Asset wall-based power equipment asset management method and system

Abstract

The application discloses a power equipment asset management method and system based on an asset wall, relates to the technical field of the asset wall, and comprises the following steps: collecting equipment full-life cycle data, and modeling a life distribution based on a logarithmic normal; establishing an initial asset wall based on the life distribution; acquiring a first extrapolation window and deriving a plurality of second extrapolation windows; extrapolating future retirement and updating quantities based on the second extrapolation windows and the life distribution; performing data analysis based on the extrapolation results, evaluating and screening the second extrapolation windows, and generating a third extrapolation window; and extrapolating based on the third extrapolation window and performing asset management. The application solves the problem that the prediction window in the prior art is difficult to balance short-term precision and long-term coverage.

Description

Technical Field

[0001] This invention relates to the field of asset wall technology, and more specifically, to a method and system for managing power equipment assets based on asset walls. Background Technology

[0002] Existing power equipment asset management technologies based on asset walls primarily involve statistically analyzing the number of power equipment in service, decommissioned, and replaced, visualizing this data as an asset wall graphic. This provides a clear view of the lifespan structure and future decommissioning distribution of the equipment group. These methods typically rely on full lifecycle data and lifespan distribution models, using the quantitative information provided by the asset wall to guide equipment replacement plans and operation and maintenance decisions. This is a crucial tool for current power system asset management.

[0003] In the management of power equipment assets within asset walls, the future extrapolation period of the wall directly affects the accuracy of forecasting equipment retirement waves and replacement needs. A short extrapolation period may fail to cover the end of the lifespan of critical equipment, while an excessively long extrapolation period may introduce uncertainty and increase forecast bias. Therefore, setting the extrapolation period for the wall is of critical technical significance for asset planning and replacement strategy formulation.

[0004] The prediction window, or extrapolation window, has a significant impact on the extrapolation results of asset walls. A high prediction window can cover a longer time period, but may lead to the accumulation of prediction errors; a low prediction window has higher prediction accuracy, but limited coverage time. Different window settings have their own limitations, affecting the accuracy and reliability of equipment asset management.

[0005] The drawback of low-predictivity windows is their short extrapolation period, which only reflects recent equipment retirement and replacement trends and cannot effectively capture long-term asset evolution patterns. This leads to a lack of foresight in equipment replacement plans, potentially failing to identify key future retirement milestones in a timely manner, thus impacting long-term asset operation and maintenance arrangements and budget planning.

[0006] The drawbacks of a high predictive window are its long coverage period, large cumulative error in the prediction process, and susceptibility to biases in lifetime distribution estimation, changes in equipment status, and external environmental factors. This can lead to deviations in the asset wall extrapolation results, affecting the determination of equipment upgrade priorities and potentially resulting in unreasonable resource allocation and management decision-making errors.

[0007] Existing technologies lack dynamic control and optimization methods for the height of the prediction window, resulting in shortcomings in balancing prediction accuracy and coverage time in power equipment asset management based on asset walls. Summary of the Invention

[0008] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a power equipment asset management method and system based on an asset wall. By constructing a multi-level extrapolation window based on the asset wall and combining it with the life distribution to predict the number of in-service equipment and the amount of replacements each year, the present invention can accurately quantify the retirement wave and replacement demand of power equipment, thus solving the problem in the prior art that the prediction window is difficult to balance short-term accuracy and long-term coverage.

[0009] One aspect of this disclosure is a power equipment asset management method based on an asset wall, comprising the following steps: constructing an initial asset wall reflecting the expected number of equipment in service and decommissioned, and extracting an initial state vector; determining the time span of a first extrapolation window based on the power equipment operation and maintenance cycle and planning cycle, and generating a corresponding time series; mapping the initial state vector to the starting point of the time series to obtain the first extrapolation window; subdividing the first extrapolation window into second extrapolation windows through a sliding time window, each second extrapolation window corresponding to a time sub-interval; calculating the in-service probability and expected number of the second extrapolation window corresponding to the time sub-interval, generating a state matrix, and establishing a bidirectional index with the time sub-interval to obtain several second extrapolation windows; predicting the number of decommissioned and replacement equipment through the second extrapolation windows and the lifetime distribution; evaluating and filtering the second extrapolation windows based on the prediction results to generate a third extrapolation window; performing extrapolation based on the third extrapolation window and performing asset management.

