A charging gun detection method and system for new energy electric vehicles

Abstract

The present invention relates to the field of electric vehicle charging gun detection, and specifically to a method and system for detecting charging guns of new energy electric vehicles. A charging gun detection system for new energy electric vehicles, comprising: a correlation index analysis module, a test necessity analysis module and a test adjustment module. The present invention realizes the dynamic optimization of the detection strategy by quantifying the correlation between multi-dimensional parameters such as environment and usage intensity and fault type in real time, breaking through the limitations of static cycles and fixed standards in traditional detection; based on the coupling analysis of degradation index and fault risk value, the system can adaptively identify high-risk working conditions, trigger the shortening of local detection cycles and the graded increase of test standards. This dynamic response mechanism not only avoids redundant detection in low-risk scenarios, but also can accurately strengthen monitoring in the early stage of key fault hazards, fundamentally balancing safety protection and detection resource efficiency.

Description

Technical Field

[0001] The present invention relates to the field of electric vehicle charging gun detection, and in particular to a method and system for detecting a charging gun of a new energy electric vehicle. Background Art

[0002] With the popularization of new energy electric vehicles, the reliability of charging guns, as the core components of power transmission, is directly related to charging safety and user experience. In actual use, charging guns are exposed to complex environments for a long time, which can easily cause faults such as contact corrosion, insulation aging, and arc damage. In severe cases, it may even lead to equipment damage or safety accidents. Traditional detection methods usually perform standard test items at a fixed cycle, but this model has significant defects: on the one hand, the fixed cycle cannot adapt to the actual degradation rate under different usage environments and working conditions, and the monitoring efficiency is low. For example, some high-intensity usage scenarios require more intensive testing, while low-risk environments may have excessive testing. On the other hand, the test standards are static and cannot be dynamically adjusted based on the real-time status of the equipment, resulting in insufficient detection sensitivity.

[0003] In the existing technology, although some solutions attempt to trigger detection alarms through a single environmental parameter, their fault prediction models often lack the ability to couple and analyze multiple parameters. For example, the combined effect of salt spray concentration and humidity will accelerate contact corrosion, and the frequency of rough plugging and unplugging is highly correlated with the risk of mechanical interlock failure. However, existing methods have not established a quantitative mapping relationship between such multi-dimensional monitoring parameters and specific fault types, resulting in insufficient risk assessment accuracy. In addition, the adjustment of detection cycles and test standards mostly relies on manual experience, which is highly subjective and lacks the support of dynamic optimization algorithms, making it difficult to respond to equipment degradation trends in a timely manner. For example, in areas with large temperature differences between day and night, the expansion and contraction of insulating materials may lead to poor contact, but traditional methods do not combine such environmental characteristics to dynamically shorten detection cycles or improve test standards.

[0004] Therefore, the present invention proposes a charging gun detection method and system for new energy electric vehicles, which can realize adaptive adjustment of the test cycle and precise classification of the test standards, and effectively solve the above technical bottlenecks. Summary of the Invention

[0005] The present invention realizes dynamic optimization of detection strategies by quantifying the correlation between multi-dimensional parameters such as environment and usage intensity and fault types in real time, breaking through the limitations of static cycles and fixed standards in traditional detection; based on the coupled analysis of degradation index and fault risk value, the system can adaptively identify high-risk working conditions, triggering the shortening of local detection cycles and the graded increase of test standards. This dynamic response mechanism not only avoids redundant detection in low-risk scenarios, but also can accurately strengthen monitoring in the early stages of critical fault hazards, fundamentally balancing safety protection and detection resource efficiency.

[0006] A method for detecting a charging gun of a new energy electric vehicle, comprising:

[0007] Calculate the correlation index between all daily monitoring parameters and various charging gun fault types. Daily monitoring parameters include temperature, humidity, salt spray concentration, day and night temperature difference, number of charging gun plug-in and unplugging, and number of times the charging gun is roughly unplugged. Fault types include contact corrosion, insulation aging, poor contact, uncontrolled temperature rise, mechanical interlock failure, arc damage, and excessive electromagnetic interference.

