Method and system for optimizing dustproof automobile charging pile with multiple protection structures

Abstract

The invention relates to the technical field of automobile charging facilities, and discloses a dustproof automobile charging pile optimization method and system with a multi-protection structure, and the method comprises the steps: analyzing the interaction correlation influence between operation state parameters and operation environment data, and setting an environment protection target corresponding to an automobile charging pile; analyzing equipment charging loss corresponding to the automobile charging pile, setting an equipment protection target of the automobile charging pile, and formulating a global protection strategy of the automobile charging pile; performing multi-modal analysis on the abnormal charging event to obtain an abnormal feature set, performing traceability analysis on the abnormal feature set to obtain a failure causal network, and determining a weak protection area in the automobile charging pile; and the regional charging shell of the weak protection region is detected, the failure probability strength corresponding to the regional charging shell is calculated, a regional protection strategy corresponding to the weak protection region is determined, protection optimization processing of the automobile charging pile is executed, and a protection result is obtained. The protection efficiency of the dustproof automobile charging pile can be improved.

Description

Technical Field

[0001] The present invention relates to a method and system for optimizing a dustproof automobile charging pile with multiple protection structures, belonging to the technical field of automobile charging facilities. Background Art

[0002] Dust-proof car charging piles are charging devices designed specifically for outdoor charging scenarios. Through sealed shells, dust-proof filters, anti-wear interfaces and other structures, they block sand, dust and debris from invading internal circuits and mechanical components. While ensuring the stability of the charging function, they extend the service life of the equipment and are suitable for electric vehicle charging needs in high-dust and harsh environments. In order to improve the user experience of car charging piles, car charging piles need to be treated with multiple protections.

[0003] The existing optimization method for multi-protection dust-proof car charging piles is to adopt a method that combines physical protection and experience-based protection. This method is to improve the protection level by adding corresponding physical protection equipment to the dust-proof car charging pile, such as filters, thickened rubber strips and other structures, and set fixed maintenance cycles and maintenance contents based on the historical experience of operation and maintenance personnel, such as cleaning the filter once a month. However, this method is relatively simple in protection and does not take into account multiple aspects of protection, which leads to low protection efficiency of dust-proof car charging piles. Therefore, a method is needed to improve the protection efficiency of dust-proof car charging piles. Summary of the Invention

[0004] The present invention provides a method and system for optimizing a dustproof automobile charging pile with multiple protection structures, the main purpose of which is to improve the protection efficiency of the dustproof automobile charging pile.

[0005] To achieve the above objectives, the present invention provides a method for optimizing a dustproof automobile charging pile with multiple protection structures, comprising:

[0006] Recording the operating status parameters and operating environment data corresponding to the dustproof automobile charging pile to be processed, analyzing the interactive correlation effects between the operating status parameters and the operating environment data, and setting an environmental protection target corresponding to the automobile charging pile based on the functional interactive effects and the operating environment data;

[0007] Obtaining vehicle charging data of the vehicle charging pile, analyzing the device charging loss corresponding to the vehicle charging pile based on the vehicle charging data, setting a device protection target for the vehicle charging pile based on the device charging loss, and formulating a global protection strategy for the vehicle charging pile in combination with the environmental protection target and the device protection target;

[0008] Querying abnormal charging events corresponding to the vehicle charging pile, performing multimodal analysis on the abnormal charging events to obtain an abnormal feature set, performing source tracing analysis on the abnormal feature set to obtain a failure causal network, and determining weak protection areas in the vehicle charging pile based on the failure causal network;

[0009] Detect the regional charging shell of the weak protection area, calculate the failure probability intensity corresponding to the regional charging shell, combine the failure probability intensity and the failure causal network, formulate a regional protection strategy corresponding to the weak protection area, combine the global protection strategy and the regional protection strategy, execute the protection optimization processing of the car charging pile, and obtain the protection result.

[0010] Optionally, analyzing the interactive correlation impact between the operating state parameter and the operating environment data includes:

[0011] Extracting the state identifier and the identification index corresponding to the operating state parameter, and constructing an operating situation diagram corresponding to the state identifier based on the identification index;

[0012] Identifying environmental factors and factor indicators in the operating environment data, and constructing factor trend graphs corresponding to the environmental factors based on the factor indicators;

[0013] Analyzing the temporal correlation between the state identifier and the environmental factor in combination with the operation status diagram and the factor trend diagram;

[0014] Based on the temporal correlation, a correlated state-environment set is selected from the state identifier and the environmental factor;

[0015] The corresponding interaction mechanism in the associated state-environment set is analyzed, and based on the interaction mechanism, an interactive correlation influence between the operating state parameter and the operating environment data is generated.

[0016] Optionally, the step of setting an environmental protection target corresponding to the vehicle charging pile in combination with the functional interaction effect and the operating environment data includes:

[0017] Perform causal relationship analysis on the functional interaction effects to obtain a causal action map;

[0018] Analyzing the environmental distribution patterns and environmental change trends corresponding to the vehicle charging piles based on the operating environment data;

[0019] Performing risk mapping on the environmental distribution pattern, the environmental change trend, and the causal effect map to obtain key environmental risk points of the vehicle charging pile;

[0020] The design industry specifications of the automobile charging pile are queried, and the environmental protection targets corresponding to the automobile charging pile are set in combination with the design industry specifications and the key environmental risk points.

[0021] Optionally, analyzing the charging loss of the device corresponding to the car charging pile based on the vehicle charging data includes:

[0022] Performing data cleaning on the vehicle charging data to obtain target charging data;

[0023] Identifying a data tag corresponding to the target charging data, and analyzing tag semantics corresponding to the data tag;

[0024] extracting charging behavior characteristics and charging fluctuation power from the target charging data based on the tag semantics;

[0025] Analyzing a charging behavior pattern corresponding to the charging behavior feature;

[0026] Calculating a power variation coefficient corresponding to the charging fluctuation power, and analyzing a behavioral charging loss corresponding to the charging behavior pattern based on the power variation coefficient;

[0027] In combination with the behavior charging loss, the device charging loss corresponding to the car charging pile is determined.

