A method and system for detecting a charging pile connector contact adhesion fault

Abstract

This invention relates to the field of electrical variable measurement technology, specifically to a method and system for detecting contact adhesion faults in charging pile connectors. The method includes: collecting multi-dimensional data related to the operation of the charging pile connector, including electrical data, mechanical structure data, material performance data, and environmental data of the connector's environment; determining multiple contact adhesion fault indicators based on the multi-dimensional data, including electrical timing coupling fault probability, electrical material coupling fault probability, and electromechanical coupling fault probability; determining the comprehensive probability of contact adhesion faults based on the multiple contact adhesion fault indicators; adjusting a preset fault threshold based on the material performance data and environmental data to obtain an adaptive threshold; and determining that a contact adhesion fault exists in the connector when the comprehensive probability is greater than the adaptive threshold. This invention achieves accurate and reliable detection of contact adhesion faults in charging pile connectors.

Description

Technical Field

[0001] This invention relates to the field of electrical variable measurement technology, specifically to a method and system for detecting contact adhesion faults in charging pile connectors. Background Technology

[0002] The rapid development of the new energy vehicle industry has driven the construction and upgrading of charging infrastructure. As the core equipment for replenishing electric vehicle power, the safety and stability of charging piles are of great significance to the popularization and application of new energy vehicles. The charging pile connector is a key component that connects the output end of the charging pile to the charging interface of the electric vehicle. It undertakes the core function of power transmission, and its working status directly determines the smoothness and safety of the charging process, making it an indispensable core hub in the charging pile system.

[0003] The contacts of a charging pile connector are the core metal components that enable conductivity. Hidden inside the connector, their health is closely related to the charging efficiency and operational safety of the charging pile. Contact adhesion is a typical type of fault in charging pile connectors. This fault directly affects the connector's on / off control function. If contact adhesion faults cannot be detected in a timely and effective manner, it can easily lead to problems such as abnormal charging and equipment damage. Therefore, conducting accurate and reliable detection of contact adhesion faults in charging pile connectors is an important prerequisite for ensuring the stable operation of the charging pile system. Summary of the Invention

[0004] To address the technical problem of the inability to detect charging pile connector contact adhesion faults in a timely and effective manner, the present invention aims to provide a method and system for detecting charging pile connector contact adhesion faults. The specific technical solution adopted is as follows: Firstly, a method for detecting contact adhesion faults in charging pile connectors is provided. This method includes: collecting multi-dimensional data related to the operation of the charging pile connector, including electrical data, mechanical structure data, material performance data, and environmental data of the connector's environment; determining multiple contact adhesion fault indicators based on the multi-dimensional data, including electrical timing coupling fault probability, electrical material coupling fault probability, and electrical-mechanical coupling fault probability; determining the comprehensive probability of contact adhesion faults based on the multiple contact adhesion fault indicators; adjusting a preset fault threshold based on the material performance data and environmental data to obtain an adaptive threshold; and determining that a contact adhesion fault exists in the connector when the comprehensive probability is greater than the adaptive threshold.

[0005] In one possible design, determining the electrical timing coupling failure probability includes: obtaining the steady-state current before the connector is disconnected from electrical data, and the instantaneous current at a preset time after disconnection; determining the current decay index based on the steady-state current and the instantaneous current, the current decay index being used to characterize the current decay rate after the connector is disconnected from the electric vehicle; and determining the electrical timing coupling failure probability based on the current decay index, the electrical timing coupling failure probability being negatively correlated with the current decay index.

[0006] In one possible design, determining the probability of electrical material coupling failure includes: determining the average current and average contact resistance based on electrical data including real-time current and contact resistance; determining the average contact temperature based on material performance data including real-time contact temperature; determining the average ambient temperature based on environmental data including real-time ambient temperature; determining a thermoelectric risk index based on average current, charging pile rated current, average contact temperature, average ambient temperature, average contact resistance, and initial contact resistance, whereby the thermoelectric risk index characterizes the risk level of contact adhesion failure due to thermoelectric effects; and determining the probability of electrical material coupling failure based on the thermoelectric risk index, whereby the probability of electrical material coupling failure is positively correlated with the thermoelectric risk index.

[0007] In one possible design, determining the probability of electromechanical coupling failure includes: obtaining a preset peak insertion / extraction force of the connector, which characterizes the mechanical engagement characteristics of the connector in a healthy state; obtaining the measured peak insertion / extraction force of the connector from mechanical structure data; determining the maximum vibration frequency from real-time vibration data included in the mechanical structure data; determining the average contact resistance based on the contact resistance included in the electrical data; determining the contact reliability index of the connector contacts based on the preset peak insertion / extraction force, the measured peak insertion / extraction force, the average contact resistance, the initial contact resistance, the maximum vibration frequency, and a preset vibration frequency threshold, whereby the contact reliability index characterizes the degree of influence of connector mechanical structure abnormalities on the contact state; and determining the probability of electromechanical coupling failure based on the contact reliability index, where the probability of electromechanical coupling failure is negatively correlated with the contact reliability index.

[0008] In one possible design, the comprehensive probability of contact adhesion failure is determined based on multiple contact adhesion failure indicators. This includes: determining the electrical-material coupling coefficient based on the correlation between electrical data and material performance data in historical data; determining the electrical-mechanical coupling coefficient based on the correlation between electrical data and mechanical structure data in historical data; determining multiple weighting coefficients that correspond one-to-one with the electrical-temporal coupling failure probability, electrical-material coupling failure probability, and electrical-mechanical coupling failure probability based on the electrical-material coupling coefficient and the electrical-mechanical coupling coefficient; and fusing the electrical-temporal coupling failure probability, electrical-material coupling failure probability, and electrical-mechanical coupling failure probability based on the multiple weighting coefficients to obtain the comprehensive probability.

[0009] In one possible design, determining the electrical-material coupling coefficient includes: extracting multiple sets of operating samples from historical data, each set of operating samples including a subset of electrical data, a subset of material performance data, and a subset of mechanical structure data within the same operating period; for each set of operating samples, determining a first correlation coefficient between the electrical data subset and the material performance data subset; and determining the electrical-material coupling coefficient based on the first correlation coefficient corresponding to each set of operating samples and the historical fault markers corresponding to each set of operating samples.

[0010] In one possible design, determining the electromechanical coupling coefficient includes: for each set of operating samples, determining a second correlation coefficient between the electrical data subset and the mechanical structure data subset; and determining the electromechanical coupling coefficient based on the second correlation coefficient corresponding to each set of operating samples and the historical fault markers corresponding to each set of operating samples.