[0008] Set decision update time points, with the time interval between each two decision update time points being t. At any decision update time point, calculate the degradation index of each daily monitoring parameter based on the average daily temperature, average daily humidity, average daily salt spray concentration, average day and night temperature difference, average daily plugging and unplugging times, and average daily rough unplugging times within the previous t time periods. Calculate the risk value of all fault types based on the degradation index of each daily monitoring parameter, and obtain the necessity score for the charging gun's electrical performance test, protection function test, and electromagnetic compatibility test, respectively.

[0009] Obtain the comprehensive necessity score of the electrical performance test, protection function test, and electromagnetic compatibility test at the current decision update time point; for any one of the electrical performance test, protection function test, and electromagnetic compatibility test, calculate the ideal execution cycle of the test based on the comprehensive importance score of the test, and use the ideal execution cycle to determine the execution time of the test; before executing the test, calculate the test standard adjustment coefficient, and use the test standard adjustment coefficient to increase the test standard of the test.

[0010] Preferably, the correlation index between all daily monitoring parameters and each fault type of the charging gun is calculated. The specific operation is as follows:

[0011] Get charging gun failure samples, each of which includes the average daily temperature, average daily humidity, average daily salt spray concentration, average day and night temperature difference, average daily plug-in and unplug times, average daily rough plug-out times, and the fault type code of the charging gun within t time before the failure. Among them, rough draw means that the user forcibly draws the gun when the charging progress is less than 80%; =1, 2, …, 7; to The codes for contact corrosion, insulation aging, poor contact, temperature rise out of control, mechanical interlock failure, arc damage, and excessive electromagnetic interference are represented in sequence. For any fault type, if the fault type occurs, the corresponding code is 1; otherwise, it is 0.

[0012] based on Daily monitoring parameters of charging gun failure samples and fault type code , =1, 2, ..., ; =1, 2, …, 6; to Corresponding to temperature, humidity, salt spray concentration, day and night temperature difference, number of times the charging gun is plugged in and out, and number of times the gun is roughly pulled out; the Pearson correlation coefficient is used to calculate the correlation index between each daily monitoring parameter and each fault type .

[0013] Preferably, the degradation index of each daily monitoring parameter is calculated, and the specific operations are as follows:

[0014] Based on the average daily temperature , using the formula Calculate the degradation index of the obtained temperature ,in, is the maximum deviation threshold, indicating the average temperature of the day Lower than or higher When the temperature degradation index is equal to 1;

[0015] Based on the average daily humidity , using the formula Calculate the humidity degradation index ,in, is the safe humidity threshold; is the maximum limit humidity threshold;

[0016] Based on the obtained daily average salt spray concentration , using the formula Calculate the degradation index of salt spray concentration ,in, is the preset salt spray concentration threshold;

[0017] Based on the average day and night temperature difference , using the formula Calculate the degradation index of the day and night temperature difference ,in, is the safe temperature difference threshold; is the maximum temperature difference threshold;

[0018] Based on the average daily plug-in and unplugging times obtained , using the formula Calculate the degradation index of the average daily plug-in and unplug times ,in, is the preset maximum number of times threshold;

[0019] Based on the average daily number of rough draws , using the formula Calculate the degradation index of the average number of rough gun draws per day .

[0020] Preferably, the risk values ​​of all fault types are calculated, and the necessity scores of the electrical performance test, protection function test, and electromagnetic compatibility test of the charging gun are obtained respectively. The specific operations are as follows:

[0021] Using the formula Calculate the The daily monitoring parameters are The weight value of each fault type ;

[0022] Using the formula Calculate the risk value of each fault type; 、 、 and The maximum value among the above is used as the necessity score of electrical performance test; and The maximum value among the two is used as the necessity score of the protection function test; and The maximum value among them is used as the necessity score of electromagnetic compatibility test.

[0023] Preferably, the comprehensive necessity scores of the electrical performance test, the protection function test, and the electromagnetic compatibility test at the current decision update time point are obtained. The specific operations are as follows:

[0024] At any decision update time point, for any one of the electrical performance test, protection function test, and electromagnetic compatibility test, based on the n necessity scores of the test obtained in the most recent nt time, calculate the average of the n necessity scores of the test to obtain the comprehensive necessity score of the test at the current decision update time point. .

