Fault detection method of direct current charging pile and related device

Abstract

The invention discloses a fault detection method of a direct current charging pile and a related device, and relates to the technical field of data processing, and the method comprises the steps: obtaining the real-time monitoring data of each to-be-detected device in the direct current charging pile; performing standardization processing on the real-time monitoring data; based on the standardized real-time monitoring data, analyzing the internal operation environment of the direct-current charging pile; carrying out device loss analysis by utilizing correlation analysis based on the operation and maintenance data and the internal operation environment analysis data; carrying out fault correlation coefficient analysis on the device based on the device loss analysis data and the internal operation environment analysis data; performing fault judgment on each device based on the standardized real-time monitoring data, the target fault correlation coefficient, the internal operation environment analysis data and the device loss analysis data; and if it is judged that the device has a fault, fault tracing is carried out based on the fault correlation coefficient in combination with a fault judgment result so as to send an abnormal alarm signal. According to the invention, the device fault detection of the direct current charging pile can achieve a more ideal effect.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a fault detection method and related devices for a DC charging pile. Background Art

[0002] At present, my country's new energy industry is in a stage of rapid development, and the new energy vehicles derived from it, which use electricity as a driving energy source, have gradually become the most popular type of vehicles. As a charging device for new energy vehicles, DC charging piles are difficult to avoid failures after they are put into use. In order to ensure the normal operation of charging piles, continuous fault detection is required. Therefore, how to accurately detect the faults of DC charging piles has become the focus of research for various companies. At present, in the fault detection of DC charging piles, there is usually a lack of analysis of the internal operating environment of DC charging piles, resulting in the failure to detect the faults of DC charging piles with more comprehensive and accurate data. Loss analysis of various components of DC charging piles is also a very important part of fault detection. At present, statistical analysis is usually performed based on the operating time and operation and maintenance frequency of various components of DC charging piles, but the data obtained by this method cannot well reflect the loss data of the components, which will affect the accuracy of fault detection of various components of DC charging piles. At the same time, for the device fault detection of DC charging piles, there is a lack of consideration of the correlation coefficient between components and faults, which not only affects the accuracy of fault judgment, but also makes it difficult to trace the faults of various components of DC charging piles, making subsequent fault handling more difficult, thereby affecting the use of DC charging piles. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art. The present invention provides a fault detection method and related devices for a DC charging pile, which improves the accuracy and comprehensiveness of device fault detection of a DC charging pile, so that the fault detection of a DC charging pile can achieve a more ideal effect.

[0004] In order to solve the above technical problems, the present invention provides a fault detection method for a DC charging pile, which is applied to a main control module and a data acquisition module of a DC charging pile, wherein the main control module is communicatively connected with the data acquisition module; the method comprises:

[0005] Acquire real-time monitoring data of each device to be detected in the DC charging pile based on the data acquisition module, and transmit the real-time monitoring data to the main control module;

[0006] The main control module performs standardization processing on the real-time monitoring data to obtain standardized real-time monitoring data;

[0007] Performing an internal operating environment analysis of the DC charging pile based on the standardized real-time monitoring data to obtain internal operating environment analysis data;

[0008] Obtaining operation and maintenance data of each device to be detected in the DC charging pile, and performing device loss analysis based on the operation and maintenance data and internal operating environment analysis data using correlation analysis to obtain device loss analysis data;

[0009] Performing a fault correlation coefficient analysis on each device to be detected based on the device loss analysis data and the internal operating environment analysis data to obtain a target fault correlation coefficient corresponding to each device to be detected;

[0010] Perform fault judgment on each to-be-detected component of the DC charging pile based on the standardized real-time monitoring data, target fault correlation coefficient, internal operating environment analysis data, and component loss analysis data;

[0011] If it is determined that the device to be detected of the DC charging pile has a fault, the fault of each device to be detected is traced based on the target fault correlation coefficient and the fault judgment result to obtain the fault tracing information, and an abnormal alarm signal is issued based on the fault tracing information and the fault judgment result.

[0012] Optionally, the performing standardization processing on the real-time monitoring data to obtain standardized real-time monitoring data includes:

[0013] Classify the real-time monitoring data based on the data attribute value characteristics to obtain the classified real-time monitoring data;

[0014] Performing data cleaning on the classified real-time monitoring data to obtain the real-time monitoring data after data cleaning;

[0015] The real-time monitoring data after cleaning is standardized based on the graph structure to obtain standardized real-time monitoring data.

[0016] Optionally, the performing of an internal operating environment analysis of a DC charging pile based on the standardized real-time monitoring data to obtain internal operating environment analysis data includes:

[0017] Constructing a three-dimensional geometric model of the interior of the DC charging pile, and using the three-dimensional geometric model to obtain internal temperature data, internal humidity data, and wind speed flow data of the DC charging pile based on the standardized real-time monitoring data;

[0018] Perform internal temperature and internal humidity fluctuation analysis based on the internal temperature data, internal humidity data and wind speed flow data to obtain internal temperature fluctuation data and internal humidity fluctuation data;

[0019] Perform variable function fitting based on the internal temperature fluctuation data and the internal humidity fluctuation data to obtain a fluctuation rate influence function;

[0020] determining a temperature boundary condition based on the internal temperature fluctuation data, and determining a humidity boundary condition based on the internal humidity fluctuation data;

[0021] Based on the fluctuation rate influence function, temperature boundary conditions and humidity boundary conditions, the internal operating environment analysis of the DC charging pile is performed using a fluid simulation model to obtain internal operating environment analysis data.

