Methods and Systems for Quality Assurance Management of Power Materials

Abstract

This invention relates to the field of power technology, specifically to a method and system for quality assurance management of power equipment. The method includes the following steps: collecting real-time operating data of various power equipment, including voltage and current, performing real-time monitoring and recording, and generating real-time performance monitoring records; based on the real-time performance monitoring records, using a sliding window to analyze performance fluctuations and generate an equipment performance stability score. This invention improves the accuracy and timeliness of data by collecting real-time operating data of power equipment, such as voltage and current, and implementing continuous monitoring and recording. Utilizing a sliding window to analyze and evaluate performance fluctuations and generate an equipment performance stability score allows for the immediate identification of potential faults and performance degradation, thereby improving the efficiency of preventative maintenance and fault response. Simultaneously, by dynamically adjusting the equipment warranty period, this management method based on the actual operating conditions of the equipment brings greater flexibility and adaptability to equipment management.

Description

Technical Field

[0001] This invention relates to the field of power technology, and in particular to a method and system for quality assurance management of power materials. Background Technology

[0002] The power equipment quality assurance management method is a approach in the power industry focused on ensuring the quality and reliability of power equipment and components. It aims to ensure, through systematic management measures and technical means, that all materials used in the power system, such as transformers, cables, and protection equipment, meet quality standards and performance requirements. However, current technologies generally employ fixed-cycle maintenance plans, which may lead to over-maintenance or under-maintenance, resulting in resource waste or increased equipment failure risks. For example, overly frequent inspections and maintenance not only increase operating costs but may also affect production efficiency due to unnecessary equipment downtime. Therefore, improvements are needed. Summary of the Invention

[0003] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method and system for quality assurance management of power materials.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: a method for quality assurance management of power materials, comprising the following steps:

[0005] Real-time operating data of various power equipment, including voltage and current, are collected, monitored, and recorded in real time to generate real-time performance monitoring records. Based on the real-time performance monitoring records, performance fluctuations are analyzed using a sliding window to generate equipment performance stability scores.

[0006] Based on the equipment performance stability score, a multi-parameter weighting analysis method is applied to adjust the influence weight of each performance parameter to generate a weight-adjusted performance score. Based on the weight-adjusted performance score, the current operating status of each device is verified by comparing it with past data and threshold settings, and the equipment stability status result is generated.

[0007] Based on the equipment stability status results, the warranty period is dynamically adjusted, and the adjusted warranty period record is generated; predictive model analysis is performed on the equipment to predict the performance stability of the equipment in the future time period, and performance prediction results are generated.

[0008] Based on equipment prediction results and real-time monitoring data, an emergency warranty process is initiated for equipment that triggers a danger threshold. The warranty requirement is fed back to the supplier, the maintenance or replacement plan is updated, and an emergency warranty trigger record is generated.

[0009] Preferably, the steps for obtaining the real-time performance monitoring records are as follows:

[0010] Voltage and current data of the power equipment are collected in real time by voltage and current sensors installed on the power equipment, and each data point is timestamped to form a real-time data record of the power equipment.

[0011] Based on the real-time data records of the power equipment, the data is formatted and cleaned, and the average current and voltage levels of the equipment are calculated to obtain real-time performance monitoring records.

[0012] Preferably, the steps for obtaining the device performance stability score are as follows:

[0013] Based on the real-time performance monitoring records, the data at each time point is read and sorted, divided into fixed-size time windows in chronological order, and partitioned time window records are generated.

[0014] Based on the time window records of the partition, the performance fluctuation value within each time window is calculated using the following formula:

[0015] ;

[0016] in, For the first Performance fluctuation values ​​within a time window For the first The first window Performance values ​​at each time point For the first Average performance of each window The number of time points within each window;

[0017] Based on the performance fluctuation value, the equipment performance stability score is calculated using the following formula:

[0018] ;

[0019] in, To score the equipment's performance stability, For the first Performance fluctuation values ​​within a time window This represents the total number of time windows.

[0020] Preferably, the steps for obtaining the weight adjustment performance score are as follows:

[0021] Based on the equipment performance stability score, parameters related to equipment performance are selected, including power efficiency, temperature stability, vibration frequency or start-up time, and a performance parameter list is generated.

[0022] Based on the aforementioned performance parameter list, the weighted performance score is calculated using the following formula:

[0023] ;

[0024] in, Adjust performance scores for weighting. For the first The weights of each performance parameter, For the first The device performance stability score for each parameter. To adjust the coefficients for the initial magnitude of influence, To adjust the coefficient of the growth rate, and is the attenuation coefficient, and sn is the number of performance parameters.

[0025] Preferably, the steps for obtaining the device stability status result are as follows:

[0026] The performance score is adjusted based on the weights, and the performance data of each device and the preset performance stability threshold are collected to obtain historical data and a threshold comparison list.

[0027] Based on the historical data and threshold comparison list, the current weight adjustment performance score of each device is compared with the historical data of the same period, the score change trend is analyzed, and the performance change analysis results of each device are obtained.

[0028] Based on the performance change analysis results of each device, the current operating status is judged to be stable by comparing it with the preset performance stability threshold. If the score is lower than the preset performance stability threshold, it is determined to be unstable, otherwise it is stable, and the device stability status result is generated.

[0029] Preferably, the steps for obtaining the adjusted warranty period record are as follows:

[0030] Collect the stability status results of the equipment, classify the status of each piece of equipment, mark the equipment that is operating stably and the equipment that is operating with fluctuations, and obtain the equipment status classification record;

[0031] Based on the equipment status classification records, the adjusted warranty period is calculated using the following formula:

[0032] ;

[0033] in, The adjusted warranty period, Based on the standard warranty period, and It is an adjustment factor. To score the equipment's performance stability, This is the equipment failure rate adjustment coefficient. The expected failure rate of the equipment;

[0034] Based on the adjusted warranty period, update the equipment's warranty record to obtain the adjusted warranty period record.