[0010] Another aspect of this disclosure is a power equipment asset management system based on an asset wall, comprising: a life distribution modeling module, an asset wall establishment module, an extrapolation window generation module, an extrapolation module, an extrapolation window filtering module, and an asset management module;

[0011] The lifetime distribution modeling module is used to collect data throughout the entire life cycle of the equipment and model the lifetime distribution based on the log-normal distribution.

[0012] The asset wall building module is used to construct an initial asset wall that reflects the expected number of equipment in service and decommissioned, and to extract the initial state vector;

[0013] An extrapolation window generation module maps the initial state vector to the starting point of the time series to obtain a first extrapolation window; the first extrapolation window is subdivided into second extrapolation windows by a sliding time window, with each second extrapolation window corresponding to a time sub-interval; the in-service probability and expected quantity of the time sub-interval corresponding to the second extrapolation window are calculated to generate a state matrix, and a bidirectional index is established with the time sub-interval to obtain several second extrapolation windows; the number of equipment decommissioned and replaced is predicted by the second extrapolation windows and the lifetime distribution.

[0014] The extrapolation window filtering module is used to evaluate and filter the second extrapolation window based on the prediction results, and generate the third extrapolation window;

[0015] The asset management module is used for extrapolation based on a third extrapolation window and for asset management.

[0016] This invention achieves proactive assessment of equipment decommissioning and replacement needs by constructing an initial asset wall and combining it with a dynamic prediction method using multi-level extrapolation windows. This enables management decisions to shift from reactive response to proactive planning, effectively mitigating operational risks and resource constraints caused by concentrated equipment decommissioning, and significantly enhancing the scientific nature of asset planning and the safety and controllability of power grid operation. Employing an optimized process of "preliminary prediction – evaluation and screening – final decision," the invention ensures that the final prediction result used for management (the third extrapolation window) is optimal through quantitative evaluation and screening of multiple prediction paths. This provides accurate and reliable data support for equipment replacement, maintenance arrangements, and budget preparation, avoiding ineffective resource investment or under-allocation, thereby improving capital utilization efficiency and reducing total lifecycle costs. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the power equipment asset management method based on an asset wall according to the present invention.

[0018] Figure 2 This is a schematic diagram of the power equipment asset management system based on the asset wall of the present invention. Detailed Implementation

[0019] 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.

[0020] Example 1, Figure 1 The present invention provides a power equipment asset management method based on an asset wall, comprising the following steps:

[0021] S1 collects equipment lifecycle data and models equipment lifespan based on a log-normal distribution.

[0022] In this embodiment, the step specifically includes:

[0023] Collect device lifecycle data;

[0024] The first life sample for each device is calculated based on device lifecycle data;

[0025] Take the natural logarithm of the first lifetime sample to generate the second lifetime sample set;

[0026] The parameter pairs of the log-normal distribution of lifetimes are obtained by analyzing the second lifetime sample set;

[0027] Construct a lifetime log-normal probability density function based on parameter pairs of the lifetime log-normal distribution;

[0028] A fitting curve is generated based on the log-normal probability density function of lifetime, and a goodness-of-fit test is performed to update the parameter pairs of the log-normal lifetime distribution.

[0029] Based on the updated parameters of the log-normal lifetime distribution, a distribution curve is plotted as the lifetime distribution.

[0030] It should be noted that the following is a calculation example for a feasible first lifetime sample:

[0031] ;

[0032] In the formula, This is the first lifetime sample for the i-th device. This is the downtime. This refers to the commissioning time.

