High-voltage system fault prediction method and device, electronic equipment and vehicle

Abstract

The invention discloses a high-voltage system fault prediction method and device, electronic equipment and a vehicle. The method comprises the following steps: acquiring operation data of a high-voltage electrical system of a target vehicle through a data acquisition module; preprocessing the operation data based on a preset parameter standard through a preprocessing module to obtain feature information; the fault prediction module transmits the feature information to the ground service part through the data transmission module; and performing fault prediction on the feature information through a fault prediction module according to a fault prediction model to obtain fault prediction data of the high-voltage electrical system. According to the invention, the features are extracted from the original operation data, and fault prediction is carried out based on the feature data without manual intervention. The efficiency and the precision of the prediction system are greatly improved, the time and the cost of manual analysis are reduced, the possibility of human errors is reduced, the accuracy and the reliability of fault prediction of the high-voltage electrical system are improved, and the method can be widely applied to the technical field of data processing.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a high-voltage system fault prediction method, device, electronic equipment and vehicle. Background Art

[0002] The high-voltage system of electric vehicles is a key system of electric vehicles. Once a fault occurs, it may cause the vehicle performance to decline or even cause a safety accident. Therefore, it is necessary to predict the fault of the high-voltage system. However, the existing fault prediction usually requires the driver to send the vehicle to a special inspection agency for maintenance and repair by maintenance personnel, and then predict the fault problems that may need maintenance to prevent based on the maintenance results and experience of the maintenance personnel. The accuracy of the existing fault prediction depends on the manual experience of specific maintenance personnel, and the maintenance process is relatively cumbersome. Summary of the invention

[0003] The present invention aims to solve the problem of related technical limitations at least to a certain extent. To this end, the present invention proposes a high-voltage system fault prediction method, device, electronic equipment and vehicle, which can efficiently and accurately predict high-voltage system faults.

[0004] On the one hand, an embodiment of the present invention provides a high voltage system fault prediction method, comprising:

[0005] The operating data of the high-voltage electrical system of the target vehicle is obtained through the data acquisition module; the operating data includes the working status and monitoring parameters of each high-voltage component in the high-voltage electrical system;

[0006] The operation data is preprocessed by the preprocessing module based on the preset parameter standards to obtain characteristic information;

[0007] The characteristic information is transmitted to the fault prediction module of the ground service part through the data transmission module; the fault prediction module is pre-set with a fault prediction model;

[0008] The fault prediction module performs fault prediction on the characteristic information according to the fault prediction model to obtain the fault prediction data of the high-voltage electrical system.

[0009] In some embodiments, the data acquisition module includes a data interface and a plurality of sensors, and each high-voltage component is provided with a sensor; obtaining the operating data of the high-voltage electrical system of the target vehicle through the data acquisition module includes the following steps:

[0010] The working status and monitoring parameters of each high-voltage component in the high-voltage electrical system are collected along the time series by sensors;

[0011] The working status and monitoring parameters of the corresponding high-voltage components collected by all sensors are summarized and transmitted to the preprocessing module through the data interface.

[0012] In some embodiments, preprocessing the operating data based on preset parameter standards to obtain characteristic information includes the following steps:

[0013] Clean the operation data based on preset parameter standards;

[0014] Perform feature extraction on the operating data after data cleaning to obtain feature information.

[0015] In some embodiments, a channel link is provided between the data transmission module and the fault prediction model; transmitting the characteristic information to the fault prediction module of the ground service part through the data transmission module includes the following steps:

[0016] The transmission status of the channel link is obtained by polling the data transmission module; the transmission status includes idle state and occupied state;

[0017] When the channel link is in an idle state, the characteristic information is encrypted and transmitted to the fault prediction model through the channel link.