[0035] Preferably, the steps for obtaining the performance prediction results are as follows:

[0036] Collect historical performance data of the equipment that needs to be predicted, including voltage, current and temperature parameters, to obtain a historical performance dataset;

[0037] Based on the historical performance dataset, the ARIMA model is applied for trend analysis. The number of moving average terms and the number of autoregressive terms in the ARIMA model are set to obtain the model configuration.

[0038] Based on the model configuration, run the ARIMA model to predict performance, analyze the trend of device performance stability over a future time period, and generate performance prediction results.

[0039] Preferably, the steps for obtaining the emergency warranty trigger record are as follows:

[0040] Based on the performance prediction results, devices that exceed the danger threshold are identified, and a list of devices that trigger emergency warranty is obtained.

[0041] Based on the list of devices that triggered emergency warranty, prepare a warranty requirement document, including the requirements for equipment maintenance or replacement, record it and send it to the supplier, and obtain feedback from the supplier.

[0042] Based on the supplier's feedback, update the equipment maintenance or replacement plan and generate an emergency warranty trigger record.

[0043] This invention provides a management system, comprising:

[0044] The data monitoring module collects real-time operating data of power equipment, including voltage and current, and generates real-time monitoring records.

[0045] The performance analysis module, based on real-time monitoring records, uses a sliding window to analyze the performance fluctuations of power equipment, obtains the equipment's performance stability indicators, and generates an equipment stability score.

[0046] The weight adjustment module adjusts the weights of various parameters affecting equipment performance based on the equipment stability score, and obtains a weight-adjusted performance score.

[0047] The status verification module uses weighted adjustment of performance scores, compares the device's historical performance data with preset thresholds, determines whether the current operating status meets expectations, and generates the device stability status result.

[0048] The warranty management module adjusts and records the warranty period based on the equipment stability status results. It also predicts the performance stability of the equipment in the future and generates performance prediction results. Based on the performance prediction results and real-time monitoring records, it triggers an emergency warranty process for equipment with risks, shares the requirements with the supplier, updates the maintenance or replacement plan, and obtains emergency warranty trigger records.

[0049] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0050] This invention improves the accuracy and timeliness of power equipment by collecting operational data such as voltage and current in real time and implementing continuous monitoring and recording. It utilizes sliding window analysis to assess performance fluctuations and generate equipment performance stability scores, allowing for the immediate identification of potential faults and performance degradation, thereby improving the efficiency of preventative maintenance and fault response. Simultaneously, by dynamically adjusting equipment warranty periods, this management method, based on actual equipment operating conditions, brings greater flexibility and adaptability to equipment management. This approach extends the maintenance cycle of stable equipment and shortens the cycle of fluctuating equipment based on equipment stability status results, saving maintenance costs and reducing unnecessary interventions. The application of predictive models such as ARIMA, combined with real-time monitoring data, further optimizes the triggering mechanism of emergency warranty procedures, improving equipment operational reliability and supply chain responsiveness. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the steps of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0053] Please see Figure 1 This invention provides a technical solution: a method for quality assurance management of power materials, comprising the following steps:

[0054] Collect real-time operating data of various power equipment, including voltage and current, for real-time monitoring and recording, and generate real-time performance monitoring records; based on the real-time performance monitoring records, use a sliding window to analyze performance fluctuations and generate equipment performance stability scores.

[0055] Based on the equipment performance stability score, a multi-parameter weighting analysis method is applied to adjust the influence weight of each performance parameter and generate a weight-adjusted performance score. Based on the weight-adjusted performance score, the current operating status of each device is verified by comparing it with past data and threshold settings, and the equipment stability status result is generated.

[0056] Based on the equipment stability status results, the warranty period is dynamically adjusted, and the adjusted warranty period record is generated; predictive model analysis is performed on the equipment to predict the performance stability of the equipment in the future time period, and performance prediction results are generated.

[0057] Based on equipment prediction results and real-time monitoring data, an emergency warranty process is initiated for equipment that triggers a danger threshold. The warranty requirement is fed back to the supplier, the maintenance or replacement plan is updated, and an emergency warranty trigger record is generated.

[0058] The steps to obtain real-time performance monitoring records are as follows:

[0059] Voltage and current data of the power equipment are collected in real time by voltage and current sensors installed on the power equipment, and each data point is timestamped to form a real-time data record of the power equipment.

[0060] Based on real-time data recordings from power equipment, the data is formatted and cleaned, and the average current and voltage levels of the equipment are calculated to obtain real-time performance monitoring records.

[0061] Specifically, multiple installation locations are selected within the power equipment to place voltage and current sensors. The number and layout of these sensor installation points are determined based on technical documents provided by the equipment manufacturer and past maintenance experience. A sampling frequency of several times per second is set, and a unique identification tag is assigned to distinguish the data sources of different sensors. Each collected voltage and current value is bound to a precise timestamp, recording the corresponding reading at that moment. In practice, the equipment's rated voltage range of 0V to 24V and current range of 0A to 5A can be used as an initial safe range. This range is provided by the manufacturer based on load capacity and insulation level, and is continuously adjusted to a more suitable level based on the fault database accumulated by the maintenance department over many years. For example, if the equipment has been operating within the 10V to 20V voltage range without abnormalities for a long time, this can be established as a relatively stable range. Subsequent maintenance personnel can conduct in-depth analysis when new equipment data reaches outside this stable range. If sensor readings momentarily deviate from this safe range, further analysis will be conducted. This requires setting alarm thresholds in conjunction with the local monitoring terminal. These thresholds are typically calculated by technicians during preliminary research based on parameters such as the equipment's overload characteristics, withstand voltage range, and ambient temperature. When exceeding limits are detected, the system continues to compare historical samples from the current batch of sensor data to screen for persistent anomalies or transient interference signals. The values ​​obtained from this series of samples are arranged in chronological order, and a simple mapping table is used to associate different sensor identifiers with their respective locations, forming a structured dataset. After all sampling is completed, a unified comparison and verification is performed. This verification process uses cross-referencing between hardware calibration values ​​and environmental noise test results to eliminate obviously unreliable outliers. If a disconnection or sensor malfunction occurs during the data acquisition process, the data is marked and included in the subsequent complete analysis. Through the above operations, the system finally obtains the on-site data collected by the equipment, sorted by timestamp and covering voltage and current values ​​at different sampling locations, forming a real-time data record of the power equipment.