[0033] It should be noted that the following is an example of calculating the parameter pairs of a feasible log-normal lifetime distribution:

[0034] ;

[0035] ;

[0036] In the formula, The mean of the lifetime sample set. The total number of devices. denoted as the standard deviation of the lifetime sample set.

[0037] The equipment life model is expressed by the following formula:

[0038] ;

[0039] In the formula, for, For timestamps.

[0040] In this embodiment, equipment lifecycle data refers to various status information and key time node records throughout the entire lifecycle of the equipment, from commissioning to decommissioning, including operation, maintenance, repair, faults, and downtime. It typically includes equipment commissioning date, fault occurrence time, maintenance records, downtime, and decommissioning time, and is used to fully describe the equipment lifecycle process, providing a data foundation for subsequent lifecycle distribution modeling.

[0041] The first lifetime sample is the lifetime value of a single piece of equipment calculated based on the equipment lifecycle data, obtained by subtracting the commissioning time from the equipment's retirement time. If the equipment has not yet been retired, a truncated lifetime sample can be used, which is the length of service time obtained by subtracting the commissioning time from the current date, and used as a right-censored sample in the lifetime distribution analysis.

[0042] The second lifetime sample set is a new sample set formed by taking the natural logarithm of the first lifetime sample. Due to the properties of the log-normal distribution, the logarithm of its random variable follows a normal distribution. Therefore, by taking the logarithm, skewed lifetime data can be transformed into an approximately normally distributed sample, providing a mathematical basis for subsequent parameter estimation and distribution fitting.

[0043] The parameter pair of a log-normal lifetime distribution typically includes a mean parameter and a standard deviation parameter, representing the mean and dispersion of the logarithmic lifetime, respectively. This parameter pair can be obtained from a second lifetime sample set using maximum likelihood estimation or moment estimation methods, thus fully describing the log-normal distribution.

[0044] The equipment life model is based on the log-normal probability density function of life, which is a mathematical function constructed based on parameter pairs. It is used to represent the probability distribution under different life values. It can reflect the failure probability distribution of equipment in different life intervals and is the core input for extrapolation analysis.

[0045] Goodness-of-fit tests are statistical methods used to verify whether the log-normal probability density function of lifespan adequately fits the actual lifespan sample distribution. Commonly used tests include the Kolmogorov-Smirnov test and the Anderson-Darling test. This test can quantify the fit bias; if the bias is too large, parameter adjustments are necessary.

[0046] Updating the parameter pairs of the log-normal lifetime distribution refers to recalculating or iteratively optimizing the sum based on the results of the goodness-of-fit test, so that the fitted curve is closer to the actual sample distribution, until the preset goodness-of-fit threshold is met, ensuring the reliability of the modeling results.

[0047] Lifetime distribution is a distribution curve plotted using the final determined log-normal distribution parameters of lifespan. It is used to visually demonstrate the statistical regularities of the lifespan of a group of equipment. Lifetime distribution serves as the foundational data input for subsequent asset wall construction, extrapolation analysis, and the formulation of update strategies.

[0048] S2, based on lifetime distribution, constructs an initial asset that reflects the expected number of equipment in service and retired.

[0049] In this embodiment, the initial asset wall is established as follows:

[0050] Group electrical equipment by category and year of commissioning;

[0051] Calculate the in-service probability curve for each category based on lifespan distribution;

[0052] Based on the preset equipment lifespan distribution, calculate the probability of in-service status and the expected number of each equipment group at different time points;

[0053] An initial asset wall matrix is ​​generated by summarizing the expected number of active and demobilized units, along with the total number of active units and the demobilization flow curve.

[0054] The initial asset wall is drawn based on the initial asset wall matrix.

[0055] This invention divides in-service equipment into several queues using equipment category and commissioning year as grouping keys. Each queue contains the total number of equipment and records the commissioning start time.

