A whole life cycle operation and maintenance decision management system based on distribution room system insulation aging

Abstract

The application discloses a full life cycle operation and maintenance decision management system based on insulation aging of a power distribution room system, and particularly relates to the field of operation and maintenance decision management, and is used for solving the problem that the existing power distribution room equipment maintenance decision fails to comprehensively consider the insulation aging state, user power failure influence and subsequent full life cycle operation and maintenance plan, resulting in that the maintenance opportunity selection lacks overall coordination and the influence of delayed maintenance is difficult to quantitatively evaluate; the maximum postponable maintenance duration of equipment is calculated, a maintenance execution period is generated in combination with user power failure sensitivity, the irreversible life loss amount formed by delayed maintenance is further used to predict the maintenance cycle compression coefficient and the maintenance type upgrade probability, the full life cycle operation and maintenance plan is dynamically adjusted, the delayed cost increment evaluation report is generated in combination with the life cycle prediction operation and maintenance cost, the comprehensive decision management of the power distribution room equipment delayed maintenance scheme is realized, and the rationality and management efficiency of the full life cycle operation and maintenance decision are improved.

Description

Technical Field

[0001] This invention relates to the field of operation and maintenance decision management technology, and more specifically, to a full life-cycle operation and maintenance decision management system based on insulation aging of power distribution room systems. Background Technology

[0002] As a crucial infrastructure in the power system responsible for power distribution, supply, and equipment operation, the substation contains switchgear, busbars, cables, transformers, and insulation accessories that operate continuously. Their insulation performance gradually ages with increasing years of operation, load changes, fluctuations in ambient temperature and humidity, and accumulated electrical stress. When the insulation condition reaches a warning level, maintenance units typically need to schedule a power outage for repairs to prevent insulation failure from further causing equipment malfunctions or even power outages.

[0003] However, power distribution rooms typically supply power to various loads, including industrial parks, commercial complexes, hospitals, data centers, and residential users. Different users have significantly different outage tolerance levels at different times, and some users also pre-declare periods when power outages are not permitted, severely restricting the maintenance window for power distribution rooms. In actual operation and maintenance, maintenance personnel need to comprehensively consider factors such as the degree of equipment insulation aging, the impact of user power outages, maintenance resource allocation, and coordination of subsequent maintenance plans. The entire maintenance decision-making process exhibits strong temporal correlation and full lifecycle management characteristics.

[0004] Most existing technologies focus on determining maintenance timing based on insulation test results or scheduling maintenance windows based on power outage plans. They lack a unified decision-making mechanism that correlates the aging state of equipment insulation, the user's sensitivity to power outages, and subsequent lifecycle maintenance plans. When maintenance windows need to be postponed due to user power outage restrictions, it is usually difficult to further assess the lasting impact of the postponement on subsequent maintenance cycles, maintenance types, and full lifecycle maintenance costs. This results in a lack of systematic basis for adjusting maintenance plans, affecting the rationality of lifecycle maintenance decisions for power distribution room equipment. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a full life cycle operation and maintenance decision management system based on insulation aging of power distribution room system to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A full lifecycle operation and maintenance decision management system based on insulation aging in power distribution room systems includes: The assessment module is used to calculate the maximum delay time for maintenance of the equipment in the power distribution room without replacement when the insulation status assessment value of the equipment reaches the preset maintenance threshold, and convert it into the maintenance rigidity level. The reporting module is used to obtain the historical time-series load and pre-reported non-outage periods of users within the power supply range of the power distribution room, and to construct the time distribution sequence of user power outage sensitivity. The delay calculation module is used to define a safe time window based on the maintenance rigidity level, match the safe time window with the user's power outage sensitivity time distribution sequence to generate a conflict intensity sequence; when there is a candidate time period that meets the set tolerance threshold, the corresponding candidate time period is used as the maintenance execution time period and the corresponding delay duration is obtained. The prediction module is used to calculate irreversible life loss based on the delay time, predict the maintenance cycle compression coefficient and the probability of maintenance type upgrade; The operation and maintenance plan generation module is used to adjust the full life cycle operation and maintenance plan of the power distribution room equipment based on the maintenance cycle compression coefficient and the upgrade probability of the maintenance type, and generate an updated operation and maintenance plan. The operation and maintenance plan decision module is used to evaluate the total cost of the updated operation and maintenance plan, generate a postponement cost increment assessment report, and determine whether to adopt the updated operation and maintenance plan based on the postponement cost increment assessment report.

[0008] As a further aspect of the present invention, the evaluation module specifically includes: Read the statistical benchmark data of fault probability corresponding to the service life of the power distribution room equipment, and the aging stage corresponding to each insulation status assessment value in the insulation degradation detection sequence. Match the fault probability value according to the service life and aging stage of the power distribution room equipment to construct the fault probability climbing sequence. The attenuation trend extrapolation function is fitted with the most recent consecutive insulation status assessment points in the insulation degradation detection sequence. The risk growth boundary is determined by the growth trajectory between the last insulation status assessment point in the fault probability climbing sequence and the preset failure fault probability. The maximum allowable delay time corresponding to the attenuation trend extrapolation function and the risk growth boundary is calculated as the maximum delayable maintenance time. The insulation status assessment value is a status quantity obtained by standardizing and fusing the insulation test data, and the insulation degradation detection sequence is constructed by arranging the insulation status assessment values ​​in chronological order. Read the original planned maintenance cycle, calculate the proportion of the maximum delayable maintenance time to the original planned maintenance cycle, and determine the maintenance rigidity level based on the range of the proportion.