[0062] Based on the real-time data records of the power equipment obtained above, all valid data points are selected and uniformly formatted. The records of each sensor are sequentially linked according to the timestamp order. The rated current range of the equipment (0A to 5A) and the rated voltage range (0V to 24V) are checked, and extreme values ​​outside these ranges are removed. The removal threshold is given by the equipment manufacturer during the testing phase and corrected by the maintenance personnel based on historical operating data. Therefore, if some extreme values ​​only appear for a very short period of time and sensor failure cannot be ruled out, they will be marked as abnormal readings and temporarily retained for further verification later. After completing the above formatting steps, the change range of each sampling point in adjacent timestamps is compared one by one using basic arithmetic. If multiple consecutive timestamps show large offsets, it is necessary to re-verify whether the sensor status in that interval is normal. If it is determined that the sensor has not failed, This indicates that such deviations are due to actual fluctuations in equipment operation. The fluctuation range is then marked independently in a variation range table. Next, the normal range data that has already been processed is retrieved, and the arithmetic mean of its voltage and current is calculated. If the monitoring results of some equipment show that the initial threshold is exceeded multiple times during a specified time period, it is necessary to manually confirm whether the threshold is reasonable and make appropriate adjustments. The threshold here is mostly determined by the equipment type and power level. It can be obtained by collecting and statistically analyzing the operating records of the same model of equipment over a period of time. If the statistics show that the average voltage of some models is more likely to be in the 10V to 12V range, the corresponding threshold can be lowered from 24V to 15V or 16V to more accurately reflect the actual situation. Finally, the normal data range after the above cleaning and screening is output and the average levels of current and voltage are calculated to obtain the real-time performance monitoring record.

[0063] The steps for obtaining the equipment performance stability score are as follows:

[0064] Based on real-time performance monitoring records, the data at each time point is read and sorted, divided into fixed-size time windows in chronological order, and partitioned time window records are generated.

[0065] Based on the time window records of each partition, calculate the performance fluctuation value within each time window. The calculation formula is as follows:

[0066] ;

[0067] in, For the first Performance fluctuation values ​​within a time window For the first The first window Performance values ​​at each time point For the first Average performance of each window The number of time points within each window;

[0068] Based on the performance fluctuation value, the equipment performance stability score is calculated using the following formula:

[0069] ;

[0070] in, To score the equipment's performance stability, For the first Performance fluctuation values ​​within a time window This represents the total number of time windows.

[0071] Specifically, based on the obtained real-time performance monitoring records, the data at each time point is read and sorted. First, the recording timeline is divided according to the device's internal sampling frequency, and each time point is numbered. Then, using the voltage range of 0V to 24V and the current range of 0A to 5A accumulated in historical monitoring as basic reference standards, the voltage and current values ​​of each data point are compared with their respective allowable ranges. For example, if the voltage of some data points exceeds 24V or falls below 0V, it is marked as a temporary abnormal reading. In subsequent steps, it is verified whether the sensor readings are inaccurate or have short-term sudden changes. If it is determined to be a faulty reading, the data point is removed; if it is transient interference, it is retained and a second comparison is performed in the subsequent data cleaning process. After completing the above basic comparison, the different time points are connected in chronological order to form a continuous time sequence, and then... To meet the needs of equipment maintenance departments, fixed-size time windows are set for partitioning. For example, in some maintenance scenarios, data monitored every 30 seconds is used as an independent window. The size of this partition is generally determined based on the equipment's operating speed and the rapid evolution of faults. Specific values ​​are obtained by recording the actual fault occurrence time during on-site evaluations and through multiple verification experiments. If multiple tests show that the equipment may experience significant voltage fluctuations within 15 seconds, the time window can be set at around 15 seconds. After the time windows are divided, the data in each window are renumbered according to the timestamp order to form separate partition records. The voltage data sequence and current data sequence within each window are stored separately for subsequent detailed comparison and calculation. Finally, after all partitions are numbered and the data is organized, the partition time window record is obtained.

[0072] The advantage of the formula is that it quantifies the difference between the performance value and the mean at each time point within each time window, thus presenting the degree of fluctuation within the current window more intuitively.

[0073] The acquisition steps are as follows: sum the performance values ​​of the aforementioned 5 sampling points and then divide by 5. This calculation process is based on the actual sampling situation of the device in the window. If some sampling points are marked as abnormal readings, they are not included in the summation, thereby obtaining the average performance value of the window.

[0074] This represents the total number of normal sampling points within the window. In the example above, it is 5. If the window size is different in other scenarios, it needs to be calculated based on the actual monitoring and sampling process.

[0075] Calculation process:

[0076] First calculate the average performance For example, if the performance values ​​of the 5 valid sampling points within this window are recorded as 41.0, 40.7, 39.5, 42.2, and 40.0 respectively (the units can be defined according to different monitoring items), then:

[0077] ;

[0078] calculate Find the values ​​and sum them.

[0079] ;

[0080] Divide by The average value was obtained as follows: ,

[0081] Take the square root of this average.

[0082] ;

[0083] The result indicates that the fluctuation value within this time window is approximately 0.9215. If the operations and maintenance department specifies in the previously established reference range that... If the value exceeds 1.5, it is considered to be too volatile, and this value has not yet been reached in this window.

[0084] formula: First, all parameters of the formula are assigned numerical values ​​and the calculation process is explained. The current monitoring period is divided into several parts. A time window, obtained from the previous... The results were 0.9215, 1.4000, and 2.0000, respectively. Specifically, this was achieved by performing the same fluctuation value calculation on the data collected from each window and then organizing the data. These three fluctuation values ​​were then substituted into the formula for step-by-step calculation. First, the values ​​were calculated separately... ,for At that time, there were:

[0085] ;

[0086] for At that time, there were:

[0087] ;

[0088] for At that time, there were:

[0089] ;

[0090] Then sum them up.