[0028] Optionally, the calculating the power variation coefficient corresponding to the charging fluctuation power includes:

[0029] identifying a power time series corresponding to the charging fluctuating power, and sorting the charging fluctuating power based on the power time series to obtain sorted fluctuating power;

[0030] The power quantity corresponding to the charging fluctuating power is counted, and the power variation coefficient corresponding to the charging fluctuating power is calculated by combining the power quantity and the sorted fluctuating power using the following formula:

[0031]

[0032] Among them, A represents the power variation coefficient corresponding to the charging fluctuation power, B a Indicates the ath power value in the sorted fluctuating power, a indicates the sequence number corresponding to the sorted fluctuating power, and N indicates the power quantity.

[0033] Optionally, performing multimodal analysis on the abnormal charging event to obtain an abnormal feature set includes:

[0034] Performing multimodal time alignment processing on the abnormal charging event to obtain a synchronous charging event matrix;

[0035] Performing sub-modal feature extraction on the synchronous charging event matrix to obtain initial modal event features;

[0036] Performing cross-modal correlation analysis on the initial modal event features to obtain an abnormal correlation feature map corresponding to the abnormal charging event;

[0037] Calculating the importance score corresponding to the initial modal event feature based on the abnormal correlation feature map;

[0038] Based on the importance score, feature optimization processing is performed on the initial modal event features to obtain an abnormal feature set.

[0039] Optionally, performing source tracing analysis on the abnormal feature set to obtain a failure causal network includes:

[0040] Calculating the similarity coefficient between each feature in the abnormal feature set;

[0041] Based on the similarity coefficient, clustering is performed on the abnormal feature set to obtain clustered abnormal features;

[0042] Performing causal test analysis on the cluster abnormal features to obtain a feature causal chain;

[0043] Analyzing the conditional dependencies between the feature causal chains, and constructing the causal impact path of the clustered abnormal features based on the conditional dependencies;

[0044] Performing logic verification on the characteristic causal chain and the causal influence path to obtain a valid causal network;

[0045] The effective causal network is quantitatively characterized to obtain an ineffective causal network.

[0046] Optionally, the calculating the failure probability intensity corresponding to the regional charging shell includes:

[0047] Performing flaw detection on the charging shell in the area to obtain a shell flaw detection report;

[0048] Extracting shell flaw detection indicators and their corresponding shell flaw detection features from the shell flaw detection test report;

[0049] Calculate the shell defect index corresponding to the regional charging shell based on the shell flaw detection index and the shell flaw detection characteristics;

[0050] Based on the shell defect index, the failure probability intensity corresponding to the regional charging shell is calculated.

[0051] Optionally, calculating the failure probability intensity corresponding to the regional charging shell based on the shell defect index includes:

[0052] The shell volume corresponding to the regional charging shell is measured, and the failure probability intensity corresponding to the regional charging shell is calculated by combining the shell volume and the shell defect index using the following formula:

[0053]

[0054] Among them, δ represents the failure probability intensity corresponding to the regional charging shell, V represents the shell volume, F represents the shell defect index, F0 represents the benchmark defect index, and m represents the morphological parameter.

[0055] In order to solve the above problems, the present invention also provides a dust-proof automobile charging pile optimization system with multiple protection structures, the system comprising:

[0056] An environmental protection target setting module is used to record the operating status parameters and operating environment data corresponding to the dust-proof automobile charging pile to be processed, analyze the interactive correlation effects between the operating status parameters and the operating environment data, and set the environmental protection target corresponding to the automobile charging pile based on the functional interaction effects and the operating environment data;

[0057] a global protection strategy formulation module, configured to obtain vehicle charging data of the vehicle charging pile, analyze the device charging loss corresponding to the vehicle charging pile based on the vehicle charging data, set the device protection target of the vehicle charging pile based on the device charging loss, and formulate a global protection strategy for the vehicle charging pile in combination with the environmental protection target and the device protection target;

[0058] a weak protection area determination module, configured to query abnormal charging events corresponding to the vehicle charging pile, perform multimodal analysis on the abnormal charging events to obtain an abnormal feature set, perform source tracing analysis on the abnormal feature set to obtain a failure causal network, and determine the weak protection area in the vehicle charging pile based on the failure causal network;

[0059] The protection optimization processing module is used to detect the regional charging shell of the weak protection area, calculate the failure probability intensity corresponding to the regional charging shell, combine the failure probability intensity and the failure causal network, formulate the regional protection strategy corresponding to the weak protection area, combine the global protection strategy and the regional protection strategy, execute the protection optimization processing of the car charging pile, and obtain the protection result.

[0060] Compared with the problems described in the background technology, the present invention can understand the coupling relationship and interaction mechanism between the environment and equipment operation by analyzing the functional interaction between the operating state parameters and the operating environment data, thereby providing an important basis for the setting of subsequent environmental protection targets. Furthermore, the present invention can understand the degree of equipment damage of the car charging pile by analyzing the charging loss of the equipment corresponding to the car charging pile based on the vehicle charging data, thereby laying an important foundation for the setting of equipment protection targets of the car charging pile. The present invention obtains an abnormal feature set by performing multimodal analysis on the abnormal charging event, which can deeply analyze the essential characteristics of the abnormal charging event from multiple dimensions and provide a comprehensive feature basis for the analysis of the subsequent fault type map. Furthermore, the present invention can understand the possibility of the regional charging shell losing its function by calculating the failure probability intensity corresponding to the regional charging shell, thereby facilitating the formulation and processing of the regional protection strategy corresponding to the weak protection area, laying a foundation for improving the protection efficiency of the dust-proof car charging pile. Therefore, the dust-proof car charging pile optimization method and system with multiple protection structures provided in the embodiment of the present invention can improve the protection efficiency of the dust-proof car charging pile. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A schematic flow chart of a method for optimizing a dustproof automobile charging pile with multiple protective structures according to an embodiment of the present invention;

[0062] Figure 2 A protection optimization process flow chart of the dustproof automobile charging pile optimization method with multiple protection structures provided by the present invention;

[0063] Figure 3 A schematic diagram of a module for implementing the method for optimizing a dust-proof automobile charging pile with multiple protection structures provided in one embodiment of the present invention.

