Update management method, system and equipment of power equipment, medium and product

Abstract

The invention discloses an updating management method, system and equipment of power equipment, a medium and a product. The method comprises the following steps: acquiring operation data of the power equipment in each link of the whole life cycle; inputting the operation data into a preset value evaluation model to calculate the value contribution of each link, and dividing each link based on the value contribution to obtain a plurality of link division results; inputting each link division result into a preset system dynamics coupling model, and solving by taking power supply reliability maximization and total life cycle cost minimization of power equipment as a collaborative optimization target to obtain a global collaborative optimization scheme and identify a core optimization node influencing global efficiency; and based on the global collaborative optimization scheme and the core optimization node, updating management is performed on the power equipment, so that the accuracy of updating management of the power equipment can be improved by implementing the method and the device.

Description

Technical Field

[0001] This invention relates to the field of power systems, and more particularly to a method, system, device, medium, and product for the management of power equipment upgrades. Background Technology

[0002] Power equipment is the material foundation for the safe, stable, and economical operation of power systems. Its entire life cycle encompasses multiple closely related stages, including planning and selection, procurement and warehousing, installation and commissioning, operation monitoring, maintenance and repair, and decommissioning. The fundamental purpose of implementing scientific renewal management for power equipment is to systematically coordinate resource input, cost consumption, and performance output across all stages of the entire life cycle. This drives a shift in management paradigm from passive, discrete, single-equipment operation and maintenance to proactive, integrated maximization of asset life-cycle value, thereby optimizing the total cost of ownership while ensuring power supply reliability.

[0003] Currently, the management of power equipment upgrades in practice mainly relies on two methods: one is based on segmented, isolated information systems to obtain scattered and localized equipment data; the other is heavily reliant on the experience and judgment of management personnel to formulate strategies. This model has significant shortcomings in the accuracy of management decisions: First, due to the lack of a unified and objective quantitative evaluation system, it is impossible to accurately calculate and compare the true value contribution and cost drivers of each stage, such as planning, operation and maintenance, and repair. For example, when distinguishing between value-added and non-value-added activities, relying solely on subjective experience can easily lead to biased and highly volatile identification results. Second, and more importantly, existing methods have failed to construct a scientific model that quantifies the dynamic coupling relationship between stages (for example, it is impossible to accurately calculate how the quality of condition monitoring affects maintenance frequency and spare parts costs). This results in any local optimization decision targeting a single stage lacking a global perspective, failing to assess its cascading impact on other stages and overall objectives. Ultimately, this leads to insufficient overall accuracy and synergistic effectiveness of upgrade management strategies, hindering the continuous improvement of asset efficiency and investment returns. Summary of the Invention

[0004] This invention provides a method, system, equipment, medium, and product for the management of power equipment updates, which can improve the accuracy of power equipment update management.

[0005] An embodiment of the present invention provides a method for updating and managing power equipment, comprising: Acquire operational data of power equipment at each stage of its entire life cycle, wherein the operational data includes operating load data, maintenance cost data, and performance status data; The operational data is input into a preset value assessment model to calculate the value contribution of each stage, and the stages are divided based on the value contribution to obtain several stage division results. The results of each of the aforementioned process segments are input into a preset system dynamics coupling model. The model is then solved with the goal of maximizing the power supply reliability of the power equipment and minimizing the total cost over the entire life cycle. This yields a global collaborative optimization scheme and identifies the core optimization nodes that affect global efficiency. Based on the global collaborative optimization scheme and the core optimization node, power equipment can be updated and managed.

[0006] This invention transforms previously ambiguous management value into calculable and comparable value contribution figures by inputting operational data into a value assessment model based on a quantitative algorithm. This allows subsequent segmentation of processes (such as value-added, necessary non-value-added, and non-value-added) to be based on objective and consistent quantitative standards. By inputting the quantitative segmentation results into a system dynamics coupling model for global collaborative optimization, it simulates and quantifies the chain reactions between processes. This ensures that the generated global collaborative optimization scheme and the identified core optimization nodes represent optimal or near-optimal solutions based on overall system efficiency, rather than simple sums of local optima. This enables management strategies to not only accurately match the current state but also possess accurate predictability of the system's dynamic evolution. Based on the global collaborative optimization scheme and the core optimization nodes, it is possible to effectively identify power equipment that needs replacement and perform update management. Compared with existing technologies, this invention can improve the accuracy of power equipment update management.