[0011] In one possible design, an adaptive threshold is obtained by adjusting a preset fault threshold based on material performance data and environmental data. This includes: determining a material correction coefficient based on the heat resistance coefficient and resistivity of the contact material in the material performance data; determining an environmental correction coefficient based on the temperature, humidity and dust concentration in the environmental data; and adjusting the preset fault threshold based on the material correction coefficient and the environmental correction coefficient to obtain the adaptive threshold.

[0012] In one possible design, the above-mentioned charging pile connector contact adhesion fault detection method further includes: when it is determined that there is an adhesion fault in the connector contacts, determining the dominant fault dimension based on the numerical relationship between the electrical timing coupling fault probability, the electrical material coupling fault probability, and the electromechanical coupling fault probability; and determining the fault cause based on the data corresponding to the dominant fault dimension.

[0013] Secondly, a charging pile connector contact adhesion fault detection system is provided, comprising: a data acquisition unit for acquiring multi-dimensional data related to the operation of the charging pile connector, including electrical data, mechanical structure data, material performance data, and environmental data of the connector's environment; an index determination unit for determining multiple contact adhesion fault indices based on the multi-dimensional data, including electrical timing coupling fault probability, electrical material coupling fault probability, and electromechanical coupling fault probability; a probability determination unit for determining the comprehensive probability of contact adhesion faults based on the multiple contact adhesion fault indices; a threshold adjustment unit for adjusting a preset fault threshold based on material performance data and environmental data to obtain an adaptive threshold; and a fault judgment unit for determining that the connector contacts have adhesion faults when the comprehensive probability is greater than the adaptive threshold.

[0014] The present invention has the following beneficial effects: In the charging pile connector contact adhesion fault detection method provided by this invention, a comprehensive perception of the contact operation status is achieved by collecting multi-dimensional data related to the operation of the charging pile connector. Then, based on the multi-dimensional data, the probabilities of electrical timing coupling faults, electrical material coupling faults, and electromechanical coupling faults are determined respectively. The contact adhesion fault is decoupled and analyzed from three dimensions: electrical response characteristics, thermoelectric effect risk, and mechanical structure reliability. This effectively distinguishes non-fault interference such as power grid fluctuations and sensor anomalies, avoiding misjudgments caused by single-parameter detection. By fusing multiple contact adhesion fault indicators to obtain the comprehensive probability of the contact adhesion fault, the organic integration of multi-dimensional information is achieved. This makes fault diagnosis more comprehensive and reliable. Simultaneously, by adjusting the preset fault threshold based on material performance data and environmental data, an adaptive threshold is obtained. This allows the judgment criteria to dynamically change with the degree of contact material degradation and real-time environmental conditions. When materials are severely aged or the environment is harsh, the detection sensitivity is automatically increased to reduce the risk of missed detections. When materials are healthy and the environment is favorable, the judgment threshold is appropriately increased to avoid false alarms. Finally, when the overall probability is greater than the adaptive threshold, the presence of a connector contact adhesion fault is determined. This achieves a synergistic improvement in detection accuracy and environmental adaptability, significantly enhancing the reliability and robustness of charging pile connector contact adhesion fault detection, and providing strong support for the safe and stable operation of charging piles. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of a charging pile connector contact adhesion fault detection system provided in one embodiment of the present invention; Figure 2 This is a flowchart illustrating a method for detecting contact adhesion faults in a charging pile connector, as provided in one embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a charging pile connector contact adhesion fault detection method and system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] In embodiments of the present invention, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in embodiments of the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0019] In the description of this invention, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" and "more than one" refer to two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] The following description, in conjunction with the accompanying drawings, details the specific scheme of the charging pile connector contact adhesion fault detection method and system provided by the present invention.

[0022] Please see Figure 1 The diagram illustrates a structural schematic of a charging pile connector contact adhesion fault detection system according to an embodiment of the present invention. Figure 1 As shown, the charging pile connector contact adhesion fault detection system 10 includes a data acquisition unit 11, an index determination unit 12, a probability determination unit 13, a threshold adjustment unit 14, and a fault judgment unit 15.

[0023] The data acquisition unit 11 is used to collect multi-dimensional data related to the operation of the charging pile connector. The multi-dimensional data includes electrical data, mechanical structure data, material performance data, and environmental data of the environment in which the connector is located.

[0024] In some embodiments, the data acquisition unit 11 collects electrical data, mechanical structure data, material performance data, and environmental data of the connector's environment in real time using various dedicated sensors. The electrical data includes real-time current throughout the charging cycle, real-time voltage throughout the charging cycle, contact resistance, and electrical curves before and after the disconnection command. The mechanical structure data includes the number of connector insertions and removals, real-time insertion and removal forces, and internal vibration data. The material performance data includes real-time contact temperature and contact resistivity changes. The environmental data includes ambient temperature, humidity, and dust concentration. The data acquisition unit 11 synchronously transmits the collected complete multidimensional data to the index determination unit 12, serving as the core data for calculating fault indicators. Simultaneously, it separately transmits the material performance data and environmental data to the threshold adjustment unit 14, providing basic data for the threshold adaptive adjustment of the threshold by the threshold adjustment unit 14.

[0025] Optionally, electrical data can be obtained through monitoring devices such as Hall current sensors and voltage sensors; mechanical structure data can be obtained through monitoring devices such as stroke counters, piezoelectric force sensors, and piezoelectric acceleration sensors; material performance data can be obtained through monitoring devices such as infrared temperature sensors and resistance sensors; and environmental data can be obtained through monitoring devices such as temperature and humidity sensors and laser dust sensors.

[0026] The indicator determination unit 12 is used to determine multiple contact adhesion fault indicators based on multidimensional data. Among them, the multiple contact adhesion fault indicators include electrical timing coupling fault probability, electrical material coupling fault probability and electromechanical coupling fault probability.