[0056] The following is a feasible example of calculating the probability curve of active duty:

[0057] ;

[0058] ;

[0059] In the formula, The cumulative distribution function is the log-normal distribution. The standard normal cumulative distribution function is... This is the average value for Class C devices. The standard deviation of Class C equipment. Probability of being in service;

[0060] Here is a feasible example of calculating the expected number of active-duty personnel:

[0061] ;

[0062] For the expected number of active duty personnel, The total number of devices. For equipment category, For the year of commissioning, Service life;

[0063] Convert the expected number of all operational queues to a calendar timeline:

[0064] ;

[0065] In the formula, z is the timestamp on the calendar timeline.

[0066] Here is an example of a feasible calculation for the expected number of retirements:

[0067] ;

[0068] In the formula, This represents the expected number of retirees.

[0069] Here is a feasible example of calculating the total number of active-duty personnel:

[0070] ;

[0071] In the formula, This refers to the total number of active-duty personnel.

[0072] Here is a feasible example of calculating the total number of demobilized personnel:

[0073] ;

[0074] In the formula, This refers to the total number of retirees.

[0075] The retirement flow curve is obtained based on the total number of retirements.

[0076] Grouping by power equipment category and commissioning year involves classifying all power equipment in the current asset ledger first according to equipment category (such as transformers, switchgear, lines, etc.), and then grouping them according to commissioning year, forming a two-dimensional grouping result of category-year, which facilitates subsequent calculation of life distribution and prediction of the number of in-service equipment by category and year.

[0077] In this embodiment, calculating the in-service probability curve for each category based on the lifetime distribution means applying the log-normal lifetime distribution obtained in the first step to each type of equipment, calculating its survival function (i.e., in-service probability) over time, and obtaining an in-service probability curve that changes with the service age of the equipment, which serves as the basic input for describing the lifetime characteristics of that type of equipment.

[0078] In this embodiment, calculating the expected number of in-service units for each commissioning queue based on lifetime distribution means that in each commissioning year queue, the expected number of in-service units for that queue is calculated year by year based on the number of such equipment and the in-service probability of the corresponding age, so that each commissioning year queue can obtain a complete time series of the number of in-service units from commissioning to the end of the prediction period.

[0079] In this embodiment, the initial asset wall matrix is ​​generated by summarizing the in-service probability curve and the expected in-service quantity. This means combining the expected in-service quantity of all equipment categories and all commissioning year queues into a two-dimensional matrix that represents the queues by rows and the calendar years by columns. Each element of the matrix represents the expected in-service value of that category-commissioning year queue in a certain year. This matrix is ​​the core data structure of the initial asset wall.

[0080] It should be noted that drawing the initial asset wall graph based on the initial asset wall matrix as the initial asset wall means visualizing the above matrix as a stacked bar chart or other two-dimensional graph, with the horizontal axis representing the year and the vertical axis representing the number of assets in service. The bars are stacked from the queues of each year of commissioning to form a "wall", which vividly reflects the asset in service structure and the distribution of future decommissioning waves, providing an intuitive basis for subsequent extrapolation analysis.

[0081] S3 determines the base forecast period as the first extrapolation window and derives multiple second extrapolation windows.

[0082] In this embodiment, specifically:

[0083] Based on the initial asset wall matrix, the active probability vector and the expected number of active assets for the current year are extracted and used as the initial state vector for extrapolation calculation.

[0084] The time span of the first extrapolation window is determined based on the operation and maintenance cycle and planning cycle of the power equipment;

[0085] Generate corresponding time series nodes based on the time span of the first extrapolation window, and map the initial state vector to the starting point of the time series to obtain the first extrapolation window;

[0086] Based on the lifetime distribution, the in-service probability and expected number of each type of equipment in the extrapolation period are calculated year by year, and the first prediction state matrix is ​​obtained by combining the first extrapolation window.

[0087] The first extrapolation window is subdivided into several overlapping and non-overlapping second extrapolation windows in the manner of sliding time windows, and each second extrapolation window corresponds to a time sub-interval;

[0088] For each second extrapolation window, the in-service probability and expected number of the sub-interval are recalculated based on the lifetime distribution to generate the state matrix of the second extrapolation window, and all state matrices are combined into a second predicted state matrix set.