[0009] As a further aspect of the present invention, the application module specifically includes: Retrieve historical time-series load records of users in the area covered by the power distribution room, calculate the average load power of each time period according to the preset statistical period, use the maximum average load power in all time periods as the normalization benchmark, calculate the proportion of the average load power of each time period to the normalization benchmark, and use it as the electricity dependence factor for the corresponding time period. Retrieve the user's pre-declared non-outage period identifiers, assign the corresponding power dependence factor to the preset rigid non-outage cost coefficient, keep the original power dependence factor unchanged for other periods, and multiply it with the average load power of the corresponding period to obtain the power outage loss per unit time for each period; arrange the power outage loss per unit time for each period in chronological order to construct the user's power outage sensitivity time distribution sequence.

[0010] As a further aspect of the present invention, the generation of the conflict intensity sequence in the delayed calculation module specifically includes: A safe time window is defined based on the date range covered by the maximum delayable maintenance time, and all dates within the safe time window that can be scheduled for power outages are used as a candidate time period set. Extract the outage sensitivity values ​​corresponding to each candidate time period from the time distribution sequence of user outage sensitivity. Sum the outage sensitivity values ​​of all users corresponding to each candidate time period to obtain the conflict intensity value of the candidate time period maintenance. Construct a conflict intensity sequence in chronological order.

[0011] As a further aspect of the present invention, the delay calculation module specifically includes obtaining the corresponding delay duration as follows: Candidate time periods with conflict intensity values ​​less than a preset tolerance threshold are selected from the conflict intensity sequence to form an executable candidate time period set. When the set of executable candidate time periods is not empty, the candidate time period closest to the current time is selected as the maintenance execution time period, and the number of days between the maintenance execution time period and the current time is calculated as the delay duration. When the set of executable candidate time periods is empty, the candidate time period with the lowest conflict intensity value is selected as the restricted maintenance execution time period, and the number of days between the restricted maintenance execution time period and the current time is calculated as the delay duration.

[0012] As a further aspect of the present invention, the prediction module specifically includes: Substitute the start and end times of the delay into the attenuation trend extrapolation function to obtain the first insulation state assessment value corresponding to the start time and the second insulation state assessment value corresponding to the end time. The difference between the first insulation state assessment value and the second insulation state assessment value is taken as the irreversible lifetime loss. Search the historical maintenance records of power distribution rooms in each region for delayed maintenance cases that have the same operating years as the current power distribution room and whose insulation status assessment value is in the same aging stage. Extract the irreversible life loss, the actual reduction ratio of the planned maintenance cycle, and the maintenance type upgrade record corresponding to each historical case. Construct a life loss impact mapping relationship with irreversible life loss and aging stage. Based on the current irreversible life loss and the aging stage corresponding to the current insulation status assessment value, the actual reduction ratio of the planned maintenance cycle is matched in the life loss impact mapping relationship as the maintenance cycle compression coefficient, and the proportion of maintenance type upgrade records under the corresponding aging stage to the total number of cases is calculated as the maintenance type upgrade probability.

[0013] As a further aspect of the present invention, the operation and maintenance plan generation module specifically includes: Read the scheduled future maintenance cycles and maintenance types from the current power distribution room's full life cycle maintenance ledger. Multiply the time interval of each scheduled maintenance by the maintenance cycle compression factor to obtain the compressed new maintenance time interval. When the probability of upgrading the maintenance type exceeds the preset upgrade judgment threshold, replace the maintenance type with an adjacent higher-level maintenance type. Regenerate the current power distribution room's full life cycle maintenance plan draft with the compressed new maintenance time interval and the replaced maintenance type.

[0014] As a further aspect of the present invention, the operation and maintenance plan decision module specifically includes: Retrieve the maintenance type, maintenance interval and resource requirements corresponding to each planned maintenance in the updated full life cycle maintenance plan draft, compare it item by item with the original planned maintenance schedule, calculate the number of new maintenance times, labor cost increase and material cost increase caused by maintenance cycle adjustment and maintenance type upgrade, summarize to obtain the full life cycle predicted maintenance cost increase, and generate a postponement cost increase assessment report. Retrieve the lifecycle-predicted maintenance costs corresponding to the original full lifecycle maintenance plan, compare the updated lifecycle-predicted maintenance costs with the original lifecycle-predicted maintenance costs, and calculate the maintenance cost increase rate. When the maintenance cost increase rate exceeds the preset cost tolerance threshold, the updated full lifecycle maintenance plan draft is deemed unacceptable; otherwise, it is deemed an acceptable solution, and the final adoption decision is output.

