Method, system and equipment for monitoring contact resistance of quick connection device and medium

Abstract

The invention discloses a rapid connection device contact resistance monitoring method, system, equipment and medium, and relates to the technical field of information, and the method comprises the steps: calculating the deviation percentage of each contact pair current and the average value of all contact pair currents, and obtaining the current distribution deviation; the current deviation occurrence frequency is compared with the amplitude deviation degree, and a suspected degradation contact pair is identified; collecting the clamping pressure of the leaf spring of the deteriorated suspected contact pair to obtain a high-risk contact pair list; the contact point temperature of the high-risk contact pair is monitored, and the contact pair with the temperature rise exceeding the normal temperature difference range is determined as a degraded contact pair; and obtaining a comprehensive degradation index in combination with the long-term current fluctuation amplitude, determining a monitoring priority according to the comprehensive degradation index, and obtaining a monitoring weight distribution table. And redistributing monitoring resources according to the monitoring weight distribution table to obtain an optimized monitoring configuration scheme. According to the method, the deviation percentage, the deviation occurrence frequency and the long-term fluctuation characteristics are introduced, so that accurate judgment of the contact state from instantaneous abnormality to trend deterioration is realized.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method, system, device, and medium for monitoring the contact resistance of a quick-connect device. Background Technology

[0002] In the field of power equipment, the rapid connection device of prefabricated low-voltage generator vehicles is a crucial link in ensuring the stability and security of power supply. Monitoring its contact resistance directly affects the reliability of equipment operation and the prevention of sudden failures. Research in this area is of undeniable value in reducing the risk of power outages and improving emergency power supply capabilities, especially under high loads or emergencies, where the performance of the connection device is particularly critical. However, a common problem in current contact resistance monitoring is the difficulty in accurately capturing subtle changes at local connection points. While many methods can measure the overall resistance value, they often overlook the complex interactions between multiple contact points, leading to insufficient ability to identify certain critical issues. This limitation stems not merely from the crudeness of monitoring methods, but from the insufficient consideration of the dynamic relationships between contact points, making it difficult to detect hidden risks in a timely manner. A deeper technical challenge lies in the core factor of uneven current distribution when multiple contact points are connected in parallel. When multiple contact points share the current, theoretically each point should share a portion of the load. However, if a contact point wears down due to long-term use, its resistance value will gradually increase. At this point, the current will naturally tend to flow to other contact points with lower resistance, resulting in a significant reduction in the current load at the wear point, even approaching zero. This uneven distribution means that in the overall resistance measurement, the abnormal signal at the wear point is masked by the low resistance value of other normal contact points, making it difficult for the monitoring system to detect this potential problem. Specifically, in the actual operation of the generator car quick-connect device, assuming multiple spring plates are connected to the busbar, one spring plate may experience poor contact due to repeated clamping, causing its resistance value to increase significantly. However, since almost all the current flows to other spring plates, the overall resistance change is negligible, and the monitoring system may be completely unable to identify this hidden danger. This situation, where local degradation is masked by the overall data, not only increases the risk of failure but may also cause local overheating or even connection failure under high load. Therefore, accurately identifying the local degradation signal masked by uneven current distribution when multiple contact points are connected in parallel becomes a key issue in improving the contact resistance monitoring accuracy of the generator car quick-connect device. Summary of the Invention

[0003] In view of the above-mentioned problems, the present invention provides a method, system, device and medium for monitoring the contact resistance of a quick connection device.

[0004] Therefore, the problem to be solved by the present invention is: how to accurately identify local degradation signals that are masked by uneven current distribution when multiple contact points are connected in parallel.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for monitoring the contact resistance of a quick-connect device, comprising: acquiring the real-time current value and spring contact resistance of each spring contact pair using an independent current sensor; calculating the percentage deviation between the current of each contact pair and the average current of all contact pairs to obtain the current distribution deviation; statistically analyzing the current distribution deviation of each contact pair within a target time period to obtain the long-term current fluctuation amplitude and fluctuation duration; comparing the frequency and amplitude deviation of the current deviation to identify suspected deterioration contact pairs; acquiring the spring clamping pressure of the suspected deterioration contact pairs; identifying contact pairs with clamping pressure below a pressure threshold and marking them as high-risk contact pairs to obtain a list of high-risk contact pairs; monitoring the contact point temperature of the high-risk contact pairs; calculating the temperature rise value relative to the ambient reference temperature; and confirming the contact resistance of each contact pair. Contact pairs whose temperature rise exceeds the normal temperature difference range are identified as deteriorated contact pairs. The current carrying capacity ratio and current distribution deviation of the deteriorated contact pairs are multiplied to obtain a deterioration ratio coefficient. Combined with the long-term current fluctuation amplitude, a comprehensive deterioration index is obtained. Based on the comprehensive deterioration index, monitoring priorities are determined and the monitoring weights of each contact pair are adjusted to obtain a monitoring weight allocation table. Monitoring resources are reallocated according to this table, and the current and temperature cluster distribution of deteriorated contact pairs is evaluated to determine the proportion of deterioration signals. The monitoring frequency and warning threshold are updated based on the duration of fluctuations to obtain an optimized monitoring configuration scheme. The instantaneous current peak value and spring sheet deformation of each contact pair are collected again to calculate the actual current sharing ratio. Based on the optimized monitoring configuration scheme, the contact resistance of each contact pair is measured to obtain a contact resistance status assessment report for the quick-connect device.

[0006] As a preferred embodiment of the contact resistance monitoring method for a quick connection device according to the present invention, the method for obtaining the current distribution deviation includes: deploying a current sensor array in the busbar connection area, configuring an independent sensor unit for each spring contact pair, capturing the instantaneous current value flowing through each spring, simultaneously applying a constant test current and measuring the terminal voltage, and calculating the contact resistance value; calculating the average current of all contact pairs at each sampling moment as a reference current, calculating the percentage deviation between the actual current of each contact pair and the reference current; performing standard deviation analysis on the deviation percentage sequence, statistically analyzing the frequency and duration of abnormal deviation states, constructing a current distribution deviation evaluation matrix, and obtaining the current distribution deviation.

[0007] As a preferred embodiment of the contact resistance monitoring method for a quick connection device according to the present invention, the identification of suspected deterioration contact pairs includes: extracting the time-series data of the deviation percentage of each contact pair within a target time period from the current distribution deviation data; calculating the difference between the maximum and minimum values ​​as the long-term current fluctuation amplitude; statistically analyzing the longest interval that continuously exceeds the threshold to obtain the fluctuation duration; comparing the deviation data with the historical monitoring period data to calculate the current frequency deviation and amplitude deviation; multiplying the frequency deviation and amplitude deviation by the fluctuation duration to obtain a deterioration risk index; sorting according to the deterioration risk index; and identifying contact pairs that exceed the risk threshold as suspected deterioration contact pairs.

[0008] As a preferred embodiment of the contact resistance monitoring method for a quick connection device according to the present invention, the method for obtaining a list of high-risk contact pairs includes: based on the pressure sensor at the root of the spring sheet of the suspected deteriorated contact pair, converting the deformation resistance change into a voltage output through a Wheatstone bridge, calculating the clamping pressure value according to the pressure and voltage calibration curve; comparing the clamping pressure value with a pressure threshold, marking contact pairs with clamping pressure less than the threshold as high-risk contact pairs, and summarizing the number, pressure value and degree of deficiency to obtain a list of high-risk contact pairs.

[0009] As a preferred embodiment of the contact resistance monitoring method for a quick connection device according to the present invention, the method for determining the deteriorated contact pair includes: for the identified high-risk contact pairs, measuring the surface temperature based on an infrared temperature sensor, and simultaneously collecting the temperature of the contact pairs with normal pressure on the same busbar as the ambient reference temperature; calculating the difference between the measured temperature of each high-risk contact pair and the ambient reference temperature to obtain the temperature rise value of each contact point; comparing the temperature rise value with a preset normal operating temperature difference range, and if the temperature rise value of a certain high-risk contact pair exceeds the upper limit of the normal operating temperature difference range, it is confirmed as a deteriorated contact pair.

[0010] As a preferred embodiment of the contact resistance monitoring method for a quick connection device according to the present invention, the method for obtaining the monitoring weight allocation table includes: calculating the ratio of the actual current to the total current of the deteriorated contact pair to obtain the current carrying ratio; multiplying the current carrying ratio by the deviation percentage to obtain the deterioration ratio coefficient, and multiplying it by the long-term current fluctuation amplitude to obtain the comprehensive deterioration index; determining the priority values ​​based on the comprehensive deterioration index in descending order, adjusting the initial weights and normalizing them to obtain the monitoring weight allocation table.