[0022] Optionally, performing device loss analysis based on the operation and maintenance data and the internal operating environment analysis data by using correlation analysis to obtain device loss analysis data includes:

[0023] Extracting historical loss data based on the operation and maintenance data, and clustering the historical loss data to obtain a plurality of clusters, wherein each cluster corresponds to a loss scenario;

[0024] Constructing a weight matrix, calculating the anti-entropy of the loss scenario corresponding to each cluster based on the weight matrix, performing a correlation analysis between the loss scenario and the device loss based on the anti-entropy of the loss scenario, and obtaining correlation analysis data;

[0025] Calculate the heat loss power based on the operation and maintenance data and the internal operation environment analysis data to obtain the target heat loss power;

[0026] Constructing a humidity field spatial model and a temperature field spatial model based on the target heat loss power and the internal operating environment analysis data;

[0027] The device loss analysis is performed based on the humidity field spatial model and the temperature field spatial model using the correlation analysis data to obtain device loss analysis data.

[0028] Optionally, performing a fault correlation coefficient analysis on each device to be detected based on the device loss analysis data and the internal operating environment analysis data to obtain a target fault correlation coefficient corresponding to each device to be detected includes:

[0029] Obtaining reference data groups of different operation stages of the DC charging pile, and determining data feature quantities of the reference data groups;

[0030] Determine the correlation coefficient of the associated fault types in different operation stages using a preset comparison table based on the data feature quantity;

[0031] Performing strong correlation analysis on the reference data group based on the correlation coefficient to obtain strong correlation data;

[0032] A fault correlation coefficient analysis model is constructed based on the strong correlation data and the correlation coefficient, and based on the fault correlation coefficient analysis model, the device loss analysis data and the internal operating environment analysis data are used to perform fault correlation coefficient analysis on each device to be detected, so as to obtain a target fault correlation coefficient corresponding to each device to be detected.

[0033] Optionally, the performing fault judgment on each to-be-detected device of the DC charging pile based on the standardized real-time monitoring data, the target fault correlation coefficient, the internal operating environment analysis data and the device loss analysis data includes:

[0034] Acquire performance characteristic data based on the fault library using the standardized real-time monitoring data, internal operating environment analysis data and device loss analysis data;

[0035] Based on the performance characteristic data and the target fault correlation coefficient, fault judgment is performed on each component to be detected of the DC charging pile.

[0036] Optionally, the performing fault tracing of each device to be detected based on the target fault correlation coefficient in combination with the fault judgment result to obtain fault tracing information includes:

[0037] Determine device fault data based on the fault judgment result, and construct a fault logic network based on the device fault data;

[0038] acquiring a fault attention feature based on the fault logic network, and determining fault path data based on the fault attention feature;

[0039] Based on the fault path data, the target fault correlation coefficient is used to perform fault cause troubleshooting to obtain fault cause troubleshooting information;

[0040] Based on the fault cause troubleshooting information, a fault induction relationship analysis is performed to obtain fault induction relationship analysis data, and based on the fault induction relationship analysis data and the fault cause troubleshooting information, fault tracing of each to-be-detected device is performed to obtain fault tracing information.

[0041] In addition, the present invention also provides a fault detection device for a DC charging pile, which is applied to a main control module and a data acquisition module of the DC charging pile, wherein the main control module is communicatively connected with the data acquisition module; the device comprises:

[0042] Data acquisition module: used to obtain real-time monitoring data of each device to be detected in the DC charging pile based on the data acquisition module, and transmit the real-time monitoring data to the main control module;

[0043] Data standardization module: used for the main control module to perform standardization processing on the real-time monitoring data to obtain standardized real-time monitoring data;

[0044] Internal operating environment analysis module: used to analyze the internal operating environment of the DC charging pile based on the standardized real-time monitoring data to obtain internal operating environment analysis data;

[0045] Device loss analysis module: used to obtain the operation and maintenance data of each device to be detected in the DC charging pile, and perform device loss analysis based on the operation and maintenance data and the internal operating environment analysis data using correlation analysis to obtain device loss analysis data;

[0046] Fault correlation coefficient analysis module: used to perform fault correlation coefficient analysis of each device to be detected based on the device loss analysis data and the internal operating environment analysis data, and obtain a target fault correlation coefficient corresponding to each device to be detected;

[0047] Fault judgment module: used to judge the faults of the components to be detected of the DC charging pile based on the standardized real-time monitoring data, the target fault correlation coefficient, the internal operating environment analysis data and the component loss analysis data;

[0048] Fault tracing module: if it is determined that a fault exists in the device to be detected of the DC charging pile, then the fault tracing module performs fault tracing on each device to be detected based on the target fault correlation coefficient and the fault judgment result, obtains fault tracing information, and issues an abnormal alarm signal based on the fault tracing information and the fault judgment result.

[0049] In addition, the present invention also provides a fault detection system for a DC charging pile, the system comprising a main control module and a data acquisition module, the main control module being communicatively connected to the data acquisition module, and the system being configured to execute the above-mentioned fault detection method for a DC charging pile.

[0050] In addition, the present invention also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the above-mentioned DC charging pile fault detection method.

[0051] In an embodiment of the present invention, the internal operating environment analysis of the DC charging pile is performed using a fluid simulation model based on the fluctuation rate influence function, temperature boundary conditions and humidity boundary conditions generated by the standardized real-time monitoring data, which can more comprehensively and accurately analyze the internal operating environment of the DC charging pile, and provide more comprehensive data for the fault judgment of each device to be detected in the DC charging pile. Based on the operation and maintenance data and the internal operating environment analysis data, the device loss analysis is performed using correlation analysis, and more accurate device loss analysis data can be obtained, so that the data can better reflect the loss of the device. Based on the device loss analysis data and the internal operating environment analysis data, the fault correlation coefficient of each device to be detected is analyzed, and the target fault correlation coefficient corresponding to each device to be detected is obtained, which can fully consider the degree of fault correlation between each device to be detected and the detected fault, so that the fault detection result is more accurate and reliable. If it is determined that there is a fault in the device of the DC charging pile, the fault source of each device to be detected is traced based on the target fault correlation coefficient combined with the fault judgment result, and more accurate fault tracing information can be obtained, and the device fault of the DC charging pile can be understood more clearly and intuitively, thereby improving the accuracy and comprehensiveness of fault detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 is a schematic flow chart of a fault detection method for a DC charging pile in an embodiment of the present invention;