[0015] The technical effects and advantages of this invention are as follows: A full lifecycle operation and maintenance decision management system based on insulation aging in power distribution room systems. This invention establishes a comprehensive decision-making mechanism that integrates insulation aging status of substation equipment, user power outage sensitivity, and full lifecycle maintenance plans. This mechanism encompasses insulation status assessment, maintenance window selection, impact analysis of delays, and maintenance plan reconfiguration. By determining the maximum delayable maintenance duration based on insulation degradation trends and selecting appropriate maintenance execution periods according to user power outage sensitivity, the coordination between maintenance timing and user power supply needs is improved. Furthermore, by predicting the subsequent maintenance cycle compression coefficient and maintenance type upgrade probability based on the irreversible lifespan loss caused by delayed maintenance, a quantitative assessment of the impact of delayed maintenance on the subsequent lifecycle of equipment is achieved. Simultaneously, the future maintenance cycle, maintenance type, and corresponding resource requirements are dynamically adjusted based on the prediction results. Combined with lifecycle-based predicted maintenance costs, a delayed cost increment assessment report is generated, enabling a comprehensive cost evaluation of delayed maintenance plans. This improves the scientific rigor and consistency of full lifecycle maintenance plan adjustments, ensuring that substation maintenance decisions consider equipment operational reliability, user power outage impacts, and subsequent maintenance costs. This provides a unified decision-making basis for refined full lifecycle maintenance management of substation equipment. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the structure of the full life cycle operation and maintenance decision management system based on the insulation aging of the power distribution room system according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1 Figure 1 This invention presents a full lifecycle operation and maintenance decision management system based on insulation aging in power distribution room systems, comprising: The assessment module is used to calculate the maximum delay time for maintenance of the equipment in the power distribution room without replacement when the insulation status assessment value of the equipment reaches the preset maintenance threshold, and convert it into the maintenance rigidity level. The reporting module is used to obtain the historical time-series load and pre-reported non-outage periods of users within the power supply range of the power distribution room, and to construct the time distribution sequence of user power outage sensitivity. The delay calculation module is used to define a safe time window based on the maintenance rigidity level, match the safe time window with the user's power outage sensitivity time distribution sequence to generate a conflict intensity sequence; when there is a candidate time period that meets the set tolerance threshold, the corresponding candidate time period is used as the maintenance execution time period and the corresponding delay duration is obtained. The prediction module is used to calculate irreversible life loss based on the delay time, predict the maintenance cycle compression coefficient and the probability of maintenance type upgrade; The operation and maintenance plan generation module is used to adjust the full life cycle operation and maintenance plan of the power distribution room equipment based on the maintenance cycle compression coefficient and the upgrade probability of the maintenance type, and generate an updated operation and maintenance plan. The operation and maintenance plan decision module is used to evaluate the total cost of the updated operation and maintenance plan, generate a postponement cost increment assessment report, and determine whether to adopt the updated operation and maintenance plan based on the postponement cost increment assessment report.

[0019] The evaluation module is used to calculate the maximum delay time for maintenance of the power distribution room equipment without replacement when the insulation status evaluation value of the equipment reaches the preset maintenance threshold, and convert it into a maintenance rigidity level.

[0020] Pre-collected baseline data on failure probabilities under different combinations of operating years and aging stages are obtained from statistical yearbooks of the power distribution equipment industry, internal equipment fault databases of power grid companies, or life test data provided by equipment manufacturers. This data is stored in the data layer of the power distribution room's full life cycle maintenance ledger in the form of a two-dimensional table. The horizontal axis represents the aging stage division interval, and the vertical axis represents the operating years. The table content is the corresponding failure probability value. The insulation degradation detection sequence is constructed as follows: after each insulation test, the measured insulation resistance, dielectric loss factor, partial discharge, and other multi-dimensional test data are weighted and fused according to preset weights. The preset weights are determined based on the contribution of each test indicator to the insulation failure judgment in historical insulation test data, and the sum of all weights is equal to one. This is converted into a one-dimensional insulation status assessment value. This assessment value is dimensionless and ranges from zero to one hundred, with smaller values ​​indicating more severe insulation degradation. The insulation status assessment values ​​obtained from each test are bound to the corresponding test timestamps and arranged in chronological order to form an insulation degradation detection sequence. The fault probability escalation sequence is constructed as follows: Each detection point in the insulation degradation detection sequence is traversed sequentially, and the corresponding insulation condition assessment value is read. The aging stage is determined based on a pre-defined aging stage division, for example, into five intervals: no aging, slight aging, moderate aging, severe aging, and critical failure. Simultaneously, the operating years of the equipment from its commissioning date to the detection point are read. Using the aging stage and operating years as joint query conditions, a precise match is performed in the fault probability statistical benchmark data table to extract the corresponding fault probability value. The fault probability values ​​obtained from matching all detection points in the insulation degradation detection sequence are arranged in chronological order of detection time to construct a fault probability escalation sequence synchronized with insulation condition degradation. When the operating years or aging stage falls within the blank area between two levels in the table during matching, the corresponding fault probability value is calculated by linear interpolation based on the fault probability values ​​of the two adjacent levels.

[0021] Multiple consecutive insulation condition assessment points are extracted from the insulation degradation detection sequence, with a preset number selected, such as five consecutive assessment points. Using the detection timestamps of these extracted assessment points as independent variables and the corresponding insulation condition assessment values ​​as dependent variables, a linear function is fitted using the least squares method to obtain a decay trend extrapolation function. This function expresses the linear decay rate of the insulation condition assessment value over time. Starting from the detection timestamp corresponding to the last insulation condition assessment point in the failure probability ramp-up sequence, and ending at the time corresponding to the preset failure probability, the trajectory of the failure probability over time between the start and end points is used as the risk growth boundary. The preset failure probability is set at the failure criterion threshold specified in the industry standard corresponding to the equipment type in the failure probability statistical benchmark data. The attenuation trend extrapolation function and the risk growth boundary are placed on the same time axis and shifted forward along the time axis by one day. The extrapolated insulation condition assessment value given by the attenuation trend extrapolation function for each day is calculated. When the fault probability value corresponding to the extrapolated insulation condition assessment value first reaches or exceeds the fault probability value of the risk growth boundary on the same day, that day is determined as the maintenance deadline that cannot be postponed any further. The number of natural days between the current date and this maintenance deadline is calculated; this number of days is the maximum allowable postponement of maintenance time.

