Operation aging detection system adapted to communication power supply

Abstract

The application belongs to the field of power supply aging detection, and relates to data analysis technology, and is used for solving the problem that the prior art cannot allocate the best analysis cycle length to the power supply in different life stages of the power supply, and specifically relates to an operation aging detection system suitable for communication power supply, which comprises an aging detection platform, wherein the aging detection platform is in communication connection with an operation detection module, a life evaluation module, a detection optimization module, an optimization verification module and a database; the application realizes accurate monitoring of the operation state of the communication power supply and accurate evaluation of the aging trend. The randomized detection cycle length breaks the limitation of the fixed mode, so that the system can adapt to the aging characteristics of the power supply in different life stages, the multi-dimensional parameter weighted analysis mechanism improves the accuracy of the aging risk evaluation, and the life stage division and the paired interval marking based on the historical data provide a basis for the dynamic optimization of the detection strategy.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of power supply aging detection, and relates to a data analysis technology, in particular to an operation aging detection system suitable for communication power supply. BACKGROUND

[0002] The operation aging detection system of communication power supply is a comprehensive platform integrating data acquisition, real-time monitoring, intelligent analysis, early warning and prediction, and maintenance management. The core purpose is to continuously "health check" the communication power supply equipment, to find the performance degradation trend caused by long-term operation and component aging in advance, so as to change passive maintenance to proactive predictive maintenance, and greatly improve the power supply reliability of communication network.

[0003] The patent for invention with publication number CN119335433B discloses a DC power supply aging test method, device and system. The test method determines the aging test result of the bidirectional DC power supply according to the second quantity and the first quantity of the total monitoring period of the bidirectional DC power supply; and improves the accuracy of the aging test result of the bidirectional DC power supply; but the test method uses a fixed test period to perform aging test on the whole life cycle of the power supply. The aging parameters of the power supply need to collect real-time electrical data and perform periodic analysis to obtain the result. If the length of the detection period is too short, the data reliability will be affected, and the result accuracy will be low. If the length of the detection period is too long, the fault early warning will not be timely. The prior art cannot allocate the best analysis period length to the power supply at different life stages, so that the operation aging detection system cannot balance the timeliness and result accuracy of early warning.

[0004] In view of the above technical problems, the present application provides a solution. SUMMARY

[0005] The application aims to provide an operation aging detection system suitable for communication power supply, which solves the problem that the prior art cannot allocate the best analysis period length to the power supply at different life stages;

[0006] The technical problem to be solved by the application is how to provide an operation aging detection system suitable for communication power supply, which can allocate the best analysis period length to the power supply at different life stages.

[0007] The object of the application can be achieved by the following technical solutions:

[0008] The operation aging detection system suitable for communication power supply comprises an aging detection platform, which is in communication connection with an operation detection module, a life evaluation module, a detection optimization module, an optimization verification module and a database.

[0009] The operation detection module is configured to detect and analyze the operation state of the communication power supply: mark the communication power supply as a detection object, generate continuous detection cycles when the detection object is running, randomly select the execution duration of each detection cycle from a standard duration range, and obtain the efficiency decay rate, insulation deterioration degree, and switch fatigue index of the detection object at the end of each detection cycle; calculate the aging risk value of the detection object by weighted summation of the efficiency decay rate, insulation deterioration degree, and switch fatigue index of the detection object in the detection cycle, and determine whether the detection object has aging risk and failure risk through the aging risk value;

[0010] The life evaluation module is configured to evaluate and analyze the operation life of the communication power supply: mark the scrapped detection object as an evaluation object, when the number of evaluation objects reaches L1, form a life set by the effective operation duration of all evaluation objects, obtain a life balance value by data cleaning of the life set, and segment the life cycle of the evaluation object into L2 operation stages by the life balance value, and mark the paired intervals of the operation stages;

[0011] The detection optimization module is configured to optimize the operation and aging detection process of the communication power supply.

[0012] The optimization verification module is configured to verify and analyze the detection optimization effect of the communication power supply.

[0013] Further, the efficiency decay rate reflects the degree of decline of the power supply in long-term operation energy conversion efficiency, which is obtained by real-time load-efficiency curve comparison and dynamic loss analysis, the insulation deterioration degree reflects the aging state of the internal insulation material of the power supply, which is obtained by combining dielectric loss monitoring and partial discharge; the switch fatigue index is for power switch devices in the power supply, which reflects the performance degradation caused by the number of switch-on / off and stress accumulation of the switch device.