[0007] Furthermore, the process of inputting the results of each of the aforementioned component divisions into a preset system dynamics coupling model, and solving the model with the goal of maximizing the power supply reliability of the power equipment and minimizing the total life-cycle cost, yields a global collaborative optimization scheme and identifies the core optimization nodes affecting global efficiency, including: The results of each segment division and its corresponding value contribution are mapped to the corresponding segment variables in the pre-constructed system dynamics coupling model; Based on the aforementioned component variables, the system uses the maximization of power supply reliability and the minimization of total life-cycle cost as multi-objective optimization functions. Simulation calculations are performed in the system dynamic coupling model to obtain the optimal combination of component variable parameters under the preset constraints. The optimal combination of component variable parameters constitutes the global collaborative optimization scheme. The sensitivity of the changes in variables at each stage during the simulation calculation process to the results of the multi-objective optimization function is analyzed, and the stages whose sensitivity exceeds a preset threshold are identified as core optimization nodes affecting the overall efficiency.

[0008] By inputting the quantified partitioning results into the system dynamic coupling model for global collaborative optimization, the chain reaction between links can be simulated and quantified. This ensures that the generated global collaborative optimization scheme and the identified core optimization nodes are optimal or suboptimal solutions based on the overall system efficiency, rather than simple superposition of local optima. This makes the management strategy not only accurately match the current state, but also have accurate predictability of the dynamic evolution of the system.

[0009] Furthermore, the step of inputting the operational data into a preset value assessment model to calculate the value contribution of each stage includes: Based on the aforementioned operational data, the accuracy assurance value coefficient, data traceability value coefficient, and compliance support value coefficient for each stage are calculated respectively. The value contribution of each stage is calculated by substituting the accuracy assurance value coefficient, the data traceability value coefficient, and the compliance support value coefficient into a preset stage value judgment formula.

[0010] By inputting operational data into a value assessment model based on quantitative algorithms, the originally vague management value can be transformed into a calculable and comparable value contribution value, so that subsequent process divisions (such as value-added, necessary non-value-added, and non-value-added) are based on objective and consistent quantitative standards.

[0011] Furthermore, the process of dividing each stage based on the value contribution yields several stage division results, including: The value contribution is compared with a preset threshold range; If the value contribution exceeds the upper limit of the threshold range, it is determined to be a value-added process; If the value contribution is within the threshold range, it is determined to be a necessary non-value-added process; If the value contribution is less than the lower limit of the threshold range, it is determined to be a non-value-added process.

[0012] By using preset thresholds to qualitatively classify processes (value-added / necessary non-value-added / non-value-added), this ensures that update management and process improvement can accurately focus on the processes that truly affect overall efficiency, avoiding resource misallocation caused by ambiguous classifications and systematically improving accuracy from the source of management decisions.

[0013] Furthermore, the updating and management of power equipment based on the global collaborative optimization scheme and the core optimization node includes: Based on the optimization parameters for the core optimization node in the global collaborative optimization scheme, several target power devices located in the link corresponding to the core optimization node and whose evaluation results are that they need to be replaced or do not meet the preset performance requirements are selected. Based on the resource allocation plan determined in the global collaborative optimization scheme, adjustment instructions are generated to centrally replace each of the target power equipment in order to update and manage the power equipment.

[0014] Based on the global collaborative optimization scheme and the core optimization node, the power equipment that needs to be replaced can be effectively identified and updated.

[0015] Furthermore, the acquisition of operational data of power equipment at each stage of its entire life cycle includes: Acquire initial operational data for power equipment at each stage of its entire life cycle; Identify abnormal data in the initial operational data and remove the abnormal data from the initial operational data to obtain the processing result; The processing results are then converted into structured data to obtain operational data.