[0091] ;

[0092] Finally divide by ,

[0093] ;

[0094] The results indicate that the equipment performance stability score is approximately 0.4234. If the monitoring personnel previously set the performance stability score threshold to 0.4000, a score below 0.4000 would be judged as an unstable state, while a score above or equal to 0.4000 would be tentatively considered a stable state. At this point, the score is slightly higher than 0.4000, indicating a relatively average level of stability in the overall assessment. If more windows are further refined or a longer observation period is observed, the final stable state of the equipment can be determined by comparing multiple score results.

[0095] The steps to obtain the weighted performance score are as follows:

[0096] Based on the equipment performance stability score, parameters related to equipment performance are selected, including power efficiency, temperature stability, vibration frequency or start-up time, and a list of performance parameters is generated.

[0097] Based on the performance parameter list, the weighted performance score is calculated using the following formula:

[0098] ;

[0099] in, Adjust performance scores for weighting. For the first The weights of each performance parameter, For the first The device performance stability score for each parameter. To adjust the coefficients for the initial magnitude of influence, To adjust the coefficient of the growth rate, and is the attenuation coefficient, and sn is the number of performance parameters.

[0100] Specifically, based on the previously obtained equipment performance stability score, parameters reflecting the equipment's operating status, such as power efficiency, temperature stability, vibration frequency, and start-up time, are selected as references. These parameters are then compared with their current values ​​and corresponding threshold ranges, taking into account real-time operating data and performance parameter ranges accumulated from past maintenance records. For example, power efficiency is typically provided by the equipment manufacturer within a reasonable range (70% to 95%). Temperature stability needs to be determined by considering the combined effects of ambient temperature and internal heat generation (e.g., 0℃ to 90℃). Vibration frequency can be referenced from previously monitored data using vibration sensors and compared with existing mechanical vibration safety standards (e.g., 0Hz to 50Hz). Start-up time can be compared through actual startup tests using records of multiple start-ups and shutdowns, and the duration of multiple start-ups and shutdowns can be compared with the equipment's specified parameters. The normal startup time (e.g., 3 to 6 seconds) is checked. If it is extremely short or far exceeds the normal startup range, it is marked as abnormal data and handed over to the maintenance personnel for manual identification. When these parameters are included in the evaluation process, factors such as equipment model and environmental requirements need to be considered. If the maintenance personnel need to monitor temperature stability more strictly in low-temperature environments, a more detailed threshold (e.g., -10℃ to 90℃) will be added to the temperature dimension. In the specific implementation process, the original records of each parameter need to be checked, analyzed and marked. The data points corresponding to each parameter are organized in sequence in the time series and compared with the threshold. If a parameter is found to exceed the threshold multiple times, it is marked as having potential risks. After completing the above checks and recording work, a list of performance parameters including power efficiency, temperature stability, vibration frequency and startup time is obtained.

[0101] The advantage of the formula is that it combines the performance stability scores with their corresponding weights, initial impact magnitudes, growth rates, and decay coefficients, thereby reflecting the different degrees of influence of each parameter on the equipment's operating status in a comprehensive score.

[0102] The steps for obtaining parameters are as follows: First, based on the equipment's design goals and statistical results of measured data, combined with the degree of influence of each parameter on the failure rate in the summary of previous maintenance, define the weight of each parameter under the current operating condition in numerical form. If temperature stability has a significant impact on a certain type of equipment, then assign it a higher weight. The values ​​range from 0.1 to 0.4. The maintenance personnel record and summarize the contribution of each parameter deviation to the probability of failure during the equipment's operating cycle, and select the specific value after multiple rounds of comparison.

[0103] The steps for obtaining parameters are as follows: use the previously obtained equipment performance stability score to break down into the corresponding parameter dimensions. For example, the temperature stability score obtained by collecting temperature data and calculating it is 0.82. This score is calculated by comparing whether the equipment temperature parameter continues to fluctuate stably in the range of 0℃ to 90℃.

[0104] The steps for obtaining the parameters are as follows: Based on the nominal values ​​of the equipment before it was put into operation and the initial impact strength obtained from actual tests of similar equipment, maintenance personnel record the impact of each parameter on the initial failure rate during 300 hours of continuous operation monitoring, forming a comparison table of the initial impact magnitude. Then, a weighted average is used to obtain the value corresponding to each parameter. value;

[0105] The steps for obtaining parameters are as follows: It is necessary to assess the growth rate of the impact of performance parameters gradually increasing within their normal range on the overall operation of the equipment. Maintenance personnel extract multiple operating curves with different durations and failure rates from previous monitoring data, statistically analyze the rate of change of each indicator segment by segment, and summarize them into a growth rate table. Then, linear fitting is performed based on the growth rate table to provide... The quantization range (e.g., 1.1 to 1.3) is then determined in conjunction with the specific equipment model;

[0106] and The steps for obtaining the parameters are as follows: To address the potential attenuation effects of each parameter, the same model of equipment is typically run in a laboratory environment. The degree of attenuation of each parameter to the performance score under extreme environments or loads is recorded. Information such as output power, maximum temperature, and vibration peak values ​​during equipment operation is collected and compared. Finally, these attenuation patterns are quantified into coefficients. and The range of values ​​(e.g.) Between 0.5 and 1.5 Between 0.2 and 0.8);

[0107] Calculation process:

[0108] Number of statistical performance parameters For example, there are currently four performance parameters: power efficiency, temperature stability, vibration frequency, and start-up time, and their respective weights are determined. , , , These figures come from multiple rounds of failure statistics and expert evaluation;

[0109] Get each parameter , , , and Values, such as the stability score of power efficiency. Temperature stability rating Vibration frequency score Startup time rating Determined by combining the aforementioned methods , , Specific values ​​such as , , etc.