[0018] In some embodiments, the fault prediction module includes a first prediction submodule and a second prediction submodule, the first prediction submodule is provided with a first prediction model based on data driving, and the second prediction submodule is provided with a second prediction model based on model driving; the method further includes the following steps:

[0019] In response to a labeling instruction of a target object, historical feature data labeled with a fault type is obtained and sorted to obtain training data;

[0020] Based on the training data, a first data-driven prediction model is obtained through machine learning or deep learning algorithm training;

[0021] Based on the physical characteristics and working principles of all high-voltage components included in the high-voltage electrical system of the target vehicle, the high-voltage electrical system is physically modeled to obtain a data model of the high-voltage electrical system as a second prediction model based on model-driven.

[0022] In some embodiments, when the fault prediction model is a second prediction model based on model-driven; performing fault prediction on the characteristic information according to the fault prediction model to obtain fault prediction data of the high-voltage electrical system includes the following steps:

[0023] Obtaining historical driving data of the target vehicle and a predicted life span of each high-voltage component in the high-voltage electrical system; the historical driving data is determined based on the driving habits of a subject driving the target vehicle;

[0024] Based on the characteristic information and the predicted life, the model-driven preliminary prediction results are obtained through the fault prediction model;

[0025] Based on the preliminary prediction results, the prediction is corrected through historical driving data to obtain fault prediction data.

[0026] In some embodiments, the method further comprises at least one of the following steps:

[0027] Optimizing and adjusting the parameter standard based on the fault prediction data to serve as the parameter standard for the next high-voltage system fault prediction preprocessing module application of the target vehicle;

[0028] Perform visualization on the fault prediction data and push the visualization results to the target terminal.

[0029] On the other hand, an embodiment of the present invention provides a high voltage system fault prediction device, comprising:

[0030] The first module is used to obtain the operating data of the high-voltage electrical system of the target vehicle through the data acquisition module; the operating data includes the working status and monitoring parameters of each high-voltage component in the high-voltage electrical system;

[0031] The second module is used to pre-process the operation data based on preset parameter standards through the pre-processing module to obtain characteristic information;

[0032] The third module is used to transmit the characteristic information to the fault prediction module of the ground service part through the data transmission module; the fault prediction module is pre-set with a fault prediction model;

[0033] The fourth module is used to perform fault prediction on the characteristic information according to the fault prediction model through the fault prediction module to obtain fault prediction data of the high-voltage electrical system.

[0034] In some embodiments, the fault prediction module includes a first prediction submodule and a second prediction submodule, the first prediction submodule is provided with a first prediction model based on data driving, and the second prediction submodule is provided with a second prediction model based on model driving; the device further includes:

[0035] The fifth module is used to respond to the labeling instruction of the target object, obtain the historical feature data labeled with the fault type and organize it to obtain training data;

[0036] A sixth module is used to obtain a data-driven first prediction model through machine learning or deep learning algorithm training based on the training data;

[0037] The seventh module is used to physically model the high-voltage electrical system based on the physical characteristics and working principles of all high-voltage components included in the high-voltage electrical system of the target vehicle, and obtain a data model of the high-voltage electrical system as a second prediction model based on model-driven.

[0038] In some embodiments, the apparatus further comprises at least one of the following:

[0039] An eighth module is used to optimize and adjust the parameter standard based on the fault prediction data to serve as the parameter standard applied by the next high-voltage system fault prediction preprocessing module of the target vehicle;

[0040] The ninth module is used to visualize the fault prediction data and push the results of the visualization processing to the target terminal.

[0041] On the other hand, an embodiment of the present invention provides an electronic device, including: a processor and a memory; the memory is used to store programs; the processor executes the program to implement the above-mentioned high-voltage system fault prediction method.

[0042] On the other hand, an embodiment of the present invention provides a computer storage medium, in which a program executable by a processor is stored. When the program executable by the processor is executed by the processor, it is used to implement the above-mentioned high-voltage system fault prediction method.

[0043] On the other hand, an embodiment of the present invention provides a vehicle, the vehicle comprising the above-mentioned high-voltage system fault prediction device or the above-mentioned electronic device.