[0027] In some embodiments, the index determination unit 12 designs corresponding calculation logic for different contact adhesion fault indices. For the electrical timing coupling fault probability, it extracts the steady-state current before the connector disconnects and the instantaneous current at a preset time after disconnection from the electrical data, and determines the current decay index characterizing the current decay rate. Then, it obtains the fault probability based on the negative correlation between the index and the probability. For the electrical material coupling fault probability, it calculates the average current and average contact resistance from the electrical data, the average contact temperature from the material performance data, and the average ambient temperature from the environmental data. It combines the charging pile rated current and the initial contact resistance to determine the thermoelectric risk index characterizing the thermoelectric fault risk. Then, it obtains the fault probability based on the positive correlation between the index and the probability. For the electrical-mechanical coupling fault probability, it extracts the insertion and extraction force data and the maximum vibration frequency from the mechanical structure data, calculates the average contact resistance from the electrical data, and determines the contact reliability index characterizing the influence of the mechanical structure on the contact state based on the preset vibration frequency threshold and the initial contact resistance. Then, it obtains the fault probability based on the negative correlation between the index and the probability. The indicator determination unit 12 synchronously transmits the calculated three contact adhesion fault indicators to the probability determination unit 13 as the core basis for calculating the comprehensive fault probability. At the same time, the three fault indicators are transmitted to the fault judgment unit 15 to provide data support for the subsequent fault cause location of the fault judgment unit 15.

[0028] The probability determination unit 13 is used to determine the comprehensive probability of contact adhesion failure based on multiple contact adhesion failure indicators.

[0029] In some embodiments, the probability determination unit 13 first analyzes the correlation between historical electrical data and material performance data, and between historical electrical data and mechanical structure data, based on the historical operating data of the charging pile connector, using a preset correlation analysis algorithm, thereby determining the electrical-material coupling coefficient and the electromechanical coupling coefficient. Then, based on the values ​​of the two coupling coefficients, it calculates three weighting coefficients that correspond one-to-one with the electrical-time coupling failure probability, the electrical-material coupling failure probability, and the electromechanical coupling failure probability. Finally, it performs a weighted fusion calculation on the three contact adhesion failure indicators according to the weighting coefficients to obtain a comprehensive probability that comprehensively reflects the possibility of connector contact adhesion failure. The probability determination unit 13 transmits the calculated comprehensive probability to the fault judgment unit 15 in real time, serving as the core numerical basis for the fault judgment unit 15 to judge contact adhesion failure.

[0030] The threshold adjustment unit 14 is used to adjust the preset fault threshold according to the material performance data and environmental data to obtain an adaptive threshold.

[0031] In some embodiments, the threshold adjustment unit 14 first extracts the heat resistance coefficient and resistivity change data of the contact material from the material performance data, and calculates a material correction coefficient that can characterize the material characteristics and degree of degradation. The worse the heat resistance of the material and the greater the change in resistivity, the smaller the material correction coefficient. Then, it extracts temperature and humidity data and dust concentration data from the environmental data, calculates the temperature and humidity deviation and dust blockage coefficient, and obtains an environmental correction coefficient that can characterize the severity of the environmental conditions. The greater the deviation of the environmental temperature and humidity from the standard value and the higher the dust concentration, the smaller the environmental correction coefficient. Finally, it combines the material correction coefficient and the environmental correction coefficient to multiply and correct the preset fault threshold to obtain an adaptive threshold that fits the current connector material state and the on-site environmental conditions. The threshold adjustment unit 14 transmits the obtained adaptive threshold to the fault judgment unit 15 as a critical reference value for the fault judgment unit 15 to perform fault judgment.

[0032] The fault determination unit 15 is used to determine that there is an adhesion fault in the connector contacts when the overall probability is greater than the adaptive threshold.

[0033] In some embodiments, the fault judgment unit 15 first compares the received comprehensive probability with an adaptive threshold. If the comprehensive probability is greater than the adaptive threshold, it directly determines that the charging pile connector contacts have an adhesion fault and triggers the corresponding alarm or recording mechanism. Furthermore, if an adhesion fault is determined, the fault judgment unit 15 further compares the values ​​of the electrical timing coupling fault probability, the electrical material coupling fault probability, and the electromechanical coupling fault probability. The dimension corresponding to the fault indicator with the largest value is determined as the dominant fault dimension. Then, based on the data type corresponding to the dominant fault dimension, the specific cause of the connector contact adhesion fault is analyzed and determined, providing a basis for subsequent targeted repairs. For example, if the electrical material coupling fault probability value is the largest, the fault cause is related to contact material deterioration and thermoelectric effects.

[0034] Please see Figure 2 The diagram illustrates a flowchart of a method for detecting contact adhesion faults in a charging pile connector according to an embodiment of the present invention, including the following steps S201-S205.

[0035] S201. Collect multi-dimensional data related to the operation of the charging pile connector.

[0036] The multidimensional data includes electrical data, mechanical structure data, material performance data, and environmental data of the environment in which the connector is located.

[0037] As one possible implementation, electrical data is acquired through Hall current sensors, voltage sensors, and contact resistance detection circuits, including real-time current, real-time voltage, contact resistance, and electrical curves before and after the disconnection command. The real-time current and voltage reflect the electrical load during charging; the contact resistance assesses the conductivity of the contacts; and the electrical curves before and after the disconnection command (i.e., waveforms of current and voltage changes over time) capture the electrical response characteristics at the moment of disconnection.

[0038] Mechanical structure data is collected using a stroke counter, piezoelectric force sensor, and piezoelectric accelerometer, including the number of connector mating / removal cycles, real-time mating / removal force data, and internal vibration data of the connector. The number of mating / removal cycles is used to accumulate the mechanical wear of the connector; the real-time mating / removal force data reflects the change in mating / removal force during each mating / removal cycle, from which the measured peak mating / removal force can be extracted; the vibration data is used to monitor the mechanical vibration of the connector during operation, from which the maximum vibration frequency can be extracted.

[0039] Material performance data is acquired using infrared temperature and resistance sensors, including real-time contact temperature and contact resistivity changes. Real-time contact temperature is used to monitor the thermal state of the contacts; contact resistivity changes are obtained by measuring the contact resistance in real time and comparing it with the initial contact resistance, and are used to characterize the degree of degradation of the contact material.

[0040] Environmental data is collected through temperature and humidity sensors and laser dust sensors, including ambient temperature, humidity and ambient dust concentration.

[0041] It should be noted that the entire process from removing the connector from the charging station to putting it back into the charging station is defined as one complete operation of the connector. The frequency of multi-dimensional data collection by various sensors shall not be less than 1 time / second to ensure that the collected data can fully reflect the state changes of the connector throughout the entire operation cycle. During the multi-dimensional data collection process, if a connector contact adhesion fault is found in the data record corresponding to a certain operation, all data collected for that operation shall be marked with the fault to provide a basis for subsequent determination of the electrical material coupling coefficient and the electromechanical coupling coefficient by combining historical data.