[0025] Preferably, for any one of the electrical performance test, the protection function test, and the electromagnetic compatibility test, the ideal execution cycle of the test is calculated based on the comprehensive importance score of the test, and the execution time of the test is determined using the ideal execution cycle. The specific operation is as follows:

[0026] At any decision update time point, for any of the electrical performance test, protection function test and electromagnetic compatibility test, the shortest execution cycle preset for the test and the longest execution cycle , using the formula Calculate the ideal execution cycle for this test ; Get the time from the last execution of the test to the present , the ideal execution cycle obtained by the application Determines the next execution time of the test. , then execute the test immediately; if and , then in Execute the test after time; if and , no operation.

[0027] Preferably, the test standard adjustment coefficient is calculated as follows:

[0028] For any of the electrical performance test, protection function test, and electromagnetic compatibility test, before the test is performed, the comprehensive necessity score of the test at the most recent decision update time point , using the formula Calculate the test standard adjustment factor ,in, , The maximum preset adjustment ratio.

[0029] A charging gun detection system for new energy electric vehicles, comprising:

[0030] Correlation index analysis module, used to calculate the correlation index of all daily monitoring parameters and various fault types of charging guns;

[0031] The test necessity analysis module includes a monitoring parameter acquisition unit, a degradation index calculation unit, a fault risk calculation unit, and a necessity score acquisition unit. The monitoring parameter acquisition unit is used to collect all daily monitoring parameters. The degradation index calculation unit is used to calculate the degradation index of each daily monitoring parameter. The fault risk calculation unit is used to calculate the risk value of all fault types based on the degradation index of each daily monitoring parameter. The necessity score acquisition unit is used to obtain the necessity scores of the charging gun's electrical performance test, protection function test, and electromagnetic compatibility test respectively.

[0032] The test adjustment module includes a test cycle adjustment unit and a test standard adjustment unit; the test cycle adjustment unit is used to obtain the comprehensive necessity score of the electrical performance test, protection function test and electromagnetic compatibility test at the current decision update time point, and calculate the ideal execution cycle of each test, and apply the ideal execution cycle to determine the execution time of each test; the test standard adjustment unit is used to calculate the test standard adjustment coefficient for any test before the execution of the test, and apply the test standard adjustment coefficient to increase the test standard of the test.

[0033] The present invention has the following advantages:

[0034] 1. The present invention achieves dynamic optimization of detection strategies by quantifying the correlation between multi-dimensional parameters such as environment and usage intensity and fault types in real time, breaking through the limitations of static cycles and fixed standards in traditional detection. Based on the coupled analysis of degradation index and fault risk value, the system can adaptively identify high-risk working conditions, triggering the shortening of local detection cycles and the graded increase of test standards. This dynamic response mechanism not only avoids redundant detection in low-risk scenarios, but also can accurately strengthen monitoring in the early stages of critical fault hazards, fundamentally balancing safety protection and detection resource efficiency.

[0035] 2. The present invention constructs a mapping network between the multi-dimensional monitoring parameters of the charging gun and complex failure modes, solving the one-sidedness problem of traditional single-parameter threshold alarms from the physical mechanism level; by establishing a quantitative weight relationship between environmental factors, operating behaviors and specific fault types such as contact corrosion and mechanical interlock failure, the system can analyze the superimposed effect of complex factors on equipment degradation, thereby improving the logical completeness of the overall risk assessment; and realizing predictive detection and control of faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a schematic diagram of the structure of the charging gun detection system for new energy electric vehicles used in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0038] Example 1, a method for detecting a charging gun of a new energy electric vehicle, comprising:

[0039] The correlation index between all daily monitoring parameters and various charging gun fault types is calculated. Daily monitoring parameters include temperature, humidity, salt spray concentration, diurnal temperature difference, number of charging gun plug-in and unplugging, and number of rough plug-in removals. Fault types include contact corrosion, insulation aging, poor contact, uncontrolled temperature rise, mechanical interlock failure, arc damage, and excessive electromagnetic interference. Charging gun failures are essentially the result of the coupling of multiple factors. For example, contact corrosion may be dominated by salt spray concentration but exacerbated by humidity, while mechanical interlock failure is highly dependent on the historical accumulation of rough plug-in and unplugging behaviors. Traditional methods only set fixed thresholds for single parameters and cannot capture the potential risks of synergistic degradation of multiple parameters. Therefore, the present invention calculates the correlation index between all monitoring parameters and fault types to reveal the dynamic contribution weight of each parameter to different faults. For example, the strong correlation between salt spray concentration and contact corrosion indicates that it needs to be included as a core indicator in risk assessment. The cross-influence of diurnal temperature difference on insulation aging and poor contact (such as differences in material expansion coefficients) can only be identified through multi-parameter coupling analysis. This systematic modeling achieves a detailed deconstruction of fault causes and improves the scientific and comprehensive nature of risk identification.