[0064] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0065] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0066] The embodiment of the present application provides a method for optimizing a dustproof automobile charging pile with multiple protective structures. The execution subject of the method for optimizing a dustproof automobile charging pile with multiple protective structures includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for optimizing a dustproof automobile charging pile with multiple protective structures can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0067] Example 1:

[0068] Reference Figure 1 FIG. 1 is a flow chart of a method for optimizing a dustproof automobile charging pile with multiple protection structures according to an embodiment of the present invention. In this embodiment, the method for optimizing a dustproof automobile charging pile with multiple protection structures includes:

[0069] S1. Record the operating status parameters and operating environment data corresponding to the dust-proof automobile charging pile to be processed, analyze the interactive correlation effects between the operating status parameters and the operating environment data, and set the environmental protection target corresponding to the automobile charging pile in combination with the functional interactive effects and the operating environment data.

[0070] By analyzing the functional interaction between the operating status parameters and the operating environment data, the present invention can understand the coupling relationship and interaction mechanism between the environment and the equipment operation, and thus provide an important basis for the setting of subsequent environmental protection goals. Among them, the dust-proof automobile charging pile is an automobile charging device with multiple dust-proof structures. In terms of functional properties and structural composition, the charging pile belongs to the category of power cabinets and can be regarded as a component of the overall power cabinet system. The operating status parameters are its real-time working status indicators (such as voltage, temperature, etc.), and the operating environment data are the characteristics of the external environment in which it is located (such as dust concentration, humidity, etc.). The interactive correlation influence is the dynamic coupling relationship and interaction mechanism between the two. Furthermore, the operating status parameters and operating environment data corresponding to the dust-proof automobile charging pile to be processed can be recorded by sensors and environmental monitoring equipment, such as current sensors, temperature sensors, dust detectors, humidity sensors, etc.

[0071] As an embodiment of the present invention, the analyzing the interactive correlation impact between the operating state parameter and the operating environment data includes:

[0072] Extracting the state identifier and the identification index corresponding to the operating state parameter, and constructing an operating situation diagram corresponding to the state identifier based on the identification index;

[0073] Identifying environmental factors and factor indicators in the operating environment data, and constructing factor trend graphs corresponding to the environmental factors based on the factor indicators;

[0074] Analyzing the temporal correlation between the state identifier and the environmental factor in combination with the operation status diagram and the factor trend diagram;

[0075] Based on the temporal correlation, a correlated state-environment set is selected from the state identifier and the environmental factor;

[0076] The corresponding interaction mechanism in the associated state-environment set is analyzed, and based on the interaction mechanism, an interactive correlation influence between the operating state parameter and the operating environment data is generated.

[0077] Among them, the state identifier and the identification indicator are respectively the characteristic label and quantitative indicator corresponding to the operating state parameter; the operating situation diagram is a dynamic visual state representation constructed by the state identifier based on the identification indicator; the environmental factors and the factor indicators are respectively the key environmental elements and measurement parameters in the operating environment data; the factor trend diagram is a visual trend representation of environmental changes constructed by the environmental factors based on the factor indicators; the temporal correlation is the time series correlation law between the state identifier and the environmental factor; the associated state-environment set is a significantly correlated combination screened out from the state identifier and the environmental factor; and the interaction mechanism is the corresponding interaction path and principle in the associated state-environment set.

[0078] Furthermore, the state identifier and identification indicator corresponding to the operating state parameter can be extracted through a data analysis algorithm; based on the identification indicator, an operating situation diagram corresponding to the state identifier can be constructed through a visual modeling tool; the environmental factors and factor indicators in the operating environment data can be identified through an environmental feature extraction model; based on the factor indicator, a factor trend diagram corresponding to the environmental factor can be constructed through a trend analysis algorithm; in combination with the operating situation diagram and the factor trend diagram, the temporal correlation between the state identifier and the environmental factor can be analyzed through a temporal correlation analysis algorithm; based on the temporal correlation, a threshold screening model can be used to screen out an associated state-environment set from the state identifier and the environmental factor; the corresponding interaction mechanism in the associated state-environment set can be analyzed through a causal reasoning model, and based on the interaction mechanism, a quantitative evaluation model can be used to generate the interactive correlation impact between the operating state parameter and the operating environment data.

[0079] The present invention sets the environmental protection target corresponding to the car charging pile by combining the functional interaction and the operating environment data, and then obtains the environmental protection target of the car charging pile, which lays a foundation for improving the protection efficiency of the car charging pile. Among them, the environmental protection target is the environmental protection target corresponding to the car charging pile, such as dustproof, humidity tolerance and temperature.

[0080] As an embodiment of the present invention, the step of setting an environmental protection target corresponding to the vehicle charging pile in combination with the functional interaction effect and the operating environment data includes:

[0081] Perform causal relationship analysis on the functional interaction effects to obtain a causal action map;

[0082] Analyzing the environmental distribution patterns and environmental change trends corresponding to the vehicle charging piles based on the operating environment data;

[0083] Performing risk mapping on the environmental distribution pattern, the environmental change trend, and the causal effect map to obtain key environmental risk points of the vehicle charging pile;

[0084] The design industry specifications of the automobile charging pile are queried, and the environmental protection targets corresponding to the automobile charging pile are set in combination with the design industry specifications and the key environmental risk points.

[0085] Among them, the causal effect map is a visualization result of the action path between environmental factors and equipment status obtained after the causal relationship of the functional interaction is decomposed; the environmental distribution law and the environmental change trend are respectively the statistical characteristics of the historical data of the operating environment corresponding to the car charging pile and the future evolution prediction results; the key environmental risk point is to perform risk mapping processing on the environmental distribution law, the environmental change trend and the causal effect map to obtain the high-risk environmental factors of the car charging pile and their action scenarios; the design industry specification is a set of mandatory and recommended technical standards for the car charging pile.

[0086] Furthermore, the causal relationship of the functional interaction effect can be decomposed through a causal link analysis algorithm to obtain a causal action map; based on the operating environment data, the environmental distribution law and environmental change trend corresponding to the car charging pile can be analyzed through a time series statistical model and a trend prediction algorithm, and the time series statistical model and the trend prediction algorithm include an autoregressive integral moving average model and an exponential smoothing method; the environmental distribution law, the environmental change trend and the causal action map can be risk mapped through a risk matrix modeling tool to obtain the key environmental risk points of the car charging pile, and the risk matrix modeling tool includes a risk probability-impact matrix generator; the design industry specifications of the car charging pile can be queried through a standardized document management system; in combination with the design industry specifications and the key environmental risk points, the environmental protection targets corresponding to the car charging pile can be set, such as setting temperature and humidity alarm thresholds with reference to the "Safety Operating Procedures for Electric Vehicle Charging Infrastructure", raising the protection level of the charging pile to IP65 for high dust areas, or formulating anti-corrosion coating thickness standards for metal parts based on the risk of coastal salt spray corrosion.