[0054] Figure 2 is a flow chart of a fault detection method for a DC charging pile in another embodiment of the present invention;

[0055] Figure 3 Schematic diagram of the structure of the fault detection system of the DC charging pile in the embodiment of the present invention;

[0056] Figure 4 It is a schematic diagram of the structural composition of a fault detection device for a DC charging pile in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Embodiment 1

[0059] See also Figure 1 , Figure 1 : is a flow chart of a fault detection method for a DC charging pile in an embodiment of the present invention, the method is applied to a main control module and a data acquisition module of a DC charging pile, the main control module is communicatively connected with the data acquisition module; the method comprises:

[0060] S11: acquiring real-time monitoring data of each device to be detected in the DC charging pile based on the data acquisition module, and transmitting the real-time monitoring data to the main control module;

[0061] In the specific implementation process of the present invention, real-time monitoring data of each device to be detected in the DC charging pile is obtained based on the data acquisition module. The devices to be detected in the DC charging pile include relays, sensors, DC contactors, charging control modules and switching power supplies, etc. The real-time monitoring data includes output voltage and current, device temperature and humidity, and contact status. The real-time monitoring data is transmitted to the main control module.

[0062] S12: the main control module performs standardization processing on the real-time monitoring data to obtain standardized real-time monitoring data;

[0063] In the specific implementation process of the present invention, the real-time monitoring data is standardized to obtain standardized real-time monitoring data, including: classifying the real-time monitoring data based on data attribute value characteristics to obtain classified real-time monitoring data; performing data cleaning on the classified real-time monitoring data to obtain cleaned real-time monitoring data; and standardizing the cleaned real-time monitoring data based on a graph structure to obtain standardized real-time monitoring data.

[0064] Specifically, the main control module receives real-time monitoring data, classifies the real-time monitoring data based on data attribute value characteristics, uses the K-means algorithm to obtain the data attribute value characteristics of the real-time monitoring data, and obtains the classified real-time monitoring data. The classified real-time monitoring data is cleaned, and the classified real-time monitoring data is cleaned of data garbled and abnormal data to obtain the real-time monitoring data after data cleaning. The cleaned real-time monitoring data is standardized based on the graph structure, and several alternative mapping relationships corresponding to the cleaned real-time monitoring data are determined according to the data attribute value characteristics, and the confidence of each alternative mapping relationship is calculated. The alternative mapping relationship with the highest confidence is used as the target mapping relationship, and the data model is read through the database tool to obtain the table and field of the data model. The graph structure is constructed according to the table and field of the data model, and the real-time monitoring data after data cleaning is standardized according to the graph structure using the target mapping relationship to obtain standardized real-time monitoring data.

[0065] S13: Analyze the internal operating environment of the DC charging pile based on the standardized real-time monitoring data to obtain internal operating environment analysis data;

[0066] In the specific implementation process of the present invention, the internal operating environment analysis of the DC charging pile is performed based on the standardized real-time monitoring data to obtain the internal operating environment analysis data, including: constructing a three-dimensional geometric model inside the DC charging pile, and obtaining the internal temperature data, internal humidity data and wind speed flow data of the DC charging pile using the three-dimensional geometric model based on the standardized real-time monitoring data; performing internal temperature and internal humidity fluctuation analysis based on the internal temperature data, internal humidity data and wind speed flow data to obtain internal temperature fluctuation data and internal humidity fluctuation data; performing variable function fitting based on the internal temperature fluctuation data and internal humidity fluctuation data to obtain a fluctuation rate influence function; determining temperature boundary conditions based on the internal temperature fluctuation data, and determining humidity boundary conditions based on the internal humidity fluctuation data; performing internal operating environment analysis of the DC charging pile using a fluid simulation model based on the fluctuation rate influence function, temperature boundary conditions and humidity boundary conditions to obtain internal operating environment analysis data.

[0067] Specifically, a three-dimensional geometric model of the interior of the DC charging pile is constructed, and a three-dimensional geometric model is established through a modeling tool using the three-dimensional data of the interior of the DC charging pile. The internal temperature data, internal humidity data and wind speed flow data of the DC charging pile are obtained using the three-dimensional geometric model based on the standardized real-time monitoring data. The internal temperature data, internal humidity data and wind speed flow data of the DC charging pile are determined using the three-dimensional geometric model according to the temperature and humidity data of each device to be detected and the wind speed data of the heat dissipation module in the standardized real-time monitoring data. Based on the internal temperature data, internal humidity data and wind speed flow data, internal temperature and internal humidity fluctuation analysis is performed, and historical internal temperature data of the DC charging pile is obtained. Several temperature trough poles are determined in the historical internal temperature data, and fluctuation functions are determined according to several temperature trough poles. Internal temperature fluctuation analysis is performed on the internal temperature data according to the temperature fluctuation function and wind speed flow data, and internal humidity fluctuation analysis is performed on the internal humidity data according to the temperature fluctuation function, to obtain internal temperature fluctuation data and internal humidity fluctuation data. Based on the internal temperature fluctuation data and the internal humidity fluctuation data, the variable function is fitted to obtain the correlation coefficient of the internal temperature fluctuation data and the internal humidity fluctuation data, and the independent variable function is fitted according to the internal temperature fluctuation data and the internal humidity fluctuation data and their correlation coefficients to obtain the fluctuation rate influence function. Based on the internal temperature fluctuation data, the temperature boundary conditions are determined, and the internal temperature fluctuation data is compared with the assimilated data in the digital twin database to calculate the deviation between the two. The temperature boundary conditions are determined by combining the calculated deviation with the fusion algorithm composed of the successive optimization algorithm and the extended Kalman filter algorithm, and the humidity boundary conditions are determined based on the internal humidity fluctuation data. The humidity boundary conditions are determined in the same way as the temperature boundary conditions. Based on the fluctuation rate influence function, temperature boundary conditions and humidity boundary conditions, a fluid simulation model is used to analyze the internal operating environment of the DC charging pile. The fluid simulation model adopts an open source field operation and processing software (Open Source Field Operation and Manipulation, OpenFOAM) model, which is used for simulation and analysis of environmental fluids. The fluctuation rate influence function, temperature boundary conditions and humidity boundary conditions are input into the fluid simulation model to perform coupling analysis of temperature and humidity scalars, and internal temperature distribution data, air flow velocity and internal humidity distribution data are obtained, that is, internal operating environment analysis data is obtained, thereby obtaining more comprehensive and accurate internal operating environment analysis data. Too high or too low temperature and humidity of the internal operating environment will affect the devices of the DC charging pile. Therefore, performing internal operating environment analysis can improve the accuracy of device fault detection.