[0022] The original planned maintenance cycle of the equipment is retrieved from the full lifecycle operation and maintenance ledger. This original planned maintenance cycle is the standard interval in days between two planned maintenance operations initially established based on the manufacturer's recommended values ​​and the power grid company's maintenance procedures. The proportion of the maximum delayable maintenance time to the original planned maintenance cycle is calculated by dividing the maximum delayable maintenance time by the number of days in the original planned maintenance cycle. A pre-defined rigidity level classification interval and the corresponding delay proportion value are established. For example, the rigidity level is divided into three levels: Rigid Level 1, Rigid Level 2, and Rigid Level 3. When the delay proportion value is less than the pre-defined first proportion threshold but greater than zero, it is classified as Rigid Level 1, indicating that the equipment has extremely limited allowable delay time and almost no flexibility in the maintenance window. When the delay proportion value is not less than the pre-defined first proportion threshold but less than the pre-defined second proportion threshold, it is classified as Rigid Level 2, indicating that the equipment has some delay leeway but still needs to be scheduled as soon as possible. When the delay proportion value is not less than the pre-defined second proportion threshold, it is classified as Rigid Level 3, indicating that the equipment still has ample delay space. The preset first and second proportional thresholds are set based on the control requirements for allowable maintenance delays for different types of equipment in the power grid company's operation and maintenance procedures. The calculated delay ratio value is compared with the above-mentioned intervals one by one to determine the interval in which the delay ratio value falls, and the corresponding rigidity level identifier is output.

[0023] The reporting module is used to obtain the historical time-series load of users within the power supply range of the power distribution room and the pre-reported periods of no power outage, and to construct a time distribution sequence of user power outage sensitivity.

[0024] The system retrieves the electricity metering terminal records of all users within the power supply range of the power distribution room from the data layer of the full life cycle operation and maintenance ledger. It extracts the load power data for each user over a complete calendar year, divided into 24 time periods according to a preset statistical period of one hour. For each user and each time period, the arithmetic mean of the load power of all sampling points within that time period is calculated to obtain the user's average load power for that time period. The system iterates through all 24 time periods for the same user, finding the maximum average load power, which is used as the normalization benchmark for that user. If a user's average load power is zero for all time periods, indicating that the user has been inactive for a long time, that user is skipped and not included in subsequent calculations. For each valid user, the proportion of average load power to the normalization benchmark is calculated for each time period. The proportion is taken as a decimal, ranging from zero to one. This proportion is the user's electricity dependence factor for the corresponding time period. The physical meaning of the electricity dependence factor is: the closer the value is to one, the closer the user's electricity demand is to its peak level during that time period, and the higher the dependence on power supply continuity. All valid users' electricity dependence factors for each time period are organized and stored in time period order, with each user corresponding to a one-dimensional array of length 24.

[0025] The system retrieves the pre-declared uninterruptible power outage period identifiers from the power distribution room's full lifecycle operation and maintenance ledger. These identifiers are declared by users through the electricity service application channel before the start of the current operation and maintenance cycle and entered into the full lifecycle operation and maintenance ledger after review and confirmation by the operation and maintenance management department. For each user and each time period, the system checks whether the corresponding time period is covered by the uninterruptible power outage period identifier. If covered, the corresponding electricity dependence factor is assigned a preset rigid uninterruptible power outage cost coefficient; if not covered, the original electricity dependence factor remains unchanged. The preset rigid uninterruptible power outage cost coefficient is determined by the operation and maintenance management department based on the average load loss value assessment results of users in the jurisdiction and the risk of uninterruptible power outage due to default, and is set as a fixed coefficient greater than one to increase the weight of uninterruptible power outage periods in the impact of power outages. After determining the weights, the power outage loss weight corresponding to each statistical period is multiplied by the average load power of the corresponding period to obtain the power outage loss per unit duration for that period. These weights are then arranged in chronological order to form a time distribution sequence of user power outage sensitivity. Each element in the time distribution sequence includes at least a period index and the corresponding power outage loss per unit duration. For users who have not declared any periods when power outages are not permitted, the original electricity dependence factor is retained in the calculation of the power outage loss per unit duration for all statistical periods.

[0026] The delay calculation module is used to define a safe time window based on the maintenance rigidity level, match the safe time window with the user power outage sensitivity time distribution sequence to generate a conflict intensity sequence; when there is a candidate time period that meets the set tolerance threshold, the corresponding candidate time period is used as the maintenance execution time period and the corresponding delay duration is obtained.