[0014] Further, the specific process of determining whether the detection object has aging risk and failure risk includes: obtaining the aging risk threshold and the failure risk threshold from the database, comparing the aging risk value with the aging risk threshold and the failure risk threshold: if the aging risk value is greater than the aging risk threshold, it is determined that the aging degree of the detection object is abnormal, a scrap signal is generated and sent to the mobile terminal of the management personnel; if the aging risk value is greater than the failure risk threshold, it is determined that the detection object has failure risk, a failure warning signal is generated and sent to the mobile terminal of the management personnel; if the aging risk value is less than the failure risk threshold, it is determined that the detection object meets the operation requirements.

[0015] Further, the specific process of data cleaning on the life set includes: calculating the variance of all elements in the life set to obtain a life balance coefficient, obtaining a life balance threshold value from the database, comparing the life balance coefficient with the life balance threshold value: if the life balance coefficient is greater than or equal to the life balance threshold value, the maximum element and the minimum element in the life set are removed, and then the life balance coefficient is recalculated, and the process is repeated until the life balance coefficient is less than the life balance threshold value; if the life balance coefficient is less than the life balance threshold value, the sum of all elements in the life set is taken to obtain a life balance value.

[0016] Further, the specific process of stage segmentation on the life cycle of the evaluation object includes: marking the ratio of the life balance value to the numerical value L2 as an equilibrium segmentation value, and segmenting the effective running time of the evaluation object according to the equilibrium segmentation value from the first to the last to obtain L2 running stages.

[0017] Further, the specific process of marking the paired interval of the running stage includes: marking all complete detection cycles of all evaluation objects in the same running stage as the paired cycles of the running stage, constructing an execution range from the maximum and minimum values of the execution time of the paired cycles, segmenting the execution range into several execution intervals, marking the sum of the total number of communication power failures and the total number of false alarms in all paired cycles in the execution interval as the screening value of the execution interval, and marking the execution interval with the smallest screening value as the paired interval of the running stage.

[0018] Further, the detection optimization module optimizes the running aging detection process of the communication power supply: after the number of evaluation objects reaches L1, the running stage is matched according to the effective running time of the detection object, and the corresponding paired interval is called, and a random value is selected from the paired interval as the execution time of the detection cycle until the detection object enters the next running stage.

[0019] Further, the specific process of the optimization verification module verifying and analyzing the detection optimization effect of the communication power supply includes: after the number of evaluation objects reaches L3, L1 and L3 are numerical constants, and L3>L1, the life balance coefficient is recalculated, the difference between the life balance coefficient corresponding to the L3 evaluation objects and the life balance coefficient corresponding to the L1 evaluation objects is marked as an effective optimization coefficient, and whether the detection optimization effect of the communication power supply meets the requirements is determined through the effective optimization coefficient.

[0020] Further, the effective optimization threshold is obtained through the database, and the effective optimization coefficient is compared with the effective optimization threshold; the specific process of determining whether the detection optimization effect of the communication power supply meets the requirements comprises: if the effective optimization coefficient is less than the effective optimization threshold, it is determined that the detection optimization effect of the communication power supply does not meet the requirements, and the life balance threshold in the life evaluation module and the value of L2 are proportionally optimized, wherein the life balance threshold is proportionally reduced, and the value of L2 is proportionally amplified; if the effective optimization coefficient is greater than or equal to the effective optimization threshold, it is determined that the detection optimization effect of the communication power supply meets the requirements, and the life set is reconstituted from the effective running time length of the evaluation object whose mark order is L1-L3, and the stage segmentation and the mark of the pairing interval are reperformed.

[0021] The present application has the following advantages:

[0022] 1. The present application realizes accurate monitoring of the running state of the communication power supply and accurate evaluation of the aging trend. The randomized detection cycle length breaks the limitation of the fixed mode, enabling the system to adapt to the aging characteristics of the power supply at different life stages, the multi-dimensional parameter weighted analysis mechanism improves the accuracy of the aging risk evaluation, the life stage division and pairing interval marking based on historical data provide a basis for the dynamic optimization of the detection strategy, and the adaptive detection mechanism significantly improves the timeliness and accuracy of the fault warning, while avoiding excessive detection and data redundancy in the early stage. Therefore, the present application effectively solves the contradiction between the timeliness and accuracy of the traditional fixed cycle detection method, provides reliable technical support for the predictive maintenance of the communication power supply, and helps to improve the power supply reliability of the communication network;

[0023] 2. The present application realizes effective cleaning of life data, eliminates the influence of outliers, improves the accuracy and representativeness of the life balance value, and ensures the adaptability and flexibility of the data cleaning process by introducing the comparison mechanism of the life balance coefficient and the life balance threshold. This method can better reflect the actual running life characteristics of the communication power supply, providing a reliable data basis for the subsequent life cycle stage segmentation, thereby improving the analysis accuracy and prediction ability of the entire running aging detection system;