[0044] The embodiment of the present invention obtains the operating data of the high-voltage electrical system of the target vehicle through a data acquisition module; the operating data includes the working status and monitoring parameters of each high-voltage component in the high-voltage electrical system; the operating data is preprocessed by a preprocessing module based on a preset parameter standard to obtain characteristic information; the characteristic information is transmitted to the fault prediction module of the ground service part through a data transmission module; a fault prediction model is pre-set in the fault prediction module; the fault prediction module predicts the characteristic information according to the fault prediction model to obtain the fault prediction data of the high-voltage electrical system. The present invention can accurately predict high-voltage system faults. The embodiment of the present invention extracts features from the original operating data through a preset device component integration specific processing flow, and then predicts faults based on the characteristic data without manual intervention. This greatly improves the efficiency and accuracy of the prediction system, reduces the time and cost of manual analysis, reduces the possibility of human error, and improves the accuracy and reliability of high-voltage electrical system fault prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings are used to provide a further understanding of the technical solution of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the technical solution of the present invention and do not constitute a limitation on the technical solution of the present invention.

[0046] Figure 1 This is a schematic diagram of an implementation environment for high voltage system fault prediction provided by an embodiment of the present invention;

[0047] Figure 2It is a flow chart of a high voltage system fault prediction method provided by an embodiment of the present invention;

[0048] Figure 3 A schematic diagram of the data collection process according to an embodiment of the present invention;

[0049] Figure 4 A schematic diagram of the data preprocessing process provided by an embodiment of the present invention;

[0050] Figure 5 A schematic diagram of the structure of a prediction system provided by an embodiment of the present invention;

[0051] Figure 6 A schematic diagram of an example of data transmission of a prediction system provided by an embodiment of the present invention;

[0052] Figure 7 A simplified flowchart of the modeling implementation provided by an embodiment of the present invention;

[0053] Figure 8 A schematic diagram of the overall flow of the prediction method provided by an embodiment of the present invention;

[0054] Fig. 9 A schematic diagram of the structure of a high voltage system fault prediction device provided by an embodiment of the present invention;

[0055] Fig.10 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0057] It should be noted that, although the functional modules are divided in the system schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first / S100", "second / S200", etc. in the specification, claims and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0058] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0059] It is understandable that the high-voltage system fault prediction method provided in the embodiment of the present invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various types of terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. In some embodiments, the terminal is a smart phone, a tablet computer, a laptop computer, and a desktop computer, but is not limited to this.

[0060] like Figure 1 FIG. 1 is a schematic diagram of an implementation environment provided by an embodiment of the present invention. Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected to a network wirelessly or wired to complete data transmission and exchange.

[0061] Server 101 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0062] In addition, the server 101 can also be a node server in the blockchain network. Blockchain is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm.

[0063] The terminal 102 may be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc. The terminal 102 may also be a vehicle-mounted terminal of various device types described above, but is not limited thereto. The terminal 102 and the server 101 may be directly or indirectly connected via wired or wireless communication, which is not limited in this embodiment of the present invention.

[0064] Based on the example Figure 1 In the implementation environment shown, an embodiment of the present invention provides a high-voltage system fault prediction method. The following is an example of applying the high-voltage system fault prediction method in the server 101. It can be understood that the high-voltage system fault prediction method can also be applied to the terminal 102.

[0065] Reference Figure 2 , Figure 2 The flowchart of the high-voltage system fault prediction method applied to the server provided by the embodiment of the present invention, the execution subject of the high-voltage system fault prediction method can be any of the aforementioned computer devices (including servers or terminals). Figure 2 , the method comprises the following steps:

[0066] S100, acquiring operation data of a high-voltage electrical system of a target vehicle through a data acquisition module;

[0067] Among them, the operating data includes the working status and monitoring parameters of each high-voltage component in the high-voltage electrical system;

[0068] It should be noted that the data acquisition module includes a data interface and multiple sensors, and each high-voltage component is provided with a sensor; in some embodiments, such as Figure 3 As shown, step S100 may include the following steps: S101, collecting the working status and monitoring parameters of each high-voltage component in the high-voltage electrical system along a time series through sensors; S102, summarizing and transmitting the working status and monitoring parameters of the corresponding high-voltage components collected by all sensors to the preprocessing module through the data interface.