[0068] S14: Acquire operation and maintenance data of each device to be detected in the DC charging pile, and perform device loss analysis based on the operation and maintenance data and the internal operating environment analysis data by using correlation analysis to obtain device loss analysis data;

[0069] In the specific implementation process of the present invention, the device loss analysis is performed based on the operation and maintenance data and the internal operating environment analysis data using correlation analysis to obtain the device loss analysis data, including: extracting historical loss data based on the operation and maintenance data, clustering based on the historical loss data, and obtaining a number of clustering clusters, wherein each clustering cluster corresponds to a loss scenario; constructing a weight matrix, calculating the anti-entropy of the loss scenario corresponding to each clustering cluster based on the weight matrix, and performing a correlation analysis between the loss scenario and the device loss based on the anti-entropy of the loss scenario to obtain correlation analysis data; calculating the heat loss power based on the operation and maintenance data and the internal operating environment analysis data to obtain the target heat loss power; constructing a humidity field space model and a temperature field space model based on the target heat loss power in combination with the internal operating environment analysis data; performing device loss analysis based on the humidity field space model and the temperature field space model using the correlation analysis data to obtain device loss analysis data.

[0070] Specifically, the operation and maintenance data of each device to be detected in the DC charging pile is obtained, the operation and maintenance data including the operation and maintenance frequency, operation and maintenance type, devices undergoing operation and maintenance, and operation and maintenance loss, etc. Historical loss data is extracted based on the operation and maintenance data, and clustering is performed based on the historical loss data. A K-means clustering algorithm is used to obtain several clustering clusters, wherein each clustering cluster corresponds to a loss scenario, and the loss scenario may include voltage deviation and frequency deviation, etc. The balanced K nearest neighbor algorithm is used to classify and optimize the data in each cluster to obtain the classification optimization results, and the association weights between the data and each cluster in the classification optimization results are calculated. A weight matrix is ​​constructed according to the association weights, and the anti-entropy of the loss scenario corresponding to each cluster is calculated based on the weight matrix. The weight matrix is ​​standardized to obtain a standardized weight matrix. The anti-entropy of the loss scenario corresponding to each cluster is calculated based on the standardized weight matrix. The correlation analysis between the loss scenario and the device loss is performed based on the anti-entropy of the loss scenario, that is, the correlation between the loss scenario and the device loss is analyzed based on the anti-entropy of the loss scenario. The higher the proportion of the anti-entropy of a certain loss scenario in the sum of the anti-entropy of all loss scenarios, the higher the correlation, that is, the correlation analysis data is obtained. The heat loss power is calculated based on the operation and maintenance data and the internal operation environment analysis data to obtain the target heat loss power. Based on the target heat loss power combined with the internal operating environment analysis data, a humidity field space model and a temperature field space model are constructed. The temperature field space model includes the temperature state of each point on the device under the action of the heat source point, and the humidity field includes the humidity condition of each point on the device. The temperature distribution function and the humidity distribution function are constructed by combining the heat loss power with the internal operating environment analysis data. The temperature field space model is constructed according to the temperature distribution function combined with the transient temperature field, and the humidity field space model is constructed according to the humidity distribution function. Based on the humidity field space model and the temperature field space model, the correlation analysis data is used to perform device loss analysis. The loss condition of each device to be detected is determined according to the humidity field space model and the temperature field space model using the correlation analysis data. For example, when the temperature distribution is high, the device will be deformed when it is electrocuted, when the voltage deviation is too high, the device operation will have deviations, or when the humidity distribution is high, the device will rust. The device loss analysis data is obtained, and more accurate device loss analysis data can be obtained. According to the device loss analysis, fault judgment can be made more reliable.

[0071] S15: performing fault correlation coefficient analysis of each device to be detected based on the device loss analysis data and the internal operating environment analysis data to obtain a target fault correlation coefficient corresponding to each device to be detected;

[0072] In the specific implementation process of the present invention, the fault correlation coefficient analysis of each device to be detected is performed based on the device loss analysis data and the internal operating environment analysis data to obtain a target fault correlation coefficient corresponding to each device to be detected, including: obtaining a reference data group of different operating stages of a DC charging pile, and determining a data feature quantity of the reference data group; determining a correlation coefficient of associated fault types in different operating stages based on the data feature quantity using a preset comparison table; performing a strong correlation analysis on the reference data group based on the correlation coefficient to obtain strong correlation data; constructing a fault correlation coefficient analysis model based on the strong correlation data and the correlation coefficient, and performing a fault correlation coefficient analysis on each device to be detected based on the fault correlation coefficient analysis model using the device loss analysis data and the internal operating environment analysis data to obtain a target fault correlation coefficient corresponding to each device to be detected.