[0042] In some embodiments, after real-time acquisition of multidimensional data is completed, the acquired raw data is preprocessed. The preprocessing operation specifically includes data cleaning and data completion. Data cleaning is used to remove outliers, noise values, and meaningless invalid data from the acquired data. Data completion is used to reasonably supplement missing data caused by non-fault factors such as temporary sensor failures and data transmission interruptions during the acquisition process, thereby ensuring the integrity, accuracy, and continuity of the multidimensional data. The preprocessed multidimensional data is uploaded to the designated storage area of ​​the charging pile connector contact adhesion fault detection system. The preprocessed data of different operating cycles and different data categories are classified, integrated, and structured for storage to construct a standardized connector contact fault detection dataset.

[0043] S202. Based on multidimensional data, determine multiple contact adhesion fault indicators.

[0044] Among them, multiple contact adhesion fault indicators include electrical timing coupling fault probability, electrical material coupling fault probability, and electromechanical coupling fault probability.

[0045] As one possible approach, when determining the probability of electrical timing coupling faults, the steady-state current before the connector disconnects and the instantaneous current at a preset time after disconnection (e.g., 5 seconds after disconnection) are first extracted from the electrical data. Then, the current decay index is determined based on the steady-state current and the instantaneous current.

[0046] It should be noted that steady-state current refers to the current value that flows steadily in the charging circuit before the disconnection command is issued, reflecting the electrical load when the contacts are normally conducting; instantaneous current at a preset time refers to the residual current value measured at a fixed time point after the disconnection command is issued, used to characterize the degree of current attenuation.

[0047] In some embodiments, the formula for determining the current decay index is as follows: In the formula, For the first Steady-state current before the connector disconnects during the operating cycle. For the first The instantaneous current at a preset time after the connector is disconnected during the operating cycle. It is a very small positive number, and an empirical value of 0.0001 can be taken to prevent the denominator from being zero. For the first The current decay index of the operating cycle is used to characterize the rate of current decay after the connector is disconnected from the electric vehicle.

[0048] Furthermore, after obtaining the current decay exponent, the probability of electrical timing coupling faults is determined based on the current decay exponent, and its formula is expressed as follows: Electrical timing coupling fault probability With current decay index There is a negative correlation: the larger the current decay exponent, the lower the probability of electrical timing coupling failure; conversely, the smaller the current decay exponent, the higher the probability of electrical timing coupling failure. This probability of electrical timing coupling failure directly reflects the electrical response characteristics when the contacts are open and serves as direct evidence for determining whether physical adhesion has occurred at the contacts.

[0049] When determining the probability of electrical material coupling failure, the real-time current and contact resistance of the connector during a single operation are first extracted from the electrical data to calculate the average current and average contact resistance; then the real-time contact temperature is extracted from the material performance data to calculate the average contact temperature, and the real-time ambient temperature is extracted from the environmental data to calculate the average ambient temperature.

[0050] It should be noted that the average current is used to characterize the average load intensity during the charging process, the average contact resistance is used to reflect the average conductivity of the contact during the operating cycle, and the difference between the average contact temperature and the average ambient temperature is used to characterize the temperature rise of the contact.

[0051] Furthermore, the current intensity coefficient is determined based on the ratio of the average current to the rated current of the charging pile; the temperature rise is determined based on the difference between the average temperature of the contact points and the average ambient temperature; and the resistance rise coefficient is determined based on the ratio of the average resistance of the contact points to the initial resistance of the contact points. Finally, by combining the temperature rise, resistance rise coefficient, and current intensity coefficient, a thermoelectric risk index is determined.

[0052] In some embodiments, the formula for calculating the thermoelectric risk index is as follows: In the formula, For the first Average contact temperature during the operating cycle. For the first The average ambient temperature during the operating cycle, This refers to the temperature increase. The temperature reference value can be the maximum permissible temperature rise of the connector contacts (e.g., 50°C) or the maximum temperature difference from historical data, used to determine the temperature increase range. Convert to dimensionless relative ratios; For the first Average contact resistance during the operating cycle For the first Initial contact resistance during the operating cycle (resistance of the contact when it is first used). This is the resistance boost factor of the contact. For the first Average current during the operating cycle This refers to the rated current of the charging station. That is, the current intensity coefficient passing through the contact point. , , The preset weighting coefficients, and The specific settings can be adjusted according to the importance of each item; for example, the speed of fast charging stations can be appropriately increased. Old piles can be appropriately raised . Indicates to Normalization can be performed, for example, by using the maximum and minimum values ​​from the historical processing to map them to the [0,1] interval using the maximum and minimum value normalization method. For the first The thermoelectric risk index of the operating cycle is used to characterize the risk of contact adhesion failure caused by thermoelectric effect.

[0053] Furthermore, after obtaining the thermoelectric risk index, the probability of electrical material coupling failure is determined based on the thermoelectric risk index, and its formula is expressed as follows: Electrical material coupling failure probability With thermoelectric risk index Positive correlation: The probability of electrical material coupling failure is assessed from the perspective of thermoelectric conversion to evaluate the impact of contact material degradation and heat accumulation on adhesion failure.

[0054] When determining the probability of electromechanical coupling failure, the preset peak insertion and extraction force of the connector is first obtained. This preset peak insertion and extraction force is the nominal mechanical engagement characteristic value of the connector under healthy conditions. It can be preset and stored in the system through the connector's factory parameters or the measured value during initial use, serving as a benchmark for subsequent assessment of mechanical wear. Next, the measured peak insertion and extraction force of the connector during this operation is extracted from the mechanical structure data. This measured peak insertion and extraction force refers to the maximum value of the insertion and extraction force curve collected during this insertion and extraction operation, reflecting the actual stress condition of the connector's engagement mechanism during the current insertion and extraction operation, and can be used to assess the degree of mechanical wear. Simultaneously, the maximum vibration frequency is extracted from the real-time vibration data included in the mechanical structure data. This maximum vibration frequency refers to the maximum frequency component of the vibration signal collected during this operation, used to characterize the severity of mechanical vibration interference experienced by the connector. Furthermore, based on the contact resistance included in the electrical data, the average contact resistance is determined. This average contact resistance reflects the average conductivity of the contacts during this operating cycle.