[0024] 3. The present application realizes reasonable segmentation of the life cycle of the evaluation object. Therefore, targeted analysis can be performed according to the characteristics of different running stages, improving the accuracy and effectiveness of the running aging detection. At the same time, through the balanced segmentation method, the length of each running stage is relatively balanced, avoiding data deviation caused by excessively long or short stages. This segmentation method is simple and easy to implement, and can adapt to different types and life characteristics of communication power supplies, having good universality and scalability;

[0025] 4. The application can determine the optimal detection cycle execution time range for each running stage. By statistically analyzing the actual operation data, the time interval with the least fault occurrence and false alarm is selected, which ensures the timeliness of detection and improves the accuracy of detection results. This data-driven optimization method can adaptively adjust the detection strategy for different models and different running stages of communication power supply, improving the intelligent level and practicality of the entire aging detection system;

[0026] 5. The application realizes closed-loop verification of the detection cycle optimization effect, dynamically adjusts the life balance threshold and the number of stage segmentation, solves the problems of early warning lag and data distortion caused by fixed detection cycle parameters, and makes the execution time of the detection cycle adaptively optimized with the change of power supply aging characteristics, maintaining the balance between fault early warning timeliness and detection result accuracy in different life stages of the power supply. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0028] Figure 1 The system block diagram of the first embodiment of the present application is shown in the figure.

[0029] Figure 2 The method flow chart of the second embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0030] The technical solutions of the present application will be described in detail below in combination with embodiments. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the present application.

[0031] In the conventional existing communication power supply running aging detection system, the test cycle with fixed execution time is used for full life cycle aging test of the power supply, which leads to the problem that the detection cycle time cannot be dynamically adjusted according to different life stages of the power supply, and further causes the contradiction between the reliability of aging parameter analysis results and the delay of fault early warning. Among them, the fixed cycle time is redundant in collecting data in the early stage of power supply running because the aging characteristics have not yet been significant, which increases the invalid calculation load; in the later stage of power supply running, the data collection interval is too long due to accelerated aging, which cannot capture the rapid change of the deterioration trend, reducing the timeliness of fault early warning.

[0032] For example, in the aging test scenario of DC power supply systems for communication base stations, the test cycle is set to a fixed duration. During the initial operation phase, the power supply's components are stable, and aging parameters fluctuate little. The data samples collected at a fixed cycle have high repeatability, making it difficult to effectively distinguish between normal fluctuations and abnormal degradation in the weighted calculation of key indicators such as efficiency decay rate and insulation deterioration, leading to a risk of misjudgment. When the power supply enters its lifespan decline phase, the stress accumulation rate of the power switching devices accelerates, and the switching fatigue index exhibits non-linear growth. However, the fixed cycle cannot shorten the test interval, resulting in blind spots in the real-time monitoring of partial discharge events or sudden changes in dielectric loss, and fault warnings lag behind the actual degradation process.

[0033] If the above problems are not addressed, the power supply aging detection system will be unable to adapt to the dynamic characteristics of its entire lifecycle, leading to increased false alarm rates and maintenance costs in the early stages, and a heightened risk of missed detections and unplanned downtime in the later stages. Simultaneously, the uneven data distribution generated at fixed intervals will interfere with the training accuracy of the lifespan assessment model, weaken the reliability of stage segmentation and pairing interval marking, ultimately causing a mismatch between maintenance strategies and the actual aging state of the power supply, threatening the continuity of power supply to communication networks.

[0034] Example 1: As Figure 1 As shown, the aging test system adapted for communication power supplies includes an aging test platform, which is communicatively connected to an operation test module, a life assessment module, a test optimization module, an optimization verification module, and a database.