[0016] Another embodiment of the present invention provides a power equipment update management system, comprising: The acquisition module is used to acquire operational data of power equipment at each stage of its entire life cycle, wherein the operational data includes operating load data, maintenance cost data, and performance status data. The segmentation module is used to input the operational data into a preset value assessment model to calculate the value contribution of each link, and to segment each link based on the value contribution to obtain several segmentation results. The solution module is used to input the division results of each link into a preset system dynamics coupling model, and solve the problem with the goal of maximizing the power supply reliability of the power equipment and minimizing the total cost of the whole life cycle, so as to obtain a global collaborative optimization scheme and identify the core optimization nodes that affect the global efficiency. The management module is used to update and manage power equipment based on the global collaborative optimization scheme and the core optimization node.

[0017] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the steps of the power equipment update management method of the present invention.

[0018] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform steps such as the power equipment update management method of the present invention.

[0019] Another embodiment of the present invention provides a computer program product, including a computer program or instructions, which, when executed by a communication device, implement the steps of the power equipment update management method of the present invention. Attached Figure Description

[0020] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating one embodiment of the power equipment update management method provided in this application; Figure 2 This is a flowchart illustrating one embodiment of steps S201 to S203 provided in this application; Figure 3 This is a schematic diagram of the structure of one embodiment of the power equipment update management system provided in this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0024] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0025] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0026] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0027] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0028] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0029] Power equipment is the material foundation for the safe, stable, and economical operation of power systems. Its entire life cycle covers many closely related stages, including planning and selection, procurement and warehousing, installation and commissioning, operation monitoring, maintenance and repair, and decommissioning and scrapping. Therefore, it is of great significance to implement the renewal management of power equipment. At present, the renewal management of power equipment generally relies on segmented information systems and experience-based judgment, which has significant deficiencies in terms of accuracy.

[0030] See Figure 1 To improve the accuracy of power equipment upgrade management, an embodiment of the present invention provides a power equipment upgrade management method, including steps S101 to S104: Step S101: Obtain operational data of the power equipment at each stage of its entire life cycle, wherein the operational data includes operating load data, maintenance cost data, and performance status data; In some embodiments, acquiring operational data of the power equipment at each stage of its entire life cycle includes: acquiring initial operational data of the power equipment at each stage of its entire life cycle; Identify abnormal data in the initial operational data and remove the abnormal data from the initial operational data to obtain the processing result; convert the processing result into structured data to obtain operational data.

[0031] In some embodiments, firstly, raw operational data for the entire lifecycle is automatically or assistedly collected by data acquisition terminals deployed at various stages (such as substations, calibration workshops, and maintenance sites). This initial operational data includes three categories: cost-related data, such as manpower hours consumed at each stage, equipment runtime, and consumable quantity; value and performance-related data, such as asset pass rate, calibration / verification error values, data record integrity fields, and compliance certificate status; and timeliness-related data, such as processing time at each stage, waiting time between stages, and asset idle time. Secondly, to prevent erroneous or extreme data from interfering with the analysis results, the mean (μ) and standard deviation (σ) of the raw operational data need to be calculated. Subsequently, abnormal data exceeding the range of μ ± 3σ are automatically identified and removed. For example, if the average processing time for a certain stage is 2 hours (μ) and the standard deviation is 0.5 hours (σ), the system will automatically remove records with processing times less than 0.5 hours or greater than 3.5 hours. Finally, since the cleaned results may still contain unstructured or semi-structured data (such as handwritten maintenance logs, non-standard format inspection reports, and image records), in order to enable them to be directly processed by quantitative algorithms such as value assessment models and system dynamics models, it is necessary to implement a data standardization process to transform unstructured data into structured data with unified coding and format according to predefined templates and rules, thereby obtaining operational data.

[0032] It should be noted that the entire life cycle covers procurement and warehousing, verification and calibration, operation and maintenance, fault handling, and scrapping and recycling.