[0027] The maximum deferable maintenance duration and the current date are read. All natural days between the current date and the corresponding end date of the maximum deferable maintenance duration are defined as the safe time window. Then, the annual power outage plan and operation and maintenance schedule of the power distribution room are retrieved. Dates with other existing maintenance tasks, statutory power supply tasks, and dates for which power outage operation tickets cannot be processed are removed. The remaining dates are used as a set of candidate time periods. Subsequently, each candidate time period is traversed sequentially. Based on the weekday attribute, time period attribute, and calendar index corresponding to the candidate date, the unit duration power outage loss degree of the corresponding date and time period is extracted from the power outage sensitivity time distribution sequence of each user. The unit duration power outage loss degree of all users for the corresponding time period is accumulated, and the accumulated result is used as the conflict intensity value of the candidate time period. To ensure comparability between different candidate time periods, when a user has multiple non-outage periods for a given date, only the outage sensitivity values ​​that completely overlap with the candidate maintenance periods are extracted for accumulation; non-overlapping periods are not included in the calculation. When a candidate maintenance period spans two statistical periods, the outage sensitivity values ​​for each of the two statistical periods are extracted separately and weighted according to the actual coverage duration to form the user's outage sensitivity contribution value for that candidate period, which is then included in the total for all users. After calculating all candidate time periods, all conflict intensity values ​​are arranged in chronological order of the candidate dates to form a conflict intensity sequence.

[0028] The system reads all elements of the conflict intensity sequence and performs a step-by-step comparison based on the tolerance thresholds pre-defined by the operation and maintenance management department. These tolerance thresholds are determined based on the statistical results of cumulative power outage impacts during historical maintenance periods, using the percentile value of the conflict intensity distribution corresponding to historically completed maintenance tasks as the setting basis. For example, the threshold is set at the 75th percentile of the historical conflict intensity distribution, ensuring consistency and repeatability of the threshold source. Subsequently, it sequentially checks whether the conflict intensity value corresponding to each candidate time period is less than the tolerance threshold. Candidate time periods that meet the condition are retained, forming an executable candidate time period set; candidate time periods that do not meet the condition are eliminated and do not participate in subsequent execution date selection. Once the executable candidate time period set is formed, all candidate time periods in the set are rearranged according to date order. The current date is read, and the calendar difference between the current date and the date of each candidate time period is calculated to obtain the corresponding interval in days. The candidate time period with the smallest interval in days is selected as the final maintenance execution time period. When multiple candidate time periods have the same interval in days as the current date, the candidate time period with the smaller conflict intensity value is selected as the final execution date.

[0029] If it is confirmed that none of the candidate time periods in the conflict intensity sequence meet the tolerance threshold requirements, the tolerance threshold will not be adjusted, nor will the conflict intensity sequence be recalculated. Instead, all elements of the conflict intensity sequence will be directly traversed, and the conflict intensity values ​​corresponding to each candidate time period will be compared. The candidate time period corresponding to the minimum conflict intensity value will be determined as the restricted maintenance execution time period. If multiple candidate time periods correspond to the same minimum conflict intensity value, they will be sorted according to their date and time, and the candidate time period with the earliest date will be selected as the restricted maintenance execution time period to avoid further extending the delay time when the conflict intensity is the same. After the restricted maintenance execution time period is determined, the current date will be read, and the difference in natural days between the restricted maintenance execution time period date and the current date will be calculated. The corresponding natural day difference will be determined as the delay duration, and the status flag indicating that the current candidate time period does not meet the tolerance threshold and the corresponding conflict intensity value will be recorded simultaneously.

[0030] The prediction module is used to calculate the irreversible life loss based on the delay time, and to predict the maintenance cycle compression coefficient and the probability of maintenance type upgrade.

[0031] The original planned maintenance date and the determined maintenance execution date are read. The original planned maintenance date is used as the start time of the postponement period, and the maintenance execution date is used as the end time of the postponement period. These are used as the two time input points for the attenuation trend extrapolation function. The attenuation trend extrapolation function uses the same function fitted based on the insulation degradation detection sequence, without establishing a new prediction model, ensuring consistency in the insulation condition prediction process. The insulation condition assessment value corresponding to the original planned maintenance date and the insulation condition assessment value corresponding to the maintenance execution date are calculated separately. Both calculations use the same insulation condition assessment system and the same standardized processing rules to avoid the impact of changes in evaluation scale on subsequent results. When the maintenance execution date spans multiple calendar days, the corresponding date's midnight is used as a unified time reference to input the attenuation trend extrapolation function, ensuring consistency in the calculation method between different substations. The insulation condition assessment value corresponding to the original scheduled maintenance date is then subtracted from the insulation condition assessment value corresponding to the maintenance execution date, and the difference is determined as the irreversible life loss. If the insulation condition assessment values ​​calculated twice are the same, the irreversible life loss is recorded as zero. If the insulation condition assessment value after the postponement is lower than the insulation condition assessment value corresponding to the original scheduled maintenance date, the difference between the two is directly used as the irreversible life loss without further normalization.

[0032] From the historical full lifecycle maintenance log, delayed maintenance cases with consistent operating years and aging stages are selected. Information such as irreversible lifespan loss, subsequent planned maintenance cycle reduction percentage, and whether the maintenance type has been upgraded are retrieved for each case. Then, all historical cases are divided into multiple consecutive lifespan loss intervals based on pre-defined lifespan loss intervals with fixed widths. The historical cases corresponding to each lifespan loss interval are then statistically analyzed according to the aging stage. For each statistical unit consisting of a lifespan loss interval and an aging stage, the arithmetic mean of the actual reduction percentage of the planned maintenance cycle for all historical cases within that unit is calculated, and the number of cases with upgraded maintenance types and the total number of such cases are counted. After completing all statistics, a lifespan loss impact mapping table is established. The mapping table uses the lifespan loss interval as the horizontal index and the aging stage as the vertical index. Each mapping unit contains at least three fields: the average actual reduction percentage of the planned maintenance cycle, the number of cases with upgraded maintenance types, and the total number of such cases.