[0069] For example, in some specific implementations, the data acquisition module is responsible for real-time acquisition of operating data of the high-voltage electrical system, including key parameters such as system voltage, system current, temperature, and operating time of high-voltage components. These data are monitored in real time by sensors and transmitted to the preprocessing module through a data interface.

[0070] S200, preprocessing the operation data based on a preset parameter standard by a preprocessing module to obtain characteristic information;

[0071] It should be noted that if Figure 4As shown, in some embodiments, the operation data is preprocessed based on preset parameter standards to obtain characteristic information, including the following steps: S201, data cleaning of the operation data based on preset parameter standards; S202, feature extraction of the cleaned operation data to obtain characteristic information.

[0072] Exemplarily, in some specific implementations, the preprocessing module performs cleaning, denoising and feature extraction on the collected data to eliminate outliers and noise in the data and extract feature information useful for fault prediction. The main working method is to perform real-time status monitoring, identify the normal and faulty states of the system, record the working time of high-voltage system components, and the switching frequency of important high-voltage components, such as high-voltage relays; at the same time, record the CPU temperature, CPU usage, and memory usage of the high-voltage system related controls, record thresholds, and over-limit alarm information. Data processing methods such as threshold judgment, mutation search, and 3σ criteria are used to process the data.

[0073] S300, transmitting the characteristic information to the fault prediction module of the ground service part through the data transmission module;

[0074] Wherein, a fault prediction model is pre-set in the fault prediction module;

[0075] It should be noted that a channel link is set between the data transmission module and the fault prediction model; in some embodiments, step S300 may include the following steps: obtaining the transmission status of the channel link through polling the data transmission module; the transmission status includes an idle state and an occupied state; when the channel link is in an idle state, encrypting the characteristic information and transmitting it to the fault prediction model through the channel link.

[0076] For example, in some specific implementations, the data transmission module encrypts and compresses the information collected by the preprocessing module and transmits it to the ground when the vehicle-to-ground channel is in an idle state.

[0077] S400, performing fault prediction on characteristic information according to a fault prediction model through a fault prediction module to obtain fault prediction data of the high voltage electrical system;

[0078] Among them, in some embodiments, the fault prediction module includes a first prediction sub-module and a second prediction sub-module, the first prediction sub-module is provided with a first prediction model based on data driving, and the second prediction sub-module is provided with a second prediction model based on model driving; the method may also include the following steps: in response to the labeling instruction of the target object, obtaining historical feature data labeled with the fault type and collating them to obtain training data; based on the training data, obtaining a first prediction model based on data driving through machine learning or deep learning algorithm training; based on the physical characteristics and working principles of all high-voltage components included in the high-voltage electrical system of the target vehicle, physically modeling the high-voltage electrical system, and obtaining a data model of the high-voltage electrical system as a second prediction model based on model driving.

[0079] Exemplarily, in some specific implementations, the fault prediction module is the core part of the present invention, and the prediction module includes a data-driven prediction submodule and a model-based prediction submodule. The data-driven prediction submodule uses machine learning or deep learning algorithms to train preprocessed data to obtain a data-driven prediction model; the model-based prediction submodule establishes a mathematical model based on the physical characteristics and working principles of the high-voltage electrical system.

[0080] It should be noted that when the fault prediction model is a second prediction model driven by a model; in some embodiments, fault prediction is performed on the characteristic information according to the fault prediction model to obtain fault prediction data of the high-voltage electrical system, which may include the following steps: obtaining the historical driving data of the target vehicle and the predicted life of each high-voltage component in the high-voltage electrical system; the historical driving data is determined based on the driving habits of the driver of the target vehicle; based on the characteristic information, a model-driven preliminary prediction result is obtained through the fault prediction model in combination with the predicted life; based on the preliminary prediction result, a corrected prediction is performed through the historical driving data to obtain fault prediction data.