[0033] It should be noted that the hardware configuration of the data acquisition terminal is as follows: integrated RFID reader / writer module (identification distance 0.5-3m, accuracy ≥99.8%, compatible with ISO 18000-6C protocol), sensing module (infrared temperature measurement: -20~85℃, error ≤±0.1℃; vibration sensor: 0-10Hz, error ≤±0.01Hz; error detection module: accuracy ≤±0.05% FS), and data entry module (high-definition camera, barcode recognition accuracy ≥99.2%; touch input screen).

[0034] For example, key information (equipment ID, failure time, maintenance action, and replaced parts) from handwritten maintenance records is extracted and filled into a standardized "maintenance work order" database; various calibration result reports are uniformly converted into XML or JSON format containing fixed fields such as "asset ID, calibration time, calibration item, standard value, measured value, error, and pass / fail status".

[0035] Step S102: Input the operational data into a preset value assessment model to calculate the value contribution of each link, and divide each link based on the value contribution to obtain several link division results; In some embodiments, inputting the operational data into a preset value assessment model to calculate the value contribution of each stage includes: calculating the accuracy assurance value coefficient, data traceability value coefficient, and compliance support value coefficient for each stage based on the operational data; and substituting the accuracy assurance value coefficient, the data traceability value coefficient, and the compliance support value coefficient into a preset stage value judgment formula to calculate the value contribution of each stage. Specifically, firstly, the accuracy assurance value coefficient can be obtained by calculating the ratio of the "stage asset qualification pass rate" to the "industry benchmark pass rate" in the operational data and multiplying it by 100. The relevant formula is: The industry benchmark pass rate is determined through industry reports or historical data statistics (e.g., the benchmark pass rate for the power industry's verification process is 98%). Then, by calculating the ratio of traceable data entries to the total number of data entries in the operational data, the data traceability rate can be obtained. Finally, by multiplying the data traceability rate by the traceability integrity score, the data traceability value coefficient, which assesses the level of data traceability and record completeness at this stage, can be obtained. Then, by calculating the ratio of compliant assets to the total number of assets processed in the operational data, the compliance rate can be obtained. Furthermore, by multiplying the "compliance rate" by the "compliance importance coefficient," the compliance support value coefficient, which measures the ability of this stage to meet legal regulations and corporate standards, can be obtained. Finally, the certainty guarantee value coefficient calculated above will be used... Data traceability value coefficient and compliance support value coefficient Substituting the values ​​into the preset value judgment formula for each stage, a comprehensive calculation is performed to determine the value contribution of that stage. The formula is a linear weighted combination model, in the form of: In the formula, where, Value judgment value for each link (unitless); The weighting coefficients (calibrated using the Analytic Hierarchy Process (AHP) combined with enterprise needs; power industry example:) ),and The unit operating cost (yuan / piece) is the cost of each link. ,and Includes costs for manpower, equipment, consumables, and venue.

[0036] It should be noted that the data traceability value coefficient The calculation formula is: Data traceability rate = number of traceable data entries / total number of data entries. The traceability integrity score is determined by expert review or automatic system judgment on a scale of 1 to 10 (10 points for data containing complete collection time, operator, and equipment number, and 2 points deducted for each missing item).

[0037] It should be noted that compliance underpins value. The calculation formula is: Compliance compliance rate = number of compliant assets / total number of assets processed. The compliance importance coefficient is assigned according to asset type (critical assets 4.0, important assets 3.0, general assets 1.5 - 2.0).

[0038] By inputting operational data into a value assessment model based on quantitative algorithms, the originally vague management value can be transformed into a calculable and comparable value contribution value, so that subsequent process divisions (such as value-added, necessary non-value-added, and non-value-added) are based on objective and consistent quantitative standards.

[0039] In some embodiments, the step of dividing each stage based on the value contribution to obtain several stage division results includes: comparing the value contribution with a preset threshold range; if the value contribution exceeds the upper limit of the threshold range, it is determined to be a value-added stage; if the value contribution is within the threshold range, it is determined to be a necessary non-value-added stage; if the value contribution is less than the lower limit of the threshold range, it is determined to be a non-value-added stage. Specifically, firstly, the calculated value contribution is... The comparison is made with a preset threshold range, and the category of the process is determined based on the comparison result. Where, if If this step is deemed a value-added step, such as core verification and precision calibration, it should be retained and optimized. If it is determined to be a necessary non-value-added step, such as the warehousing registration and scrapping filing steps, then redundancy needs to be reduced; if If it is determined to be a non-value-added process (such as duplicate data entry and excessive inspection process), it will be directly eliminated.