[0011] The beneficial effects of this preferred technical solution are: this step can quantitatively reflect the degree of impact of different degradation contacts on the overall operation of the system, shifting the focus of monitoring from "whether degradation has occurred" to "the magnitude of the degradation impact", and providing an objective basis for the differentiated allocation of subsequent monitoring resources.

[0012] As a preferred embodiment of the contact resistance monitoring method for a quick connection device described in this invention, the optimized monitoring configuration scheme includes: reallocating the sampling frequency and bandwidth according to the monitoring weight allocation table; collecting real-time current and temperature values ​​of deteriorated contact pairs to construct a two-dimensional data point set; using a clustering algorithm to divide the data into three clusters; calculating the proportion of deteriorated signals; adjusting the sampling frequency and temperature warning limit based on the duration of historical fluctuations; and summarizing the updated monitoring frequency, warning limit, and resource quota to obtain the optimized monitoring configuration scheme.

[0013] The beneficial effects of this preferred technical solution are as follows: by dynamically adjusting the monitoring resources according to the monitoring weight allocation results, the monitoring system can prioritize high-risk contact pairs under limited resource conditions, improve the sensitivity of capturing deterioration signals, and avoid over-monitoring of stable contact pairs, thereby achieving a balance between monitoring efficiency and monitoring accuracy.

[0014] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a contact resistance monitoring system for a quick connection device, comprising: a preliminary judgment module, a degradation determination module, a solution formulation module, and an implementation module; the preliminary judgment module collects the real-time current value and spring contact resistance of each spring contact pair through independent current sensors, calculates the percentage deviation of the current of each contact pair from the average current of all contact pairs to obtain the current distribution deviation, statistically analyzes the current distribution deviation of each contact pair within a target time period to obtain the long-term current fluctuation amplitude and fluctuation duration, compares the frequency and amplitude deviation of the current deviation, and identifies suspected degradation contact pairs; the degradation determination module collects the spring clamping pressure of the suspected degradation contact pairs, identifies contact pairs with clamping pressure below a pressure threshold, marks them as high-risk contact pairs, obtains a list of high-risk contact pairs, monitors the contact point temperature of the high-risk contact pairs, and calculates... The temperature rise value relative to the ambient reference temperature is calculated, and contact pairs whose temperature rise exceeds the normal temperature difference range are identified as deteriorated contact pairs. The scheme formulation module multiplies the current carrying capacity ratio and current distribution deviation of the deteriorated contact pairs to obtain a deterioration ratio coefficient. Combined with the long-term current fluctuation amplitude, a comprehensive deterioration index is obtained. Based on the comprehensive deterioration index, the monitoring priority is determined and the monitoring weight of each contact pair is adjusted to obtain a monitoring weight allocation table. Based on the monitoring weight allocation table, monitoring resources are reallocated, the current and temperature cluster distribution of deteriorated contact pairs are evaluated, the proportion of deterioration signals is determined, and the monitoring frequency and warning threshold are updated based on the duration of fluctuations to obtain an optimized monitoring configuration scheme. The implementation module collects the instantaneous current peak value and spring sheet deformation of each contact pair again, calculates the actual current sharing ratio, and measures the contact resistance of each contact pair according to the optimized monitoring configuration scheme to obtain a contact resistance status assessment report for the quick connection device.

[0015] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of a quick connection device contact resistance monitoring method as described above.

[0016] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a method for monitoring the contact resistance of a quick connection device as described above.

[0017] The beneficial effects of this invention are as follows: By collaboratively monitoring and comprehensively analyzing the multi-dimensional characteristics such as current distribution, contact pressure, and temperature rise of each spring contact pair in a quick-connect device, this invention can effectively identify local contact degradation risks that are easily masked by traditional overall resistance monitoring methods, even when multiple contact pairs are working in parallel and current sharing is uneven. By introducing deviation percentage, deviation frequency, and long-term fluctuation characteristics, it achieves accurate judgment of contact status from instantaneous anomalies to trend degradation, and combines clamping pressure and temperature rise information to verify the physical mechanism of degradation results, thereby improving the accuracy and reliability of degradation identification. Furthermore, based on comprehensive degradation indicators, it dynamically adjusts monitoring weights and monitoring resource allocation, enabling the monitoring system to focus on high-risk contact pairs, reducing resource consumption while ensuring overall monitoring efficiency. Finally, under high load conditions, it comprehensively evaluates contact resistance, current sharing capacity, and mechanical deformation, generating a contact resistance status assessment report, providing intuitive and reliable technical basis for the safe operation, predictive maintenance, and operation and maintenance decisions of quick-connect devices. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0019] Figure 1 This is a flowchart of a method for monitoring the contact resistance of a quick connection device in Example 1. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a method for monitoring the contact resistance of a quick connection device, comprising: S1: Collect the real-time current value of each spring contact pair and the contact resistance of the spring sheet through an independent current sensor, calculate the percentage deviation of the current of each contact pair from the average current of all contact pairs, and obtain the current distribution deviation.

[0023] S2: Statistically analyze the current distribution deviation of each contact pair within the target time period to obtain the long-term current fluctuation amplitude and fluctuation duration. Compare the frequency and amplitude deviation of the current deviation to identify suspected deterioration contact pairs.

[0024] S3: Collect the spring clip clamping pressure of suspected deteriorated contact pairs, identify contact pairs with clamping pressure below the pressure threshold, mark them as high-risk contact pairs, and obtain a list of high-risk contact pairs.

[0025] S4: Monitor the temperature of high-risk contact points, calculate the temperature rise relative to the ambient reference temperature, and identify contact pairs whose temperature rise exceeds the normal temperature difference range as deteriorated contact pairs.

[0026] S5: Multiply the current carrying capacity ratio of the deteriorated contact pair by the current distribution deviation to obtain the deterioration ratio coefficient. Combine this with the long-term current fluctuation amplitude to obtain the comprehensive deterioration index. Based on the comprehensive deterioration index, determine the monitoring priority and adjust the monitoring weight of each contact pair to obtain the monitoring weight allocation table.

[0027] S6: Based on the monitoring weight allocation table, reallocate monitoring resources, evaluate the cluster distribution of current and temperature in deteriorated contacts, determine the proportion of deterioration signals, and update the monitoring frequency and early warning threshold in combination with the duration of fluctuations to obtain the optimized monitoring configuration scheme.

[0028] S7: Collect the instantaneous current peak value and spring sheet deformation of each contact pair again, calculate the actual current sharing ratio, measure the contact resistance of each contact pair according to the optimized monitoring configuration scheme, and obtain the contact resistance status assessment report of the quick connection device.

[0029] It should be noted that most existing monitoring technologies for contact resistance of quick-connect devices use overall resistance or total circuit parameters as evaluation criteria. This makes it difficult to distinguish the true working state of individual contact units when multiple contact points are working in parallel. When the contact resistance of individual contact parts increases due to wear, loosening, or elastic decay, the abnormality is often masked by the current sharing of other normal contact channels, resulting in insignificant changes in the overall measurement results and making it difficult to detect localized degradation early. At the same time, existing methods usually focus on the monitoring of single electrical parameters and lack joint analysis of mechanical and thermal characteristics directly related to contact degradation, such as clamping pressure and temperature rise. This makes it impossible to verify the causes of degradation from the perspective of physical mechanisms, leading to a high risk of misjudgment and missed judgment.

[0030] Therefore, in response to the above problems, such as Figure 1 As shown, through steps S1-S7, the current values ​​of each contact pair are collected in real time based on independent current sensors. The percentage of current distribution deviation is calculated, and combined with historical data statistical analysis, suspected deteriorated contact pairs with abnormal current fluctuations are identified. Then, the spring clamping pressure and contact point temperature changes are monitored. When the pressure is lower than the preset value and the temperature rise exceeds the normal range, it is confirmed as a deteriorated contact pair. Based on the deterioration ratio and fluctuation amplitude, the monitoring priority is determined, the monitoring weight allocation of each contact pair is dynamically adjusted, the monitoring resource allocation scheme is re-optimized, and the instantaneous current peak and spring deformation are evaluated under high load conditions. Finally, a contact resistance status assessment report is generated, realizing predictive maintenance and intelligent management of the contact performance of the quick-connect device.