[0055] It should be noted that there is an inherent coupling relationship between mechanical wear and resistance degradation. Mechanical wear leads to a decrease in contact pressure, which in turn causes an increase in contact resistance. Both factors jointly affect the contact reliability of the contact. Therefore, when constructing the contact reliability index, the coupling effect of mechanical and resistance factors should be considered. At the same time, vibration, as an independent environmental disturbance factor, can have a cumulative effect on contact reliability.

[0056] Furthermore, based on the ratio of the measured peak insertion / extraction force to the preset peak insertion / extraction force, an insertion / extraction force retention coefficient is determined; based on the ratio of the initial contact resistance to the average contact resistance, a resistance health coefficient is determined; multiplying the insertion / extraction force retention coefficient and the resistance health coefficient yields the mechanical resistance coupling coefficient, which comprehensively characterizes the coupled influence of mechanical wear and resistance degradation on contact reliability. Simultaneously, based on the ratio of the preset vibration frequency threshold to the maximum vibration frequency, a vibration suppression coefficient is determined, which characterizes the degree of vibration interference suppression. Finally, by combining the mechanical resistance coupling coefficient and the vibration suppression coefficient, a contact reliability index is determined.

[0057] In some embodiments, the formula for determining the contact reliability index is as follows: In the formula, For the first The measured peak insertion and extraction force during the operating cycle, The preset peak insertion and extraction force of the connector, This is the insertion and extraction force retention coefficient. The larger this ratio is, the closer the measured peak insertion and extraction force is to the initial value, meaning that the mechanical wear is less. For the first Average contact resistance during the operating cycle The initial resistance of the contact is... This is the resistance health factor; the larger the value, the less severe the resistance degradation. The mechanical-resistance coupling coefficient is represented by two coefficients, both of which are "the larger the better" and their product indicates that the coupling coefficient is large when both mechanical and resistance are healthy. The deterioration of either factor will reduce the coupling coefficient, reflecting the inherent coupling relationship between mechanical wear and resistance degradation. The preset vibration frequency threshold is (i.e., the maximum acceptable vibration frequency at which the connector can function normally). For the first The maximum vibration frequency during the operating cycle, This is the vibration suppression coefficient. The larger the ratio, the weaker the vibration is relative to the acceptable threshold, meaning the smaller the vibration interference. , The preset weighting coefficients, and This setting is used to balance the impact of intrinsic factors (mechanical wear and resistance degradation) and extrinsic factors (vibration interference) on contact reliability. The specific setting can be adjusted according to the application scenario; for example, it can be appropriately increased for connectors intended for long-term service. To highlight the coupled effects of mechanical wear and electrical resistance degradation, the voltage can be appropriately increased for scenarios with harsh vibration environments. To highlight the impact of vibration interference. Indicates to Normalization can be performed, for example, by using the maximum and minimum values ​​from the historical processing to map them to the [0,1] interval using the maximum and minimum value normalization method. For the first The contact reliability index during the operating cycle is used to characterize the comprehensive impact of the connector's mechanical structure condition on the contact reliability. The larger the value, the healthier the mechanical structure condition and the higher the contact reliability; conversely, the smaller the value, the higher the risk that abnormal mechanical structure may lead to poor contact.

[0058] Furthermore, after obtaining the contact reliability index, the probability of electromechanical coupling failure is determined based on the contact reliability index, and its formula is expressed as follows: Electromechanical coupling failure probability Contact reliability index The probability of electromechanical coupling failure is negatively correlated. The indirect impact of mechanical factors on contact adhesion failure is assessed from the perspective of mechanical wear and vibration interference.

[0059] S203. Determine the overall probability of contact adhesion failure based on multiple contact adhesion failure indicators.

[0060] One possible implementation involves first extracting multiple sets of operational samples from historical operational data. Each set corresponds to a complete connector operation cycle and includes subsets of electrical data, material performance data, and mechanical structure data within the same operational period. The electrical data subset includes real-time current and contact resistance during the operation; the material performance data subset includes real-time contact temperature and resistivity changes during the operation; and the mechanical structure data subset includes insertion / extraction force data and vibration data during the operation. Each set of operational samples is also associated with historical fault markers to indicate whether a contact adhesion fault occurred during the operation (e.g., a fault marker of 1 indicates a fault occurred, and 0 indicates no fault occurred).

[0061] Furthermore, for each set of operating samples, a first correlation coefficient is calculated between the electrical data subset and the material performance data subset, and a second correlation coefficient is calculated between the electrical data subset and the mechanical structure data subset. The first and second correlation coefficients can be calculated using the Pearson correlation coefficient or other correlation analysis algorithms to measure the degree of linear correlation between the two sets of data sequences. Their values ​​range from -1 to 1, with positive values ​​indicating positive correlation and negative values ​​indicating negative correlation. A larger absolute value indicates a stronger linear correlation.

[0062] After obtaining multiple first correlation coefficients and multiple second correlation coefficients corresponding to multiple sets of operating samples, the electrical material coupling coefficient and the electromechanical coupling coefficient are determined by combining the historical fault markers of each set of operating samples. In some embodiments, operating samples that have experienced faults can be assigned higher weights, for example, the electrical material coupling coefficient can be calculated using the following formula: In the formula, The total number of samples to run. For the first The first correlation coefficient between the electrical data subset and the material performance data subset in each operating sample. For the first The historical fault flags for each running sample are 1 when there is a fault and 0 when there is no fault. This is the historical fault frequency factor, which can be preset based on the overall frequency of fault occurrence in historical data, for example, by taking the value as the ratio of the number of historical fault samples to the total number of samples. In this way, samples more closely associated with faults occupy a larger proportion of the coupling coefficient, thus more accurately reflecting the data correlation strength under actual fault modes.

[0063] It needs to be explained that, The original calculated values ​​(including the sign) are used directly because the correlation may contain information about the fault mechanism. For example, if the electrical data and the material performance data are positively correlated, the correlation coefficient will be large and positive during the fault, which helps to increase the coupling coefficient. If they are negatively correlated, the value may be negative during the fault, and the contribution to the coupling coefficient will be reduced accordingly, which is consistent with the actual physical relationship under different fault modes.

[0064] Similarly, the electromechanical coupling coefficient is determined using the following formula: In the formula, For the first The second correlation coefficient between the electrical data subset and the mechanical structure data subset in each operating sample, and the other parameters are the same as those in the formula for calculating the electrical material coupling coefficient mentioned above, will not be repeated here.