[0033] The system reads the irreversible life loss calculated for the current power distribution room, determines its corresponding life loss interval according to the life loss interval division rules, and determines the corresponding aging stage based on the current insulation status assessment value. Then, it uses both the life loss interval and aging stage indices to jointly search the life loss impact mapping table, locates the corresponding mapping unit, and directly reads the average actual reduction ratio of the planned maintenance cycle recorded in that mapping unit, determining it as the maintenance cycle compression coefficient for the current power distribution room. Next, it reads the number of maintenance type upgrade cases and the total number of corresponding cases recorded in the same mapping unit, dividing the number of maintenance type upgrade cases by the total number of corresponding cases to obtain the maintenance type upgrade probability under the current aging stage and the current life loss interval. When the current irreversible life loss is located at the boundary of two life loss intervals, it reads the data recorded in two adjacent mapping units and performs linear interpolation calculations according to the distance ratio between the current irreversible life loss and the two interval boundaries to obtain the corresponding maintenance cycle compression coefficient and maintenance type upgrade probability.

[0034] The operation and maintenance plan generation module is used to adjust the full life cycle operation and maintenance plan of the power distribution room equipment based on the maintenance cycle compression coefficient and the upgrade probability of the maintenance type, and generate an updated operation and maintenance plan.

[0035] The system reads all scheduled but not yet executed maintenance records from the current power distribution room's full lifecycle maintenance log. Each planned maintenance record includes at least the planned maintenance date, planned maintenance interval, maintenance type, resource requirement category, and corresponding equipment identifier. These records are then arranged in chronological order of planned maintenance dates to form a future planned maintenance sequence. Next, the system reads the obtained maintenance cycle compression coefficient and adjusts the planned maintenance intervals in the future planned maintenance sequence according to a unified adjustment rule. The maintenance cycle compression coefficient applies a uniform ratio to all future planned maintenance time periods. For example, when the maintenance cycle compression coefficient is 0.90, the original planned maintenance cycle of 24 months is adjusted to 21.6 months, and the original planned maintenance cycle of 12 months is adjusted to 10.8 months. The planned maintenance dates are then recalculated after rounding to the nearest calendar day according to a unified rounding rule.

[0036] To ensure the correct chronological order of all planned maintenance dates, after correcting the current maintenance record date, the corrected planned maintenance date is used as the starting date for the next planned maintenance interval. All future planned maintenance dates are calculated sequentially forward, avoiding overlap or reversal caused by independently adjusting maintenance intervals. Once all planned maintenance dates are updated, the obtained maintenance type upgrade probability is read and compared with a preset upgrade threshold. This preset upgrade threshold is determined based on historical delayed maintenance case statistics; for example, the median of the upgrade probability corresponding to an upgrade in all historical cases is used as the upgrade threshold. When the maintenance type upgrade probability reaches the upgrade threshold, the current maintenance type is adjusted to the adjacent higher-level maintenance type according to the pre-established maintenance level correspondence in the power distribution room operation and maintenance specifications. This correspondence is established using a fixed mapping method; for example, routine maintenance is upgraded to special maintenance, and special maintenance is upgraded to comprehensive maintenance. If the current maintenance type already belongs to the pre-set highest level, the original maintenance type is maintained and no further upgrades are made. After adjusting the maintenance cycle and maintenance type, the standard resource configuration list corresponding to each maintenance type is read synchronously. The personnel configuration, tool configuration, spare parts configuration, and expected power outage duration corresponding to the adjusted maintenance type are rematched and rewritten into the corresponding planned maintenance record. Finally, the current full life cycle operation and maintenance plan draft for the current power distribution room is regenerated according to the adjusted planned maintenance dates, maintenance intervals, maintenance types, and corresponding resource configurations. Each plan record retains both the original plan content and the adjusted content, and records the basis for the adjustment, including the maintenance cycle compression coefficient, the probability of maintenance type upgrade, and the upgrade judgment result. This ensures that the subsequent life cycle operation and maintenance cost assessment can accurately trace the source of each adjustment. Throughout the update process, all future plans are continuously corrected using a unified rule without changing the completed maintenance records and historical operation and maintenance data.

[0037] In the operation and maintenance plan decision module, a delayed cost increment assessment report is generated to evaluate the total cost of the updated operation and maintenance plan, and the updated operation and maintenance plan is adopted based on the delayed cost increment assessment report.