[0040] It should be noted that when obtaining the process segmentation results, it is necessary to eliminate waste and redundancy through quantitative accounting and targeted optimization, targeting the specific waste types of the measured assets. Waste type identification includes: focusing on time-related waste (verification waiting waste, process delay waste) and value-related waste (over-calibration waste, idle asset waste, and duplicate data entry waste). At this point, it is necessary to calculate the total waste over the entire cycle. The calculation formula is as follows: Where W is the total waste amount (yuan) throughout the entire cycle; Let be the unit time cost (yuan / hour) for the i-th type of time waste. ; For the i-th type of time-wasted time (hours), the waiting and lag time recorded by the collection terminal is statistically analyzed; The number (pieces) of assets that constitute the j-th type of value waste, such as the number of overcalibrated assets or assets that have been idle for more than 3 months; The unit value (yuan / unit) of asset of type j is calculated based on the asset purchase cost after depreciation.

[0041] It should be noted that for non-value-added processes: they are directly eliminated, such as deleting duplicate data entry procedures, and data is entered once and shared throughout the entire process through a collaborative platform, reducing data entry time and error rate. For necessary non-value-added processes: wasted time is reduced, with the goal of... This reduces costs by over 30%. For example, by parallel processing to synchronize warehousing registration and inspection appointments, what previously required two days (warehousing registration followed by inspection appointment) can now be completed in one day. Standardized operating procedures, such as warehousing registration operation specifications, clearly define the operation time, responsible person, and standards for each step, reducing operational errors and time waste. Value-added aspects include optimizing resource allocation (e.g., based on...). The number of calibration equipment should be dynamically adjusted. A high verification value indicates a high level of accuracy assurance, allowing for the addition of verification equipment, improving verification efficiency, and avoiding wasted time due to insufficient equipment.

[0042] By using preset thresholds to qualitatively classify processes (value-added / necessary non-value-added / non-value-added), this ensures that update management and process improvement can accurately focus on the processes that truly affect overall efficiency, avoiding resource misallocation caused by ambiguous classifications and systematically improving accuracy from the source of management decisions.

[0043] Step S103: Input the results of each link division into a preset system dynamic coupling model, and solve the problem with the goal of maximizing the power supply reliability of the power equipment and minimizing the total cost of the whole life cycle, so as to obtain a global collaborative optimization scheme and identify the core optimization nodes that affect the global efficiency. Please refer to Figure 2In some embodiments, step S103 includes steps S201 to S203: Step S201: Map the results of each segment division and the corresponding value contribution to the corresponding segment variables in the pre-constructed system dynamics coupling model; In some embodiments, the results of the segmentation of each stage (i.e., value-added stages and necessary non-value-added stages) and their corresponding quantitative value contributions are instantiated as a stage variable in the model. This stage variable includes the cost characterizing that stage. Value output Attributes such as... Simultaneously, based on historical operational data, regression analysis is used to determine the coupling coefficient between any two stages, p and q. This coefficient quantifies the direct impact of the efficiency or quality of the preceding stage on the subsequent stage (e.g., the impact coefficient of the verification stage on the operation and maintenance stage). It can be set to 0.6).

[0044] Step S202: Based on the aforementioned link variables, using the maximization of power supply reliability and the minimization of total life cycle cost as multi-objective optimization functions, simulation calculations are performed in the system dynamic coupling model to obtain the optimal link variable parameter combination under the preset constraints, thus forming the optimal link variable parameter combination constituting the global collaborative optimization scheme. In some embodiments, in the system dynamics model that completes the mapping between variables and parameters, a multi-objective optimization function is constructed, wherein the function aims to maximize power supply reliability and the total life-cycle cost. Minimization is the core objective, and it can also be incorporated into the total duration of the entire cycle. Subsidiary objectives such as "minimize" are incorporated into the constraints to obtain a multi-objective optimization function. By calling system dynamics simulation tools such as Vensim and AnyLogic, the mapped process variables and parameters are input, and the model is run to perform iterative simulation calculations. Finally, a set of process variable parameter combinations that can make the multi-objective function reach the optimal balance (such as the optimal resource allocation ratio of each process, processing time target, and cost control target, etc.) is solved. This parameter combination constitutes the global collaborative optimization scheme.