[0081] Exemplarily, in some specific implementations, when the fault prediction model applies a second prediction model based on model-driven, it can refer to the life prediction information of key components of the high-voltage system in the model database, obtain the model-driven prediction results through model analysis, and make a corrected prediction based on the driving habits in the user's historical data to obtain the final fault prediction result.

[0082] Among them, in some embodiments, the method also includes at least one of the following steps: optimizing and adjusting the parameter standard based on the fault prediction data to serve as the parameter standard applied by the next high-voltage system fault prediction preprocessing module of the target vehicle; visualizing the fault prediction data and pushing the results of the visualization processing to the target terminal.

[0083] For example, in some specific implementations, based on the fault prediction data, the threshold and other information of the pre-processed data cleaning process can be optimized through machine learning of the neural network and transmitted to the pre-processing module as the parameter standard applied to the next high-voltage system fault prediction of the target vehicle. The fault prediction module can also formulate corresponding maintenance strategies and optimization measures based on the prediction results, and perform visual display so that maintenance personnel can timely understand the fault conditions of the high-voltage electrical system.

[0084] In order to explain the principle of the technical solution of the present invention in detail, the overall process of the present invention is described below in combination with some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and cannot be regarded as a limitation of the present invention.

[0085] First of all, it should be noted that the high-voltage system of electric vehicles is a key system of electric vehicles. Once a failure occurs, it may cause vehicle performance to decline or even cause safety accidents. Therefore, it is necessary to predict the failure of the high-voltage system. While improving the reliability and availability of the vehicle, it can also timely discover potential safety hazards and effectively avoid or reduce safety accidents caused by high-voltage system failures through preventive maintenance.

[0086] In view of this, the present invention proposes a fault prediction system for a high-voltage electrical system of an electric vehicle and a corresponding implementation method thereof. The advantage of this method is that deep learning technology can automatically extract features from raw data without human intervention. This greatly improves the efficiency and accuracy of the prediction system, reduces the time and cost of manual analysis, reduces the possibility of human error, and improves the accuracy and reliability of high-voltage electrical system fault prediction. At the same time, through data preprocessing and feature extraction, outliers and noise in the data are effectively eliminated, the performance of the prediction model is improved, and the remaining computing power of each controller on the vehicle and the remaining transmission capacity of the communication channel are effectively utilized. At the same time, a more powerful data model is established on the ground for complex calculations and adaptive upgrade iterations, and the data preprocessing link of the on-board part is reversely upgraded to increase the effectiveness of model data acquisition.

[0087] like Figure 5 As shown, the present invention proposes a fault prediction system for a high-voltage electrical system of an electric vehicle, including a data acquisition module, a preprocessing module, a data transmission module and a fault prediction module. Figure 6 As shown, it is an example of data transmission of a fault prediction system for a high-voltage electrical system of an electric vehicle (it should not be regarded as a limitation to the present invention, and the specific data interaction signal details can be adaptively adjusted based on the actual application scenario).

[0088] The data acquisition module is responsible for real-time collection of high-voltage electrical system operation data, including key parameters such as system voltage, system current, temperature, and high-voltage component operation time. These data are monitored in real time through sensors and transmitted to the pre-processing module through the data interface.

[0089] The preprocessing module cleans, denoises and extracts features from the collected data to eliminate outliers and noise in the data and extract feature information that is useful for fault prediction. The main working method is to conduct real-time status monitoring, identify the normal and faulty states of the system, record the working time of high-voltage system components, and the switching frequency of important high-voltage components, such as high-voltage relays; at the same time, record the CPU temperature, CPU usage, and memory usage of the high-voltage system-related controls, record thresholds, and over-limit alarm information. Data processing methods such as threshold judgment, mutation search, and 3σ criteria are used to process the data.