[0087] S2. Obtain vehicle charging data of the car charging pile, analyze the equipment charging loss corresponding to the car charging pile based on the vehicle charging data, set the equipment protection target of the car charging pile based on the equipment charging loss, and formulate a global protection strategy for the car charging pile in combination with the environmental protection target and the equipment protection target.

[0088] The present invention analyzes the equipment charging loss corresponding to the car charging pile based on the vehicle charging data, thereby understanding the degree of equipment damage of the car charging pile, and thus laying an important basis for the subsequent setting of equipment protection targets of the car charging pile. The vehicle charging data is a multi-dimensional data set of user behavior and equipment status collected during the charging process of the car charging pile, and the equipment charging loss is the degree of equipment damage caused by user behavior during the charging process corresponding to the car charging pile. Furthermore, the vehicle charging data of the car charging pile can be obtained through an external smart device, such as a charging operation management platform.

[0089] As an embodiment of the present invention, analyzing the charging loss of the device corresponding to the car charging pile based on the vehicle charging data includes:

[0090] Performing data cleaning on the vehicle charging data to obtain target charging data;

[0091] Identifying a data tag corresponding to the target charging data, and analyzing tag semantics corresponding to the data tag;

[0092] extracting charging behavior characteristics and charging fluctuation power from the target charging data based on the tag semantics;

[0093] Analyzing a charging behavior pattern corresponding to the charging behavior feature;

[0094] Calculating a power variation coefficient corresponding to the charging fluctuation power, and analyzing a behavioral charging loss corresponding to the charging behavior pattern based on the power variation coefficient;

[0095] In combination with the behavior charging loss, the device charging loss corresponding to the car charging pile is determined.

[0096] Among them, the target charging data is the valid data subset of the vehicle charging data after cleaning and denoising; the data label is the attribute classification identifier corresponding to the target charging data (such as charging mode, time period label); the label semantics is the business meaning explanation corresponding to the data label (such as the "fast charging" label indicates that the charging power is ≥30kW); the charging behavior characteristics and the charging fluctuation power are respectively the user charging habit parameters (such as charging time) and power fluctuation indicators recorded in the target charging data; the charging behavior pattern is the typical charging behavior type corresponding to the charging behavior characteristics (such as high-frequency fast charging type); the power variation coefficient represents the quantitative value of the stability of the fluctuation amplitude corresponding to the charging fluctuation power; the behavioral charging loss is the energy loss value caused by the specific behavior corresponding to the charging behavior pattern.

[0097] Furthermore, the vehicle charging data can be cleaned by a median filtering algorithm to obtain target charging data; the data labels corresponding to the target charging data can be identified by an NLP classification model; the label semantics corresponding to the data labels can be analyzed by a semantic analysis method; based on the label semantics, the charging behavior characteristics and charging fluctuation power in the target charging data can be extracted by feature engineering tools (such as Python Pandas); the charging behavior pattern corresponding to the charging behavior characteristics can be analyzed by a decision tree method; based on the value of the power variation coefficient, the behavioral charging loss corresponding to the charging behavior pattern is analyzed, such as when the power variation coefficient is greater than 0.3, it is determined that the ripple loss under the charging behavior pattern accounts for more than 40%; combined with the behavioral charging loss, the equipment charging loss corresponding to the car charging pile is determined; such as the behavioral charging loss is accumulated and summed with the internal inherent loss of the charging pile (such as line resistance loss, control module standby loss), and environmental impact loss (such as additional loss due to insulation aging caused by high temperature).

[0098] Furthermore, as an optional embodiment of the present invention, the calculating the power variation coefficient corresponding to the charging fluctuating power includes:

[0099] identifying a power time series corresponding to the charging fluctuating power, and sorting the charging fluctuating power based on the power time series to obtain sorted fluctuating power;

[0100] The power quantity corresponding to the charging fluctuating power is counted, and the power variation coefficient corresponding to the charging fluctuating power is calculated by combining the power quantity and the sorted fluctuating power using the following formula:

[0101]

[0102] Among them, A represents the power variation coefficient corresponding to the charging fluctuation power, B a Indicates the ath power value in the sorted fluctuating power, a indicates the sequence number corresponding to the sorted fluctuating power, and N indicates the power quantity.

[0103] Among them, the power time series is the power time point corresponding to the charging fluctuating power. Furthermore, the power time series corresponding to the charging fluctuating power can be identified by a time point identification tool, and the time point identification tool is compiled by JAVA language; based on the power time series, the charging fluctuating power can be sorted by a numerical sorting algorithm to obtain the sorted fluctuating power.

[0104] The present invention sets the equipment protection target of the automobile charging pile based on the equipment charging loss, and then obtains the equipment protection target of the automobile charging pile. In combination with the environmental protection target and the equipment protection target, a global protection strategy of the automobile charging pile is formulated to achieve coupled control of equipment loss, wherein the equipment protection target is the core protection index of the equipment of the automobile charging pile (such as the upper limit of the junction temperature of the power device), and the global protection strategy is the environment and equipment protection scheme of the automobile charging pile. Furthermore, based on the equipment charging loss, the equipment protection target of the automobile charging pile is set, such as when the equipment charging loss When the power consumption is higher than the rated threshold, the power module temperature monitoring threshold is set to be lowered by 10°C and the insulation detection cycle is shortened to once a day to provide early warning of overheating risks and reduce the insulation aging rate; combined with the environmental protection goals and the equipment protection goals, a global protection strategy for the car charging pile is formulated. First, redundant heat dissipation channels and moisture-proof sealing structures are designed for high temperature and high humidity environments, and then the power module layout is optimized for high-frequency charging scenarios to reduce current fluctuation losses. Finally, the environmental sensor linkage mechanism is integrated to formulate a global protection strategy covering "environmental adaptability design-equipment loss control-dynamic response strategy" to achieve multi-dimensional protection of charging piles under complex working conditions.

[0105] S3. Query abnormal charging events corresponding to the automobile charging pile, perform multimodal analysis on the abnormal charging events to obtain an abnormal feature set, perform source tracing analysis on the abnormal feature set to obtain a failure causal network, and determine weak protection areas in the automobile charging pile based on the failure causal network.