[0040] Set the decision update time point. The time interval between each two decision update time points is t, which can be manually set to 1-4 weeks and adjusted according to actual needs. In coastal areas with severe salt fog fluctuations or high frequency of charging station use, t can be shortened to 1 week to quickly respond to environmental changes. In scenarios with stable climate and low usage intensity, t can be extended to 1 month to reduce the calculation frequency while ensuring risk coverage. At any decision update time point, based on the average daily temperature, average daily humidity, average daily salt fog concentration, average day and night temperature difference, average daily plugging and unplugging times, and average daily rough unplugging times in the previous t time period, the degradation index of each daily monitoring parameter is calculated. The degradation index reflects the cumulative degree of deviation of the parameter from the appropriate range. Based on the degradation index of each daily monitoring parameter, the risk value of all fault types is calculated, and the necessity score of the electrical performance test, protection function test, and electromagnetic compatibility test of the charging gun is obtained separately. This design avoids the interference of single instantaneous data noise through periodic data backtracking and dynamic modeling, and realizes the synchronous optimization of detection strategy and actual degradation process.

[0041] Obtain the comprehensive necessity score of electrical performance test, protection function test and electromagnetic compatibility test at the current decision update time point; for any one of the electrical performance test, protection function test and electromagnetic compatibility test, calculate the ideal execution cycle of the test based on the comprehensive importance score of the test, and use the ideal execution cycle to determine the execution time of the test; before the test is executed, calculate the test standard adjustment coefficient, and use the test standard adjustment coefficient to increase the test standard of the test. For example: when the coefficient is 1.2, the electrical performance temperature rise limit is reduced from 85℃ to 71℃, and the contact resistance tolerance range is compressed by 20%; the pass coefficient is consistent with the standard. Accurate linear / nonlinear mapping can achieve intelligent adaptation of risk stratification response and detection accuracy under the premise of ensuring safety. The core design concept here is that the standard is strict when the working conditions are good. The logic is that when the charging gun is in good working conditions, the system determines that the current environment has less potential degradation pressure on the equipment. At this time, actively raising the test standard is essentially a proactive self-inspection reinforcement strategy. Because under good working conditions, the equipment is in a "stable state" and the risk of surface failure is low, but micro-corrosion or material fatigue may accumulate at an extremely slow rate. By raising the standard, such micro-degradation trends that are "hidden" under conventional detection standards can be exposed in advance to avoid accumulation into sudden failures.

[0042] Calculate the correlation index between all daily monitoring parameters and various charging gun fault types. The specific operations are as follows:

[0043] Get charging gun failure samples, each of which includes the average daily temperature, average daily humidity, average daily salt spray concentration, average day and night temperature difference, average daily plug-in and unplug times, average daily rough plug-out times, and the fault type code of the charging gun within t time before the failure. Among them, rough draw means that the user forcibly draws the gun when the charging progress is less than 80%; =1, 2, …, 7; to The codes for contact corrosion, insulation aging, poor contact, temperature rise out of control, mechanical interlock failure, arc damage, and excessive electromagnetic interference are represented in sequence. For any fault type, if the fault type occurs, the corresponding code is 1; otherwise, it is 0.

[0044] based on Daily monitoring parameters of charging gun failure samples and fault type code , =1, 2, ..., ; =1, 2, …, 6; to Corresponding to temperature, humidity, salt spray concentration, day and night temperature difference, number of times the charging gun is plugged in and out, and number of times the gun is roughly pulled out; using the formula Calculate the correlation index between temperature, humidity, salt spray concentration, day and night temperature difference, number of times the charging gun is plugged in and out, and number of times the gun is pulled out roughly and each fault type ,in, For daily monitoring parameters exist The average value of the charging gun failure samples; Code the fault type exist The average value of the charging gun failure samples.