[0073] Specifically, obtain reference data groups of different operation stages of the DC charging pile, the operation stages include starting, charging and pausing charging, etc. The reference data groups include reference fault data of the devices to be detected in different operation stages, and determine the data feature quantity of the reference data group. Based on the data feature quantity, the correlation coefficient of the associated fault type in different operation stages is determined using a preset comparison table, and the corresponding correlation coefficient is matched in the preset comparison table according to the data feature quantity. Based on the correlation coefficient, a strong correlation analysis is performed on the reference data group, and a strong correlation analysis is performed through correlation analysis and frequent item set mining to obtain strongly correlated data. Based on the strongly correlated data and the correlation coefficient, a fault correlation coefficient analysis model is constructed, and the fault correlation coefficient analysis model is a recurrent neural network. Based on the fault correlation coefficient analysis model, the device loss analysis data and the internal operating environment analysis data are used to perform fault correlation coefficient analysis of each device to be detected, and the target fault correlation coefficient corresponding to each device to be detected is obtained. The device fault correlation probability and the fault occurrence probability can be determined through the fault correlation coefficient, so as to improve the reliability of fault detection and fault tracing.

[0074] S16: Performing fault judgment on each to-be-detected component of the DC charging pile based on the standardized real-time monitoring data, the target fault correlation coefficient, the internal operating environment analysis data, and the component loss analysis data;

[0075] In the specific implementation process of the present invention, the fault judgment of each component to be detected of the DC charging pile is performed based on the standardized real-time monitoring data, the target fault correlation coefficient, the internal operating environment analysis data and the device loss analysis data, including: obtaining performance feature data based on the fault library using the standardized real-time monitoring data, the internal operating environment analysis data and the device loss analysis data; and performing fault judgment on each component to be detected of the DC charging pile based on the performance feature data and the target fault correlation coefficient.

[0076] Specifically, based on the fault library, the performance characteristic data is obtained by using the standardized real-time monitoring data, the internal operating environment analysis data and the device loss analysis data. The performance characteristic data is the change data compared with the preset standard normal state data in the fault library. Based on the performance characteristic data and the target fault correlation coefficient, the fault judgment is performed on each device to be detected of the DC charging pile. The performance characteristic data and the target fault correlation coefficient are input into the fault judgment model to perform fault judgment on each device to be detected of the DC charging pile. If it is judged that the devices to be detected of the DC charging pile have no fault, the real-time monitoring data of each device to be detected will continue to be collected. If it is judged that the devices to be detected of the DC charging pile have faults, step S17 is entered. The faults of the devices to be detected include relay adhesion, sensor failure, etc.

[0077] S17: If it is determined that the components to be detected of the DC charging pile are faulty, the faults of the components to be detected are traced based on the target fault correlation coefficient and the fault judgment result to obtain fault tracing information, and an abnormal alarm signal is issued based on the fault tracing information and the fault judgment result.

[0078] In the specific implementation process of the present invention, the fault tracing of each device to be detected based on the target fault correlation coefficient combined with the fault judgment result to obtain fault tracing information includes: determining device fault data based on the fault judgment result, and constructing a fault logic network based on the device fault data; acquiring fault attention features based on the fault logic network, and determining fault path data based on the fault attention features; performing fault cause investigation based on the fault path data using the target fault correlation coefficient to obtain fault cause investigation information; performing fault induction relationship analysis based on the fault cause investigation information to obtain fault induction relationship analysis data, and performing fault tracing of each device to be detected based on the fault induction relationship analysis data and the fault cause investigation information to obtain fault tracing information.

[0079] Specifically, the device fault data is determined based on the fault judgment result, the device fault condition and fault point data are determined according to the fault judgment result, that is, the device fault data is determined, and a fault logic network is constructed based on the device fault data, and the device fault data is used to construct the logic network according to the preset fault logic network generation template. The fault attention feature is obtained based on the fault logic network, and the fault attention feature is obtained based on the fault logic network using the attention mechanism, and the fault path data is determined based on the fault attention feature. According to the fault attention feature, several candidate fault path data are determined in the fault path database, and the confidence of each candidate fault path data is calculated, and the candidate fault path data with the highest confidence is used as the final fault path data. Based on the fault path data, the target fault correlation coefficient is used to perform fault cause investigation, and according to the target fault correlation coefficient and the fault path data, the hierarchical identification and inter-layer association in the fault cause network are used to perform fault cause investigation, and fault cause investigation information is obtained. Based on the fault cause troubleshooting information, a fault induction relationship analysis is performed to obtain fault induction relationship analysis data. The fault induction relationship may include direct induced faults and indirect induced faults. Based on the fault induction relationship analysis data and the fault cause troubleshooting information, the faults of each device to be detected are traced to obtain fault tracing information. The specific fault induction factors can be known through the fault tracing information, so as to provide more specific and detailed fault information, improve the efficiency of subsequent fault repair, and avoid excessive impact on DC charging piles. An abnormal alarm signal is issued based on the fault tracing information and the fault judgment result, and relevant personnel are notified in time to perform maintenance.

[0080] In an embodiment of the present invention, the internal operating environment analysis of the DC charging pile is performed using a fluid simulation model based on the fluctuation rate influence function, temperature boundary conditions and humidity boundary conditions generated by the standardized real-time monitoring data, which can more comprehensively and accurately analyze the internal operating environment of the DC charging pile, and provide more comprehensive data for the fault judgment of each device to be detected in the DC charging pile. Based on the operation and maintenance data and the internal operating environment analysis data, the device loss analysis is performed using correlation analysis, and more accurate device loss analysis data can be obtained, so that the data can better reflect the loss of the device. Based on the device loss analysis data and the internal operating environment analysis data, the fault correlation coefficient of each device to be detected is analyzed, and the target fault correlation coefficient corresponding to each device to be detected is obtained, which can fully consider the degree of fault correlation between each device to be detected and the detected fault, so that the fault detection result is more accurate and reliable. If it is determined that there is a fault in the device of the DC charging pile, the fault source of each device to be detected is traced based on the target fault correlation coefficient combined with the fault judgment result, and more accurate fault tracing information can be obtained, and the device fault of the DC charging pile can be understood more clearly and intuitively, thereby improving the accuracy and comprehensiveness of fault detection.