[0106] The present invention obtains an abnormal feature set by performing multimodal analysis on the abnormal charging event, and can deeply analyze the essential characteristics of the abnormal charging event from multiple dimensions, providing a comprehensive feature basis for the subsequent analysis of the fault type map, wherein the abnormal charging data is the charging process data corresponding to the automobile charging pile that deviates from the normal operating state (such as overvoltage, overcurrent, and temperature abnormality data), and the abnormal feature set is a set of key abnormal attributes extracted after multi-dimensional analysis of the abnormal charging event (such as voltage harmonics exceeding the standard, hot spot temperature exceeding the limit, etc.). Furthermore, the abnormal charging event corresponding to the automobile charging pile can be queried through the built-in sensor of the charging pile.

[0107] As an embodiment of the present invention, the multimodal analysis of the abnormal charging event to obtain the abnormal feature set includes:

[0108] Performing multimodal time alignment processing on the abnormal charging event to obtain a synchronous charging event matrix;

[0109] Performing sub-modal feature extraction on the synchronous charging event matrix to obtain initial modal event features;

[0110] Performing cross-modal correlation analysis on the initial modal event features to obtain an abnormal correlation feature map corresponding to the abnormal charging event;

[0111] Calculating the importance score corresponding to the initial modal event feature based on the abnormal correlation feature map;

[0112] Based on the importance score, feature optimization processing is performed on the initial modal event features to obtain an abnormal feature set.

[0113] Among them, the synchronous charging event matrix is ​​a spatiotemporal synchronous data matrix formed after the abnormal charging event is subjected to multimodal time alignment processing (such as a voltage, temperature, and communication log data array aligned according to a unified timestamp); the initial modal event feature is a basic feature set obtained by extracting single-modal features from the synchronous charging event matrix (such as independent waveform features and statistical features of each modality); the abnormal correlation feature map is a feature correlation relationship network corresponding to the abnormal charging event obtained by cross-modal correlation analysis of the initial modal event features (using a graph structure to display the causal, temporal or correlation relationship between multimodal features); the importance score is a quantitative evaluation value based on the feature contribution corresponding to the initial modal event feature (such as a feature importance ranking index calculated by algorithms such as SHAP value and information gain).

[0114] Furthermore, the abnormal charging events can be subjected to multimodal time alignment processing through a timestamp synchronization algorithm to obtain a synchronous charging event matrix, such as dynamic time warping (DTW); the synchronous charging event matrix can be subjected to submodal feature extraction through a single-modal feature extraction algorithm to obtain initial modal event features, such as using fast Fourier transform (FFT) to extract frequency domain features such as harmonic distortion rate and fundamental phase difference from the electrical parameter matrix, using a threshold segmentation algorithm to extract the temperature extremes and heat dissipation gradients of the hot spot area of ​​the power module from the thermal imaging matrix, and using natural language processing (NLP) to extract features such as protocol interaction delay rate and message verification failure frequency from the communication log matrix; association rule mining can be used to extract the features of the synchronous charging event matrix. An algorithm (such as Apriori) performs cross-modal correlation analysis on the initial modal event features to obtain an abnormal correlation feature map corresponding to the abnormal charging event; based on the abnormal correlation feature map, the importance score corresponding to the initial modal event feature can be calculated by a graph node centrality algorithm (such as PageRank); based on the importance score, the initial modal event features are subjected to feature optimization processing by a threshold screening method to obtain an abnormal feature set. For example, the importance score threshold is set to 0.3, and features with scores higher than the threshold are retained (such as harmonic distortion rate > 20%, hot spot temperature > 110°C, protocol retransmission number > 3 times / minute, etc.), and redundant low-contribution features are eliminated.

[0115] The present invention obtains a failure causal network by tracing the source of the abnormal feature set, thereby understanding the key failure nodes and propagation paths of the abnormal feature set, and thus laying an important foundation for determining the weak protection areas in the automobile charging pile. Among them, the failure causal network is a directed graph of the causal relationship between the features of the abnormal feature set, which intuitively displays the causal dependence and conduction mechanism between the feature variables.

[0116] As an embodiment of the present invention, the tracing and parsing of the abnormal feature set to obtain a failure causal network includes:

[0117] Calculating the similarity coefficient between each feature in the abnormal feature set;

[0118] Based on the similarity coefficient, clustering is performed on the abnormal feature set to obtain clustered abnormal features;

[0119] Performing causal test analysis on the cluster abnormal features to obtain a feature causal chain;

[0120] Analyzing the conditional dependencies between the feature causal chains, and constructing the causal impact path of the clustered abnormal features based on the conditional dependencies;

[0121] Performing logic verification on the characteristic causal chain and the causal influence path to obtain a valid causal network;

[0122] The effective causal network is quantitatively characterized to obtain an ineffective causal network.

[0123] Among them, the similarity coefficient represents the quantitative value of the degree of association between each feature in the abnormal feature set (such as Pearson correlation coefficient, mutual information value, etc.); the clustered abnormal feature is a feature cluster (feature grouping with similarity higher than a threshold) obtained by clustering the abnormal feature set based on the similarity coefficient; the feature causal chain is the intra-cluster feature causal relationship chain (including causal direction and action order) confirmed after causal test analysis of the clustered abnormal feature; the conditional dependency is the probabilistic dependency between the feature causal chains (such as the conditional probability association in the Bayesian network); the causal influence path is the cross-cluster causal transmission path of the clustered abnormal feature (indirect causal action link between feature clusters); the effective causal network is a set of real causal relationships that conform to physical principles and business logic and are retained after logical verification of the feature causal chain and the causal influence path.

[0124] Furthermore, the similarity coefficient between each feature in the abnormal feature set can be calculated by the Pearson correlation coefficient algorithm; based on the similarity coefficient, the abnormal feature set can be clustered by the K-means clustering algorithm to obtain clustered abnormal features; the clustered abnormal features can be subjected to causal test analysis by Granger causality test or DAGitty causal inference to obtain a feature causal chain; the conditional dependency between the feature causal chains can be analyzed by Bayesian network; based on the conditional dependency, the clustered abnormal features can be constructed by a causal graph model (such as do-calculus) The causal influence path of the feature; the feature causal chain and the causal influence path can be logically checked by combining the charging pile design document with the national standard (such as GB / T18487.1-2015) to obtain a valid causal network; the valid causal network is quantitatively characterized to obtain a failure causal network, and the processing steps are: assigning values ​​to causal edges based on the weighted calculation of the p-value of the causal test and the absolute value of the similarity coefficient (weight ratio 0.6:0.4), visualizing the network structure through the Gephi tool, and finally generating a failure causal network in the form of a weighted directed acyclic graph (DAG).