[0031] Example 2, a second embodiment of the present invention, differs from the first embodiment in that: a method for monitoring the contact resistance of a quick connection device further includes, in step S1, obtaining the current distribution deviation, which includes the following steps A1-A3: A1: Deploy a current sensor array in the busbar connection area. Each spring contact pair is equipped with an independent sensor unit to capture the instantaneous current value flowing through each spring. At the same time, apply a constant test current and measure the terminal voltage to calculate the contact resistance value.

[0032] A2: Calculate the average current of all contact pairs at each sampling time as the reference current, and calculate the percentage deviation between the actual current of each contact pair and the reference current.

[0033] A3: Perform standard deviation analysis on the deviation percentage sequence, statistically analyze the frequency and duration of abnormal deviation states, construct a current distribution deviation evaluation matrix, and obtain the current distribution deviation.

[0034] Specifically, a Hall current sensor array is deployed in the busbar connection area of ​​the prefabricated low-voltage generator vehicle. Each spring contact pair is equipped with an independent sensor unit. The sensors capture the instantaneous current value flowing through each spring at a preset sampling frequency through the principle of magnetic field induction. At the same time, a constant test current is applied across each contact pair and the terminal voltage is measured. The contact resistance value between each spring and the busbar is calculated according to Ohm's law, and the time-series current data and contact resistance data of each contact pair are obtained. The arithmetic mean of the current values ​​of all contact pairs at each sampling moment is calculated based on the time-series current data as the reference current. For each contact pair, the actual current value is subtracted from the reference current, divided by the reference current, and multiplied by 100% to obtain the current deviation percentage of each contact pair at each sampling moment. If the absolute value of the deviation percentage of a contact pair exceeds a preset first threshold, the contact pair is marked as having an abnormal deviation state at that moment. A sliding window analysis is performed on the current deviation percentage sequence, with the window size set to the preset number of sampling points. The standard deviation of the deviation percentage within each window is calculated as the fluctuation index of the contact pair. The frequency and duration of abnormal deviation states are statistically analyzed. Based on the fluctuation index, abnormal deviation frequency, and duration of each contact pair, a current distribution deviation evaluation matrix containing the deviation characteristics of each contact pair is constructed to obtain the current distribution deviation.

[0035] In the prefabricated low-voltage generator vehicle quick connection device, the Hall current sensor array is deployed in a surrounding arrangement.

[0036] Specifically, each spring contact is surrounded by an independent Hall effect sensor. The sensor’s magnetic core surrounds the conductive path. When current passes through the spring, a magnetic field proportional to the current is generated in the magnetic core. The Hall element detects the magnetic field strength and converts it into a voltage signal. After passing through the signal conditioning circuit, a digital signal corresponding to the instantaneous current value is output.

[0037] In one embodiment, the contact resistance is measured by applying a constant test current across each spring contact. A test current source generates a constant current in the milliampere range, which is injected into one end of the spring contact via a first wire and flows out from the other end of the busbar via a second wire. Simultaneously, a high-impedance voltmeter is used to measure the voltage drop across the contact interface between the spring contact and the busbar. The contact resistance value is calculated using Ohm's law R=U / I. The injection point of the test current and the voltage measurement point are independent of each other, avoiding the influence of the wire resistance on the measurement results.

[0038] Preferably, the sliding window analysis method evaluates the dynamic characteristics of current deviation by setting a fixed-size time window. The window moves gradually along the time axis, moving one sampling point at a time. At each window position, the standard deviation of all deviation percentage data within the window is calculated. This standard deviation reflects the degree of fluctuation in the contact pair current deviation. At the same time, the frequency of abnormal deviation states within the window is obtained by dividing the number of times abnormal deviation states occur by the window size. The number of sampling points with consecutive abnormal deviation states is taken as the duration. These parameters together constitute the core elements of the current distribution deviation evaluation matrix. Each row of the matrix corresponds to a contact pair, and the columns store the fluctuation index, abnormal deviation frequency, and maximum duration data, respectively.

[0039] Furthermore, in step S2, identifying suspected deterioration contact pairs includes the following steps B1-B3: B1: Extract the time series data of the percentage deviation of each contact pair within the target time period from the current distribution deviation data, calculate the difference between the maximum and minimum values ​​as the long-term current fluctuation amplitude, and obtain the fluctuation duration by counting the longest interval that continuously exceeds the threshold.

[0040] B2: Compare the deviation data with those from the historical monitoring period to calculate the current frequency deviation and amplitude deviation.

[0041] B3: Multiply the frequency deviation and amplitude deviation by the duration of fluctuation to obtain the degradation risk index. Sort by degradation risk index and identify contact pairs that exceed the risk threshold as suspected degradation contact pairs.

[0042] Specifically, from the current distribution deviation data acquired in the previous stage, the time series data of the deviation percentage of each contact pair within the target time period are extracted. The difference between the maximum and minimum values ​​of all deviation percentages for each contact pair within that time period is calculated as the long-term current fluctuation amplitude. The longest period in which the deviation percentage continuously exceeds a preset threshold is statistically analyzed. The number of continuous sampling points in this period is multiplied by the sampling period to obtain the fluctuation duration, forming a fluctuation characteristic record for each contact pair. Based on the fluctuation characteristic record, historical deviation data of each contact pair within the past preset monitoring period is collected. The number of deviation occurrences for each contact pair is calculated and divided by the total number of samplings to obtain the historical deviation occurrence frequency. The mean and standard deviation of the historical deviation percentage sequence are calculated as historical benchmark values. The frequency deviation is obtained by subtracting the historical deviation occurrence frequency from the deviation occurrence frequency of the current time period. The amplitude deviation is obtained by subtracting the historical fluctuation amplitude mean from the current fluctuation amplitude and then dividing by the historical standard deviation. Degradation is determined by frequency deviation and amplitude deviation. If the frequency deviation of a contact pair is greater than a preset frequency threshold and the amplitude deviation is greater than a preset amplitude threshold, the contact pair is determined to have a degradation trend. The frequency deviation and amplitude deviation are multiplied by the duration of the fluctuation to obtain the degradation risk index. The contact pairs are sorted from high to low according to the degradation risk index, and the contact pairs with the degradation risk index exceeding the preset risk threshold are identified as suspected degradation contact pairs.

[0043] In this embodiment of the application, step B1 involves time-series analysis of the deviation percentage time-series data, segmenting the deviation percentage data of each contact pair according to a preset time window, including the following steps B111-B113: B111: The deviation percentage time-series data of each contact pair is processed by sliding segmentation according to the preset time window length. Each time window contains several consecutive sampling points. By traversing all deviation percentage data in the window, the maximum deviation value and the minimum deviation value in the window are identified, and the difference between the two is used as the local fluctuation amplitude corresponding to the time window, which is used to characterize the fluctuation strength of the current distribution deviation in the time interval.

[0044] B112: Based on the calculation of the local fluctuation amplitude of each time window, it is determined whether the percentage of deviation continuously exceeds the preset threshold. When the same contact pair is in the state of exceeding the threshold in multiple adjacent sampling points, the start sampling time and end sampling time of the continuous exceeding interval are recorded, and the fluctuation duration corresponding to the continuous exceeding interval is calculated according to the sampling period.

[0045] B113: Combining the local fluctuation amplitude results obtained from each time window with the corresponding continuous over-limit duration information, the maximum local fluctuation amplitude is selected as the long-term current fluctuation amplitude of the contact pair within the target time period, and the longest continuous over-limit interval is selected as the fluctuation duration of the contact pair, forming a fluctuation characteristic record for subsequent frequency deviation and amplitude deviation calculation.

[0046] It should be noted that the establishment of historical benchmarks involves statistical analysis of long-term monitoring data. The system establishes a historical data cache to store deviation data for each contact pair over several past monitoring periods. Each monitoring period contains a fixed number of sampling points. For each contact pair, the number of times its deviation percentage exceeds a threshold within the historical period is counted, and this number is divided by the total number of samplings to obtain the historical deviation frequency. Simultaneously, the mean and standard deviation of the relevant sequences are calculated as historical characteristic parameters for that contact pair.

[0047] In an optional implementation, for time series analysis of deviation percentage time series data, a time series analysis method based on the statistical analysis of the range of segmented time intervals can also be used, including the following steps B121-B123: B121: For the deviation percentage time series data of each contact pair within the target time period, the time series data is divided into multiple continuous statistical intervals according to the preset time interval length. Each statistical interval covers several continuous sampling points. The deviation percentage data in each statistical interval is traversed and statistically analyzed to determine the maximum deviation value and the minimum deviation value in the statistical interval. The difference between the two is used as the interval fluctuation amplitude corresponding to the statistical interval, which is used to characterize the overall fluctuation level of the current distribution deviation within the time interval.