[0081] Embodiment 2

[0082] See also Figure 2 , Figure 2 : is a flow chart of a fault detection method for a DC charging pile in another embodiment of the present invention, the method is applied to a main control module and a data acquisition module of a DC charging pile, the main control module is communicatively connected with the data acquisition module; the method comprises:

[0083] S201: Acquire real-time monitoring data of each device to be detected in the DC charging pile based on the data acquisition module, and transmit the real-time monitoring data to the main control module;

[0084] S202: The main control module performs standardization processing on the real-time monitoring data to obtain standardized real-time monitoring data;

[0085] S203: Analyze the internal operating environment of the DC charging pile based on the standardized real-time monitoring data to obtain internal operating environment analysis data;

[0086] S204: Acquire operation and maintenance data of each device to be detected in the DC charging pile, and perform device loss analysis based on the operation and maintenance data and the internal operating environment analysis data by using correlation analysis to obtain device loss analysis data;

[0087] S205: Acquire reference data groups of different operation stages of the DC charging pile, and determine data feature quantities of the reference data groups;

[0088] S206: Determine the correlation coefficient of the associated fault types in different operation stages using a preset comparison table based on the data feature quantity;

[0089] S207: performing strong correlation analysis on the reference data group based on the correlation coefficient to obtain strong correlation data;

[0090] S208: constructing a fault correlation coefficient analysis model based on the strong correlation data and the correlation coefficient, and performing fault correlation coefficient analysis of each device to be detected using the device loss analysis data and the internal operating environment analysis data based on the fault correlation coefficient analysis model to obtain a target fault correlation coefficient corresponding to each device to be detected;

[0091] S209: judging whether each to-be-detected component of the DC charging pile has a fault based on the standardized real-time monitoring data, the target fault correlation coefficient, the internal operating environment analysis data, and the component loss analysis data;

[0092] S210: If it is determined that the device to be detected of the DC charging pile has a fault, the fault of each device to be detected is traced based on the target fault correlation coefficient and the fault judgment result to obtain fault tracing information, and an abnormal alarm signal is issued based on the fault tracing information and the fault judgment result.

[0093] In an embodiment of the present invention, the internal operating environment analysis of the DC charging pile is performed using a fluid simulation model based on the fluctuation rate influence function, temperature boundary conditions and humidity boundary conditions generated by the standardized real-time monitoring data, which can more comprehensively and accurately analyze the internal operating environment of the DC charging pile, and provide more comprehensive data for the fault judgment of each device to be detected in the DC charging pile. Based on the operation and maintenance data and the internal operating environment analysis data, the device loss analysis is performed using correlation analysis, and more accurate device loss analysis data can be obtained, so that the data can better reflect the loss of the device. Based on the device loss analysis data and the internal operating environment analysis data, the fault correlation coefficient of each device to be detected is analyzed, and the target fault correlation coefficient corresponding to each device to be detected is obtained, which can fully consider the degree of fault correlation between each device to be detected and the detected fault, so that the fault detection result is more accurate and reliable. If it is determined that there is a fault in the device of the DC charging pile, the fault source of each device to be detected is traced based on the target fault correlation coefficient combined with the fault judgment result, and more accurate fault tracing information can be obtained, and the device fault of the DC charging pile can be understood more clearly and intuitively, thereby improving the accuracy and comprehensiveness of fault detection.

[0094] Embodiment 3

[0095] See also Figure 3 , Figure 3 3 is a schematic diagram of the structure of the fault detection system of the DC charging pile in the embodiment of the present invention, the system includes a main control module 31 and a data acquisition module 32, the main control module 31 is communicatively connected with the data acquisition module 32, and the system is configured to execute the fault detection method of the DC charging pile in the above embodiment.

[0096] In the specific implementation of the present invention, the main control module 31 has several fault detection functions. The main control module can realize the control of the operation of the charging module and the closure of the contactor, the monitoring and adjustment of the output voltage and current, and the heat dissipation adjustment in the cabin. Based on the dual-processor design of microcontroller + microprocessor, more complex data calculations and more efficient and flexible application designs can be realized, and multiple module control modes such as full matrix, dual matrix, and ring can be supported to adapt to different application scenarios. The data acquisition module 32 includes a DC meter, a temperature and humidity sensor, and a wind speed sensor.

[0097] at the same time, Figure 3The fault detection system of the DC charging pile shown does not constitute a limitation on all components, and may include more or fewer components than shown in the figure, or combine certain components. The specific implementation method can be referred to the above embodiment, which will not be repeated here.

[0098] In an embodiment of the present invention, the internal operating environment analysis of the DC charging pile is performed using a fluid simulation model based on the fluctuation rate influence function, temperature boundary conditions and humidity boundary conditions generated by the standardized real-time monitoring data, which can more comprehensively and accurately analyze the internal operating environment of the DC charging pile, and provide more comprehensive data for the fault judgment of each device to be detected in the DC charging pile. Based on the operation and maintenance data and the internal operating environment analysis data, the device loss analysis is performed using correlation analysis, and more accurate device loss analysis data can be obtained, so that the data can better reflect the loss of the device. Based on the device loss analysis data and the internal operating environment analysis data, the fault correlation coefficient of each device to be detected is analyzed, and the target fault correlation coefficient corresponding to each device to be detected is obtained, which can fully consider the degree of fault correlation between each device to be detected and the detected fault, so that the fault detection result is more accurate and reliable. If it is determined that there is a fault in the device of the DC charging pile, the fault source of each device to be detected is traced based on the target fault correlation coefficient combined with the fault judgment result, and more accurate fault tracing information can be obtained, and the device fault of the DC charging pile can be understood more clearly and intuitively, thereby improving the accuracy and comprehensiveness of fault detection.