[0090] The data transmission module encrypts and compresses the information collected by the preprocessing module and transmits it to the ground when the vehicle-to-ground channel is in an idle state.

[0091] The fault prediction module is the core part of the present invention. Figure 7 As shown in the figure, it is a simplified process for modeling implementation. The prediction module includes a data-driven prediction submodule and a model-based prediction submodule. The data-driven prediction submodule uses machine learning or deep learning algorithms to train the preprocessed data to obtain a data-driven prediction model; the model-based prediction submodule establishes a mathematical model based on the physical characteristics and working principles of the high-voltage electrical system, and refers to the life prediction information of the key components of the high-voltage system in the model database. The model-driven prediction results are obtained through model analysis, and the driving habits in the user's historical data are combined to make corrections and predictions to obtain the final fault prediction results. The fault prediction module formulates corresponding maintenance strategies and optimization measures based on the prediction results, and displays them visually so that maintenance personnel can understand the fault conditions of the high-voltage electrical system in a timely manner. When necessary, remind users to go to the store early for repair and maintenance to improve the safety and reliability of electric vehicles.

[0092] Specifically, Figure 8As shown, the electric vehicle high-voltage electrical system fault prediction method based on the above system architecture includes the following steps: S1: Identify the key features of the vehicle's high-voltage system and establish a high-voltage system model, including but not limited to the relevant vehicle controller, battery BMS, electric drive controller MCU, air conditioning controller ATC, OBC and DC / DC and other high-voltage component controllers; S2: Collect the working status and parameters of high-voltage components, system voltage, system current, temperature, high-voltage component running time, controller CPU temperature, CPU usage, memory usage and switching frequency of high-voltage control components through the data acquisition module; S3: Pre-analyze the voltage and current parameter information of each high-voltage assembly collected in the high-voltage system loop, the working status and time of the system controller, and perform Data cleaning, removal of outliers, noise and duplicate data; S4: The transmission module transmits valuable feature values ​​through an encrypted channel to the fault prediction module on the ground; S5: Predictive modeling and optimization are carried out through the machine algorithm of the neural network, and the user's historical habits are used as correction parameters; at the same time, a professional judgment mechanism can be introduced to further improve the accuracy of model judgment. Fault prediction mainly includes prediction of possible faults and prediction of component life; S6: Through the machine learning of the neural network, the threshold and other information of the pre-processed data cleaning process are optimized and transmitted to the pre-processing module; S7: The prediction results and countermeasures are visualized to the maintenance personnel, and selectively pushed to the driver according to the time dimension and severity dimension of the fault prediction.

[0093] In summary, compared with the prior art, the present invention has at least the following beneficial effects:

[0094] 1. Comprehensive prediction method: Combining data-driven and model-driven prediction methods to achieve accurate prediction of high-voltage electrical system faults. This comprehensive method has higher prediction accuracy and reliability than a single prediction method.

[0095] 2. User historical data correction: The prediction results are corrected using the user's historical driving habit data, which improves the pertinence and accuracy of the prediction results for individual users.

[0096] 3. Secure data transmission: Encrypted and compressed data transmission is performed when the vehicle-to-ground channel is idle, ensuring data security and transmission efficiency.

[0097] 4. Visual display of fault prediction results: Through visual display, maintenance personnel can intuitively understand the fault conditions of the high-voltage electrical system, improving maintenance efficiency and accuracy.

[0098] 5. Maintenance strategy driven by prediction results: Targeted maintenance strategies and optimization measures are formulated based on the prediction results, which improves the safety and reliability of electric vehicles and provides users with a better user experience.

[0099] On the other hand, Fig. 9 As shown, an embodiment of the present invention provides a high voltage system fault prediction device 900, which may include:

[0100] The first module 910 is used to obtain the operating data of the high-voltage electrical system of the target vehicle through the data acquisition module; the operating data includes the working state and monitoring parameters of each high-voltage component in the high-voltage electrical system;

[0101] The second module 920 is used to pre-process the operation data based on a preset parameter standard through a pre-processing module to obtain characteristic information;

[0102] The third module 930 is used to transmit the characteristic information to the fault prediction module of the ground service part through the data transmission module; the fault prediction module is pre-set with a fault prediction model;

[0103] The fourth module 940 is used to perform fault prediction on the characteristic information according to the fault prediction model through the fault prediction module to obtain fault prediction data of the high-voltage electrical system.