[0048] B122: After calculating the fluctuation range of each statistical interval, it is determined whether the percentage deviation in each statistical interval is generally in a state of exceeding the preset threshold. When a certain contact pair has a percentage deviation that continuously exceeds the preset threshold in multiple adjacent statistical intervals, the adjacent statistical intervals are merged into the same continuous abnormal segment, and the fluctuation duration corresponding to the contact pair is determined according to the merged time span.

[0049] B123: By comparing and analyzing the fluctuation amplitudes of all statistical intervals, the interval fluctuation amplitude with the largest value is selected as the long-term current fluctuation amplitude of the contact pair within the target time period. At the same time, the longest time span in the continuous abnormal section is taken as the fluctuation duration of the contact pair, thus forming fluctuation characteristic data for subsequent frequency deviation and amplitude deviation calculation.

[0050] In another alternative implementation, for time series analysis of deviation percentage time series data, a time series analysis method based on deviation stability segment identification can also be used, including the following steps B131-B133: B131: Continuously scan the time series data of the deviation percentage of each contact pair within the target time period. Based on the changing trend between adjacent sampling points of the deviation percentage, identify the continuous sampling points whose deviation change amplitude is within the preset change threshold range as the same stable segment, and take the sampling points with significantly increased deviation change amplitude as the boundary of the stable segment, thereby dividing the entire time series data into multiple mutually independent deviation stable segments.

[0051] B132: For each stable deviation segment, the maximum and minimum values ​​of the deviation percentage within the segment are calculated, and the difference between the two is taken as the segment fluctuation amplitude of the stable segment. At the same time, it is determined whether the deviation percentage within the stable segment is continuously higher than the preset threshold. When the stable segment is continuously in the state of exceeding the threshold, the time length corresponding to the stable segment is taken as the candidate value of the fluctuation duration of the contact pair.

[0052] B133: Among all stable deviation segments, the fluctuation amplitude corresponding to the stable segment with the largest fluctuation amplitude is selected as the long-term current fluctuation amplitude of the contact pair. Among the stable segments that meet the threshold condition, the stable segment with the longest time length is selected as the final fluctuation duration, thereby obtaining fluctuation characteristic results equivalent to other time series analysis methods, which are used for subsequent calculation of the degradation risk index.

[0053] Preferably, both frequency deviation and amplitude deviation are calculated using the standardized difference method to ensure the comparability of deviations between different contact pairs. Frequency deviation is calculated by the standardized difference between the frequency of deviation in the current monitoring cycle and historical frequencies, while amplitude deviation is also based on standardization. When both exceed their respective thresholds, it indicates a significant change in current distribution characteristics.

[0054] In this embodiment of the application, step B3, the calculation of the degradation risk index comprehensively considers the magnitude and persistence of the deviation, including the following steps B311-B313: B311: Multiply the frequency deviation by the amplitude deviation to obtain the deviation product used to comprehensively characterize the abnormal intensity of the current distribution.

[0055] B312: Based on the deviation product, extract the duration of fluctuation corresponding to the contact pair, and multiply the deviation product by the duration of fluctuation to obtain the degradation risk index. The duration of fluctuation is used to reflect the persistence of the abnormal deviation state in the time dimension, so that short-term anomalies and long-term anomalies can be distinguished in risk assessment.

[0056] B313: Sort all contact pairs in descending order of their corresponding degradation risk indices, and identify contact pairs whose degradation risk indices exceed a preset risk threshold as suspected degradation contact pairs, thereby prioritizing contact pairs that simultaneously possess the characteristics of significant deviation, high frequency of occurrence, and long duration of abnormal state.

[0057] For example, if the deviation frequency of a certain spring contact pair in the current monitoring period is 0.35, while its historical average is 0.15, the frequency deviation is 0.2, and the fluctuation amplitude increases from 5% in the past to 15%, the amplitude deviation after standardization is 2.5, and the fluctuation duration reaches 30 sampling periods, then the deterioration risk index is 0.2×2.5×30=15. When the preset risk threshold is 10, this contact pair is identified as a suspected deterioration contact pair.

[0058] In an optional implementation, the degradation risk index can also be calculated based on a degradation risk index calculation method using deviation-weighted combination and persistence modulation, including the following steps B321-B323: B321: For each contact pair, obtain its frequency deviation and amplitude deviation, and weight the frequency deviation and amplitude deviation according to the preset importance weight. By summing the weighted frequency deviation and the weighted amplitude deviation, a comprehensive deviation index is obtained to characterize the comprehensive degree of current distribution abnormality.

[0059] B322: Combine the comprehensive deviation index with the duration of fluctuation corresponding to the contact pair. By applying duration modulation to the comprehensive deviation index, a degradation risk index is obtained, so that contact pairs with longer abnormal state durations obtain higher risk assessment results when the degree of deviation is similar.

[0060] B323: Sort the contact pairs according to their degradation risk index, and identify the contact pairs whose degradation risk index exceeds the preset risk threshold as suspected degradation contact pairs. This allows for effective differentiation and screening of degradation risks without changing the original monitoring data source.

[0061] In another alternative implementation, the degradation risk index can also be calculated based on a degradation risk index calculation method that combines deviation grading mapping and persistent accumulation, including the following steps B331-B333: B331: For each contact pair, its frequency deviation and amplitude deviation are mapped to a preset deviation level range, and the corresponding level value is determined according to the range in which the deviation is located. This is used to transform the continuously changing deviation parameter into a discrete risk characterization quantity, thereby reducing the single-point amplification effect of extreme deviation values ​​on the risk assessment results.

[0062] B332: Combine the frequency deviation level value and the amplitude deviation level value to obtain the deviation level combination value. Accumulate the deviation level combination value according to the sampling period within the target time period so that the persistence of the abnormal deviation state in the time dimension can be reflected through the cumulative result, thereby forming a degradation risk index.

[0063] B333: The degradation risk index of each contact pair is compared and ranked, and contact pairs with degradation risk indices exceeding the preset risk threshold are identified as suspected degradation contact pairs, thereby achieving effective identification of contact degradation risk caused by abnormal current distribution over a long period of time.

[0064] Furthermore, in step S3, obtaining the list of high-risk contact pairs includes the following steps C1-C2: C1: Based on the pressure sensor at the root of the spring plate, which is suspected of being deteriorated, the change in deformation resistance is converted into voltage output through a Wheatstone bridge, and the clamping pressure value is calculated according to the pressure and voltage calibration curve.

[0065] C2: Compare the clamping pressure value with the pressure threshold, mark the contact pairs with clamping pressure less than the threshold as high-risk contact pairs, and summarize the number, pressure value and degree of deficiency to obtain a list of high-risk contact pairs.

[0066] Specifically, for identified suspected deterioration contact pairs, a piezoresistive pressure sensor is installed at the root of the spring plate. The sensor senses the deformation of the spring plate when clamping the busbar through a strain gauge. The deformation causes a change in resistance, which is converted into a voltage output through a Wheatstone bridge circuit. The voltage signal is collected and calculated into the actual clamping pressure value according to a preset pressure-voltage calibration curve. The clamping pressure value is compared with a preset pressure threshold. If the clamping pressure of a suspected deterioration contact pair is less than the threshold, the contact pair is determined to have insufficient clamping force and marked as high-risk. A list of high-risk contact pairs is compiled, including the contact pair number, corresponding pressure value, and degree of pressure deficiency.

[0067] In one embodiment, the piezoresistive pressure sensor uses a metal strain gauge as the sensing element, which is installed at the connection between the spring plate and the fixed bracket. When the spring plate clamps the busbar, the pressure generated acts on the strain gauge, causing a change in its resistance value. The amount of change is proportional to the applied pressure. Through a Wheatstone bridge circuit composed of four strain gauges, the minute resistance change is converted into a measurable voltage signal output.

[0068] Specifically, the pressure-voltage calibration curve is obtained through pre-calibration experiments. During calibration, a known pressure value, ranging from 0 to 100 kPa, is applied to the sensor using a standard pressure generator, with increments of 10 kPa. The corresponding output voltage is recorded each time, establishing a pressure-voltage correlation curve. This curve is stored in the monitoring device's storage unit in the form of a data table or a fitted equation. During actual measurement, the corresponding clamping pressure value is calculated by referring to the calibration curve based on the acquired voltage value.