[0125] The present invention determines the weak protection area in the car charging pile based on the failure causal network, and then accurately identifies the area in the car charging pile that is relatively fragile and needs protection, thereby facilitating subsequent related protection processing analysis, wherein the weak protection area is the area in the car charging pile that is prone to a higher probability of failure. Furthermore, based on the failure causal network, by identifying the starting node of the causal chain (such as the physical component corresponding to the feature with high causal effect weight), analyzing the risk transmission path density (such as the module with cross-cluster causal edge concentration) and evaluating the coverage gap of the protection measures (such as the key node without redundant design), the weak protection area in the car charging pile is determined.

[0126] S4. Determine the regional charging shell of the weak protection area, calculate the failure probability intensity corresponding to the regional charging shell, combine the failure probability intensity and the failure causal network, formulate the regional protection strategy corresponding to the weak protection area, combine the global protection strategy and the regional protection strategy, execute the protection optimization processing of the car charging pile, and obtain the protection result.

[0127] By calculating the failure probability intensity corresponding to the regional charging shell, the present invention can understand the possibility of the regional charging shell losing its function, thereby facilitating the formulation and processing of the regional protection strategy corresponding to the weak protection area, laying the foundation for improving the protection efficiency of the dust-proof automobile charging pile, wherein the regional charging shell is the key physical carrier of the weak protection area (a shell component that undertakes the functions of power transmission and equipment protection, which is prone to failure due to environmental, mechanical or electrical factors); the failure probability intensity represents a quantitative indicator of the possibility of failure corresponding to the regional charging shell. Furthermore, the regional charging shell of the weak protection area can be determined through the charging pile design blueprint.

[0128] As an embodiment of the present invention, calculating the failure probability intensity corresponding to the regional charging shell includes:

[0129] Performing flaw detection on the charging shell in the area to obtain a shell flaw detection report;

[0130] Extracting shell flaw detection indicators and their corresponding shell flaw detection features from the shell flaw detection test report;

[0131] Calculate the shell defect index corresponding to the regional charging shell based on the shell flaw detection index and the shell flaw detection characteristics;

[0132] Based on the shell defect index, the failure probability intensity corresponding to the regional charging shell is calculated.

[0133] Among them, the shell flaw detection report is the test result file obtained by flaw detection of the regional charging shell, the shell flaw detection index and the shell flaw detection characteristics are the quantitative parameters and defect feature descriptions in the shell flaw detection report respectively, and the shell defect index represents the comprehensive quantitative value of the defect severity corresponding to the regional charging shell.

[0134] Furthermore, the charging shell in the area can be inspected by an ultrasonic flaw detector to obtain a shell flaw detection report;

[0135] The shell flaw detection indicators and their corresponding shell flaw detection features in the shell flaw detection test report can be extracted through a trained convolutional neural network model, such as a FasterR-CNN model;

[0136] In combination with the shell flaw detection index and the shell flaw detection characteristics, the shell defect index corresponding to the regional charging shell is calculated. For example, based on a weighted summation model, weights are assigned to different defect types (such as cracks and pores) (such as crack weight 0.6, pore weight 0.3), and indicators such as defect size and quantity are normalized and multiplied by the corresponding weights to sum them; or a fuzzy comprehensive evaluation method is used to convert qualitative characteristics (such as defect direction and distribution density) into quantitative values ​​through a membership function, and the comprehensive index is calculated in combination with the quantitative indicators.

[0137] Furthermore, as an optional embodiment of the present invention, the calculating of the failure probability intensity corresponding to the regional charging shell based on the shell defect index includes:

[0138] The shell volume corresponding to the regional charging shell is measured, and the failure probability intensity corresponding to the regional charging shell is calculated by combining the shell volume and the shell defect index using the following formula:

[0139]

[0140] Among them, δ represents the failure probability intensity corresponding to the regional charging shell, V represents the shell volume, F represents the shell defect index, F0 represents the benchmark defect index, and m represents the morphological parameter.

[0141] Among them, the shell volume is a quantitative value of the occupied space size corresponding to the regional charging shell calculated by characterization parameters such as length, width, and thickness; the benchmark defect index is determined by material fatigue testing or historical data, and is a quantitative value of the critical defect that causes the shell to begin to have the risk of failure. The morphological parameter is a quantitative indicator reflecting the inherent nonlinear relationship between the defect index and the failure probability. Furthermore, the shell volume corresponding to the regional charging shell can be measured by a three-dimensional laser scanner; the morphological parameters can be obtained by performing mechanical tests and life tests on multiple charging shells with different defect conditions, recording their defect indicators and failure conditions, and then using statistical methods such as regression analysis to substitute these data into the failure probability intensity formula, and calculate the morphological parameters by minimizing the error method (such as the least squares method).

[0142] The present invention formulates a regional protection strategy corresponding to the weak protection area by combining the failure probability strength and the failure causal network, which can accurately locate the weak points of the automobile charging pile protection and strengthen the protection in a targeted manner to reduce the risk of local failure; combining the global protection strategy and the regional protection strategy, the protection optimization processing of the automobile charging pile is performed to obtain a protection result, thereby improving the overall protection performance of the automobile charging pile, ensuring its safe and stable operation, and reducing the occurrence of failures and accidents. Among them, the regional protection strategy is a combination of special protection measures (such as reinforcement, material replacement, structure optimization, etc.) formulated for the failure risk characteristics (such as defect distribution, stress concentration points, etc.) of the weak protection area. Furthermore, combined with the failure probability strength, The degree of failure and the failure causal network are used to formulate regional protection strategies corresponding to the weak protection areas. For example, for areas with high failure probability and concentrated defects, local reinforcement (such as adding reinforcing ribs, pasting carbon fiber cloth) is used to reduce stress concentration, and high-toughness materials are replaced to improve crack resistance; or for areas where failure is caused by temperature-sensitive defects, heat dissipation devices are added to reduce the operating temperature, and the flaw detection cycle is adjusted to monitor the development of defects in real time; combined with the global protection strategy and the regional protection strategy, the protection optimization processing of the automobile charging pile can be performed by combining manual and intelligent equipment to obtain protection results. Specifically, in order to further intuitively understand the protection optimization processing in the optimization method of the dust-proof automobile charging pile with multiple protection structures in this application, you can refer to Figure 2 As shown, it is a protection optimization processing flow chart of the dustproof automobile charging pile optimization method with multiple protection structures provided by the present invention. It should be noted that, in the present invention, Figure 2 The presented flowchart is only used for the protection optimization processing of the dust-proof automobile charging pile optimization method with multiple protection structures, and is not limited to the protection optimization processing of the dust-proof automobile charging pile optimization method with multiple protection structures in actual different application scenarios.