[0035] The running detection module is used for detecting and analyzing the running state of the communication power supply: the communication power supply is marked as a detection object, a continuous detection period is generated when the detection object is running, the execution time length of each detection period is randomly selected from a standard time length range, and the efficiency attenuation rate, insulation deterioration degree and switch fatigue index of the detection object are obtained at the end time of each detection period; the efficiency attenuation rate reflects the degree of decline of the energy conversion efficiency of the power supply in long-term operation, is realized through real-time load-efficiency curve comparison and dynamic loss analysis, the insulation deterioration degree focuses on the aging state of the internal insulation materials (such as capacitors, transformer windings) of the power supply, and is combined with dielectric loss monitoring and partial discharge analysis; the switch fatigue index is for the power switch device (such as MOSFET, IGBT) in the power supply, and reflects the performance degradation caused by the number of turn-on / off and stress accumulation; the aging risk value of the detection object is calculated by weighted summation of the efficiency attenuation rate, insulation deterioration degree and switch fatigue index of the detection object in the detection period, the aging risk threshold value and the failure risk threshold value are obtained through the database, and the aging risk value is compared with the aging risk threshold value and the failure risk threshold value: if the aging risk value is greater than the aging risk threshold value, it is determined that the aging degree of the detection object is abnormal, a scrap signal is generated and sent to the mobile terminal of the management personnel; if the aging risk value is greater than the failure risk threshold value, it is determined that the detection object has a failure risk, a failure warning signal is generated and sent to the mobile terminal of the management personnel; if the aging risk value is less than the failure risk threshold value, it is determined that the detection object meets the operation requirements.

[0036] Specifically, in the real-time load-efficiency curve comparison process, the actual efficiency data of the power supply under different load rates is continuously collected, and the difference is calculated with the efficiency value of the corresponding load point in the standard curve, the dynamic loss analysis further obtains the total loss of the power device under the variable load condition through integral operation, and the efficiency attenuation rate is generated after the two are weighted. The dielectric loss monitoring device periodically measures the dielectric loss tangent value of the insulation material, and the local discharge sensor captures the amplitude and frequency of the discharge pulse in a high-frequency sampling mode, and the insulation deterioration degree is obtained by linear superposition after normalization processing of the two data. The turn-on / off times of the switch device are directly recorded by the counter, the equivalent mechanical stress is calculated according to the voltage spike and current surge data when the switch is actuated, and the switch fatigue index is generated by exponential weighting. Therefore, the fusion of multi-dimensional data sources ensures that the calculation basis of the aging risk value has higher reliability and comprehensiveness.

[0037] As a preferred embodiment, the scheme of the application is implemented as follows:

[0038] The efficiency degradation rate reflects the degree of energy conversion efficiency reduction of the power supply in long-term operation. Specifically, the efficiency degradation rate is obtained by comparing the real-time load-efficiency curve with dynamic loss analysis. For example, the input power and output power of the power supply can be measured under different load conditions to calculate the real-time efficiency curve. Comparing this curve with the efficiency curve of the initial state of the power supply, the efficiency reduction percentage is obtained. At the same time, by measuring the loss power of each stage of the power supply circuit, the trend of loss change over time is analyzed, and the efficiency degradation rate is comprehensively obtained.

[0039] The insulation deterioration degree reflects the aging state of the internal insulation material of the power supply. Further, the insulation deterioration degree is obtained by combining dielectric loss monitoring and partial discharge. The dielectric loss monitoring can be realized by measuring the dielectric loss tangent of the insulation material. The partial discharge detection adopts ultrasonic or electromagnetic wave detection method to detect the occurrence of micro-discharge in the insulation material. Thus, the dielectric loss and partial discharge data are comprehensively analyzed to obtain the quantitative index of the insulation deterioration degree.

[0040] The switch fatigue index is aimed at the power switch device in the power supply, and reflects the performance degradation caused by the number of switch-on / off and stress accumulation of the switch device. Specifically, by monitoring the change trend of the turn-on time, turn-off time, on-resistance and other parameters of the switch device, and combining the accumulation of switch times, a fatigue model can be established to quantify the switch fatigue degree. For example, the parameter reference value in the initial state can be set, and as the number of switch times increases, the deviation of each parameter is calculated, and the switch fatigue index is comprehensively obtained.

[0041] Specifically, when the detection period ends, the system automatically calls the threshold data stored in the database, and compares the calculated aging risk value with the two preset threshold values. When the aging risk value exceeds the aging risk threshold value, the device scrapping process is triggered and a scrapping instruction containing the device number and detection timestamp is generated, which is pushed to the specified terminal through the mobile communication network. When the aging risk value is between the failure risk threshold value and the aging risk threshold value, an early warning information containing the risk level and the recommended maintenance measures is generated and pushed to the same terminal. When the aging risk value is lower than the failure risk threshold value, the system updates the state identifier of the detection object to normal operation. The determination process realizes risk grading management through hierarchical threshold setting, in which the failure risk threshold value considers the repairable threshold value of the power supply device, and the aging risk threshold value corresponds to the irreversible performance degradation critical point. After the mobile terminal receives the signal, different sound and light alarm modes are triggered and maintenance work orders are generated, and the maintenance personnel can take differential disposal measures according to the signal type.