[0110] Calculate item by item and divide by Then with the corresponding Multiply them, then add all the results together to get the final product. value;

[0111] For example, if the calculation result is 0.1497, and the operations and maintenance department defined the following in the early monitoring: If the score is below 0.5, further investigation of the equipment is required. A comprehensive score of 0.49 or 0.48 indicates a certain risk. A score above 0.8 indicates relative stability. If the score is between 0.5 and 0.8, it means that many parameters are within the normal range, but some dimensions are slightly fluctuating. The comprehensive score within different threshold ranges is helpful for subsequent equipment management and maintenance judgment.

[0112] The steps for obtaining the equipment stability status results are as follows:

[0113] Based on the weighted adjustment of performance scores, the performance data of each device and the preset performance stability threshold are collected to obtain historical data and a threshold comparison list.

[0114] Based on historical data and a threshold comparison list, the current weight adjustment performance score of each device is compared with historical data from the same period, the score change trend is analyzed, and the performance change analysis results of each device are obtained.

[0115] Based on the performance change analysis results of each device, the current operating status is judged to be stable by comparing it with the preset performance stability threshold. If the score is lower than the preset performance stability threshold, it is judged to be unstable, otherwise it is stable, and the device stability status result is generated.

[0116] Specifically, based on the previously obtained weighted performance score adjustment, it is necessary to obtain the statistical data of each device's operating indicators over different time periods and collect the corresponding preset performance stability thresholds. First, refer to the device's technical specifications to determine the allowable ranges for key indicators, such as temperature between 0℃ and 90℃, pressure between 0MPa and 2MPa, current between 0A and 5A, and voltage between 0V and 24V. During device operation and maintenance, further adjustments to the specific upper and lower limits are made based on failure rate survey results. Historical data for these indicators are matched with corresponding timestamps and compared with the threshold ranges. If certain data repeatedly fall on the edge of the range or exceed the range, it may indicate related problems. These issues can be addressed by... The data is further verified to check for abnormal sensor readings. If the sensor is confirmed to be normal after manual or automatic comparison, the record that exceeds or falls below the threshold can be marked as a potential risk. At the same time, it is also necessary to collect the operating history of the same batch of equipment under the same or similar environment to check whether there are significant differences between the equipment. For example, whether the temperature rise trend of the same model of equipment under the same load conditions is consistent. If individual equipment deviates significantly from most equipment, it can be marked in the summary table and focused on in subsequent stages. Through these operations, the numerical changes of each equipment in the past multiple operating cycles and the comparison with the threshold are finally integrated to form the historical data and threshold comparison list of the equipment.

[0117] Based on the historical data and threshold comparison list obtained above, it is necessary to cross-compare the current weighted performance score of each device with historical data from the same period. First, the scores within the same or adjacent time periods are ranked to check whether there is a continuous increase or decrease in parameters such as power efficiency, temperature stability, current load, and vibration frequency. Each comparison result should be accompanied by a corresponding timestamp and annotations about the operating environment, such as whether the ambient temperature is 3°C to 5°C higher than usual or other factors that may cause score fluctuations. Then, the trend of score changes is traced back. If the score shows a gradual decrease or significant increase in fluctuation across multiple consecutive measurement points, it is necessary to verify whether there is a lag in equipment maintenance or other issues. The issue of aging of key components can often be confirmed by referring to data records from previous operating weeks and in conjunction with regular maintenance files. If the score changes relatively steadily within a continuous monitoring period, it can be temporarily treated as normal. To facilitate subsequent statistics, the multiple score results of each device in the key section can be summarized. For example, the score under high temperature conditions is in the range of 0.6 to 0.7, or the score under high load conditions is in the range of 0.7 to 0.8. By combining the scores of these different scenarios with their corresponding environmental and operating conditions, the changing pattern of the score with operating conditions and historical status can be clearly presented. Finally, based on the above comparison and analysis, the performance change analysis results of each device can be obtained.

[0118] Based on the performance change analysis results of each device obtained above, the current weighted performance score needs to be compared with the preset performance stability threshold for judgment. Specifically, the score and its threshold are listed for each device in the evaluation table. If the score is found to be lower than the threshold range provided by the device design or operation and maintenance department, it is marked as unstable during the evaluation. For example, if the threshold of a certain unit is specified to be between 0.50 and 1.00, and its score has been lower than 0.50 many times recently, subsequent actions will be triggered immediately. If the score is maintained at 0.60 or higher, it is classified as stable. In order to avoid missed judgments or misjudgments, a critical range (such as 0.45 to 0.50) can be set around the threshold. For devices within the critical range, the monitoring frequency is increased or short-term inspections are arranged during subsequent operation. If multiple instances of being lower than the threshold are still found during the inspection, it is clearly marked as unstable. This mark is associated with the device number and timestamp and saved. Through this set of filtering logic, the current stability label of each device can be viewed in the scheduling system, and subsequent investigation or maintenance can be carried out for unstable devices. Through the above steps, the device stability status result is generated.

[0119] The steps for obtaining the adjusted warranty period records are as follows:

[0120] Collect equipment stability status results, classify the status of each piece of equipment, mark the equipment that is operating stably and the equipment that is operating with fluctuations, and obtain equipment status classification records;

[0121] Based on the equipment status classification records, the adjusted warranty period is calculated using the following formula:

[0122] ;

[0123] in, The adjusted warranty period, Based on the standard warranty period, and It is an adjustment factor. To score the equipment's performance stability, This is the equipment failure rate adjustment coefficient. The expected failure rate of the equipment;

[0124] Update the equipment's warranty record according to the adjusted warranty period to obtain the adjusted warranty period record.