[0099] Embodiment 4

[0100] See also Figure 4 , Figure 4 : is a structural schematic diagram of a fault detection device for a DC charging pile in an embodiment of the present invention, the device is applied to a main control module and a data acquisition module of a DC charging pile, the main control module is communicatively connected with the data acquisition module; the device comprises:

[0101] Data acquisition module 41: used to acquire real-time monitoring data of each device to be detected in the DC charging pile based on the data acquisition module, and transmit the real-time monitoring data to the main control module;

[0102] Data standardization module 42: used for the main control module to perform standardization processing on the real-time monitoring data to obtain standardized real-time monitoring data;

[0103] Internal operating environment analysis module 43: used to perform internal operating environment analysis of the DC charging pile based on the standardized real-time monitoring data to obtain internal operating environment analysis data;

[0104] Device loss analysis module 44: used to obtain operation and maintenance data of each device to be detected in the DC charging pile, and perform device loss analysis based on the operation and maintenance data and the internal operating environment analysis data using correlation analysis to obtain device loss analysis data;

[0105] Fault correlation coefficient analysis module 45: used to perform fault correlation coefficient analysis of each device to be detected based on the device loss analysis data and the internal operating environment analysis data, and obtain a target fault correlation coefficient corresponding to each device to be detected;

[0106] Fault judgment module 46: used to judge the fault of each to-be-detected component of the DC charging pile based on the standardized real-time monitoring data, the target fault correlation coefficient, the internal operating environment analysis data and the component loss analysis data;

[0107] Fault tracing module 47: if it is determined that the device to be detected of the DC charging pile has a fault, then based on the target fault correlation coefficient and the fault judgment result, the fault tracing information is obtained, and an abnormal alarm signal is issued based on the fault tracing information and the fault judgment result.

[0108] In the specific implementation process of the present invention, the specific implementation methods of the device items can be referred to the above embodiments, which will not be repeated here.

[0109] In an embodiment of the present invention, the internal operating environment analysis of the DC charging pile is performed using a fluid simulation model based on the fluctuation rate influence function, temperature boundary conditions and humidity boundary conditions generated by the standardized real-time monitoring data, which can more comprehensively and accurately analyze the internal operating environment of the DC charging pile, and provide more comprehensive data for the fault judgment of each device to be detected in the DC charging pile. Based on the operation and maintenance data and the internal operating environment analysis data, the device loss analysis is performed using correlation analysis, and more accurate device loss analysis data can be obtained, so that the data can better reflect the loss of the device. Based on the device loss analysis data and the internal operating environment analysis data, the fault correlation coefficient of each device to be detected is analyzed, and the target fault correlation coefficient corresponding to each device to be detected is obtained, which can fully consider the degree of fault correlation between each device to be detected and the detected fault, so that the fault detection result is more accurate and reliable. If it is determined that there is a fault in the device of the DC charging pile, the fault source of each device to be detected is traced based on the target fault correlation coefficient combined with the fault judgment result, and more accurate fault tracing information can be obtained, and the device fault of the DC charging pile can be understood more clearly and intuitively, thereby improving the accuracy and comprehensiveness of fault detection.

[0110] A computer-readable storage medium provided in an embodiment of the present invention stores a computer program on the computer-readable storage medium, and when the program is executed by a processor, a fault detection method for a DC charging pile in any one of the above embodiments is implemented. Wherein, the computer-readable storage medium includes but is not limited to any type of disk (including floppy disk, hard disk, optical disk, CD-ROM, and magneto-optical disk), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (EraSable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic card or optical card. That is, the storage device includes any medium that stores or transmits information in a readable form by a device (for example, a computer, a mobile phone), which can be a read-only memory, a disk or an optical disk, etc.

[0111] In addition, the above is a detailed introduction to a fault detection method and related devices for a DC charging pile provided in an embodiment of the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A DC charging pile fault detection method, characterized in that: A main control module and a data acquisition module applied to a DC charging pile, wherein the main control module is communicatively connected with the data acquisition module; the method comprises: Acquire real-time monitoring data of each device to be detected in the DC charging pile based on the data acquisition module, and transmit the real-time monitoring data to the main control module; The main control module performs standardization processing on the real-time monitoring data to obtain standardized real-time monitoring data; Performing an internal operating environment analysis of the DC charging pile based on the standardized real-time monitoring data to obtain internal operating environment analysis data; Obtaining operation and maintenance data of each device to be detected in the DC charging pile, and performing device loss analysis based on the operation and maintenance data and internal operating environment analysis data using correlation analysis to obtain device loss analysis data; Performing a fault correlation coefficient analysis on each device to be detected based on the device loss analysis data and the internal operating environment analysis data to obtain a target fault correlation coefficient corresponding to each device to be detected; Perform fault judgment on each to-be-detected component of the DC charging pile based on the standardized real-time monitoring data, target fault correlation coefficient, internal operating environment analysis data, and component loss analysis data; If it is determined that the device to be detected of the DC charging pile has a fault, the fault of each device to be detected is traced based on the target fault correlation coefficient and the fault judgment result to obtain the fault tracing information, and an abnormal alarm signal is issued based on the fault tracing information and the fault judgment result.

2. The fault detection method for a DC charging pile according to claim 1, characterized in that: The step of performing standardization processing on the real-time monitoring data to obtain standardized real-time monitoring data includes: Classify the real-time monitoring data based on the data attribute value characteristics to obtain the classified real-time monitoring data; Performing data cleaning on the classified real-time monitoring data to obtain the real-time monitoring data after data cleaning; The real-time monitoring data after cleaning is standardized based on the graph structure to obtain standardized real-time monitoring data.