[0143] Compared with the problems described in the background technology, the present invention can understand the coupling relationship and interaction mechanism between the environment and equipment operation by analyzing the functional interaction between the operating state parameters and the operating environment data, thereby providing an important basis for the setting of subsequent environmental protection targets. Furthermore, the present invention can understand the degree of equipment damage of the car charging pile by analyzing the charging loss of the equipment corresponding to the car charging pile based on the vehicle charging data, thereby laying an important foundation for the setting of equipment protection targets of the car charging pile. The present invention obtains an abnormal feature set by performing multimodal analysis on the abnormal charging event, which can deeply analyze the essential characteristics of the abnormal charging event from multiple dimensions and provide a comprehensive feature basis for the analysis of the subsequent fault type map. Furthermore, the present invention can understand the possibility of the regional charging shell losing its function by calculating the failure probability intensity corresponding to the regional charging shell, thereby facilitating the formulation and processing of the regional protection strategy corresponding to the weak protection area, laying a foundation for improving the protection efficiency of the dust-proof car charging pile. Therefore, the dust-proof car charging pile optimization method and system with multiple protection structures provided in the embodiment of the present invention can improve the protection efficiency of the dust-proof car charging pile.

[0144] Example 2:

[0145] like Figure 3 The figure shows a functional module diagram of a dustproof automobile charging pile optimization system with multiple protection structures according to the present invention.

[0146] The dustproof vehicle charging pile optimization system 300 with multiple protection structures described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the system can include an environmental protection target setting module 301, a global protection strategy formulation module 302, a weak protection area determination module 303, and a protection optimization processing module 304. A module, also referred to as a unit, is a series of computer program segments that can be executed by an electronic device processor and perform a fixed function, and is stored in the electronic device's memory.

[0147] In the embodiment of the present invention, the functions of each module / unit are as follows:

[0148] The environmental protection target setting module 301 is used to record the operating status parameters and operating environment data corresponding to the dust-proof automobile charging pile to be processed, analyze the interactive correlation between the operating status parameters and the operating environment data, and set the environmental protection target corresponding to the automobile charging pile based on the functional interaction and the operating environment data;

[0149] The global protection strategy formulation module 302 is used to obtain vehicle charging data of the vehicle charging pile, analyze the device charging loss corresponding to the vehicle charging pile based on the vehicle charging data, set the device protection target of the vehicle charging pile based on the device charging loss, and formulate a global protection strategy for the vehicle charging pile in combination with the environmental protection target and the device protection target;

[0150] The weak protection area determination module 303 is used to query the abnormal charging events corresponding to the automobile charging pile, perform multimodal analysis on the abnormal charging events to obtain an abnormal feature set, perform source tracing analysis on the abnormal feature set to obtain a failure causal network, and determine the weak protection area in the automobile charging pile based on the failure causal network;

[0151] The protection optimization processing module 304 is used to detect the regional charging shell of the weak protection area, calculate the failure probability intensity corresponding to the regional charging shell, combine the failure probability intensity and the failure causal network, formulate the regional protection strategy corresponding to the weak protection area, combine the global protection strategy and the regional protection strategy, execute the protection optimization processing of the car charging pile, and obtain the protection result.

[0152] In detail, the modules in the dustproof car charging pile optimization system 300 with multiple protection structures in the embodiment of the present invention are used in the same manner as above. Figure 1 The same technical means as the optimization method of the dust-proof automobile charging pile with multiple protection structures described in the above are used and can produce the same technical effects, so they will not be repeated here.

[0153] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing a dustproof automobile charging pile with multiple protection structures, characterized in that: The method comprises: Recording the operating status parameters and operating environment data corresponding to the dustproof automobile charging pile to be processed, analyzing the interactive correlation effects between the operating status parameters and the operating environment data, and setting an environmental protection target corresponding to the automobile charging pile based on the functional interactive effects and the operating environment data; Obtaining vehicle charging data of the vehicle charging pile, analyzing the device charging loss corresponding to the vehicle charging pile based on the vehicle charging data, setting a device protection target for the vehicle charging pile based on the device charging loss, and formulating a global protection strategy for the vehicle charging pile in combination with the environmental protection target and the device protection target; Querying abnormal charging events corresponding to the vehicle charging pile, performing multimodal analysis on the abnormal charging events to obtain an abnormal feature set, performing source tracing analysis on the abnormal feature set to obtain a failure causal network, and determining weak protection areas in the vehicle charging pile based on the failure causal network; Detect the regional charging shell of the weak protection area, calculate the failure probability intensity corresponding to the regional charging shell, combine the failure probability intensity and the failure causal network, formulate a regional protection strategy corresponding to the weak protection area, combine the global protection strategy and the regional protection strategy, execute the protection optimization processing of the car charging pile, and obtain the protection result.

2. The method for optimizing a dustproof automobile charging pile with multiple protection structures according to claim 1, characterized in that: The analyzing the interactive correlation impact between the operating state parameter and the operating environment data includes: Extracting the state identifier and the identification index corresponding to the operating state parameter, and constructing an operating situation diagram corresponding to the state identifier based on the identification index; Identifying environmental factors and factor indicators in the operating environment data, and constructing factor trend graphs corresponding to the environmental factors based on the factor indicators; Analyzing the temporal correlation between the state identifier and the environmental factor in combination with the operation status diagram and the factor trend diagram; Based on the temporal correlation, a correlated state-environment set is selected from the state identifier and the environmental factor; The corresponding interaction mechanism in the associated state-environment set is analyzed, and based on the interaction mechanism, an interactive correlation influence between the operating state parameter and the operating environment data is generated.