[0045] It should be noted that the total life cycle cost The calculation formula is: ,in, The total cost over the entire lifecycle is in yuan; l represents the total number of steps in the process. The fixed cost (yuan / cycle) for the k-th stage is as follows: ; Let be the unit variable cost (yuan / unit) for the k-th stage, where ; Number of assets processed in stage k (pieces); Let the unit risk cost (yuan / piece) be the cost of the kth stage, where, ; Let be the probability of risk occurring in the k-th stage (0.02 - 0.1), where .

[0046] It should be noted that maximizing power supply reliability can be correlated and transformed into a value contribution coefficient, etc. Maximizing calculable comprehensive benefit indicators, the calculation formula is as follows: ,in, Value contribution coefficient (value unit / yuan cost); The accuracy value coefficient for the k-th stage (1-5 points, key assets 5 points, important assets 3-4 points, general assets 1-2 points); The asset qualification pass rate (%) for stage k; The efficiency value coefficient for the k-th stage (1-3 points, 3 points for meeting the standard within the time limit, 2 points for basically meeting the standard, and 1 point for not meeting the standard). The timeliness compliance rate (%) of the kth stage; The compliance value coefficient for the k-th stage (1-4 points, 4 points for serious impact on compliance, 2-3 points for moderate impact, and 1 point for minor impact); The compliance rate (%) of the k-th stage; Let $k$ be the total cost (in yuan) for the k-th stage.

[0047] It should be noted that the total duration of the entire cycle... The calculation formula is: ,in, Let k be the processing time of the k-th stage. The duration of the connection between stages; To improve collaborative efficiency.

[0048] Step S203: Analyze the sensitivity of the changes in the variables of each stage during the simulation calculation process to the result of the multi-objective optimization function, and identify the stages whose sensitivity exceeds a preset threshold as core optimization nodes affecting the overall efficiency.

[0049] In some embodiments, during and after the above simulation calculations, the sensitivity of the impact of changes in variables (parameters or states) at each stage on the results of the multi-objective optimization function (especially the power supply reliability and total cost objectives) is analyzed. Specifically, this is achieved by observing the cost of manually fine-tuning a certain stage in the simulation. or value output At that time, the global value contribution coefficient With total cost The magnitude of change is used to quantify the global impact of a particular stage. Stages where even minor changes cause significant fluctuations in the global optimization objective are classified as high-sensitivity stages. Typically, the value contribution coefficient... Lower cost and its own cost The higher-level links have the most significant negative impact on the goals of "cost minimization" and "value maximization," and are therefore identified as the core optimization nodes affecting overall efficiency.

[0050] By simulating different optimization schemes, the optimal solution is selected, and the core optimization node is locked. (High-efficiency links), and make resource allocation and process adjustments to these nodes, break down barriers between links, achieve overall process synergy optimization, and avoid the problem of low overall efficiency caused by local optimization.

[0051] By inputting the quantified partitioning results into the system dynamic coupling model for global collaborative optimization, the chain reaction between links can be simulated and quantified. This ensures that the generated global collaborative optimization scheme and the identified core optimization nodes are optimal or suboptimal solutions based on the overall system efficiency, rather than simple superposition of local optima. This makes the management strategy not only accurately match the current state, but also have accurate predictability of the dynamic evolution of the system.

[0052] Step S104: Based on the global collaborative optimization scheme and the core optimization node, update and manage the power equipment.