[0089] By establishing a bidirectional index between the second predicted state matrix set and its corresponding time sub-intervals, several second extrapolation windows are obtained.

[0090] In this embodiment, the initial state vector for extrapolation calculation refers to the active probability vector and the expected number of active assets extracted at the current year node based on the initial asset wall matrix. These two vectors together characterize the asset health status and quantity distribution at the current time point, providing the starting conditions for subsequent extrapolation calculations.

[0091] In this embodiment, the time span of the first extrapolation window refers to the length of the future time interval determined based on the typical operation and maintenance cycle of the equipment (such as maintenance cycle, life cycle) and asset planning cycle (such as five-year or ten-year planning period), which is used to define the overall range of the extrapolation prediction.

[0092] Time series nodes refer to a set of time points obtained by uniformly or discretizing the time span of the first extrapolation window according to the planned nodes. These nodes will serve as the output positions of the extrapolation results, ensuring that the prediction results correspond to specific years or months.

[0093] The first extrapolation window refers to the baseline window that maps the initial state vector to the starting point of the aforementioned time series, and then extrapolates the future in-service probability and expected number of each type of equipment year by year based on the lifetime distribution, forming a complete state matrix covering the entire extrapolation period. This window is used to describe the future asset evolution trend.

[0094] Subdividing the time window by sliding means dividing the time span of the first extrapolation window by a certain window length (such as 1 year or 2 years) and step size, thereby generating multiple time sub-intervals that may overlap or not overlap, for more precise observation of asset changes in different time periods.

[0095] The second extrapolation window refers to treating each time sub-interval as an independent prediction window, calculating the in-service probability curve and expected quantity curve for that interval based solely on the lifetime distribution, forming a corresponding state matrix, thereby capturing the asset evolution characteristics of the subdivided time periods.

[0096] The second predicted state matrix set refers to the set of all state matrices of the second extrapolation windows arranged in the order of time sub-intervals. This set retains the global trend while providing local details, and can support subsequent multidimensional comparison and filtering analysis.

[0097] The bidirectional indexing of time sub-intervals refers to establishing a bidirectional mapping relationship between each second extrapolation window and its time sub-interval, so that the corresponding state matrix can be quickly retrieved by time, and the corresponding time range can be retrieved by the state matrix, which facilitates subsequent data processing and decision scheduling.

[0098] S4 uses a second extrapolation window combined with lifespan distribution to predict the amount of equipment decommissioning and replacement.

[0099] In this embodiment, the step specifically includes:

[0100] Initialize the extrapolation state based on the second extrapolation window;

[0101] Based on the initial state and equipment lifespan distribution, predict the number of in-service equipment at each future time point;

[0102] Based on the predicted changes in the number of in-service equipment, determine the corresponding equipment replacement demand;

[0103] The number of in-service equipment and the demand for equipment replacement are summarized to form a complete set of forecast results.

[0104] The extrapolation state is the current extrapolation calculation starting state obtained by initializing the state matrix of the second extrapolation window. It includes the in-service probability vector and the expected number of in-service vector at the beginning of the time sub-interval, which serve as the basic input for subsequent year-by-year extrapolation.

[0105] The extrapolated number of in-service equipment refers to the calculation of the decay of the probability of in-service equipment and the corresponding change in the expected number of equipment in service year by year according to the time series under the given extrapolation state and life distribution conditions, so as to obtain the prediction result of the number of in-service equipment in each future year, which is used to reflect the natural retirement process of equipment.

[0106] The update quantity is the number of new devices needed at each point in time to maintain asset size or meet planning goals. It is calculated by comparing the target number of devices in service with the extrapolated number of devices in service, reflecting future asset replenishment or expansion needs.

[0107] The second extrapolation window's extrapolation result set refers to the result set formed by summarizing the in-service quantity sequence and the corresponding update quantity sequence obtained from year-by-year extrapolation in chronological order. This result set completely records the natural retirement and replenishment plans of assets within the time sub-interval, serving as input data for subsequent evaluation and screening. This embodiment is successful.