[0104] In some embodiments, the fault prediction module includes a first prediction submodule and a second prediction submodule, the first prediction submodule is provided with a first prediction model based on data driving, and the second prediction submodule is provided with a second prediction model based on model driving; the device may also include:

[0105] The fifth module is used to respond to the labeling instruction of the target object, obtain the historical feature data labeled with the fault type and organize it to obtain training data;

[0106] A sixth module is used to obtain a data-driven first prediction model through machine learning or deep learning algorithm training based on the training data;

[0107] The seventh module is used to physically model the high-voltage electrical system based on the physical characteristics and working principles of all high-voltage components included in the high-voltage electrical system of the target vehicle, and obtain a data model of the high-voltage electrical system as a second prediction model based on model-driven.

[0108] In some embodiments, the device may further include at least one of the following:

[0109] An eighth module is used to optimize and adjust the parameter standard based on the fault prediction data to serve as the parameter standard applied by the next high-voltage system fault prediction preprocessing module of the target vehicle;

[0110] The ninth module is used to visualize the fault prediction data and push the results of the visualization processing to the target terminal.

[0111] The contents of the method embodiments of the present invention are all applicable to the device embodiments. The functions specifically implemented by the device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0112] On the other hand, an embodiment of the present invention further provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above-mentioned high-voltage system fault prediction method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0113] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0114] like Fig.10 As shown, Fig.10 The hardware structure of an electronic device 1000 of another embodiment is illustrated. The electronic device 1000 includes:

[0115] The processor 1001 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention.

[0116] The memory 1002 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1002 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1002, and the processor 1001 calls and executes the network node population optimization method of the embodiment of the present invention;

[0117] Input / output interface 1003, used to implement information input and output;

[0118] The communication interface 1004 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0119] A bus 1005 , which transmits information between various components of the device (e.g., the processor 1001 , the memory 1002 , the input / output interface 1003 , and the communication interface 1004 );

[0120] The processor 1001 , the memory 1002 , the input / output interface 1003 and the communication interface 1004 are connected to each other in communication within the device via the bus 1005 .

[0121] The electronic device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0122] The contents of the method embodiments of the present invention are all applicable to the electronic device embodiments. The functions specifically implemented by the electronic device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0123] Another aspect of an embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the above method.

[0124] It should be noted that the computer-readable medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (Compact Disc Read to Only Memory, CD to ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0125] The contents of the method embodiments of the present invention are all applicable to the computer-readable storage medium embodiments. The functions specifically implemented by the computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0126] The embodiment of the present invention also discloses a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device can read the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the above method.

[0127] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0128] It should be noted that, although several modules of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0129] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD to ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation of the present invention.

[0130] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0131] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0132] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0133] The logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by an instruction execution device, apparatus or device (such as a computer-based device, a device including a processor, or other device that can fetch instructions from an instruction execution device, apparatus or device and execute the instructions), or in conjunction with such instruction execution device, apparatus or device. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution device, apparatus or device, or in conjunction with such instruction execution device, apparatus or device.

[0134] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0135] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution device. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0136] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0137] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0138] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A high voltage system fault prediction method, characterized in that: The following steps are involved: Acquire the operating data of the high-voltage electrical system of the target vehicle through the data acquisition module; the operating data includes the working status and monitoring parameters of each high-voltage component in the high-voltage electrical system; Preprocessing the operating data based on preset parameter standards through a preprocessing module to obtain characteristic information; The characteristic information is transmitted to a fault prediction module of a ground service part through a data transmission module; a fault prediction model is pre-set in the fault prediction module; The fault prediction module performs fault prediction on the characteristic information according to the fault prediction model to obtain fault prediction data of the high-voltage electrical system.

