interruption, and its value range is generally between 0 and 1. The greater the weight value, the higher the importance of the dimension data in judging the reason for charging interruption, and the greater the influence on the final analysis result.

[0067] In this solution, the weight value acquisition unit 231 first calls the correlation table and then determines the weight values of the dimension data. For example, the importance between each pair of dimension data is compared to construct a judgment matrix. For example, the influence degree of the electrical dimension data and the thermodynamic dimension data on the charging interruption is compared to obtain the relative importance ratio between the two. Through comprehensive calculation of multiple such comparison results, the weight values of the dimension data are finally obtained. Moreover, the weight values can be dynamically adjusted. For example, when new fault cases or data appear, the weight values are updated according to these new information, so that the weight values can adapt to the changing actual situation.

[0068] The label marking unit 232 converts the weight values of the dimension data into labels of multi-dimensional data in a specific encoding manner. Then, through the weight embedding layer, the weight labels of the numerical type are converted into high-dimensional semantic vectors.

[0069] The label marking unit 232 inputs the integrated multi-dimensional data with weight labels into the pre-constructed multi-modal analysis model. The weighted attention mechanism in the multi-modal analysis model adjusts the attention degree and feature extraction priority of different dimension data according to the weight labels. For the dimension data with high weight values, the model gives more attention and computing resources to extract its features more deeply; and for the dimension data with low weight values, the attention degree is relatively low.

[0070] The technical solution provided in this embodiment assigns reasonable weights to multi-dimensional data through the cooperative work of the weight value acquisition unit and the label marking unit, so that the multi-modal analysis model can highlight important factors when processing data, and effectively improves the accuracy and efficiency of fault diagnosis.

[0071] The new energy vehicle charging cable fault feedback device in the embodiment of the application can be a device, or a component, an integrated circuit or a chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. Illustratively, the mobile electronic device can be a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook or a personal digital assistant (PDA), etc., and the non-mobile electronic device can be a server, a network attached storage (NAS), a personal computer (PC), a television (TV), a teller machine or a self-service machine, etc., and the embodiment of the application is not limited in this regard.

[0072] The fault feedback device of the new energy automobile charging cable in the embodiment of the application can be a device with an operating system. The operating system can be an Android operating system, can be an IOS operating system, and can also be other possible operating systems, which are not limited in the embodiment of the application.

[0073] Embodiment three Figure 3 is a flowchart of the fault feedback method of the new energy automobile charging cable provided in the embodiment three of the application. As shown in Figure 3 , the method specifically comprises the following steps: S301, in the working state of the new energy automobile charging cable, multi-dimensional data of the charging cable is acquired; wherein the multi-dimensional data comprises electrical dimension data, thermodynamic dimension data and optical dimension data; S302, in the case of identifying charging interruption of the charging cable, multi-dimensional data of the charging cable in the charging process is acquired; S303, the multi-dimensional data is input into a pre-constructed multi-modal analysis model, and a multi-dimensional data and charging interruption correlation index is output; S304, data of the correlation index is arranged in descending order, an identification result is obtained, and a fault data analysis result feedback is performed.

[0074] Further, the multi-dimensional data is input into a pre-constructed multi-modal analysis model, and a multi-dimensional data and charging interruption correlation index is output, comprising: acquiring a pre-constructed correlation table of each dimension data and charging interruption, and determining a weight value of each dimension data according to the correlation table; the weight value of each dimension data is taken as a label of the multi-dimensional data, and is input into a pre-constructed multi-modal analysis model to identify a multi-dimensional data and charging interruption correlation index.

[0075] The fault feedback method of the new energy automobile charging cable provided in the embodiment of the application has the functions and execution processes corresponding to the device of the above-mentioned embodiment, and achieves the corresponding beneficial effects. To avoid repetition, it will not be repeated here.

[0076] Embodiment four As shown in Figure 4 , the embodiment of the application further provides an electronic device 400, which comprises a processor 401, a memory 402, a program or instruction stored in the memory 402 and executable on the processor 401. The program or instruction is executed by the processor 401 to implement the processes of each embodiment of the above-mentioned new energy automobile charging cable fault feedback device, and can achieve the same technical effects. To avoid repetition, it will not be repeated here.

[0077] It should be noted that the electronic device in the embodiments of the present application includes the mobile electronic device and the non-mobile electronic device described above.

[0078] Embodiment five The embodiments of the present application also provide a readable storage medium, which stores a program or instructions, and the program or instructions are executed by a processor to realize each process of the new energy automobile charging cable fault feedback device embodiments described above and achieve the same technical effects. To avoid repetition, details are not described here.

[0079] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes a computer readable storage medium, such as a computer readable memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, etc.

[0080] It should be noted that in this document, the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusions, so that processes, methods, articles or devices including a series of elements not only include those elements, but also include other elements not explicitly listed, or include elements inherent to such processes, methods, articles or devices. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element 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 the order of performing the functions as shown or discussed, but can also include performing the functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method can be performed in an order different from the described order, and various steps can also be added, omitted or combined. In addition, the features described with reference to some examples can be combined in other examples.