[0108] S5. Based on the extrapolation results, perform data analysis to evaluate and filter the second extrapolation window, and generate the third extrapolation window.

[0109] In this embodiment, the above steps are specifically as follows:

[0110] For each second extrapolation window, extract several dimensions of equipment asset extrapolation features;

[0111] The extrapolation characteristics of each equipment asset are standardized using the mean and standard deviation within the window.

[0112] Weights are assigned to the extrapolated characteristics of each standardized equipment asset, and the weight values ​​are determined based on management strategies and planning objectives.

[0113] The standardized equipment asset extrapolation characteristics of each second extrapolation window are weighted and summed to obtain a comprehensive weighted evaluation value;

[0114] Sort according to the comprehensive weighted evaluation value of each second extrapolation window;

[0115] Within the device limitations, select the second extrapolation window with the highest weighted value as the third extrapolation window.

[0116] In this embodiment, the equipment asset extrapolation features refer to the multi-dimensional indicators extracted from the extrapolation results of each second extrapolation window, which are used to quantify the characteristics of future asset status, including annual fluctuations in the number of in-service units, annual peak retirement volume, annual peak replacement volume, retirement ratio of key equipment categories, and deviation of lifespan distribution, etc., which are used to describe the stability, risk, and resource demand of the asset wall extrapolation results.

[0117] The comprehensive weighted evaluation value refers to the value obtained by weighting and summing all standardized features of each second extrapolation window according to their corresponding weights. This value can quantify the merits and demerits of the window in future asset management and facilitate cross-window comparison and screening.

[0118] It should be noted that the third extrapolation window refers to the extrapolation window formed by selecting several windows with the highest weighting values ​​from the sorted second extrapolation windows under equipment limitations and management constraints. This window is used for the next step of final asset extrapolation and asset management decision-making and is an optimized screening result of the second extrapolation window.

[0119] S6 performs extrapolation based on the third extrapolation window and manages assets.

[0120] In this embodiment, the above steps are specifically as follows:

[0121] Within the time interval of the third extrapolation window, the number of in-service equipment in each category and commissioning year is calculated annually, while the annual decommissioning and replacement volumes are updated to form a continuous annual extrapolation curve:

[0122] Summarize the annual active quantity, decommissioning quantity, and update quantity of all third extrapolation windows, generate asset status, and draw an asset wall graphic;

[0123] The asset wall graphic displays the distribution of the number of assets in service, decommissioned, and replaced in each year;

[0124] The asset wall graphic serves as a benchmark for asset management, enabling the monitoring and management of assets.

[0125] By calculating the number of in-service equipment for each equipment category and the commissioning year queue year by year, the number of in-service equipment can be calculated year by year within the time interval covered by the third extrapolation window, based on the lifespan distribution and extrapolation status of each equipment category and its commissioning year queue, in order to reflect the changing trend of assets over time.

[0126] Annual retirement volume refers to the number of equipment that is taken out of service each year due to the expiration of its service life or planned retirement. This indicator is used to describe the natural consumption and replacement needs of equipment and has reference value for asset management and maintenance planning.

[0127] Annual replacement volume refers to the number of devices that need to be added or supplemented each year based on the projected number of devices in service and planning targets, in order to maintain asset size or meet future power load and maintenance needs. This indicator is used to guide equipment procurement and replacement plans.

[0128] A continuous annual extrapolation curve is a graph that plots the number of active assets, decommissioned assets, and replacement assets calculated year by year in chronological order. It is used to visually display the evolution trend of future asset status over time, facilitating decision-making.

[0129] Asset status refers to the overall asset data set formed by aggregating the annual number of in-service, decommissioned, and updated assets from all third extrapolation windows. This data set is used to comprehensively reflect the equipment status at various future time points and provides a data foundation for asset management.