[0065] Furthermore, based on the electrical material coupling coefficient and the electromechanical coupling coefficient, multiple weighting coefficients are determined that correspond one-to-one with the electrical timing coupling failure probability, the electrical material coupling failure probability, and the electromechanical coupling failure probability.

[0066] In some embodiments, the multiple weighting coefficients include a first weighting coefficient corresponding to the probability of electrical timing coupling faults, a second weighting coefficient corresponding to the probability of electrical material coupling faults, and a third weighting coefficient corresponding to the probability of electromechanical coupling faults. Since current attenuation is the most direct evidence for judging physical adhesion of contacts, the electrical timing coupling coefficient is set to a preset fixed value of 1, serving as the benchmark coupling coefficient and reflecting its dominant role in fault judgment. The second weighting coefficient is positively correlated with the electrical material coupling coefficient; that is, the larger the electrical material coupling coefficient, the stronger the correlation between electrical data and material performance data, and therefore, a greater weight should be given to the probability of electrical material coupling faults during fusion. Similarly, the third weighting coefficient is positively correlated with the electromechanical coupling coefficient. Simultaneously, the sum of the first, second, and third weighting coefficients is a fixed value, for example, set to 1, to ensure that the overall probability remains within a uniform dimensional range.

[0067] In some embodiments, the specific calculation formulas for the multiple weighting coefficients are as follows: In the formula, The coupling coefficient of the electrical materials, The electromechanical coupling coefficient is given, while the electrical timing coupling coefficient is fixed at 1. This is the first weighting coefficient corresponding to the electrical timing coupling fault probability. This is the second weighting coefficient corresponding to the probability of electrical material coupling failure. This is the third weighting coefficient corresponding to the probability of electromechanical coupling faults, and .

[0068] Furthermore, after obtaining the weighting coefficients, the electrical timing coupling failure probability, electrical material coupling failure probability, and electromechanical coupling failure probability are fused based on the multiple weighting coefficients to obtain a comprehensive probability.

[0069] In some embodiments, the formula for determining the overall probability is as follows: In the formula, This represents the probability of electrical timing coupling faults. The probability of electrical material coupling failure. The probability of electromechanical coupling failure. , , These are the corresponding weighting coefficients. This is the overall probability of contact sticking failure, with a value between 0 and 1. The higher the value, the higher the probability of contact sticking failure.

[0070] Understandably, by combining the above-mentioned fusion process, the three different dimensions of fault probability indicators are organically combined, which not only highlights the dominant role of current decay as direct evidence, but also dynamically adjusts the weights according to the actual correlation between different dimensions in historical data, thus achieving effective fusion of multi-dimensional information.

[0071] S204. Based on material performance data and environmental data, adjust the preset fault threshold to obtain an adaptive threshold.

[0072] It should be noted that the preset fault threshold is a pre-set fault judgment benchmark value used to distinguish between normal and faulty contact states under standard operating conditions. In some embodiments, the preset fault threshold is determined as follows: No-interference samples are selected from historical operating data. No-interference samples refer to operating samples where the contact resistance value is equal to the standard contact resistance value and the environmental data meets standard environmental conditions, which can be set as a temperature of 25°C and humidity of 60%. For each no-interference sample, the comprehensive probability of contact adhesion failure is calculated according to the aforementioned steps, and the maximum value among all no-interference sample comprehensive probabilities is taken as the preset fault threshold. This preset fault threshold reflects the highest fault probability level that may be reached during normal operation under conditions of healthy contact materials and ideal environment; exceeding this value indicates a possible abnormality.

[0073] As one possible approach, the material correction coefficient is first determined based on the heat resistance coefficient and resistivity of the contact material in the material performance data. The material correction coefficient is used to reflect the influence of the contact material's own characteristics and its degree of deterioration on the susceptibility to failure.

[0074] In some embodiments, the heat resistance coefficient of the contact material is a preset value, used to characterize the inherent high-temperature resistance of the contact material. A larger heat resistance coefficient indicates a stronger ability of the contact to withstand high temperatures and a lower tendency to sticking failure. The resistivity change amplitude is used to characterize the degree of degradation of the contact during long-term use, and can be obtained by comparing the real-time measured contact resistivity with the initial contact resistivity. For example, the resistivity change amplitude can be expressed as the absolute difference between the current resistivity and the initial resistivity. The material correction coefficient is positively correlated with the heat resistance coefficient of the contact material, that is, the larger the heat resistance coefficient, the larger the material correction coefficient, thereby relatively increasing the adaptive threshold and reducing the possibility of false alarms; the material correction coefficient is negatively correlated with the resistivity change amplitude, that is, the larger the resistivity change amplitude (the more severe the degradation), the smaller the material correction coefficient, thereby decreasing the adaptive threshold and increasing the sensitivity to failures of aging contacts. Optionally, the calculation formula for the material correction coefficient is as follows: In the formula, The heat resistance coefficient of the contact material. For the first The magnitude of resistivity change during the operating cycle, The initial resistivity of the contact. The relative change in resistivity characterizes the degree of material degradation. This is the material degradation attenuation factor, which decreases as the degree of degradation increases. The better the inherent heat resistance of the material and the less severe the subsequent degradation, the higher the material correction factor. The logic that the larger the value, the higher the fault threshold should be. However, when the inherent high-temperature resistance of the contact material is poor and its subsequent degradation is severe (large changes in contact resistivity), the material correction coefficient should be adjusted accordingly. The smaller.

[0075] Furthermore, based on the temperature, humidity, and dust concentration in the environmental data, an environmental correction factor is determined. This environmental correction factor is used to reflect the impact of external environmental factors on the susceptibility of contact failures.

[0076] In some embodiments, the temperature deviation is determined based on the ratio of real-time ambient temperature to standard temperature; the humidity deviation is determined based on the ratio of real-time ambient humidity to standard humidity; the temperature deviation and humidity deviation are multiplied (or the maximum value is taken, or a weighted sum is calculated) to obtain the combined temperature and humidity deviation, which characterizes the degree of influence of the synergistic effect of temperature and humidity on the susceptibility of contact failure; the dust blockage coefficient is determined by comparing the real-time dust concentration with the maximum acceptable dust concentration at the contact. A larger temperature and humidity deviation indicates a harsher environment, which is more likely to accelerate contact aging and induce failure; a higher dust concentration indicates more severe surface contamination of the contact, which may lead to unstable contact impedance and increase the risk of failure. Therefore, the environmental correction coefficient is negatively correlated with both temperature and humidity deviation and dust concentration; that is, the harsher the environment, the smaller the environmental correction coefficient, thereby reducing the adaptive threshold and improving the sensitivity of fault detection. Optionally, the formula for calculating the environmental correction coefficient is as follows: In the formula, The temperature and humidity deviation can be calculated based on the ratio of real-time temperature and humidity to standard temperature and humidity (e.g., 25℃, 60%). Real-time dust concentration; This is the maximum acceptable dust concentration at the contact point, and it is a preset value. This is the real-time dust blockage coefficient, characterizing the degree of dust pollution. The greater the deviation in temperature and humidity or the higher the dust concentration, the larger the denominator, and the greater the environmental correction factor. The smaller the value, the lower the adaptive threshold will be, in order to cope with the risk of missed fault reports in harsh environments.