[0042] The life evaluation module is used for evaluating and analyzing the operation life of the communication power supply: marking the scrapped detection objects as evaluation objects, when the number of the evaluation objects reaches L1, the valid operation time length of all the evaluation objects constitutes a life set, data cleaning is performed on the life set: variance calculation is performed on all the elements in the life set to obtain a life balance coefficient, the life balance threshold value is obtained through a database, and the life balance coefficient is compared with the life balance threshold value: if the life balance coefficient is greater than or equal to the life balance threshold value, the maximum element and the minimum element in the life set are removed, and then the life balance coefficient is recalculated, and the process is repeated until the life balance coefficient is less than the life balance threshold value; if the life balance coefficient is less than the life balance threshold value, the sum of all the elements in the life set is taken to obtain a life balance value.

[0043] The variance calculation is used for quantifying the discrete degree of the data in the life set, and the life balance threshold value is used as a critical standard for determining whether the data needs to be cleaned. When the life balance coefficient exceeds the threshold value, the maximum value and the minimum value are removed through iteration, and the interference of abnormal data on the overall balance is gradually eliminated until the data distribution tends to be stable. Finally, the life balance value is obtained by taking the average value, which ensures that the value can represent the typical life characteristics of the evaluation objects.

[0044] Specifically, first, the variance of all the valid operation time lengths in the life set is calculated to obtain a life balance coefficient to measure the data volatility. If the coefficient exceeds the preset life balance threshold value, it indicates that there are significant abnormal values in the data, and at this time, the maximum value and the minimum value need to be removed, the variance is recalculated, and the comparison process is repeated. The iteration operation continues until the variance of the remaining data is lower than the threshold value, at which time the data distribution tends to be balanced. Finally, the average value of the cleaned life set is taken to obtain the life balance value. Through this method, abnormal data is effectively excluded, and the calculation of the life balance value is no longer affected by the extreme value, thereby providing a reliable basis for the subsequent stage segmentation and improving the accuracy of the optimization adjustment of the detection cycle.

[0045] As a preferred embodiment, the scheme of the application is implemented as follows: the specific process of data cleaning of the life set includes: first, the variance of all the elements in the life set is calculated to obtain a life balance coefficient. For example, assuming that the life set contains 10 elements, which are 5000 hours, 5200 hours, 4800 hours, 5100 hours, 5300 hours, 4900 hours, 5400 hours, 5050 hours, 5150 hours and 4950 hours. The variance of these elements is calculated to obtain a life balance coefficient of 35000.

[0046] Next, the life balance threshold is obtained from the database, for example, set to 30000. The calculated life balance coefficient 35000 is compared with the life balance threshold 30000. Since 35000 is greater than 30000, the maximum element 5400 hours and the minimum element 4800 hours in the life set are removed.

[0047] After removal, the life balance coefficient is recalculated for the remaining 8 elements, resulting in a new life balance coefficient of 28000. At this time, 28000 is less than the life balance threshold 30000, which meets the condition, and the removal process is stopped.

[0048] Finally, the sum of the remaining 8 elements is taken to obtain the average life balance value of 5081.25 hours. This life balance value will be used in the subsequent life cycle stage segmentation process.

[0049] The ratio of the life balance value to the value L2 is marked as the balance segmentation value, and the effective running time of the evaluation object is segmented according to the balance segmentation value from the first to the last to obtain L2 running stages. All complete detection cycles of all evaluation objects in the same running stage are marked as paired cycles of the running stage, and the maximum and minimum values of the execution time of the paired cycles constitute the execution range, which is divided into several execution intervals. The sum of the total number of communication power failures and the total number of false alarms in all paired cycles in the execution interval is marked as the screening value of the execution interval, and the execution interval with the smallest screening value is marked as the paired interval of the running stage.

[0050] Among them, the calculation of the balance segmentation value is realized by the ratio of the life balance value to L2, which represents the average duration benchmark of each running stage. The segmentation of the running stage is divided into L2 continuous time periods from the starting point of the effective running time, with the balance segmentation value as the unit. The duration of each running stage is dynamically determined by the balance segmentation value, ensuring that the stage division maintains a proportional relationship with the overall life balance value. For example, when the life balance value is 1000 hours and L2 is 5, the balance segmentation value is 200 hours, and the effective running time is divided into 5 continuous 200-hour stages.

[0051] Specifically, after determining the life balance value, the reference duration of each stage is obtained by dividing it by the preset L2 value. The effective running duration of the evaluation object is sequentially divided according to the reference duration, forming a plurality of continuous running stages. During the division process, the starting point of each stage seamlessly connects with the end point of the previous stage, ensuring the continuity of time division. In this way, the division of the running stage not only considers the overall life balance value, but also combines the preset number of stages, so that the optimization of the subsequent detection cycle can be dynamically adjusted according to the aging characteristics of different stages. For example, in the early running stage, the detection cycle can be allocated a longer execution duration to reduce the detection frequency, while in the later stage, the execution duration is shortened to improve the detection timeliness.