[0045] Calculate the degradation index of each daily monitoring parameter. The specific operations are as follows:

[0046] Based on the average daily temperature , using the formula Calculate the degradation index of the obtained temperature ,in, is the maximum deviation threshold, indicating the average temperature of the day Lower than or higher When the temperature degradation index is equal to 1; Set by experts based on experience;

[0047] Based on the average daily humidity , using the formula Calculate the humidity degradation index ,in, is the safe humidity threshold; is the maximum limit humidity threshold; and The default values ​​are 55% and 85% respectively, which can be adjusted by experts based on experience;

[0048] Based on the obtained daily average salt spray concentration , using the formula Calculate the degradation index of salt spray concentration ,in, The preset salt spray concentration threshold can be set to 1mg / m 3 ;

[0049] Based on the average day and night temperature difference , using the formula Calculate the degradation index of the day and night temperature difference ,in, is the safe temperature difference threshold; is the maximum temperature difference threshold; and The default values ​​are 5℃ and 20℃ respectively, which can also be adjusted by experts based on experience;

[0050] Based on the average daily plug-in and unplugging times obtained , using the formula Calculate the degradation index of the average daily plug-in and unplug times ,in, is the preset maximum number of times threshold;

[0051] Based on the average daily number of rough draws , using the formula Calculate the degradation index of the average number of rough gun draws per day .

[0052] Calculate the risk values ​​for all fault types and obtain the necessity scores for the charging gun's electrical performance test, protection function test, and electromagnetic compatibility test. The specific operations are as follows:

[0053] Using the formula Calculate the The daily monitoring parameters are The weight value of each fault type ;

[0054] Using the formula Calculate the risk value of each fault type; 、 、 and The maximum value among the above is used as the necessity score of electrical performance test; and The maximum value among the two is used as the necessity score of the protection function test; and The maximum value among them is used as the necessity score of electromagnetic compatibility test.

[0055] Obtain the comprehensive necessity score of electrical performance test, protection function test, and electromagnetic compatibility test at the current decision update time point. The specific operations are as follows:

[0056] At any decision update time point, for any one of the electrical performance test, protection function test, and electromagnetic compatibility test, based on the n necessity scores of the test obtained in the most recent nt time, calculate the average of the n necessity scores of the test to obtain the comprehensive necessity score of the test at the current decision update time point. .

[0057] For any of the electrical performance test, protection function test, and electromagnetic compatibility test, the ideal execution cycle of the test is calculated based on the comprehensive importance score of the test. The ideal execution cycle is used to determine the execution time of the test. The specific operations are as follows:

[0058] At any decision update time point, for any of the electrical performance test, protection function test and electromagnetic compatibility test, the shortest execution cycle preset for the test and the longest execution cycle , using the formula Calculate the ideal execution cycle for this test ; Get the time from the last execution of the test to the present , the ideal execution cycle obtained by the application Determines the next execution time of the test. , then execute the test immediately; if and , then in Execute the test after time; if and , no operation is performed; in traditional charging gun testing, the inspection cycle of electrical performance testing is 1-3 months / time, the inspection cycle of protection function testing is 3-6 months / time, and the inspection cycle of electromagnetic compatibility testing is 6-12 months / time; the present invention dynamically quantifies environmental parameters and historical fault data to assign precise elastic cycles to the three types of tests, and covers the high-incidence period of hidden dangers through high-frequency and high-standard "targeted testing"; when the risk is low, strict but low-frequency testing is used to save costs; the hidden degradation not covered by the conventional cycle is exposed more likely through dynamic standards, realizing a "low-cost and high-density" safety line of defense.

[0059] Calculate the test standard adjustment coefficient as follows:

[0060] For any of the electrical performance test, protection function test, and electromagnetic compatibility test, before the test is performed, the comprehensive necessity score of the test at the most recent decision update time point , using the formula Calculate the test standard adjustment factor ,in, , The default value is 1.3, which can be set by experts based on experience. The larger the setting, the stricter the test standard for the charging gun. Apply the test standard adjustment coefficient , and raised the test standards for this test.

[0061] Example 2, a charging gun detection system for new energy electric vehicles, such as Figure 1 Shown, including:

[0062] The correlation index analysis module calculates the correlation index between all daily monitoring parameters and various charging gun fault types. Daily monitoring parameters include temperature, humidity, salt spray concentration, day and night temperature difference, number of charging gun plug-in and unplugging, and number of times the charging gun is roughly unplugged. Fault types include contact corrosion, insulation aging, poor contact, uncontrolled temperature rise, mechanical interlock failure, arc damage, and excessive electromagnetic interference.