[0125] Specifically, the stability status results of each device are collected and categorized. First, all obtained stability status indicators are combined with the corresponding historical maintenance list for each device. From this, the operating records of each device in the previous stage are extracted, including whether the performance stability score has repeatedly fallen below a specific threshold (e.g., 0.50 to 0.60), whether the temperature has repeatedly exceeded 90℃, and whether the current has repeatedly exceeded 5A. These classification markers identify devices that meet either the stable operation or fluctuating operation categories. When grouping devices of the same type, information such as device model and power rating needs to be considered to ensure that devices of the same model or power rating are clustered together for easy comparison. If some devices indicate in their fault records that they have been triggered multiple times... Overload alarms or instances of abnormally long startup times (e.g., exceeding the predetermined standard of 7 seconds) can be specially marked in this record for more intensive inspections or tests if necessary. If the threshold established by the maintenance department requires further investigation when both 5A current and 24V voltage fluctuate simultaneously, such cases can be listed separately and compared with the same batch of equipment through a centralized query function. If different devices exhibit significant differences or frequent fluctuations under the same load conditions, these devices are recorded as operating in a fluctuating state and added to the corresponding group. After classifying stable and fluctuating states, the latest status of each device is updated and categorized using timestamps and reason annotations to obtain a device status classification record.

[0126] The advantage of the formula is that it integrates the performance stability score, failure rate factors and benchmark warranty period of the equipment during operation, which makes the warranty period closer to the real-time usage of the equipment.

[0127] The steps for obtaining parameters are as follows: before the equipment is put into use, the manufacturer and the maintenance party jointly provide the parameters based on the results of the equipment's factory test and standard operating conditions, and combine them with the historical life statistics of similar equipment to form a basic duration (e.g., 12 months or 24 months).

[0128] and The parameter acquisition process involves equipment monitoring personnel conducting centralized tests on the equipment's operating characteristics under different load environments, temperature ranges, and vibration intensities. This is done by collecting a series of maintenance data and combining it with the actual wear and tear of the equipment under high load conditions to set the adjustment range. Between 0.1 and 0.5) and exponential decay rate ( (Between 0.2 and 0.8), after multiple rounds of comparative testing, the optimal value is recorded as the value corresponding to the device. and ;

[0129] The steps to obtain the score are as follows: extract the final score value (e.g., 0.75 or 0.80) of the device from the previously obtained device performance stability score. This score is obtained by calculating the degree of data fluctuation within the time window and adjusting the weight in the early stage.

[0130] The steps for obtaining parameters are as follows: During continuous operation monitoring, observe the failure frequency of the equipment under different operating conditions such as normal load and extreme load; compare this frequency with the average failure rate of similar equipment in the industrial field; and then summarize the results using a weighted method. It may be in the range of 0.1 to 0.3, and the specific value was obtained after multiple evaluations and statistics.

[0131] The steps for obtaining parameters are as follows: calculate the historical failure rate of the main vulnerable components during equipment operation; extract the damage status of relevant components from each fault location data after formal operation and calculate the time average failure rate; compare this value with existing industrial data and then make corrections to obtain the expected failure rate of the equipment (e.g., 0.02 or 0.03).

[0132] Calculation process:

[0133] Determine the values ​​of each parameter, for example Months , , , , ,

[0134] First calculate ,

[0135] calculate ,

[0136] Summation ,

[0137] Multiply ,Right now (Unit: number of months);

[0138] The results indicate that the calculated adjusted warranty period is approximately 14.55 months. If monitoring personnel pre-determine that a re-verification is required after 15 months, this value can be compared with the 15-month boundary. If subsequent operational observations reveal a gradual improvement in equipment performance (making... Increase or (Reduced), then during recalculation... It may be extended further, if it occurs Significantly increased The corresponding shortening;

[0139] Based on the adjusted warranty period obtained earlier, for each device, operational data continues to be tracked and important indicators such as current and voltage are summarized in the new period. Then, this real-time collected information is compared with the previously determined failure rate reference range (e.g., the number of failures should not exceed 2 times per quarter). If the number of device failures is found to be significantly higher than this reference range in a certain statistical period, the device is recorded in the operation and maintenance system and its specific failure rate is checked to see if it has increased (e.g., from 0.03 to 0.05). If it has indeed increased, the warranty period is recalculated and the relevant departments are notified immediately to intervene in maintenance. After completing this series of query and comparison operations, the adjusted new warranty period is written into the device file and an update mark is formed. For devices that have not experienced significant fluctuations in this inspection period, the previous period information is retained and the current date stamp is added to obtain the adjusted warranty period record.

[0140] The steps to obtain performance prediction results are as follows:

[0141] Collect historical performance data of the equipment that needs to be predicted, including voltage, current and temperature parameters, to obtain a historical performance dataset;

[0142] Based on historical performance datasets, the ARIMA model is applied for trend analysis. The number of moving average terms and the number of autoregressive terms in the ARIMA model are set to obtain the model configuration.

[0143] Based on the model configuration, run the ARIMA model to predict performance, analyze the stability trend of equipment performance over a future time period, and generate performance prediction results.

[0144] Specifically, when collecting historical performance data for equipment requiring prediction, it's necessary to first confirm the collection scope and frequency. Then, retrieve the voltage, current, and temperature values ​​of the equipment over several past periods (e.g., 180 consecutive days) from previous maintenance records, and associate each record with a timestamp. If the maintenance department has set a safe range of 0V to 24V for voltage, a rated range of 0A to 5A for current, and an operating boundary of 0℃ to 90℃ for temperature, then each record needs to be compared to see if it exceeds the corresponding range. If some voltage points are found to be higher than 24V or temperature data exceeds 90℃, these are marked as potential anomalies, and these markings are subsequently confirmed. For example,... Check the dedicated fault log information to see if a fault alarm was triggered at that time. If it is confirmed that there is no sensor error, retain the abnormal reading for analysis of its impact on subsequent predictions. After comparing the overall data and forming a continuous time series, it is necessary to additionally identify the sub-intervals of the equipment under different operating conditions (such as high load or high temperature environment) and record their numerical distribution. This sub-interval information can provide a basis for differential analysis of the prediction model. For example, if the voltage fluctuation characteristics of the equipment in the high temperature range are significantly different from those in the normal temperature range, then the range can be modeled separately or processed by adding additional feature quantities when building the model later. After these operations are completed, the historical performance dataset can be obtained.