[0130] Asset wall graphics refer to visualizing the status of assets as a two-dimensional or stacked bar chart. The horizontal axis represents the year, the vertical axis represents the quantity, and different colors or stacking levels represent the number of assets in service, the number of decommissioned assets, and the number of replacement assets, which vividly presents the distribution structure of equipment assets and future replacement plans.

[0131] Asset management benchmarks refer to the asset wall diagram as a reference, used to monitor the deviation between the actual equipment operation and the predicted state, to achieve closed-loop control and dynamic adjustment of asset management, and to guide operation and maintenance decisions, budget allocation and update plans.

[0132] This invention achieves refined prediction and management of the entire lifecycle of power equipment by constructing an initial asset wall based on lifespan distribution and combining it with a multi-level extrapolation window method. By calculating the number of in-service, decommissioned, and replacement units annually, and performing multi-dimensional feature weighted evaluation and screening in the second extrapolation window, it can identify key decommissioning nodes and equipment replacement needs in advance, effectively avoiding operational risks caused by concentrated equipment decommissioning, improving the scientific nature and controllability of asset planning, and providing data support and decision-making basis for operation and maintenance, replacement, and budget arrangements.

[0133] Through the final extrapolation of the third extrapolation window and the visualization of the asset wall, the status of assets is presented intuitively and managed dynamically. The asset wall graphic displays the distribution of the number of assets in service, decommissioned, and replaced in each year, enabling managers to monitor the health status of assets in real time, rationally arrange equipment replacement plans, and form a closed-loop control between planned goals and actual operation. This improves equipment replacement efficiency, reduces operation and maintenance costs, and enhances the predictability and reliability of power system asset management.

[0134] Example 2, Figure 2A power equipment asset management system based on an asset wall is presented, comprising a lifespan distribution modeling module, an asset wall establishment module, an extrapolation window generation module, an extrapolation module, an extrapolation window filtering module, and an asset management module. The lifespan distribution modeling module is used to establish an equipment lifespan model based on the equipment's full lifespan data and a log-normal distribution. The asset wall establishment module is used to construct an initial asset wall reflecting the expected number of equipment in service and decommissioned. The extrapolation window generation module uses the basic prediction period as the first extrapolation window, predicts equipment status based on the lifespan model, and obtains a first prediction status matrix. The first extrapolation window is divided into multiple prediction sub-intervals to generate multiple second extrapolation windows. The equipment status prediction matrix for each second extrapolation window is calculated based on the lifespan model, forming a second prediction status matrix set. The extrapolation window filtering module evaluates and filters the second prediction status matrix set to determine the optimal prediction interval as the third extrapolation window. The asset management module is used to manage power equipment assets based on the prediction results of the third extrapolation window.

[0135] In the embodiments provided by this invention, it should be understood that the disclosed system can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0136] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0137] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0138] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0139] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0140] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0141] In the embodiments provided in this disclosure, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0142] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0143] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A power equipment asset management method based on asset walls, characterized in that, Includes the following steps: Based on the collected lifespan data of power equipment, the parameters of the log-normal distribution are estimated; Based on the parameters of the log-normal distribution, a log-normal probability density function is constructed; A fitting curve is generated based on the log-normal probability density function, and a goodness-of-fit test is performed. The parameters of the log-normal distribution are updated accordingly. Based on the updated parameters of the log-normal distribution, a distribution curve is plotted as the lifetime distribution; Group electrical equipment by category and year of commissioning; Based on the preset equipment lifespan distribution, calculate the probability of in-service status and the expected number of each equipment group at different time points; summarize the expected number of in-service status and the expected number of decommissioning status of all equipment groups, generate the initial asset wall data model, and extract the initial state vector; Based on the operation and maintenance cycle and planning cycle of power equipment, determine the time span of the first extrapolation window and generate the corresponding time series; The initial state vector is mapped to the starting point of the time series to obtain the first extrapolation window; The first extrapolation window is subdivided into a second extrapolation window by sliding a time window, and each second extrapolation window corresponds to a time sub-interval; Calculate the in-service probability and expected number of the time sub-interval corresponding to the second extrapolation window, generate a state matrix, and establish a bidirectional index with the time sub-interval to obtain several second extrapolation windows; Predict the amount of equipment decommissioning and replacement by using a second extrapolation window and lifetime distribution; The second extrapolation window is evaluated and filtered based on the prediction results to generate the third extrapolation window; Extrapolation is performed based on a third extrapolation window, and asset management is also carried out.