[0052] Wherein, when the maximum and minimum values of the execution duration of the pairing cycle constitute an execution range, the execution range covers the duration data of all historical detection cycles in the running stage; when the execution range is divided into execution intervals, the number of intervals is dynamically adjusted according to the span of the execution range, for example, when the execution range is 10-30 minutes, it can be divided into 10-15, 15-20, 20-25, 25-30 four intervals; in the screening value calculation process, the statistics of the total number of faults and the total number of false alarms are based on the detection cycle execution records and fault alarm logs stored in the database.

[0053] Specifically, after completing the division of the running stage, first extract all the detection cycle data actually executed by the evaluation object in this stage to determine the distribution range of its execution duration. By dividing the execution range into multiple intervals, the sum of the number of fault occurrences and false alarms corresponding to each interval is calculated, and the interval with the lowest screening value indicates that the detection cycle in this duration interval is most reasonable, which can effectively capture real faults and reduce false alarms. For example, when the execution range of a running stage is 20-40 minutes, after being divided into 20-25, 25-30, 30-35, 35-40 four intervals, if the screening value of the 25-30 minute interval is 3 times, and the screening values of other intervals are all higher than 5 times, then this interval is marked as a pairing interval. The subsequent detection optimization module will preferentially select the execution duration of the detection cycle from this interval, realizing the dynamic optimization configuration of the detection cycle parameters in different running stages.

[0054] The detection optimization module is used for optimizing the running aging detection process of the communication power supply: after the number of evaluation objects reaches L1, the running stage is matched according to the effective running duration of the detection object, and the corresponding pairing interval of the running stage is called to randomly select a value as the execution duration of the detection cycle until the detection object enters the next running stage.

[0055] Specifically, when the evaluation objects accumulate to a preset number L1, the system determines the running stage to which the current detected object belongs according to the accumulated running time length of the current detected object, and the system randomly extracts an execution time length from the interval as the current detection period, for example, 40 minutes is randomly selected in the range of [30, 50] minutes as the period execution time length. The execution time length is continuously applied until the total running time length of the detected object reaches the next stage segmentation threshold, for example, when the total time length exceeds twice the balanced segmentation value, the pairing interval of the third stage is automatically switched to. By dynamically adjusting the execution time length range, the detection period can adapt to the change of aging rate in different stages.

[0056] The optimization verification module is used to verify and analyze the detection optimization effect of the communication power supply: after the number of evaluation objects reaches L3, L1 and L3 are numerical constants, and L3>L1, the life balance coefficient corresponding to the L3 evaluation objects is recalculated. The difference between the life balance coefficient corresponding to the L1 evaluation objects is marked as an effective optimization coefficient, the effective optimization threshold is obtained through the database, and the effective optimization coefficient is compared with the effective optimization threshold: if the effective optimization coefficient is less than the effective optimization threshold, it is determined that the detection optimization effect of the communication power supply does not meet the requirements, and the numerical values of the life balance threshold and L2 in the life evaluation module are proportionally optimized, wherein the life balance threshold is proportionally reduced, and the numerical value of L2 is proportionally enlarged; if the effective optimization coefficient is greater than or equal to the effective optimization threshold, it is determined that the detection optimization effect of the communication power supply meets the requirements, and the effective running time of the evaluation object whose marking order is located in L1-L3 is used to reconstitute the life set and reperform the stage segmentation and the marking of the pairing interval.

[0057] Wherein, the setting of L3 needs to be greater than L1 to form verification data with larger sample size, and to ensure the reliability of the verification result; the calculation of the effective optimization coefficient reflects the influence degree of the detection period adjustment on the stability of the life evaluation by comparing the difference between the life balance coefficients before and after optimization; the comparison of the effective optimization threshold is used to judge whether the optimization strategy achieves the expected effect, if not, the parameter adjustment mechanism is triggered, if yes, the data updating mechanism is triggered. The reduction of the life balance threshold and the enlargement of the numerical value of L2 form a reverse adjustment, the former improves the strictness of data cleaning, and the latter increases the fineness of running stage division. The reacquisition of the effective running time of the evaluation object whose marking order is located in L1-L3 ensures that the subsequent stage segmentation is based on the latest data.