3. The fault detection method for a DC charging pile according to claim 1, characterized in that: The internal operating environment analysis of the DC charging pile is performed based on the standardized real-time monitoring data to obtain the internal operating environment analysis data, including: Constructing a three-dimensional geometric model of the interior of the DC charging pile, and using the three-dimensional geometric model to obtain internal temperature data, internal humidity data, and wind speed flow data of the DC charging pile based on the standardized real-time monitoring data; Perform internal temperature and internal humidity fluctuation analysis based on the internal temperature data, internal humidity data and wind speed flow data to obtain internal temperature fluctuation data and internal humidity fluctuation data; Perform variable function fitting based on the internal temperature fluctuation data and the internal humidity fluctuation data to obtain a fluctuation rate influence function; determining a temperature boundary condition based on the internal temperature fluctuation data, and determining a humidity boundary condition based on the internal humidity fluctuation data; Based on the fluctuation rate influence function, temperature boundary conditions and humidity boundary conditions, the internal operating environment analysis of the DC charging pile is performed using a fluid simulation model to obtain internal operating environment analysis data.

4. The fault detection method for a DC charging pile according to claim 1, characterized in that: The performing device loss analysis based on the operation and maintenance data and the internal operation environment analysis data by using correlation analysis to obtain device loss analysis data includes: Extracting historical loss data based on the operation and maintenance data, and clustering the historical loss data to obtain a plurality of clusters, wherein each cluster corresponds to a loss scenario; Constructing a weight matrix, calculating the anti-entropy of the loss scenario corresponding to each cluster based on the weight matrix, performing a correlation analysis between the loss scenario and the device loss based on the anti-entropy of the loss scenario, and obtaining correlation analysis data; Calculate the heat loss power based on the operation and maintenance data and the internal operation environment analysis data to obtain the target heat loss power; Constructing a humidity field spatial model and a temperature field spatial model based on the target heat loss power and the internal operating environment analysis data; The device loss analysis is performed based on the humidity field spatial model and the temperature field spatial model using the correlation analysis data to obtain device loss analysis data.

5. The fault detection method for a DC charging pile according to claim 1, characterized in that: The performing of the fault correlation coefficient analysis of each device to be detected based on the device loss analysis data and the internal operating environment analysis data to obtain a target fault correlation coefficient corresponding to each device to be detected includes: Obtaining reference data groups of different operation stages of the DC charging pile, and determining data feature quantities of the reference data groups; Determine the correlation coefficient of the associated fault types in different operation stages using a preset comparison table based on the data feature quantity; Performing strong correlation analysis on the reference data group based on the correlation coefficient to obtain strong correlation data; A fault correlation coefficient analysis model is constructed based on the strong correlation data and the correlation coefficient, and based on the fault correlation coefficient analysis model, the device loss analysis data and the internal operating environment analysis data are used to perform fault correlation coefficient analysis on each device to be detected, so as to obtain a target fault correlation coefficient corresponding to each device to be detected.

6. The fault detection method of a DC charging pile according to claim 1, characterized in that: The method of performing fault judgment on each to-be-detected device of the DC charging pile based on the standardized real-time monitoring data, the target fault correlation coefficient, the internal operating environment analysis data and the device loss analysis data includes: Acquire performance characteristic data based on the fault library using the standardized real-time monitoring data, internal operating environment analysis data and device loss analysis data; Based on the performance characteristic data and the target fault correlation coefficient, fault judgment is performed on each component to be detected of the DC charging pile.

7. The fault detection method of a DC charging pile according to claim 1, characterized in that: The step of performing fault tracing of each device to be detected based on the target fault correlation coefficient and the fault judgment result to obtain fault tracing information includes: Determine device fault data based on the fault judgment result, and construct a fault logic network based on the device fault data; acquiring a fault attention feature based on the fault logic network, and determining fault path data based on the fault attention feature; Based on the fault path data, the target fault correlation coefficient is used to perform fault cause troubleshooting to obtain fault cause troubleshooting information; Based on the fault cause troubleshooting information, a fault induction relationship analysis is performed to obtain fault induction relationship analysis data, and based on the fault induction relationship analysis data and the fault cause troubleshooting information, fault tracing of each to-be-detected device is performed to obtain fault tracing information.

8. A fault detection device for a DC charging pile, characterized in that: A main control module and a data acquisition module applied to a DC charging pile, wherein the main control module is communicatively connected with the data acquisition module; the device comprises: Data acquisition module: used to obtain real-time monitoring data of each device to be detected in the DC charging pile based on the data acquisition module, and transmit the real-time monitoring data to the main control module; Data standardization module: used for the main control module to perform standardization processing on the real-time monitoring data to obtain standardized real-time monitoring data; Internal operating environment analysis module: used to analyze the internal operating environment of the DC charging pile based on the standardized real-time monitoring data to obtain internal operating environment analysis data; Device loss analysis module: used to obtain the operation and maintenance data of each device to be detected in the DC charging pile, and perform device loss analysis based on the operation and maintenance data and the internal operating environment analysis data using correlation analysis to obtain device loss analysis data; Fault correlation coefficient analysis module: used to perform fault correlation coefficient analysis of each device to be detected based on the device loss analysis data and the internal operating environment analysis data, and obtain a target fault correlation coefficient corresponding to each device to be detected; Fault judgment module: used to judge the faults of the components to be detected of the DC charging pile based on the standardized real-time monitoring data, the target fault correlation coefficient, the internal operating environment analysis data and the component loss analysis data; Fault tracing module: if it is determined that a fault exists in the device to be detected of the DC charging pile, then the fault tracing module performs fault tracing on each device to be detected based on the target fault correlation coefficient and the fault judgment result, obtains fault tracing information, and issues an abnormal alarm signal based on the fault tracing information and the fault judgment result.

9. A fault detection system for a DC charging pile, characterized in that: The system includes a main control module and a data acquisition module, wherein the main control module is in communication with the data acquisition module, and the system is configured to execute the fault detection method for a DC charging pile according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed on an electronic device, the electronic device executes the fault detection method for a DC charging pile according to any one of claims 1 to 7.

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