[0069] It should be noted that the pressure threshold is set based on the elastic modulus of the spring sheet material and the required contact area. When the clamping pressure is below this threshold, the contact area between the spring sheet and the busbar decreases, the contact resistance increases, and local overheating is likely to occur when a large current passes through. The system marks contact pairs with insufficient pressure as high-risk and records their numbers, measured pressure values, and the percentage of insufficient pressure in the inventory, providing maintenance personnel with accurate maintenance information.

[0070] Furthermore, in step S4, the determination of deteriorated contact pairs includes the following steps D1-D3: D1: For identified high-risk contact pairs, the surface temperature is measured based on an infrared temperature sensor, and the temperature of contact pairs with normal pressure on the same busbar is collected as the ambient reference temperature.

[0071] D2: Calculate the difference between the measured temperature of each high-risk contact pair and the ambient reference temperature to obtain the temperature rise value of each contact point.

[0072] D3: Compare the temperature rise value with the preset normal operating temperature difference range. If the temperature rise value of a high-risk contact pair exceeds the upper limit of the normal operating temperature difference range, it is identified as a deteriorated contact pair.

[0073] Specifically, for identified high-risk contact pairs, infrared temperature sensors are deployed at their contact points. These sensors measure surface temperature by receiving infrared radiation signals emitted from the contact points. Simultaneously, the temperature of contact pairs with normal pressure on the same busbar is collected as the ambient reference temperature. The difference between the measured temperature of each high-risk contact pair and the ambient reference temperature is calculated to obtain the temperature rise value for each contact point. This temperature rise value is compared with a preset normal operating temperature range. If the temperature rise value of a high-risk contact pair exceeds the upper limit of the normal operating temperature range, the contact pair is determined to be abnormally overheating due to poor contact, and is identified as a degraded contact pair. The degraded contact pair's number, temperature rise value, and exceedance range are recorded to identify the degraded contact pair.

[0074] Specifically, the infrared temperature sensor employs a non-contact temperature measurement method, determining the surface temperature of a target object by detecting the infrared radiation energy emitted by the object. In the quick-connect device of the prefabricated low-voltage generator vehicle, the sensor is installed at a certain distance from the contact point, avoiding insulation problems caused by direct contact. The sensor's optical system focuses the infrared radiation onto the thermopile detector, outputting an electrical signal proportional to the temperature.

[0075] In this embodiment of the application, step D1, the selection of the environmental reference temperature adopts the same busbar comparison method, including the following steps D111-D113: D111: After completing the identification of high-risk contact pairs, from the remaining contact pairs set in parallel with the high-risk contact pairs on the same busbar, select contact pairs whose clamping pressure is higher than the preset pressure threshold and are not marked as high-risk as normal contact pairs, and synchronously collect the surface temperature values ​​of normal contact pairs at the same time through infrared temperature sensors.

[0076] D112: Statistical processing is performed on the surface temperature values ​​corresponding to the normal contact pair set. After removing abnormal temperature values ​​that deviate significantly from the group distribution, the arithmetic mean of the remaining temperature values ​​is calculated. The arithmetic mean is used as the environmental reference temperature under the same busbar to characterize the normal operating temperature level of the busbar under the current load and environmental conditions.

[0077] D113: Using the ambient reference temperature as the reference benchmark for temperature rise calculation, the difference between the measured temperature of each high-risk contact pair and the ambient reference temperature is calculated to obtain the corresponding temperature rise value. This enables accurate determination of local heating anomalies in contact pairs while eliminating the influence of ambient temperature changes and overall load fluctuations.

[0078] In an optional implementation, the environmental reference temperature can also be selected using an environmental reference temperature selection method based on the lowest stable temperature of the same busbar, including the following steps D121-D123: D121: On the same busbar, infrared thermometry is performed on all contact pairs whose clamping pressure meets the pressure threshold requirement and are not marked as high-risk, to obtain the set of surface temperature values ​​corresponding to the same sampling time, and the set of temperature values ​​is screened for stability, and contact pairs whose temperature fluctuation exceeds the preset stability threshold are removed.

[0079] D122: Among the temperature values ​​that pass the stability screening, the temperature value with the lowest value and whose change range is less than the preset threshold in multiple consecutive sampling periods is selected as the environmental reference temperature corresponding to the same busbar, which is used to characterize the lowest stable operating temperature level of the busbar under the current working conditions.

[0080] D123: The measured temperature of each high-risk contact pair is compared with the ambient reference temperature to calculate the corresponding temperature rise value. This allows the temperature rise judgment to be based on the contact pair with the most stable thermal state and the best contact state in the busbar, thereby effectively amplifying and identifying local abnormal heating phenomena.

[0081] In another alternative implementation, the environmental reference temperature can also be selected using a method based on the quantile value of the temperature distribution within the same busbar, including the following steps D131-D133: D131: Within the same busbar range, infrared temperature is collected for all contact pairs that are not marked as high-risk, forming a temperature distribution dataset for the contact pairs corresponding to that busbar, and the temperature distribution dataset is sorted.

[0082] D132: Select the temperature value in the preset low quantile interval from the sorted temperature distribution dataset as the environmental reference temperature. The low quantile interval is used to characterize the temperature level of the contact pair in the busbar that is in normal contact and is less affected by local heating.

[0083] D133: Based on the ambient reference temperature, calculate the temperature rise between the measured temperature of each high-risk contact pair and the ambient reference temperature, so that the temperature rise determination is based on the overall temperature distribution characteristics of the same busbar, thus maintaining the stability and robustness of the selected ambient reference temperature even in the presence of individual abnormal temperature rise points.

[0084] It should be noted that the normal operating temperature range is determined based on the thermal characteristics of the contact materials and the rated current. When the contact resistance increases, the local power loss increases, causing the temperature rise to exceed the normal range.

[0085] For example, if the temperature rise of a high-risk contact pair reaches 25°C, while the upper limit of the normal operating temperature difference range is 15°C, the excess is 10°C, indicating that the contact pair has deteriorated significantly. The system will identify it as a deteriorated contact pair and record the relevant parameters to provide a basis for subsequent maintenance decisions.

[0086] Furthermore, in step S5, obtaining the monitoring weight allocation table includes the following steps E1-E3: E1: Calculate the ratio of the actual current to the total current of the deteriorated contact pair to obtain the current carrying capacity ratio.

[0087] E2: Multiply the current carrying capacity ratio by the deviation percentage to obtain the degradation ratio coefficient, and multiply it by the long-term current fluctuation amplitude to obtain the comprehensive degradation index.

[0088] E3: Based on the comprehensive deterioration index, priority values ​​are determined in descending order, the initial weights are adjusted and normalized to obtain the monitoring weight allocation table.

[0089] Specifically, the current carrying capacity ratio is obtained by dividing the actual current value of the identified deteriorated contact pair by the sum of the current values ​​of all contact pairs. The percentage deviation of the current distribution for the deteriorated contact pair is obtained from the previous steps. The current carrying capacity ratio is multiplied by the deviation percentage to obtain the deterioration proportion coefficient. Simultaneously, the long-term current fluctuation amplitude recorded in historical monitoring for the deteriorated contact pair is extracted. The deterioration proportion coefficient is multiplied by the fluctuation amplitude to obtain the comprehensive deterioration index. All deteriorated contact pairs are sorted in descending order according to the comprehensive deterioration index. The contact pair ranked first is assigned the highest priority value, and the values ​​are assigned in descending order to obtain the monitoring priority value for each contact pair. The initial weight of each contact pair in the total resistance measurement is multiplied by the ratio of its priority value to the sum of all priority values ​​to obtain the adjusted monitoring weight for that contact pair. Based on the adjusted monitoring weight, the weights of all contact pairs are normalized so that the sum of the weights equals 1. The contact pair number, initial weight, adjusted weight, and normalized final monitoring weight are summarized in a table to obtain the monitoring weight allocation table for each contact pair.

[0090] In this embodiment of the application, the calculation of the degradation ratio coefficient in step E2 comprehensively considers the current carrying capacity and the degree of deviation of the contact pair, including the following steps E211-E213: E211: For each identified deteriorated contact pair, obtain its actual current value within the current monitoring period, and calculate the ratio of this actual current value to the sum of the actual current values ​​of all contact pairs to obtain the current carrying capacity ratio of the deteriorated contact pair, which is used to characterize the actual carrying capacity of the contact pair in the overall current transmission.