[0058] Specifically, when the number of evaluation objects accumulates to L3, the system automatically recalculates the lifetime balancing coefficient at this number and performs a difference operation with the coefficient at the original L1 number to obtain an effective optimization coefficient. This coefficient is compared with a preset threshold. If it is lower than the threshold, it indicates insufficient optimization effect, requiring a reduction in the lifetime balancing threshold to decrease data cleaning tolerance, while simultaneously increasing the granularity of the L2 numerical refinement stage segmentation to improve the accuracy of dynamic adjustment of the detection cycle. If the threshold is reached, new sample data from the L1-L3 interval are used to reconstruct the lifetime set, and stage segmentation and pairing marking are performed to ensure the system continuously adapts to changes in power supply aging characteristics.

[0059] Example 2: Figure 2 As shown, the aging test method adapted for communication power supplies includes the following steps:

[0060] Step 1: Detect and analyze the operating status of the communication power supply: Mark the communication power supply as the detection object, generate a continuous detection cycle when the detection object is running, obtain the aging risk value of the detection object at the end of each detection cycle, and determine whether the detection object has fault risk and aging risk through the aging risk value;

[0061] Step 2: Evaluate and analyze the service life of the communication power supply: Mark the scrapped test objects as evaluation objects, divide the operation phase when the number of evaluation objects reaches L1, and mark the pairing intervals of the operation phase.

[0062] Step 3: Optimize the aging test process of the communication power supply: After the number of evaluation objects reaches L1, optimize the execution time of the test cycle according to the pairing interval;

[0063] Step 4: Verify and analyze the detection and optimization effect of the communication power supply: After the number of evaluation objects reaches L3, calculate the effective optimization coefficient, and use the effective optimization coefficient to determine whether the detection and optimization effect of the communication power supply meets the requirements.

[0064] An aging detection system adapted for communication power supplies is used to mark communication power supplies as detection objects during operation. Continuous detection cycles are generated as the detection objects run. At the end of each detection cycle, the aging risk value of the detection object is obtained, and the system determines whether the detection object has fault risk or aging risk based on this value. Scrapped detection objects are marked as evaluation objects. When the number of evaluation objects reaches L1, the operation phase is segmented, and the paired intervals of the operation phase are marked. After the number of evaluation objects reaches L1, the execution time of the detection cycle is optimized based on the paired intervals. After the number of evaluation objects reaches L3, an effective optimization coefficient is calculated, and the system determines whether the optimization effect of the communication power supply detection meets the requirements based on the effective optimization coefficient.

[0065] The above merely provides the illustration and description of the structure of the present application. Those skilled in the art can make various modifications or supplements to the described specific embodiments or replace them with similar ways, as long as they do not deviate from the structure of the present application or exceed the scope defined by the claims, which shall belong to the protection scope of the present application.

[0066] In the description of the present specification, the description referring to the terms "one embodiment", "an example", "a specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. The illustrative description of the above terms in the present specification does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0067] The preferred embodiments of the present application disclosed above are only used to help explain the present application. The preferred embodiments do not describe all the details and limit the present application to the specific embodiments. Obviously, many modifications and changes can be made according to the content of the present specification. The present specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present application, so that those skilled in the art can well understand and utilize the present application. The present application is limited only by the claims and their full scope and equivalents.

Claims

1. An operational aging detection system adapted for communication power supplies, characterized in that, It includes an aging testing platform, which is communicatively connected to an operation testing module, a life assessment module, a testing optimization module, an optimization verification module, and a database; The operation detection module is used to detect and analyze the operating status of the communication power supply: the communication power supply is marked as the detection object, and a continuous detection cycle is generated when the detection object is running. The execution time of each detection cycle is randomly selected from the standard time range. At the end of each detection cycle, the efficiency decay rate, insulation degradation degree and switching fatigue index of the detection object are obtained. The aging risk value of the test object is obtained by weighted summation of the efficiency decay rate, insulation degradation degree and switch fatigue index during the test cycle. The aging risk value is used to determine whether the test object has aging risk and failure risk. The lifespan assessment module is used to assess and analyze the operating lifespan of the communication power supply: the scrapped test object is marked as the assessment object; when the number of assessment objects reaches L1, the effective operating time of all assessment objects constitutes the lifespan set; the lifespan set is cleaned to obtain the lifespan equilibrium value; the lifespan of the assessment object is divided into L2 operating stages by the lifespan equilibrium value; and the paired intervals of the operating stages are marked. The detection optimization module is used to optimize the aging detection process of the communication power supply. The optimization verification module is used to verify and analyze the detection optimization effect of the communication power supply; The specific process of marking the pairing intervals in the operation phase includes: marking all complete detection cycles corresponding to all evaluation objects within the same operation phase as the pairing cycles of the operation phase; using the maximum and minimum execution durations of the pairing cycles to form the execution range; dividing the execution range into several execution intervals; marking the sum of the total number of communication power supply failures and the total number of false alarms within all pairing cycles in the execution interval as the screening value of the execution interval; and marking the execution interval with the smallest screening value as the pairing interval of the operation phase. The detection optimization module optimizes the operation aging detection process of the communication power supply: after the number of evaluation objects reaches L1, the operation stage is matched according to the effective running time of the detection object, and the corresponding pairing interval of the operation stage is retrieved. A value is randomly selected from the pairing interval as the execution time of the detection cycle until the detection object enters the next operation stage.