[0145] Based on the historical performance dataset obtained above, the ARIMA model needs to be applied for trend analysis. First, select time-series segments of the equipment under standard load conditions from the dataset and ensure that the sampling frequency is sufficiently uniform, such as once per minute or once every ten seconds. Then, arrange these time-series data in chronological order and construct training samples. Next, the number of moving average terms and the number of autoregressive terms in the ARIMA model need to be set. The specific order can be selected based on the observation results of the ACF (autocorrelation function) and PACF (partial autocorrelation function) graphs by the equipment operation and maintenance department in previous experiments. For example, it may be found that the number of autoregressive terms is 2 and the number of moving average terms is 1, which better reflects the fluctuation trend of the equipment. At the same time, seasonal or periodic factors need to be considered. If they exist, additional processing should be performed. For cases where the temperature reading rises quickly and falls slowly, temperature can be regarded as a key feature and voltage and current can be input as parallel features into the time-series model. In addition, if there are many types of equipment or a large sample span, group modeling is required. Each group of equipment is matched with a similar model configuration according to its power level and thermal balance characteristics. Finally, after completing these settings, the configuration items of the ARIMA model are obtained.

[0146] Based on the previously obtained model configuration, the historical time-series data of the equipment is read point by point during the training process, and the three parameters of voltage, current and temperature are mapped to the corresponding timestamps. During the training phase, the ARIMA parameters need to be fitted with data from past periods in batches, and the difference between the predicted and actual values ​​is compared after each batch. If the difference exceeds the allowable range specified by the operation and maintenance department in advance (for example, the prediction deviation is greater than ±5% or the temperature prediction deviates from the actual value by more than 3℃), the model order or other fine-tuning items are corrected after this iteration and the next batch of training continues. After multiple iterations, if the prediction deviation can be kept within the allowable range for most periods, it means that the model is basically applicable. Then, in the actual prediction phase, the recently acquired voltage and current information can be input into the model in the same order, and the model will automatically predict the operating trend of temperature and other key parameters in the future period. If the results show that the equipment voltage or temperature has a tendency to continuously develop towards the boundary limit, a prompt can be added in the operation and maintenance system. Through this prediction operation, the performance stability trend of the equipment in the future period is comprehensively analyzed and the corresponding data sequence is output, and finally the performance prediction result is generated.

[0147] The steps to obtain the emergency warranty trigger record are as follows:

[0148] Based on the performance prediction results, devices that exceed the danger threshold are identified, and a list of devices that trigger emergency warranty is obtained;

[0149] Based on the list of equipment that triggers emergency warranty, prepare warranty requirement documents, including requirements for equipment maintenance or replacement, record and send them to the supplier, and obtain feedback from the supplier;

[0150] Based on supplier feedback, update the equipment maintenance or replacement plan and generate emergency warranty trigger records.

[0151] Specifically, based on the performance prediction results obtained above, it is necessary to compare the operating data of all equipment in the future period and identify equipment with dangerous conditions. The monitoring data is compared with the danger thresholds set in advance by the operation and maintenance department. For example, for equipment with a temperature range of 0℃ to 90℃, 90℃ can be used as the danger threshold, and for equipment with a current range of 0A to 5A, 5A can be used as the corresponding danger threshold. If the current exceeds 5A or the temperature exceeds 90℃ multiple times during monitoring or prediction, the equipment is determined to be at high risk. This is then checked in conjunction with the alarm records of the past few months in the operation and maintenance experience database to see if any issues have already occurred. If multiple edge alarms are triggered, these devices are included in the category of devices that may fail or have a rapidly increasing failure rate. Then, based on details such as the specific device model, power rating, and operating environment, it is confirmed whether to add them to the danger list. For example, if the current of the same model of device generally increases to about 4.7A after the load increases by 10% but does not exceed the 5A threshold, it is not included for the time being. If individual devices continuously and significantly fluctuate to 5.2A in a short period of time, they are immediately judged to be out of limit. All devices that meet or exceed the danger threshold are summarized and fixedly marked in subsequent steps to form an ordered list, resulting in the list of devices that trigger emergency warranty.

[0152] Based on the previously obtained list of equipment triggering emergency warranty, a warranty requirement document needs to be prepared, and the main risk factors for each piece of equipment need to be identified. For example, the number of failures, startup time, and abnormal temperature points in the past 30 days may be used to list them as actual maintenance or replacement needs. If the startup time of a certain piece of equipment is consistently higher than the predetermined standard set by the maintenance department (e.g., 7 seconds) and the temperature exceeds 90°C several times, it will be included in the replacement plan. If other equipment only experiences short-term current overload, it will be included in the maintenance plan. These requirements should be recorded uniformly, along with detailed information such as equipment identification number, model, and operating time. After completing this list, it should be sent to the corresponding manufacturing supplier, and feedback on the supply of replacement parts or maintenance materials should be awaited. If the supplier's feedback indicates that certain models do not have readily available spare parts and need to be ordered in advance or that the fault point needs further confirmation, then communication with the supplier will continue in the next stage. If the supplier confirms that it can deliver in a timely manner or provide a backup model, this step can be confirmed. Finally, a new maintenance and replacement schedule will be formed based on all feedback information, and feedback from the supplier will be obtained.

[0153] Based on the supplier feedback received earlier, the maintenance or replacement plans for the corresponding equipment need to be updated. For example, equipment with confirmed faulty parts that can be delivered within a week should be prioritized for maintenance next week. At the same time, equipment that requires temporary borrowing of other models of parts should be sorted, taking into account the failure rate and high-risk indicators such as temperature and current registered in the maintenance system. If the high-risk indicators are prominent, they should be prioritized for handling. If the supplier indicates a shortage of the same model of parts and that delivery will be delayed by at least two weeks, this message should be recorded and an assessment should be made as to whether the current equipment can maintain low-load operation for two weeks to avoid secondary failures. Then, all equipment maintenance and replacement plans should be compiled and combined with the equipment number and the expected maintenance period. If some equipment has been confirmed to need replacement during this process, it indicates that the replacement time is in line with the factory's production line rhythm. After completing the above operations, an emergency warranty trigger record can be generated.