[0091] E212: Obtain the percentage of current distribution deviation corresponding to the deteriorated contact pair, and multiply the current carrying ratio by the percentage of current distribution deviation to obtain the deterioration ratio coefficient, so that the deterioration ratio coefficient can simultaneously reflect the proportion of current carried by the contact pair and the degree of its abnormal current distribution.

[0092] E213: The obtained degradation ratio coefficient is used as the basic input for subsequent comprehensive degradation index calculation. It is used to characterize the degree of influence of the degradation state of the contact pair on the overall current distribution and system operation. The larger the degradation ratio coefficient, the more significant the potential impact of the degradation of the contact pair on the system.

[0093] In an optional implementation, the degradation percentage factor can also be calculated based on a degradation percentage factor calculation method that maps current carrying capacity to deviation level, including the following steps E221-E223: E221: For each deteriorated contact pair, calculate the proportion of its actual current value to the total current of all contact pairs to obtain the current carrying capacity ratio. Based on the numerical range of the current carrying capacity ratio, map it to the corresponding carrying capacity level value to reduce the amplified effect of instantaneous extreme current on the calculation results.

[0094] E222: Divide the current distribution deviation percentage of the deteriorated contact pair into intervals, determine the corresponding deviation level value according to the interval of the deviation percentage, and combine the load-bearing level value and the deviation level value to obtain the deterioration ratio coefficient, so that the deterioration ratio coefficient can reflect the strength of the influence of the deteriorated contact pair on the overall current distribution in the form of a level.

[0095] E223: The degradation ratio coefficient is used to characterize the relative impact of degradation contacts on the overall system and serves as an input parameter for subsequent comprehensive degradation index calculation, thereby improving the robustness of the calculation process to abnormal values ​​while maintaining the consistency of degradation ranking results.

[0096] In another alternative implementation, the degradation ratio can also be calculated based on a degradation ratio calculation method that combines current carrying capacity ratio and deviation weighting, including the following steps E231-E233: E231: For each deteriorated contact pair, calculate its current carrying capacity ratio and current distribution deviation percentage, and weight the current carrying capacity ratio and current distribution deviation percentage according to the preset importance weight to reflect their relative contribution to the deterioration impact assessment.

[0097] E232: The weighted current carrying capacity ratio and the weighted current distribution deviation percentage are combined to obtain the degradation ratio coefficient. This allows the degradation ratio coefficient to flexibly adjust the influence weights of the current contribution and the degree of deviation abnormality according to different operating scenarios.

[0098] E233: The degradation ratio coefficient is used as a quantitative result to describe the degree of influence of degraded contacts on the overall current distribution of the system, and is used for the calculation of subsequent comprehensive degradation index and the determination of monitoring weights, thereby achieving effective differentiation of the degree of degradation.

[0099] It should be noted that the comprehensive degradation index assesses the severity of contact pair degradation by combining the degradation percentage coefficient and the long-term current fluctuation amplitude. The long-term current fluctuation amplitude reflects the instability of the current distribution in the contact pair; a large fluctuation amplitude indicates that the contact condition is unstable and prone to sudden changes. The comprehensive degradation index, obtained by multiplying the degradation percentage coefficient by the fluctuation amplitude, takes into account both the current degradation state and the degradation trend.

[0100] Preferably, the monitoring priority is assigned using an inverse proportional allocation method. The first-ranked deteriorated contact pair is assigned a value N, the second-ranked is assigned N-1, and so on, where N is the total number of deteriorated contact pairs. This assignment method ensures that contact pairs with more severe deterioration receive higher priority values. When adjusting the monitoring weights, the initial weight of each contact pair is multiplied by the ratio of its priority value to the sum of all priority values, thus dynamically adjusting the allocation of monitoring resources according to the degree of deterioration.

[0101] For example, weight normalization is achieved by dividing the adjusted weights of all contact pairs by the sum of their weights. Suppose there are three degraded contact pairs with adjusted weights of 0.5, 0.3, and 0.4, and a total weight of 1.2. The normalized weights would be 0.417, 0.25, and 0.333, with a total weight of exactly 1.

[0102] In one embodiment, the monitoring weight allocation table includes five columns of data: contact pair number, initial weight, adjustment factor, adjusted weight, and final monitoring weight. This table provides a quantitative basis for resource allocation in the monitoring system, enabling the settings of monitoring frequency, sampling accuracy, and alarm thresholds to be configured differently based on the weight of each contact pair, thus achieving focused monitoring of high-risk contact pairs.

[0103] Furthermore, in step S6, obtaining the optimized monitoring configuration scheme includes the following steps F1-F3: F1: Based on the monitoring weight allocation table, the sampling frequency and bandwidth are redistributed according to the weights, and the real-time current and temperature values ​​of the deteriorated contact are collected to construct a two-dimensional data point set.

[0104] F2: The clustering algorithm is used to divide the signal into three clusters, calculate the proportion of degraded signals, and adjust the sampling frequency and temperature warning limit based on the duration of historical fluctuations.

[0105] F3: Summarize the updated monitoring frequency, warning thresholds, and resource quotas to obtain an optimized monitoring configuration scheme.

[0106] Specifically, based on the weight values ​​in the monitoring weight allocation table for each contact pair obtained in the previous step, the sampling frequency and data transmission bandwidth of the monitoring sensors are redistributed according to the weight. Deteriorated contact pairs with higher weights receive higher sampling frequencies. Simultaneously, real-time current and temperature values ​​of the deteriorated contact pairs are collected to construct a two-dimensional data point set. The K-means clustering algorithm is used to cluster the two-dimensional data point set, setting the number of clusters to three, corresponding to normal, mildly deteriorated, and severely deteriorated states, respectively. The coordinates of the center point of each cluster and the number of data points within each cluster are obtained. The proportion of data points in the cluster where the temperature exceeds a preset threshold or the current is below a preset threshold is calculated to obtain the proportion of deterioration signals. At the same time, the duration of fluctuations for each deteriorated contact pair is extracted from historical monitoring records. The monitoring parameters are adjusted based on the proportion of deterioration signals and the duration of fluctuations. If the proportion of deterioration signals for a certain contact pair exceeds a preset proportion threshold and the duration of fluctuations exceeds a preset duration threshold, the monitoring sampling frequency for that contact pair is increased to the current frequency multiplied by a preset coefficient, and the temperature warning threshold is decreased to the current threshold value multiplied by a preset adjustment coefficient, resulting in updated monitoring frequencies and warning thresholds. The updated monitoring frequencies, temperature warning thresholds, and resource allocation ratios calculated based on weights are summarized to form a configuration parameter table containing contact pair number, monitoring sampling frequency, upper temperature warning limit, lower current warning limit, and monitoring resource quota, resulting in an optimized monitoring configuration scheme.

[0107] In one implementation, the reallocation of monitoring resources is based on a weight-driven resource scheduling mechanism. Specifically, the system dynamically adjusts the operating parameters of the data acquisition unit according to the weight values ​​of each contact pair in the monitoring weight allocation table. For high-risk contact pairs with a weight value of 0.4, their sampling frequency is set to four times the base frequency, i.e., from once per second to four times per second. At the same time, a wider data transmission channel is allocated to this contact pair, increasing the bandwidth from the original 10kbps to 40kbps. This differentiated resource allocation allows the system to focus on severely degraded contact pairs while maintaining basic monitoring of other contact pairs.

[0108] It should be noted that the construction of the two-dimensional data point set involves data time synchronization and spatial mapping. The system collects the current and temperature values ​​of each deteriorated contact pair at the same time, forming a two-dimensional coordinate point. The horizontal axis represents the current carrying capacity, and the vertical axis represents the contact point temperature. After continuous acquisition, each contact pair forms a point cloud distribution in two-dimensional space. The point cloud of normally functioning contact pairs is concentrated in the low temperature and high current region, while the point cloud of deteriorated contact pairs is biased towards the high temperature and low current region.

[0109] Preferably, the application process of the K-means clustering algorithm includes three stages: initialization, iteration, and convergence. In the initialization stage, the system randomly selects three points in two-dimensional space as initial cluster centers, representing normal, slightly degraded, and severely degraded states, respectively. In the iteration stage, the algorithm calculates the Euclidean distance from each data point to the three cluster centers, assigns the data point to the cluster containing the nearest cluster center, and then recalculates the mean of all data points within each cluster as the new cluster center. After multiple iterations, when the change in the cluster center position is less than a preset threshold, the algorithm converges, ultimately obtaining three stable clusters. By analyzing the center coordinates of each cluster, the system can identify which cluster corresponds to the normal state and which clusters correspond to the degraded state. The center of the normal state cluster is usually located in a high-current, low-temperature region, while the center of the severely degraded cluster is located in a low-current, high-temperature region.