[0081] From the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment method can be realized by means of software and necessary general hardware platform, of course, it can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a plurality of instructions for making a terminal (which can be a mobile phone, computer, server, or network equipment, etc.) execute the method described in each embodiment of the present application.

[0082] The embodiments of the present application are described above with reference to the drawings, but the present application is not limited to the specific embodiments described above, and the specific embodiments described above are merely illustrative, but not restrictive, and those of ordinary skill in the art can make many forms without departing from the purpose of the present application and the scope protected by the claims under the inspiration of the present application, which all belong to the protection of the present application.

[0083] The above are only the preferred embodiments of the present application and the technical principles used. The present application is not limited to the specific embodiments described herein, and various obvious changes, re-adjustments and replacements made by those skilled in the art will not deviate from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and more other equivalent embodiments can be included without departing from the concept of the present application, and the scope of the present application is determined by the scope of the claims.

Claims

1. A fault feedback device for a charging cable for new energy vehicles, characterized in that, The device includes: The data acquisition module is used to acquire multi-dimensional data of the charging cable when the new energy vehicle charging cable is in operation; wherein, the multi-dimensional data includes electrical dimension data, thermodynamic dimension data and optical dimension data; The data scheduling module is used to acquire multi-dimensional data of the charging cable during the charging process when the charging cable is detected to be interrupted. The data analysis module is used to input the multi-dimensional data into a pre-built multimodal analysis model and output the correlation index between the multi-dimensional data and charging interruption. The fault feedback module is used to arrange the data of the correlation indicators in descending order to obtain the identification results and to provide feedback on the data analysis results of the fault.

2. The fault feedback device for the new energy vehicle charging cable according to claim 1, characterized in that, The data analysis module includes: The weight value acquisition unit is used to acquire a pre-built correlation table between data of each dimension and charging interruption, and to determine the weight value of each dimension of data based on the correlation table. The labeling unit is used to use the weight values ​​of the data in each dimension as labels for the multi-dimensional data, input them into a pre-built multimodal analysis model, and identify the correlation indicators between the multi-dimensional data and the charging interruption.

3. The fault feedback device for the new energy vehicle charging cable according to claim 1, characterized in that, The device further includes a model optimization module, which is used for: Obtain historical fault data within the current charging pile's preset range; Based on the analysis results of the historical fault data, the multimodal analysis model is dynamically optimized.

4. The fault feedback device for the new energy vehicle charging cable according to claim 1, characterized in that, The device further includes: The micro-meteorological data acquisition module is used to acquire weather data at the location of the charging station; Accordingly, the data analysis module is also used for: The multi-dimensional data and the weather data are input into a pre-built multimodal analysis model to identify the correlation indicators between the multi-dimensional data and the weather data and charging interruption.

5. The fault feedback device for the new energy vehicle charging cable according to claim 1, characterized in that, The device further includes: The data acquisition and control module is used to determine the acquisition duration for multi-dimensional data when the charging cable is interrupted.

6. The fault feedback device for the new energy vehicle charging cable according to claim 5, characterized in that, The device further includes: The data playback module is used to play back the multi-dimensional data within the acquisition time in the form of data curves when a data playback instruction is received.

7. The fault feedback device for the new energy vehicle charging cable according to claim 1, characterized in that, The device further includes: The correlation index retrieval module is used to retrieve and display the multi-dimensional data output by the multimodal analysis model and the correlation index of charging interruption when a correlation index retrieval instruction is received.

8. A fault feedback method for a charging cable for new energy vehicles, characterized in that, The method includes: While the charging cable for a new energy vehicle is in operation, acquire multi-dimensional data of the charging cable; wherein, the multi-dimensional data includes electrical dimension data, thermodynamic dimension data, and optical dimension data; When a charging cable charging interruption is detected, multi-dimensional data of the charging cable during the charging process can be obtained. The multi-dimensional data is input into a pre-built multimodal analysis model, and the multi-dimensional data and charging interruption correlation index are output. The data of the correlation indicators are arranged in descending order to obtain the identification results, and the data analysis results of the fault are fed back.

9. The fault feedback method for new energy vehicle charging cables according to claim 8, characterized in that, The multi-dimensional data is input into a pre-built multimodal analysis model, which outputs a correlation index between the multi-dimensional data and charging interruption, including: Obtain a pre-built correlation table between data of each dimension and charging interruption, and determine the weight value of data of each dimension based on the correlation table; The weight values ​​of each dimension of data are used as labels for the multi-dimensional data and input into a pre-built multimodal analysis model to identify the correlation indicators between the multi-dimensional data and charging interruption.

10. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the fault feedback method for a new energy vehicle charging cable as described in any one of claims 8-9.

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