[0053] In some embodiments, step S104 includes: based on the optimization parameters for the core optimization node in the global collaborative optimization scheme, selecting a number of target power devices located within the link corresponding to the core optimization node and whose evaluation results indicate that they need to be replaced or do not meet the preset performance requirements; and generating an adjustment instruction for centralized replacement of each of the target power devices according to the resource allocation plan determined in the global collaborative optimization scheme to update and manage the power devices. Specifically, firstly, based on the specific optimization parameters (such as expected value contribution improvement targets, allowable cost budgets, and resource allocation requirements) of the identified core optimization nodes in the global collaborative optimization scheme, several target power devices located in the specific management links (such as "operation monitoring" or "maintenance and repair") corresponding to the core optimization node are automatically selected from the power equipment asset pool. These devices are determined to be "needing replacement" or do not meet the preset performance and reliability requirements through value assessment and status monitoring. Subsequently, according to the resource allocation and execution plan formulated in the global collaborative optimization scheme, which aims to maximize the overall power supply reliability and minimize the total cost, a clear scheduling and adjustment instruction for the unified and centralized replacement of all the above-mentioned target power devices is generated. This instruction is then issued to the corresponding procurement, logistics, and field operation systems, thereby executing equipment updates in a collaborative and efficient manner and completing precise management of the entire life cycle of power equipment.

[0054] Based on the global collaborative optimization scheme and the core optimization node, the power equipment that needs to be replaced can be effectively identified and updated.

[0055] In some embodiments, this application further includes: generating cross-stage resource allocation instructions based on the optimization parameters for the core optimization node in the global collaborative optimization scheme and preset resource dynamic allocation rules; tilting the resources of the power equipment towards the core optimization node to achieve collaborative management of the entire life cycle of the power equipment.

[0056] In some embodiments, based on the optimization parameters for the core optimization node in the global collaborative optimization scheme and preset resource dynamic allocation rules, cross-stage resource allocation instructions are generated. Specifically, this involves: firstly, extracting the optimization parameters for the identified core optimization node from the global collaborative optimization scheme, including the node's expected value contribution coefficient and the allowed increase in cost budget. Simultaneously, obtaining the current value contribution coefficient of high-value contribution stages (such as efficient and stable verification stages) from the system dynamics model or collaborative platform. And potential resource redundancy. Next, a pre-defined dynamic resource allocation rule model is applied for calculation. A key calculation formula in this model is the resource allocation amount calculation formula: In this formula, The amount of resources that can be allocated; Value coefficient of high-value contributing links; The value coefficient of the core optimization node that requires preferential resources for low-value contribution links. The total amount of idle resources, such as idle equipment hours and manpower hours that can be flexibly allocated, is used to calculate the specific amount of resources that should theoretically be allocated from high-value links to core optimization nodes. Finally, based on this calculation result, the system automatically generates an executable resource allocation instruction that includes the specific allocation object (such as equipment number, personnel team), allocation quantity and time node.

[0057] It should be noted that by building a collaborative platform and formulating dynamic allocation rules, data sharing and resource collaboration can be achieved across all stages. The specific steps are as follows: Building a collaborative management platform: Formulating unified coding rules for asset IDs (such as "industry code-asset type-purchase year-serial number") and XML format for verification data (including fields such as asset ID, verification time, error value, and qualification status); ensuring compatibility with API / SDK protocols of ERP, asset management systems, and verification and calibration systems, enabling real-time sharing of asset information, cost data, and value indicators (data update delay ≤50ms), breaking down data silos, and providing a data foundation for cross-stage collaboration.

[0058] In some embodiments, the resources of power equipment are tilted towards the core optimization node to achieve collaborative management of the entire life cycle of the power equipment. This includes: acquiring target operational data after tilting the resources of the power equipment towards the core optimization node, inputting the target operational data into a preset collaborative effect evaluation model, obtaining evaluation results, and continuing until the evaluation results meet preset conditions to determine that the collaborative management of the entire life cycle of the power equipment is complete. Specifically, firstly, resource allocation instructions are issued through the established unified collaborative management platform. After the instructions are executed, resources (such as expert manpower and precision calibration equipment) are actually allocated to the core optimization node. Then, the system continuously collects "target operational data" after the instructions are executed through data acquisition terminals (integrating RFID, sensors, etc.) deployed at each stage, including key indicators such as the improvement in processing efficiency of the core optimization node, changes in cost consumption, and overall power supply reliability. These data are input into the preset collaborative effect evaluation model in real time for quantitative evaluation. The model includes a series of effect evaluation formulas, specifically: ; ; ; in, The goal is to improve collaborative efficiency (target ≥ 20%). Cost savings rate (target ≥ 15%) Value contribution improvement rate (target ≥ 10%); the subscript "before" represents data before optimization, and "after" represents data after optimization. The system will continuously monitor these evaluation results. If the evaluation results meet the preset optimization target conditions (e.g., ≥20%, and ≥15% and (≥10%) Only then is it confirmed that the resource allocation operation for the core optimization node in this round has been completed, thereby achieving effective collaborative management of the entire life cycle of the power equipment.