[0077] Finally, the preset fault threshold is adjusted based on the material correction coefficient and the environmental correction coefficient to obtain the adaptive threshold.

[0078] In some embodiments, the formula for determining the adaptive threshold is as follows: In the formula, The adjusted adaptive threshold, A preset fault threshold is set (an example value of 0.85, which can be adjusted within the range of 0.8-0.95 according to actual data and scenarios). This is the material correction factor. This is an environmental correction factor. Furthermore, to ensure the threshold is not too low, leading to frequent false alarms, [the following is used]: The function performs a maximum value operation and sets the lower limit of the adaptive threshold (0.8), which ensures that the adaptive threshold is not lower than 0.8, thus avoiding unnecessary alarms caused by excessively low thresholds due to extreme environments or severe degradation.

[0079] Understandably, through the above process, the fault judgment threshold is adaptively adjusted, so that the fault judgment standard can dynamically change according to the material aging degree of the contact and the real-time environmental conditions. This avoids missed judgments due to material deterioration or harsh environment, and also prevents misjudgments due to overly strict standards.

[0080] S205. If the overall probability is greater than the adaptive threshold, it is determined that there is an adhesion fault in the connector contacts.

[0081] As one possible implementation, the comprehensive probability of contact adhesion failure determined in step S203 above and the adaptive threshold determined in step S204 above are compared. If the comprehensive probability is greater than the adaptive threshold, it is determined that the connector contact has an adhesion failure, and the corresponding alarm or recording mechanism is triggered; otherwise, if the comprehensive probability is less than or equal to the adaptive threshold, it is determined that the connector contact is in a normal state, and monitoring continues in the next operating cycle.

[0082] In some embodiments, after determining that the connector contacts have an adhesion fault, the dominant fault dimension is further determined based on the numerical relationship between the probabilities of electrical timing coupling faults, electrical material coupling faults, and electromechanical coupling faults. Specifically, the magnitudes of the probabilities of electrical timing coupling faults, electrical material coupling faults, and electromechanical coupling faults are compared, and the fault index dimension corresponding to the maximum value is determined as the dominant fault dimension. For example, if the electrical material coupling fault probability is the highest, it indicates that the fault is mainly related to contact material degradation and thermoelectric effects, and the dominant fault dimension is electrical-material synergistic anomaly; if the electromechanical coupling fault probability is the highest, it indicates that the fault is mainly related to mechanical wear, insertion / extraction force attenuation, or vibration interference, and the dominant fault dimension is electro-mechanical synergistic anomaly; if the electrical timing coupling fault probability is the highest, it indicates that the fault mainly manifests as abnormal current attenuation, which may be directly related to physical adhesion of the contacts, and the dominant fault dimension is electrical timing anomaly.

[0083] After determining the dominant fault dimension, further detailed analysis of the fault causes is conducted based on the data corresponding to that dominant fault dimension. For example, when the dominant fault dimension is an electrical-material coordination anomaly, the focus can be on analyzing parameters such as average contact temperature, temperature rise, and resistance rise coefficient to determine whether the excessive contact temperature rise is caused by long-term overload, poor heat dissipation, or material aging. When the dominant fault dimension is an electrical-mechanical coordination anomaly, the focus can be on analyzing parameters such as measured peak insertion and extraction force, insertion and extraction force attenuation coefficient, and maximum vibration frequency to determine whether the decreased contact reliability is caused by mechanical wear, poor insertion, or external vibration. When the dominant fault dimension is an electrical timing anomaly, the focus can be on analyzing the current curves before and after the disconnection command to determine whether the inability to disconnect normally is caused by contact welding or mechanical jamming.

[0084] The above-described fault location process can not only accurately detect contact adhesion faults, but also provide maintenance personnel with clear fault guidance, making it easier to quickly develop targeted repair solutions, such as replacing aging contacts, adjusting mechanical structures, improving the operating environment, or strengthening heat dissipation measures, thereby significantly reducing fault diagnosis time and repair costs.

[0085] Understandably, in the charging pile connector contact adhesion fault detection method provided in this embodiment of the invention, a comprehensive perception of the contact operation status is achieved by collecting multi-dimensional data related to the operation of the charging pile connector. Then, based on the multi-dimensional data, the probabilities of electrical timing coupling faults, electrical material coupling faults, and electromechanical coupling faults are determined respectively. The contact adhesion fault is decoupled and analyzed from three dimensions: electrical response characteristics, thermoelectric effect risk, and mechanical structure reliability. This effectively distinguishes non-fault interference such as power grid fluctuations and sensor anomalies, avoiding misjudgments caused by single-parameter detection. By fusing multiple contact adhesion fault indicators to obtain the comprehensive probability of contact adhesion faults, multi-dimensional information is realized. The organic integration of these technologies makes fault diagnosis more comprehensive and reliable. Simultaneously, by adjusting the preset fault threshold based on material performance data and environmental data, an adaptive threshold is obtained. This allows the judgment criteria to dynamically change with the degree of contact material degradation and real-time environmental conditions. When materials are severely aged or the environment is harsh, the detection sensitivity is automatically increased to reduce the risk of missed detections. When materials are healthy and the environment is favorable, the judgment threshold is appropriately increased to avoid false alarms. Finally, when the overall probability exceeds the adaptive threshold, the presence of a connector contact adhesion fault is determined. This achieves a synergistic improvement in detection accuracy and environmental adaptability, significantly enhancing the reliability and robustness of charging pile connector contact adhesion fault detection, and providing strong support for the safe and stable operation of charging piles.