2. The high voltage system fault prediction method according to claim 1, characterized in that: The data acquisition module includes a data interface and a plurality of sensors, and each of the high-voltage components is provided with the sensor; the operation data of the high-voltage electrical system of the target vehicle is obtained through the data acquisition module, including the following steps: Collecting the working status and monitoring parameters of each high-voltage component in the high-voltage electrical system along a time series by means of the sensor; The working status and the monitoring parameters of the corresponding high-voltage components collected by all the sensors are summarized and transmitted to the preprocessing module through the data interface.

3. The high voltage system fault prediction method according to claim 1, characterized in that: The preprocessing of the operating data based on the preset parameter standard to obtain characteristic information includes the following steps: Performing data cleaning on the operation data based on the preset parameter standard; Feature extraction is performed on the operating data after the data cleaning to obtain the feature information.

4. The high voltage system fault prediction method according to claim 1, characterized in that: A channel link is provided between the data transmission module and the fault prediction model; the step of transmitting the characteristic information to the fault prediction module of the ground service part through the data transmission module comprises the following steps: The transmission status of the channel link is obtained by polling the data transmission module; the transmission status includes an idle state and an occupied state; When the channel link is in an idle state, the characteristic information is encrypted and transmitted to the fault prediction model through the channel link.

5. The high voltage system fault prediction method according to claim 1, characterized in that: The fault prediction module includes a first prediction submodule and a second prediction submodule, wherein the first prediction submodule is provided with a first prediction model based on data driving, and the second prediction submodule is provided with a second prediction model based on model driving; the method further includes the following steps: In response to a labeling instruction of a target object, historical feature data labeled with a fault type is obtained and sorted to obtain training data; Based on the training data, the first data-driven prediction model is obtained through machine learning or deep learning algorithm training; Based on the physical characteristics and working principles of all the high-voltage components included in the high-voltage electrical system of the target vehicle, the high-voltage electrical system is physically modeled to obtain a data model of the high-voltage electrical system as the second prediction model based on model-driven.

6. The high voltage system fault prediction method according to claim 5, characterized in that: When the fault prediction model is the second prediction model based on model-driven; performing fault prediction on the characteristic information according to the fault prediction model to obtain fault prediction data of the high-voltage electrical system comprises the following steps: Acquiring historical driving data of the target vehicle and a predicted life span of each high-voltage component in the high-voltage electrical system; the historical driving data is determined based on the driving habits of a driver of the target vehicle; Based on the characteristic information and in combination with the predicted life, a model-driven preliminary prediction result is obtained through the fault prediction model; Based on the preliminary prediction result, a revised prediction is performed using the historical driving data to obtain the fault prediction data.

7. The high voltage system fault prediction method according to claim 1, characterized in that: The method further comprises at least one of the following steps: Optimizing and adjusting the parameter standard based on the fault prediction data to serve as the parameter standard applied by the preprocessing module for predicting the next high-voltage system fault of the target vehicle; Visualization processing is performed on the fault prediction data, and the result of the visualization processing is pushed to a target terminal.

8. A high voltage system fault prediction device, characterized in that: include: The first module is used to obtain the operating data of the high-voltage electrical system of the target vehicle through the data acquisition module; the operating data includes the working state and monitoring parameters of each high-voltage component in the high-voltage electrical system; The second module is used to pre-process the operation data based on a preset parameter standard through a pre-processing module to obtain characteristic information; The third module is used to transmit the characteristic information to the fault prediction module of the ground service part through the data transmission module; the fault prediction module is pre-set with a fault prediction model; The fourth module is used to perform fault prediction on the characteristic information according to the fault prediction model through the fault prediction module to obtain fault prediction data of the high-voltage electrical system.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 7.

10. A vehicle, characterized in that: The vehicle includes the high voltage system failure prediction device according to claim 8 or the electronic device according to claim 9.

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