2. The power equipment asset management method based on asset walls according to claim 1, characterized in that, The parameters for estimating the log-normal distribution include: performing a logarithmic transformation on the lifetime data, and calculating the parameter pairs of the log-normal distribution based on the transformed data.

3. The power equipment asset management method based on asset walls according to claim 2, characterized in that, The specific construction steps of the initial asset wall also include: generating a visual representation of the initial asset wall based on the initial asset wall data model.

4. The power equipment asset management method based on asset walls according to claim 3, characterized in that, The calculation of the in-service probability and expected number of each equipment group at different time points includes: for each equipment group, deriving its in-service probability curve over time based on the lifespan distribution; and calculating the expected in-service number and expected decommissioning number of the equipment group year by year based on the in-service probability curve and the initial number of equipment.

5. The power equipment asset management method based on asset walls according to claim 4, characterized in that, The method of predicting equipment retirement and replacement rates using a second extrapolation window and lifetime distribution includes: Initialize the extrapolation state based on the second extrapolation window; Based on the initial state and equipment lifespan distribution, predict the number of in-service equipment at each future time point; Based on the predicted changes in the number of in-service equipment, determine the corresponding equipment replacement demand; The number of in-service equipment and the demand for equipment replacement are summarized to form a complete set of forecast results.

6. The power equipment asset management method based on asset walls according to claim 5, characterized in that, The process of evaluating and filtering the second extrapolation window based on the prediction results to generate the third extrapolation window is as follows: Multiple evaluation features are extracted from the prediction results of the second extrapolation window, and the evaluation features are standardized. Weights are assigned based on the standardized evaluation features and weighted calculations are performed to obtain the comprehensive weighted evaluation value for each window; The second extrapolation window is sorted according to the comprehensive weighted evaluation value, and the optimal third extrapolation window is selected based on the sorting result.

7. The power equipment asset management method based on asset walls according to claim 6, characterized in that, The extrapolation based on the third extrapolation window and the asset management are specifically as follows: Based on the selected forecast window, generate continuous time series forecast data containing the number of equipment in service, the number of decommissioned, and the number of replacements; A dynamic asset wall visualization graphic is constructed based on the predicted data; The dynamic asset wall graphic serves as a benchmark for asset management, used to monitor and manage power equipment assets.

8. A system using the power equipment asset management method based on asset walls as described in any one of claims 1-7, characterized in that, It includes a lifetime distribution modeling module, an asset wall creation module, an extrapolation window generation module, an extrapolation module, an extrapolation window filtering module, and an asset management module; The lifetime distribution modeling module is used to collect data throughout the entire life cycle of the equipment and model the lifetime distribution based on the log-normal distribution. The asset wall building module is used to construct an initial asset wall that reflects the expected number of equipment in service and decommissioned, and to extract the initial state vector; An extrapolation window generation module maps the initial state vector to the starting point of the time series to obtain a first extrapolation window; the first extrapolation window is subdivided into second extrapolation windows by a sliding time window, with each second extrapolation window corresponding to a time sub-interval; the in-service probability and expected quantity of the time sub-interval corresponding to the second extrapolation window are calculated to generate a state matrix, and a bidirectional index is established with the time sub-interval to obtain several second extrapolation windows; the number of equipment decommissioned and replaced is predicted by the second extrapolation windows and the lifetime distribution. The extrapolation window filtering module is used to evaluate and filter the second extrapolation window based on the prediction results, and generate the third extrapolation window; The asset management module is used for extrapolation based on a third extrapolation window and for asset management.

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