[0086] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0087] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for detecting contact adhesion faults in charging pile connectors, characterized in that, The method includes: Collect multi-dimensional data related to the operation of the charging pile connector, including electrical data, mechanical structure data, material performance data, and environmental data of the environment in which the connector is located. Based on the multidimensional data, multiple contact adhesion fault indicators are determined, including electrical timing coupling fault probability, electrical material coupling fault probability, and electromechanical coupling fault probability. Based on the multiple contact adhesion failure indicators, determine the overall probability of contact adhesion failure; Based on the material performance data and the environmental data, the preset fault threshold is adjusted to obtain an adaptive threshold. If the overall probability is greater than the adaptive threshold, it is determined that there is an adhesion fault in the connector contacts.

2. The method for detecting contact adhesion faults in charging pile connectors according to claim 1, characterized in that, Determining the electrical timing coupling fault probability includes: Obtain the steady-state current before the connector is disconnected from the electrical data, and the instantaneous current at a preset time after disconnection; Based on the steady-state current and the instantaneous current, a current decay index is determined, which is used to characterize the current decay rate after the connector is disconnected from the electric vehicle. The electrical timing coupling failure probability is determined based on the current decay index, and the electrical timing coupling failure probability is negatively correlated with the current decay index.

3. The method for detecting contact adhesion faults in charging pile connectors according to claim 1, characterized in that, Determining the probability of electrical material coupling failure includes: Based on the electrical data, including real-time current and contact resistance, determine the average current and average contact resistance. The average temperature of the contact points is determined based on the real-time contact temperature included in the material performance data. The average ambient temperature is determined based on the real-time ambient temperature included in the environmental data. The thermoelectric risk index is determined based on the average current, the rated current of the charging pile, the average temperature of the contact, the average ambient temperature, the average resistance of the contact, and the initial resistance of the contact. The thermoelectric risk index is used to characterize the degree of risk of contact adhesion failure due to thermoelectric effect. The probability of electrical material coupling failure is determined based on the thermoelectric risk index, and the probability of electrical material coupling failure is positively correlated with the thermoelectric risk index.

4. The method for detecting contact adhesion faults in charging pile connectors according to claim 1, characterized in that, Determining the probability of the electromechanical coupling fault includes: Obtain the preset peak insertion and extraction force of the connector, which is used to characterize the mechanical engagement characteristics of the connector in a healthy state; The measured peak insertion and extraction force of the connector is obtained from the mechanical structure data; The maximum vibration frequency is determined from the real-time vibration data included in the mechanical structure data; Based on the contact resistance included in the electrical data, determine the average contact resistance; Based on the preset peak insertion and extraction force, the measured peak insertion and extraction force, the average contact resistance, the initial contact resistance, the maximum vibration frequency, and the preset vibration frequency threshold, the contact reliability index of the connector contact is determined. The contact reliability index is used to characterize the degree of influence of abnormal mechanical structure of the connector on the contact state of the contact. The probability of electromechanical coupling failure is determined based on the contact reliability index, and the probability of electromechanical coupling failure is negatively correlated with the contact reliability index.

5. The method for detecting contact adhesion faults in charging pile connectors according to claim 1, characterized in that, Based on the multiple contact adhesion failure indicators, the overall probability of contact adhesion failure is determined, including: The electrical-material coupling coefficient is determined based on the degree of correlation between electrical data and material performance data in historical data. The electromechanical coupling coefficient is determined based on the degree of correlation between electrical data and mechanical structure data in historical data. Based on the electrical material coupling coefficient and the electromechanical coupling coefficient, determine multiple weighting coefficients that correspond one-to-one with the electrical timing coupling failure probability, the electrical material coupling failure probability, and the electromechanical coupling failure probability; The combined probability is obtained by fusing the electrical timing coupling failure probability, the electrical material coupling failure probability, and the electromechanical coupling failure probability based on the multiple weighting coefficients.

6. The method for detecting contact adhesion faults in charging pile connectors according to claim 5, characterized in that, Determining the electrical material coupling coefficient includes: Multiple sets of operational samples are extracted from historical data. Each set of operational samples includes a subset of electrical data, a subset of material performance data, and a subset of mechanical structure data within the same operating period. For each set of operating samples, determine the first correlation coefficient between the electrical data subset and the material performance data subset; The electrical material coupling coefficient is determined based on the first correlation coefficient corresponding to each set of operating samples and the historical fault markers corresponding to each set of operating samples.

7. The method for detecting contact adhesion faults in charging pile connectors according to claim 6, characterized in that, Determining the electromechanical coupling coefficient includes: For each set of operating samples, determine a second correlation coefficient between the electrical data subset and the mechanical structure data subset; The electromechanical coupling coefficient is determined based on the second correlation coefficient corresponding to each set of operating samples and the historical fault markers corresponding to each set of operating samples.

8. The method for detecting contact adhesion faults in charging pile connectors according to claim 1, characterized in that, Based on the material performance data and the environmental data, the preset fault threshold is adjusted to obtain an adaptive threshold, including: Based on the heat resistance coefficient and resistivity of the contact material in the material performance data, determine the material correction coefficient; Based on the temperature, humidity, and dust concentration in the environmental data, determine the environmental correction factor; The preset fault threshold is adjusted based on the material correction coefficient and the environmental correction coefficient to obtain the adaptive threshold.

9. The method for detecting contact adhesion faults in charging pile connectors according to claim 1, characterized in that, The method further includes: In the case where it is determined that there is an adhesion fault at the connector contact, the dominant fault dimension is determined based on the numerical relationship between the electrical timing coupling fault probability, the electrical material coupling fault probability, and the electromechanical coupling fault probability. The cause of the failure is determined based on the data corresponding to the dominant failure dimension.

10. A charging pile connector contact adhesion fault detection system, characterized in that, include: The data acquisition unit is used to collect multi-dimensional data related to the operation of the charging pile connector. The multi-dimensional data includes the connector's electrical data, mechanical structure data, material performance data, and environmental data of the environment in which the connector is located. The indicator determination unit is used to determine multiple contact adhesion fault indicators based on the multidimensional data. The multiple contact adhesion fault indicators include electrical timing coupling fault probability, electrical material coupling fault probability, and electromechanical coupling fault probability. The probability determination unit is used to determine the comprehensive probability of contact adhesion failure based on the multiple contact adhesion failure indicators. A threshold adjustment unit is used to adjust a preset fault threshold according to the material performance data and the environmental data to obtain an adaptive threshold. The fault determination unit is used to determine that there is an adhesion fault in the connector contacts when the overall probability is greater than the adaptive threshold.

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