[0038] The original full lifecycle maintenance plan and the updated full lifecycle maintenance plan draft are read synchronously. Both are arranged in chronological order of planned maintenance dates, and a one-to-one correspondence is established based on the planned maintenance date, maintenance type, and corresponding equipment identifier. When a new maintenance node is added to the updated plan, the node is marked as a new maintenance task. When both the original and updated plans have corresponding maintenance nodes, the maintenance time interval and maintenance type are further compared to see if they have changed. Subsequently, the standard resource requirements corresponding to different maintenance types are read based on the resource quota table pre-established by the maintenance management department. The resource quota table includes at least standard man-hours, a standard spare parts list, and a standard consumable material list. The man-hours are converted into labor costs according to the maintenance type and hourly rate, and the spare parts and materials are converted into material costs based on the standard purchase price formed in the most recent procurement cycle. When a shortened maintenance cycle leads to an increase in the number of maintenance tasks within the lifecycle, all labor and material costs corresponding to the new maintenance tasks are included in the new costs. When the maintenance type is upgraded, labor and material costs are recalculated based on the resource quotas corresponding to the upgraded maintenance type, and compared item by item with the costs corresponding to the original maintenance type. The difference is determined as the cost increment. For maintenance tasks that simultaneously experience an increase in the number of maintenance tasks and an upgrade in the maintenance type, the two costs are calculated separately and then summed to avoid double counting. After comparing all planned maintenance records, all new maintenance tasks, labor cost increments, and material cost increments are summarized separately and further summed to form the predicted maintenance cost increment for the entire lifecycle. Subsequently, a delayed cost increment assessment report is generated. The report includes at least the number of new maintenance tasks, labor cost increment, material cost increment, source of cost increment, corresponding planned maintenance date, and reason for adjustment. Each cost retains the corresponding maintenance record index.

[0039] The system retrieves both the original and updated lifecycle-based predicted maintenance costs from the existing full lifecycle maintenance plan. Both costs are calculated using a unified statistical methodology, including labor, material, and organization costs for all planned maintenance within the lifecycle, excluding historical costs incurred from completed maintenance, ensuring consistency in the comparison. The system then calculates the percentage increase in the updated lifecycle-based predicted maintenance cost relative to the original, and defines this percentage as the maintenance cost increase rate. The preset cost tolerance threshold is determined based on historical power distribution room lifecycle maintenance data. It statistically analyzes the distribution of cost increase rates for all normally executed lifecycle maintenance plans over several years, selecting the 90% percentile as the cost tolerance threshold. For example, if the statistical result is 15%, then 15% is determined as the maintenance cost tolerance threshold for this power distribution room. After comparison, if the rate of increase in maintenance costs exceeds the cost tolerance threshold, it is determined that the current postponement of maintenance will cause the future lifecycle maintenance cost growth to exceed the established management scope. The updated full lifecycle maintenance plan draft is marked as unacceptable, while the original full lifecycle maintenance plan is retained as the subsequent implementation plan. If the rate of increase in maintenance costs does not exceed the cost tolerance threshold, it is determined that the updated full lifecycle maintenance plan draft meets the cost control requirements. It is marked as an acceptable plan, and the updated plan is written into the full lifecycle maintenance ledger as a new formal maintenance plan. At the same time, the original lifecycle predicted maintenance cost, the updated lifecycle predicted maintenance cost, the rate of increase in maintenance costs, the cost tolerance threshold, and the final judgment result used in this judgment are recorded to ensure that the subsequent maintenance plan adjustment process has complete historical traceability and consistent judgment standards.

[0040] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0041] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0042] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0043] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0044] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0045] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0046] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0047] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0048] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A full lifecycle operation and maintenance decision management system based on insulation aging in power distribution room systems, characterized in that, include: The assessment module is used to calculate the maximum delay time for maintenance of the equipment in the power distribution room without replacement when the insulation status assessment value of the equipment reaches the preset maintenance threshold, and convert it into the maintenance rigidity level. The reporting module is used to obtain the historical time-series load and pre-reported non-outage periods of users within the power supply range of the distribution room, and to construct a time distribution sequence of user power outage sensitivity, specifically including: Retrieve historical time-series load records of users in the area covered by the power distribution room, calculate the average load power of each time period according to the preset statistical period, use the maximum average load power in all time periods as the normalization benchmark, calculate the proportion of the average load power of each time period to the normalization benchmark, and use it as the electricity dependence factor for the corresponding time period. Retrieve the user's pre-declared non-outage period identifier, assign the power dependence factor corresponding to the non-outage period to the preset rigid non-outage cost coefficient, keep the original power dependence factor unchanged for other periods, and multiply it with the average load power of the corresponding period to obtain the power outage loss per unit time for each period. Arrange the power outage loss per unit duration in time sequence to construct a time distribution sequence of user power outage sensitivity. The delay calculation module is used to define a safe time window based on the maintenance rigidity level, match the safe time window with the user's power outage sensitivity time distribution sequence to generate a conflict intensity sequence; when there is a candidate time period that meets the set tolerance threshold, the corresponding candidate time period is used as the maintenance execution time period and the corresponding delay duration is obtained. The prediction module is used to calculate irreversible life loss based on the delay time, predict the maintenance cycle compression coefficient and the probability of maintenance type upgrade; The operation and maintenance plan generation module is used to adjust the full life cycle operation and maintenance plan of the power distribution room equipment based on the maintenance cycle compression coefficient and the upgrade probability of the maintenance type, and generate an updated operation and maintenance plan. The operation and maintenance plan decision module is used to evaluate the total cost of the updated operation and maintenance plan, generate a postponement cost increment assessment report, and determine whether to adopt the updated operation and maintenance plan based on the postponement cost increment assessment report.