3. The method for optimizing a dustproof automobile charging pile with multiple protection structures according to claim 1, characterized in that: The step of setting an environmental protection target corresponding to the vehicle charging pile in combination with the functional interaction effect and the operating environment data includes: Perform causal relationship analysis on the functional interaction effects to obtain a causal action map; Analyzing the environmental distribution patterns and environmental change trends corresponding to the vehicle charging piles based on the operating environment data; Performing risk mapping on the environmental distribution pattern, the environmental change trend, and the causal effect map to obtain key environmental risk points of the vehicle charging pile; The design industry specifications of the automobile charging pile are queried, and the environmental protection targets corresponding to the automobile charging pile are set in combination with the design industry specifications and the key environmental risk points.

4. The method for optimizing a dustproof automobile charging pile with multiple protection structures according to claim 1, characterized in that: The analyzing, based on the vehicle charging data, the device charging loss corresponding to the vehicle charging pile includes: Performing data cleaning on the vehicle charging data to obtain target charging data; Identifying a data tag corresponding to the target charging data, and analyzing tag semantics corresponding to the data tag; extracting charging behavior characteristics and charging fluctuation power from the target charging data based on the tag semantics; Analyzing a charging behavior pattern corresponding to the charging behavior feature; Calculating a power variation coefficient corresponding to the charging fluctuation power, and analyzing a behavioral charging loss corresponding to the charging behavior pattern based on the power variation coefficient; In combination with the behavior charging loss, the device charging loss corresponding to the car charging pile is determined.

5. The method for optimizing a dustproof automobile charging pile with multiple protection structures according to claim 4, characterized in that: The calculating the power variation coefficient corresponding to the charging fluctuation power includes: identifying a power time series corresponding to the charging fluctuating power, and sorting the charging fluctuating power based on the power time series to obtain sorted fluctuating power; The power quantity corresponding to the charging fluctuating power is counted, and the power variation coefficient corresponding to the charging fluctuating power is calculated by combining the power quantity and the sorted fluctuating power using the following formula: Among them, A represents the power variation coefficient corresponding to the charging fluctuation power, B a Indicates the ath power value in the sorted fluctuating power, a indicates the sequence number corresponding to the sorted fluctuating power, and N indicates the power quantity.

6. The method for optimizing a dustproof automobile charging pile with multiple protection structures according to claim 1, characterized in that: The multimodal analysis of the abnormal charging event is performed to obtain an abnormal feature set, including: Performing multimodal time alignment processing on the abnormal charging event to obtain a synchronous charging event matrix; Performing sub-modal feature extraction on the synchronous charging event matrix to obtain initial modal event features; Performing cross-modal correlation analysis on the initial modal event features to obtain an abnormal correlation feature map corresponding to the abnormal charging event; Calculating the importance score corresponding to the initial modal event feature based on the abnormal correlation feature map; Based on the importance score, feature optimization processing is performed on the initial modal event features to obtain an abnormal feature set.

7. The method for optimizing a dustproof automobile charging pile with multiple protection structures according to claim 1, characterized in that: The tracing and parsing of the abnormal feature set to obtain a failure causal network includes: Calculating the similarity coefficient between each feature in the abnormal feature set; Based on the similarity coefficient, clustering is performed on the abnormal feature set to obtain clustered abnormal features; Performing causal test analysis on the cluster abnormal features to obtain a feature causal chain; Analyzing the conditional dependencies between the feature causal chains, and constructing the causal impact path of the clustered abnormal features based on the conditional dependencies; Performing logic verification on the characteristic causal chain and the causal influence path to obtain a valid causal network; The effective causal network is quantitatively characterized to obtain an ineffective causal network.

8. The method for optimizing a dustproof automobile charging pile with multiple protection structures according to claim 1, wherein: The calculating the failure probability intensity corresponding to the regional charging shell includes: Performing flaw detection on the charging shell in the area to obtain a shell flaw detection report; Extracting shell flaw detection indicators and their corresponding shell flaw detection features from the shell flaw detection test report; Calculate the shell defect index corresponding to the regional charging shell based on the shell flaw detection index and the shell flaw detection characteristics; Based on the shell defect index, the failure probability intensity corresponding to the regional charging shell is calculated.

9. The method for optimizing a dustproof automobile charging pile with multiple protection structures according to claim 8, characterized in that: The calculating, based on the shell defect index, the failure probability intensity corresponding to the regional charging shell includes: The shell volume corresponding to the regional charging shell is measured, and the failure probability intensity corresponding to the regional charging shell is calculated by combining the shell volume and the shell defect index using the following formula: Among them, δ represents the failure probability intensity corresponding to the regional charging shell, V represents the shell volume, F represents the shell defect index, F0 represents the benchmark defect index, and m represents the morphological parameter.

10. A dustproof car charging pile optimization system with multiple protection structures, characterized in that: The system comprises: An environmental protection target setting module is used to record the operating status parameters and operating environment data corresponding to the dust-proof automobile charging pile to be processed, analyze the interactive correlation effects between the operating status parameters and the operating environment data, and set the environmental protection target corresponding to the automobile charging pile based on the functional interaction effects and the operating environment data; a global protection strategy formulation module, configured to obtain vehicle charging data of the vehicle charging pile, analyze the device charging loss corresponding to the vehicle charging pile based on the vehicle charging data, set the device protection target of the vehicle charging pile based on the device charging loss, and formulate a global protection strategy for the vehicle charging pile in combination with the environmental protection target and the device protection target; a weak protection area determination module, configured to query abnormal charging events corresponding to the vehicle charging pile, perform multimodal analysis on the abnormal charging events to obtain an abnormal feature set, perform source tracing analysis on the abnormal feature set to obtain a failure causal network, and determine the weak protection area in the vehicle charging pile based on the failure causal network; The protection optimization processing module is used to detect the regional charging shell of the weak protection area, calculate the failure probability intensity corresponding to the regional charging shell, combine the failure probability intensity and the failure causal network, formulate the regional protection strategy corresponding to the weak protection area, combine the global protection strategy and the regional protection strategy, execute the protection optimization processing of the car charging pile, and obtain the protection result.

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