[0110] For example, the calculation of the degradation signal ratio reflects the distribution of degradation data in the overall data. The system first determines whether each data point belongs to an abnormal state based on preset temperature and current abnormal thresholds. The temperature abnormal threshold is determined according to the contact material and operating conditions, and is usually set to the upper limit of the normal operating temperature difference (e.g., 85℃). The current abnormal threshold is usually set to 30% of the rated current. When the temperature of a data point exceeds the temperature abnormal threshold or the current is lower than the current abnormal threshold, it is marked as abnormal. Then, the clusters of these abnormal data points are counted, and the percentage of the number of abnormal data points to the total number of data points is calculated to obtain the degradation signal ratio. The higher the ratio, the more severe the degradation of the contact pair.

[0111] In one embodiment, the extraction of the duration of fluctuation involves time-series analysis of historical data. The system retrieves the deviation data sequence for each deterioration contact pair from historical monitoring records, i.e., the difference sequence between a certain parameter and the standard value, identifies the time period in which the deviation continuously exceeds the threshold by 5 units, and calculates the length of the longest continuous excess period as the duration of fluctuation. This parameter reflects the stability of the deterioration state; the longer the duration, the more stable the deterioration, and the more frequent the monitoring is required.

[0112] Understandably, the dynamic adjustment of monitoring parameters adopts a tiered adjustment strategy. When the proportion of degradation signals exceeds 50% and the fluctuation duration exceeds 100 sampling cycles, the system determines that the contact pair is in a high-risk state and takes two adjustment measures: First, the monitoring sampling frequency is increased by a preset multiple (usually 2 to 5 times) so that the system can capture the degradation process more intensively; second, the upper limit of temperature warning is lowered by a preset percentage (usually reduced by 10% to 20%), for example, from the initial setting value to a more stringent threshold, so that the system can trigger the warning earlier. This adjustment enables the system to detect abnormal trends earlier and provide timely warnings before a fault occurs.

[0113] Specifically, the calculation of monitoring resource quotas takes into account the overall system resource constraints. The system's total sampling capacity and transmission bandwidth are limited and need to be rationally allocated among all contact pairs. The resource quota equals the percentage of the adjusted weight of that contact pair relative to the sum of the weights of all contact pairs multiplied by the total system resource quantity. This allocation method ensures that key contact pairs receive sufficient resources while avoiding excessive resource concentration. Furthermore, the implementation of the optimized monitoring configuration scheme requires parameter reconfiguration of the monitoring equipment. The system distributes the parameters from the configuration parameter table to the corresponding sensors and data acquisition modules. The sensors adjust their internal timers according to the new sampling frequency, and the data acquisition modules adjust the comparator thresholds according to the new warning limits, thus achieving a complete closed loop from resource allocation to actual monitoring.

[0114] For example, a prefabricated low-voltage generator vehicle's quick-connect device contains 10 contact pairs, of which 3 are identified as deteriorated contact pairs. Through the above-mentioned optimized configuration, these 3 deteriorated contact pairs occupy 60% of the system's monitoring resources, with a sampling frequency of 5 times per second, and the temperature warning threshold is reduced to 80°C. Meanwhile, the remaining 7 normal contact pairs share 40% of the resources, maintaining the basic sampling frequency. This differentiated configuration improves the sensitivity of monitoring potential fault points.

[0115] Furthermore, step S7 specifically includes: Under high load conditions, the instantaneous peak current of each contact pair is collected using a current transformer. Simultaneously, a displacement sensor measures the deformation of the spring sheet relative to its initial position. The instantaneous peak current is divided by the sum of the peak currents of all contact pairs to obtain the actual current sharing ratio of each contact pair. When the current carried by a contact pair deviates significantly from the average value, it is identified as a current concentration flow state. Based on the actual current sharing ratio, the previously optimized monitoring parameters are used to monitor each contact pair at an updated sampling frequency. The spring sheet contact resistance value of each contact pair under the current load is measured using the four-wire method. The measured resistance value is compared with the initial resistance value under rated conditions to calculate the resistance change rate. The resistance change rate, current sharing ratio, deformation, and degradation status indicators of each contact pair are summarized and categorized according to contact pair number, current contact resistance value, resistance change rate, actual current sharing ratio, spring sheet deformation, and degradation degree to form an evaluation matrix containing comprehensive performance parameters of all contact pairs, resulting in a contact resistance status evaluation report for the quick-connect device.

[0116] In one implementation, high-load conditions are identified by monitoring the total current value. When the output current of the generator reaches more than 80% of the rated current, the system determines that it has entered a high-load condition. At this time, the current density borne by each contact pair is close to the design limit, and any poor contact will lead to local overheating. Therefore, it is necessary to focus on monitoring the current sharing and mechanical deformation of each contact pair.

[0117] It should be noted that acquiring instantaneous current peak values ​​requires high-speed sampling capability. The current transformer continuously acquires the current signal at a sampling rate of not less than 10kHz, capturing the peak points of the current waveform. These peaks reflect the current carrying capacity of the contact pair under transient impact. The deformation is measured by a laser displacement sensor or strain gauge. The sensor is installed at the fixed end of the spring sheet to measure the displacement change of the spring sheet under the combined action of clamping force and electrodynamic force.

[0118] Specifically, the determination of concentrated current flow is based on the principle of statistical deviation, that is, identifying outliers by analyzing the dispersion of data distribution. The system first calculates the average current of all contact pairs, i.e., the total current divided by the number of contact pairs; then it calculates the standard deviation, reflecting the dispersion of the current values. When the actual current of a contact pair exceeds the average plus twice the standard deviation, or is lower than the average minus twice the standard deviation, the contact pair is considered to be in a concentrated current flow state. This state indicates that the current distribution is severely uneven, with some contact pairs bearing too much or too little current load.

[0119] Preferably, the calculation of the resistance change rate requires the establishment of an initial benchmark. In the initial stage of equipment commissioning, the system measures the contact resistance of each contact pair under rated operating conditions as the initial value and stores it in the database. In subsequent monitoring, the measured resistance value is compared with the initial value to calculate the change rate. When the change rate exceeds 30%, it indicates that the contact performance has significantly degraded.

[0120] For example, the evaluation matrix is ​​constructed using a multi-dimensional data fusion method, integrating the data of each parameter through a weighted average algorithm, with weights allocated according to the importance of the parameters. Each row of the matrix represents a contact pair, and the columns contain six key parameters: the contact pair number for unique identification, the current contact resistance value reflecting the real-time contact status, the resistance change rate characterizing the degree of degradation, the actual current sharing ratio showing the load distribution, the spring deformation reflecting the mechanical condition, and the degradation degree indicator providing a qualitative evaluation. These parameters together constitute a complete performance profile of the contact pair.

[0121] In one embodiment, the assessment report includes trend analysis and risk warning information in addition to the data matrix. The system compares current data with historical data, identifies contact pairs that are experiencing accelerated degradation, and marks high-risk items in red and medium-risk items in yellow in the report, providing an intuitive reference for maintenance decisions.

[0122] Example 3, the third embodiment of the present invention, differs from the previous two embodiments in that it provides a contact resistance monitoring system for a quick-connect device, comprising a preliminary judgment module, a degradation determination module, a scheme formulation module, and an implementation module. The preliminary judgment module collects real-time current values ​​and spring contact resistance of each spring contact pair using independent current sensors, calculates the percentage deviation between the current of each contact pair and the average current of all contact pairs, obtains the current distribution deviation, statistically analyzes the current distribution deviation of each contact pair within a target time period, obtains the long-term current fluctuation amplitude and fluctuation duration, compares the frequency and amplitude deviation of the current deviation, and identifies suspected degradation contact pairs. The degradation determination module collects the spring clamping pressure of suspected degradation contact pairs, identifies contact pairs with clamping pressure below a pressure threshold, marks them as high-risk contact pairs, obtains a list of high-risk contact pairs, and monitors the contact point temperature of high-risk contact pairs. The temperature rise is calculated relative to the ambient reference temperature, and contact pairs whose temperature rise exceeds the normal temperature difference range are identified as deteriorated contact pairs. The scheme formulation module multiplies the current carrying capacity ratio and current distribution deviation of the deteriorated contact pairs to obtain a deterioration ratio coefficient. Combined with the long-term current fluctuation amplitude, a comprehensive deterioration index is obtained. Based on the comprehensive deterioration index, the monitoring priority is determined and the monitoring weight of each contact pair is adjusted to obtain a monitoring weight allocation table. Based on the monitoring weight allocation table, monitoring resources are reallocated, the current and temperature cluster distribution of deteriorated contact pairs is evaluated, the proportion of deterioration signals is determined, and the monitoring frequency and warning threshold are updated based on the duration of fluctuations to obtain an optimized monitoring configuration scheme. The implementation module collects the instantaneous current peak value and spring deformation of each contact pair again, calculates the actual current sharing ratio, and measures the contact resistance of each contact pair according to the optimized monitoring configuration scheme to obtain a contact resistance status assessment report for the quick connection device.