2. The full lifecycle operation and maintenance decision management system based on insulation aging of a power distribution room system according to claim 1, characterized in that, The evaluation module specifically includes: Read the statistical benchmark data of fault probability corresponding to the service life of the power distribution room equipment, and the aging stage corresponding to each insulation status assessment value in the insulation degradation detection sequence. Match the fault probability value according to the service life and aging stage of the power distribution room equipment to construct the fault probability climbing sequence. The attenuation trend extrapolation function is fitted with the most recent consecutive insulation status assessment points in the insulation degradation detection sequence. The risk growth boundary is determined by the growth trajectory between the last insulation status assessment point in the fault probability climbing sequence and the preset failure fault probability. The maximum allowable delay time corresponding to the attenuation trend extrapolation function and the risk growth boundary is calculated as the maximum delayable maintenance time. The insulation status assessment value is a status quantity obtained by standardizing and fusing the insulation test data, and the insulation degradation detection sequence is constructed by arranging the insulation status assessment values ​​in chronological order. Read the original planned maintenance cycle, calculate the proportion of the maximum delayable maintenance time to the original planned maintenance cycle, and determine the maintenance rigidity level based on the range of the proportion.

3. The full lifecycle operation and maintenance decision management system based on insulation aging of a power distribution room system according to claim 1, characterized in that, The delay calculation module specifically includes generating the conflict intensity sequence: A safe time window is defined based on the date range covered by the maximum delayable maintenance time, and all dates within the safe time window that can be scheduled for power outages are used as a candidate time period set. Extract the outage sensitivity values ​​corresponding to each candidate time period from the time distribution sequence of user outage sensitivity. Sum the outage sensitivity values ​​of all users corresponding to each candidate time period to obtain the conflict intensity value of the candidate time period maintenance. Construct a conflict intensity sequence in chronological order.

4. The full lifecycle operation and maintenance decision management system based on insulation aging of a power distribution room system according to claim 1, characterized in that, In the delay calculation module, obtaining the corresponding delay duration specifically includes: Candidate time periods with conflict intensity values ​​less than a preset tolerance threshold are selected from the conflict intensity sequence to form an executable candidate time period set. When the set of executable candidate time periods is not empty, the candidate time period closest to the current time is selected as the maintenance execution time period, and the number of days between the maintenance execution time period and the current time is calculated as the delay duration. When the set of executable candidate time periods is empty, the candidate time period with the lowest conflict intensity value is selected as the restricted maintenance execution time period, and the number of days between the restricted maintenance execution time period and the current time is calculated as the delay duration.

5. The full lifecycle operation and maintenance decision management system based on insulation aging of a power distribution room system according to claim 1, characterized in that, The prediction module specifically includes: Substitute the start and end times of the delay into the attenuation trend extrapolation function to obtain the first insulation state assessment value corresponding to the start time and the second insulation state assessment value corresponding to the end time. The difference between the first insulation state assessment value and the second insulation state assessment value is taken as the irreversible lifetime loss. Search the historical maintenance records of power distribution rooms in each region for delayed maintenance cases that have the same operating years as the current power distribution room and whose insulation status assessment value is in the same aging stage. Extract the irreversible life loss, the actual reduction ratio of the planned maintenance cycle, and the maintenance type upgrade record corresponding to each historical case. Construct a life loss impact mapping relationship with irreversible life loss and aging stage. Based on the current irreversible life loss and the aging stage corresponding to the current insulation status assessment value, the actual reduction ratio of the planned maintenance cycle is matched in the life loss impact mapping relationship as the maintenance cycle compression coefficient, and the proportion of maintenance type upgrade records under the corresponding aging stage to the total number of cases is calculated as the maintenance type upgrade probability.

6. The full lifecycle operation and maintenance decision management system based on insulation aging of a power distribution room system according to claim 1, characterized in that, The operation and maintenance plan generation module specifically includes: Read the scheduled future maintenance cycles and maintenance types from the current power distribution room's full life cycle maintenance ledger. Multiply the time interval of each scheduled maintenance by the maintenance cycle compression factor to obtain the compressed new maintenance time interval. When the probability of upgrading the maintenance type exceeds the preset upgrade judgment threshold, replace the maintenance type with an adjacent higher-level maintenance type. Regenerate the current power distribution room's full life cycle maintenance plan draft with the compressed new maintenance time interval and the replaced maintenance type.

7. The full lifecycle operation and maintenance decision management system based on insulation aging of a power distribution room system according to claim 1, characterized in that, The operation and maintenance plan decision module specifically includes: Retrieve the maintenance type, maintenance interval and resource requirements corresponding to each planned maintenance in the updated full life cycle maintenance plan draft, compare it item by item with the original planned maintenance schedule, calculate the number of new maintenance times, labor cost increase and material cost increase caused by maintenance cycle adjustment and maintenance type upgrade, summarize to obtain the full life cycle predicted maintenance cost increase, and generate a postponement cost increase assessment report. Retrieve the lifecycle-predicted maintenance costs corresponding to the original full lifecycle maintenance plan, compare the updated lifecycle-predicted maintenance costs with the original lifecycle-predicted maintenance costs, and calculate the maintenance cost increase rate. When the maintenance cost increase rate exceeds the preset cost tolerance threshold, the updated full lifecycle maintenance plan draft is deemed unacceptable; otherwise, it is deemed an acceptable solution, and the final adoption decision is output.

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