[0063] The test necessity analysis module includes a monitoring parameter collection unit, a degradation index calculation unit, a fault risk calculation unit, and a necessity score acquisition unit; the monitoring parameter collection unit is used to collect all daily monitoring parameters; the degradation index calculation unit is used to calculate the degradation index of each daily monitoring parameter based on the average daily temperature, average daily humidity, average daily salt spray concentration, average day and night temperature difference, average daily plugging and unplugging times, and average daily rough unplugging times within the previous t time at any decision update time point; the fault risk calculation unit is used to calculate the risk value of all fault types based on the degradation index of each daily monitoring parameter; the necessity score acquisition unit is used to obtain the necessity scores of the electrical performance test, protection function test, and electromagnetic compatibility test of the charging gun respectively;

[0064] The test adjustment module includes a test cycle adjustment unit and a test standard adjustment unit; the test cycle adjustment unit is used to obtain the comprehensive necessity score of the electrical performance test, protection function test and electromagnetic compatibility test at the current decision update time point; for any one of the electrical performance test, protection function test and electromagnetic compatibility test, based on the comprehensive importance score of the test, the ideal execution cycle of the test is calculated, and the execution time of the test is determined by applying the ideal execution cycle; the test standard adjustment unit is used to calculate the test standard adjustment coefficient for the test before any test is executed, and apply the test standard adjustment coefficient to increase the test standard of the test.

[0065] It should be understood that those skilled in the art may make improvements or modifications based on the above description, and all such improvements and modifications shall fall within the scope of protection of the appended claims. Any portion of this specification not described in detail is prior art known to those skilled in the art.