[0123] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0124] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0125] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0126] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented in combination with any of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0127] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for monitoring the contact resistance of a quick-connect device, characterized in that: include, The real-time current value of each spring contact pair and the contact resistance of the spring sheet are collected by an independent current sensor. The percentage deviation of the current of each contact pair from the average current of all contact pairs is calculated to obtain the current distribution deviation. By statistically analyzing the current distribution deviation of each contact pair within the target time period, the long-term current fluctuation amplitude and fluctuation duration are obtained. By comparing the frequency and amplitude deviation of the current deviation, suspected deterioration contact pairs are identified. Collect the spring clip clamping pressure of the suspected deteriorated contact pairs, identify contact pairs with clamping pressure below the pressure threshold, mark them as high-risk contact pairs, and obtain a list of high-risk contact pairs. Monitor the temperature of the contact points of the high-risk contact pairs, calculate the temperature rise value compared with the ambient reference temperature, and confirm that the contact pairs whose temperature rise exceeds the normal temperature difference range are deteriorated contact pairs. The degradation ratio coefficient is obtained by multiplying the current carrying capacity ratio of the deteriorated contact pair by the current distribution deviation. The comprehensive degradation index is obtained by combining the long-term current fluctuation amplitude. Based on the comprehensive degradation index, the monitoring priority is determined and the monitoring weight of each contact pair is adjusted to obtain the monitoring weight allocation table. Based on the monitoring weight allocation table, monitoring resources are reallocated, the cluster distribution of current and temperature of deteriorated contacts is evaluated, the proportion of deterioration signals is determined, and the monitoring frequency and warning threshold are updated in combination with the duration of fluctuations to obtain an optimized monitoring configuration scheme. The instantaneous current peak value and spring sheet deformation of each contact pair are collected again to calculate the actual current sharing ratio. Based on the optimized monitoring configuration scheme, the contact resistance of each contact pair is measured to obtain a contact resistance status assessment report of the quick connection device.

2. The method for monitoring contact resistance of a quick-connect device as described in claim 1, characterized in that: The obtained current distribution deviation includes, A current sensor array is deployed in the busbar connection area. Each spring contact pair is equipped with an independent sensor unit to capture the instantaneous current value flowing through each spring, while applying a constant test current and measuring the terminal voltage to calculate the contact resistance value. Calculate the average current of all contact pairs at each sampling time as the reference current, and calculate the percentage deviation between the actual current of each contact pair and the reference current; Standard deviation analysis is performed on the deviation percentage sequence to statistically analyze the frequency and duration of abnormal deviation states, and a current distribution deviation evaluation matrix is ​​constructed to obtain the current distribution deviation.

3. The method for monitoring contact resistance of a quick connection device as described in claim 2, characterized in that: The identification of suspected deteriorated contact pairs includes, From the current distribution deviation data, extract the time series data of the deviation percentage of each contact pair within the target time period, calculate the difference between the maximum and minimum values ​​as the long-term current fluctuation amplitude, and count the longest interval that continuously exceeds the threshold to obtain the fluctuation duration. Compare the deviation data with those from historical monitoring periods to calculate the current frequency deviation and amplitude deviation; The degradation risk index is obtained by multiplying the frequency deviation and amplitude deviation by the duration of fluctuation. Contact pairs that exceed the risk threshold are identified as suspected degradation contact pairs according to the degradation risk index.

4. The method for monitoring contact resistance of a quick connection device as described in claim 3, characterized in that: The list of high-risk contact pairs includes, Based on the pressure sensor at the root of the spring plate with suspected deterioration, the change in deformation resistance is converted into voltage output through a Wheatstone bridge, and the clamping pressure value is calculated according to the pressure and voltage calibration curve. The clamping pressure value is compared with the pressure threshold. Contact pairs with clamping pressure less than the threshold are marked as high-risk contact pairs. The numbers, pressure values ​​and degree of deficiency are summarized to obtain a list of high-risk contact pairs.

5. The method for monitoring contact resistance of a quick connection device as described in claim 4, characterized in that: The methods for determining the deteriorated contact pairs include, For identified high-risk contact pairs, the surface temperature is measured using an infrared temperature sensor, while the temperature of contact pairs with normal pressure on the same busbar is collected as the ambient reference temperature. The temperature rise value of each contact point is obtained by calculating the difference between the measured temperature of each high-risk contact pair and the ambient reference temperature. The temperature rise value is compared with the preset normal operating temperature difference range. If the temperature rise value of a high-risk contact pair exceeds the upper limit of the normal operating temperature difference range, it is identified as a deteriorated contact pair.

6. The method for monitoring contact resistance of a quick connection device as described in claim 5, characterized in that: The obtained monitoring weight allocation table includes, The current carrying capacity ratio is obtained by calculating the ratio of the actual current to the total current of the deteriorated contact pair. Multiplying the current carrying capacity ratio by the deviation percentage yields the degradation ratio coefficient, which is then multiplied by the long-term current fluctuation amplitude to obtain the comprehensive degradation index. Priority values ​​are determined by arranging the comprehensive degradation indicators in descending order, adjusting the initial weights and normalizing them to obtain the monitoring weight allocation table.

7. The method for monitoring contact resistance of a quick connection device as described in claim 6, characterized in that: The optimized monitoring configuration scheme includes, Based on the monitoring weight allocation table, the sampling frequency and bandwidth are redistributed according to weight, and a two-dimensional data point set is constructed by collecting real-time current and temperature values ​​of the deteriorated contact pairs. The data is divided into three clusters using a clustering algorithm. The proportion of degraded signals is calculated, and the sampling frequency and temperature warning limit are adjusted based on the duration of historical fluctuations. The updated monitoring frequency, warning thresholds, and resource quotas are summarized to obtain an optimized monitoring configuration scheme.

8. A contact resistance monitoring system for a quick-connect device, employing the contact resistance monitoring method for a quick-connect device as described in any one of claims 1 to 7, characterized in that: It includes a preliminary judgment module, a degradation determination module, a solution formulation module, and an implementation module; The preliminary discrimination module collects the real-time current value and spring contact resistance of each spring contact pair through an independent current sensor, calculates the percentage deviation of the current of each contact pair from the average current of all contact pairs, obtains the current distribution deviation, counts the current distribution deviation of each contact pair within the target time period, obtains the long-term current fluctuation amplitude and fluctuation duration, compares the frequency and amplitude deviation of the current deviation, and identifies the suspected deteriorated contact pairs. The degradation determination module collects the spring clip clamping pressure of the suspected degradation contact pairs, identifies contact pairs with clamping pressure below the pressure threshold, marks them as high-risk contact pairs, obtains a list of high-risk contact pairs, monitors the contact point temperature of the high-risk contact pairs, calculates the temperature rise value relative to the ambient reference temperature, and confirms that contact pairs with temperature rise exceeding the normal temperature difference range are degraded contact pairs. The scheme formulation module multiplies the current carrying ratio of the deteriorated contact pair by the current distribution deviation to obtain the deterioration ratio coefficient, combines it with the long-term current fluctuation amplitude to obtain a comprehensive deterioration index, determines the monitoring priority based on the comprehensive deterioration index and adjusts the monitoring weight of each contact pair to obtain a monitoring weight allocation table, reallocates monitoring resources based on the monitoring weight allocation table, evaluates the current and temperature cluster distribution of the deteriorated contact pair, determines the proportion of deterioration signals, and updates the monitoring frequency and warning threshold based on the duration of fluctuations to obtain an optimized monitoring configuration scheme. The implementation module again collects the instantaneous current peak value and spring sheet deformation of each contact pair, calculates the actual current sharing ratio, measures the contact resistance of each contact pair according to the optimized monitoring configuration scheme, and obtains a contact resistance status assessment report of the quick connection device.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the contact resistance monitoring method for a quick connection device according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the contact resistance monitoring method for a quick connection device according to any one of claims 1 to 7.

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