2. The operational aging detection system adapted for communication power supplies according to claim 1, characterized in that, Efficiency degradation rate reflects the degree of decline in energy conversion efficiency of the power supply during long-term operation. It is obtained through real-time load-efficiency curve comparison and dynamic loss analysis. Insulation degradation reflects the aging state of the internal insulation material of the power supply. It is obtained by combining dielectric loss monitoring and partial discharge. Switch fatigue index is for the power switching devices in the power supply. It reflects the performance degradation caused by the number of turn-on / turn-off cycles and stress accumulation of the switching devices.

3. The operational aging detection system adapted for communication power supplies according to claim 2, characterized in that, The specific process for determining whether a test object has aging risk and failure risk includes: obtaining aging risk thresholds and failure risk thresholds from the database; comparing the aging risk value with the aging risk thresholds and failure risk thresholds; if the aging risk value is greater than the aging risk threshold, the test object is determined to be abnormally aged, a scrap signal is generated, and the scrap signal is sent to the mobile terminal of the management personnel; if the aging risk value is greater than the failure risk threshold, the test object is determined to have failure risk, a failure warning signal is generated, and the failure warning signal is sent to the mobile terminal of the management personnel; if the aging risk value is less than the failure risk threshold, the test object is determined to meet the operating requirements.

4. The operational aging detection system adapted for communication power supplies according to claim 3, characterized in that, The specific process of data cleaning for the lifetime set includes: calculating the variance of all elements in the lifetime set to obtain the lifetime equilibrium coefficient; obtaining the lifetime equilibrium threshold from the database; comparing the lifetime equilibrium coefficient with the lifetime equilibrium threshold; if the lifetime equilibrium coefficient is greater than or equal to the lifetime equilibrium threshold, then the largest and smallest elements in the lifetime set are removed, and the lifetime equilibrium coefficient is recalculated, and so on, until the lifetime equilibrium coefficient is less than the lifetime equilibrium threshold; if the lifetime equilibrium coefficient is less than the lifetime equilibrium threshold, then the sum of all elements in the lifetime set is taken as the average value to obtain the lifetime equilibrium value.

5. The operational aging detection system adapted for communication power supplies according to claim 4, characterized in that, The specific process of dividing the life cycle of the evaluation object into stages includes: marking the ratio of the life balance value to the value L2 as the balance division value, and dividing the effective running time of the evaluation object into L2 running stages according to the balance division value from first to last.

6. The operational aging detection system adapted for communication power supplies according to claim 5, characterized in that, The specific process of the optimization verification module to verify and analyze the detection optimization effect of the communication power supply includes: after the number of evaluation objects reaches L3, L1 and L3 are both numerical constants, and L3>L1, the lifetime equalization coefficient is recalculated, and the difference between the lifetime equalization coefficients corresponding to L3 evaluation objects and the lifetime equalization coefficients corresponding to L1 evaluation objects is marked as the effective optimization coefficient. The detection optimization effect of the communication power supply is judged to meet the requirements by using the effective optimization coefficient.

7. The operational aging detection system adapted for communication power supplies according to claim 6, characterized in that, The effective optimization threshold is obtained from the database, and the effective optimization coefficient is compared with the effective optimization threshold. The specific process for determining whether the detection optimization effect of the communication power supply meets the requirements includes: if the effective optimization coefficient is less than the effective optimization threshold, the detection optimization effect of the communication power supply is determined to be unsatisfactory. The lifetime equalization threshold and L2 values ​​in the lifetime assessment module are optimized proportionally, where the lifetime equalization threshold is proportionally reduced and the L2 value is proportionally increased. If the effective optimization coefficient is greater than or equal to the effective optimization threshold, the detection optimization effect of the communication power supply is determined to be satisfactory. The lifetime set is reconstructed from the effective runtime of the assessment objects with the marking order located in L1-L3, and the stage segmentation and pairing interval marking are re-performed.

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