Claims

1. A method for detecting a charging gun of a new energy electric vehicle, characterized in that: include: Calculate the correlation index between all daily monitoring parameters and various charging gun fault types. Daily monitoring parameters include temperature, humidity, salt spray concentration, day and night temperature difference, number of charging gun plug-in and unplugging, and number of times the charging gun is roughly unplugged. Fault types include contact corrosion, insulation aging, poor contact, uncontrolled temperature rise, mechanical interlock failure, arc damage, and excessive electromagnetic interference. Set decision update time points, with the time interval between each two decision update time points being t. At any decision update time point, calculate the degradation index of each daily monitoring parameter based on the average daily temperature, average daily humidity, average daily salt spray concentration, average day and night temperature difference, average daily plugging and unplugging times, and average daily rough unplugging times within the previous t time periods. Calculate the risk value of all fault types based on the degradation index of each daily monitoring parameter, and obtain the necessity score for the charging gun's electrical performance test, protection function test, and electromagnetic compatibility test, respectively. Obtain the comprehensive necessity score of the electrical performance test, protection function test, and electromagnetic compatibility test at the current decision update time point; for any one of the electrical performance test, protection function test, and electromagnetic compatibility test, calculate the ideal execution cycle of the test based on the comprehensive importance score of the test, and use the ideal execution cycle to determine the execution time of the test; before executing the test, calculate the test standard adjustment coefficient, and use the test standard adjustment coefficient to increase the test standard of the test; Calculate the correlation index between all daily monitoring parameters and various charging gun fault types. The specific operations are as follows: Get charging gun failure samples, each of which includes the average daily temperature, average daily humidity, average daily salt spray concentration, average day and night temperature difference, average daily plug-in and unplug times, average daily rough plug-out times, and the fault type code of the charging gun within t time before the failure. Among them, rough draw means that the user forcibly draws the gun when the charging progress is less than 80%; =1, 2, …, 7; to The codes for contact corrosion, insulation aging, poor contact, temperature rise out of control, mechanical interlock failure, arc damage, and excessive electromagnetic interference are represented in sequence. For any fault type, if the fault type occurs, the corresponding code is 1; otherwise, it is 0. based on Daily monitoring parameters of charging gun failure samples and fault type code , =1, 2, ..., ; =1, 2, …, 6; to Corresponding to temperature, humidity, salt spray concentration, day and night temperature difference, number of times the charging gun is plugged in and out, and number of times the gun is roughly pulled out; the Pearson correlation coefficient is used to calculate the correlation index between each daily monitoring parameter and each fault type ; Calculate the degradation index of each daily monitoring parameter. The specific operations are as follows: Based on the average daily temperature , using the formula Calculate the degradation index of the obtained temperature ,in, is the maximum deviation threshold, indicating the average temperature of the day Lower than or higher When the temperature degradation index is equal to 1; Based on the average daily humidity , using the formula Calculate the humidity degradation index ,in, is the safe humidity threshold; is the maximum limit humidity threshold; Based on the obtained daily average salt spray concentration , using the formula Calculate the degradation index of salt spray concentration ,in, is the preset salt spray concentration threshold; Based on the average day and night temperature difference , using the formula Calculate the degradation index of the day and night temperature difference ,in, is the safe temperature difference threshold; is the maximum temperature difference threshold; Based on the average daily plug-in and unplugging times obtained , using the formula Calculate the degradation index of the average daily plug-in and unplug times ,in, is the preset maximum number of times threshold; Based on the average daily number of rough draws , using the formula Calculate the degradation index of the average number of rough gun draws per day ; Calculate the risk values ​​for all fault types and obtain the necessity scores for the charging gun's electrical performance test, protection function test, and electromagnetic compatibility test. The specific operations are as follows: Using the formula Calculate the The daily monitoring parameters are The weight value of each fault type ; Using the formula Calculate the risk value of each fault type; 、 、 and The maximum value among the above is used as the necessity score of electrical performance test; and The maximum value among the two is used as the necessity score of the protection function test; and The maximum value among them is used as the necessity score of electromagnetic compatibility test; Obtain the comprehensive necessity score of electrical performance test, protection function test, and electromagnetic compatibility test at the current decision update time point. The specific operations are as follows: At any decision update time point, for any one of the electrical performance test, protection function test, and electromagnetic compatibility test, based on the n necessity scores of the test obtained in the most recent nt time, calculate the average of the n necessity scores of the test to obtain the comprehensive necessity score of the test at the current decision update time point. ; For any of the electrical performance test, protection function test, and electromagnetic compatibility test, the ideal execution cycle of the test is calculated based on the comprehensive importance score of the test. The ideal execution cycle is used to determine the execution time of the test. The specific operations are as follows: At any decision update time point, for any of the electrical performance test, protection function test and electromagnetic compatibility test, the shortest execution cycle preset for the test and the longest execution cycle , using the formula Calculate the ideal execution cycle for this test ; Get the time from the last execution of the test to the present , the ideal execution cycle obtained by the application Determines the next execution time of the test. , then execute the test immediately; if and , then in Execute the test after time; if and , no operation; Calculate the test standard adjustment coefficient as follows: For any of the electrical performance test, protection function test, and electromagnetic compatibility test, before the test is performed, the comprehensive necessity score of the test at the most recent decision update time point , using the formula Calculate the test standard adjustment factor ,in, , The maximum preset adjustment ratio.

2. A charging gun detection system for new energy electric vehicles, characterized in that: The system is applied to the method for detecting a charging gun of a new energy electric vehicle as described in claim 1, comprising: Correlation index analysis module, used to calculate the correlation index of all daily monitoring parameters and various fault types of charging guns; The test necessity analysis module includes a monitoring parameter acquisition unit, a degradation index calculation unit, a fault risk calculation unit, and a necessity score acquisition unit. The monitoring parameter acquisition unit is used to collect all daily monitoring parameters. The degradation index calculation unit is used to calculate the degradation index of each daily monitoring parameter. The fault risk calculation unit is used to calculate the risk value of all fault types based on the degradation index of each daily monitoring parameter. The necessity score acquisition unit is used to obtain the necessity scores of the charging gun's electrical performance test, protection function test, and electromagnetic compatibility test respectively. The test adjustment module includes a test cycle adjustment unit and a test standard adjustment unit; the test cycle adjustment unit is used to obtain the comprehensive necessity score of the electrical performance test, protection function test and electromagnetic compatibility test at the current decision update time point, and calculate the ideal execution cycle of each test, and apply the ideal execution cycle to determine the execution time of each test; the test standard adjustment unit is used to calculate the test standard adjustment coefficient for any test before the execution of the test, and apply the test standard adjustment coefficient to increase the test standard of the test.

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