[0154] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for managing the quality of electric power materials, characterized by, The method comprises the following steps: Collecting real-time operation data of various power equipment, including voltage and current, for real-time monitoring and recording to generate real-time performance monitoring records; Based on the real-time performance monitoring records, using a sliding window to analyze performance fluctuations to generate a device performance stability score; Based on the device performance stability score, applying a multi-parameter weight analysis method to adjust the influence weight of each performance parameter to generate a weight-adjusted performance score; Based on the weight-adjusted performance score, comparing past data and threshold settings to verify whether the current operation state of each device is stable to generate a device stability state result; According to the device stability state result, dynamically adjusting the warranty period to generate an adjusted warranty period record; Performing a prediction model analysis on the device to predict the performance stability of the device in a future time period to generate a performance prediction result; According to the device prediction result and real-time monitoring data, triggering an emergency warranty process for a device that triggers a dangerous threshold, feeding back warranty requirements to a supplier, updating maintenance or replacement plans, and generating an emergency warranty trigger record; The real-time performance monitoring record acquisition step is: Real-time collection of voltage and current data of power equipment through voltage and current sensors installed on the power equipment, timestamping each data point to form real-time data records of the power equipment; Based on the real-time data records of the power equipment, format and clean the data, calculate the average current and voltage level of the device, and obtain the real-time performance monitoring record; The device performance stability score acquisition step is: Based on the real-time performance monitoring record, read and sort the data at each time point, divide into fixed-size time windows in chronological order, and generate partitioned time window records; According to the partitioned time window records, calculate the performance fluctuation value in each time window, the calculation formula is: ； wherein, is a performance fluctuation value for the th time window, is a performance value for the th window at the th time point, is a performance mean value for the th window, is the number of time points within each window. According to the performance fluctuation value, calculate the device performance stability score, the calculation formula is: ； wherein, is a device performance stability score, is a performance fluctuation value for the time window, is the number of all time windows; The weight-adjusted performance score acquisition step is: Based on the device performance stability score, filter parameters related to device performance, including power efficiency, temperature stability, vibration frequency, or startup time, to generate a performance parameter list; Based on the performance parameter list, calculate the weight-adjusted performance score, the calculation formula is: ； wherein, is a weight adjustment performance score, is a weight of the th performance parameter, is a weight of the th parameter, is a coefficient for adjusting an initial impact size, is a coefficient for adjusting a growth rate, and is a decay coefficient, and sn is the number of performance parameters. The adjusted warranty period record acquisition step is: Collect the device stability state result, classify the state of each device, mark the devices running stably and running fluctuantly, and obtain a device state classification record; Based on the device state classification record, calculate the adjusted warranty period, the calculation formula is: ； wherein, is the adjusted warranty period, is the baseline warranty period, and is the adjustment factor, is the device performance stability score, is the device failure rate adjustment factor, is the expected failure rate of the device; According to the adjusted warranty period, update the warranty record of the device to obtain the adjusted warranty period record.

2. The electric power asset guarantee management method according to claim 1, characterized by, The device stability state result acquisition step is: Based on the weight-adjusted performance score, collect the performance data of each device and the preset performance stability threshold to obtain a historical data and threshold comparison list; Based on the historical data and threshold comparison list, compare the current weight-adjusted performance score of each device with the historical same period data, analyze the score change trend, and obtain the performance change analysis result of each device; According to the performance change analysis result of each device, it is judged whether the current running state is stable or not by comparing with the preset performance stability threshold value. If the score is lower than the preset performance stability threshold value, it is determined to be unstable, otherwise it is stable, and a device stability state result is generated.

3. The electric power asset guarantee management method according to claim 1, characterized by, The performance prediction result acquisition step is: Collecting historical performance data of the device to be predicted, including voltage, current and temperature parameters, to obtain a historical performance data set; Based on the historical performance data set, applying an ARIMA model for trend analysis, setting the moving average term number and the autoregressive term number of the ARIMA model, and obtaining a model configuration; According to the model configuration, running the ARIMA model for performance prediction, analyzing the device performance stability trend in the future time period, and generating a performance prediction result.

4. The electric power asset guarantee management method according to claim 1, characterized by, The emergency warranty trigger record acquisition step is: Based on the performance prediction result, identifying the device exceeding the danger threshold to obtain a list of devices triggering emergency warranty; Based on the list of devices triggering emergency warranty, preparing a warranty requirement file including the requirements for device maintenance or replacement, recording and sending to the supplier to obtain supplier feedback; According to the supplier feedback, updating the maintenance or replacement plan of the device, and generating an emergency warranty trigger record.

5. The management system of the electric power asset guarantee management method according to any one of claims 1 to 4, characterized by, It includes: A data monitoring module for collecting real-time power equipment operation data including voltage and current, and generating real-time monitoring records; A performance analysis module for applying a sliding window to analyze the performance fluctuation of the power equipment based on the real-time monitoring records, obtaining the performance stability index of the device, and obtaining the device stability score; A weight adjustment module for adjusting the weight of each parameter affecting the performance of the device based on the device stability score, obtaining the weight-adjusted performance score; A state verification module for comparing the historical performance data of the device with the preset threshold value by using the weight-adjusted performance score, judging whether the current running state meets the expectation or not, and generating a device stability state result; A warranty management module for adjusting the warranty period and recording according to the device stability state result, predicting the performance stability of the device in the future time period, generating a performance prediction result, triggering an emergency warranty process for the device at risk based on the performance prediction result and the real-time monitoring record, sharing the requirements with the supplier, updating the maintenance or replacement plan, and obtaining an emergency warranty trigger record.

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