[0059] This allows redundant resources (such as idle equipment and manpower) from high-value contribution stages to low-value contribution, high-cost stages, thereby optimizing resource allocation and improving overall process efficiency.

[0060] This collaborative management based on global optimization plans and core nodes can guide targeted adjustments to resources and processes, ensuring that management actions (such as resource allocation and process reengineering) are precisely focused on scientifically identified key bottlenecks and value gaps, enabling optimization strategies to be implemented accurately.

[0061] This invention transforms previously ambiguous management value into calculable and comparable value contribution figures by inputting operational data into a value assessment model based on a quantitative algorithm. This allows subsequent segmentation of processes (such as value-added, necessary non-value-added, and non-value-added) to be based on objective and consistent quantitative standards. By inputting the quantitative segmentation results into a system dynamics coupling model for global collaborative optimization, it simulates and quantifies the chain reactions between processes. This ensures that the generated global collaborative optimization scheme and the identified core optimization nodes represent optimal or near-optimal solutions based on overall system efficiency, rather than simple sums of local optima. This enables management strategies to not only accurately match the current state but also possess accurate predictability of the system's dynamic evolution. Based on the global collaborative optimization scheme and the core optimization nodes, it is possible to effectively identify power equipment that needs replacement and perform update management. Compared with existing technologies, this invention can improve the accuracy of power equipment update management.

[0062] like Figure 3 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided; An embodiment of the present invention provides a power equipment update management system, comprising: The acquisition module 100 is used to acquire operational data of power equipment at each stage of its entire life cycle, wherein the operational data includes operating load data, maintenance cost data, and performance status data. The segmentation module 200 is used to input the operational data into a preset value assessment model to calculate the value contribution of each link, and to segment each link based on the value contribution to obtain several segmentation results. The solution module 300 is used to input the division results of each link into a preset system dynamics coupling model, and solve the problem with the goal of maximizing the power supply reliability of the power equipment and minimizing the total cost of the whole life cycle, so as to obtain a global collaborative optimization scheme and identify the core optimization nodes that affect the global efficiency. The management module 400 is used to update and manage power equipment based on the global collaborative optimization scheme and the core optimization node.

[0063] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the power equipment update management method provided by any of the above-described method embodiments of the present invention.

[0064] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0065] Based on the above-described embodiments of the power equipment update management method, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the power equipment update management method of any embodiment of the present invention.

[0066] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0067] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0068] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0069] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the power equipment update management method described in any of the above-described method embodiments of the present invention.

[0070] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0071] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for managing the renewal of power equipment, characterized in that, include: Acquire operational data of power equipment at each stage of its entire life cycle, wherein the operational data includes operating load data, maintenance cost data, and performance status data; The operational data is input into a preset value assessment model to calculate the value contribution of each stage, and the stages are divided based on the value contribution to obtain several stage division results. The results of each of the aforementioned process segments are input into a preset system dynamics coupling model. The model is then solved with the goal of maximizing the power supply reliability of the power equipment and minimizing the total cost over the entire life cycle. This yields a global collaborative optimization scheme and identifies the core optimization nodes that affect global efficiency. Based on the global collaborative optimization scheme and the core optimization node, power equipment can be updated and managed.

2. The method for managing the renewal of power equipment according to claim 1, characterized in that, The process involves inputting the results of each stage division into a preset system dynamics coupling model, and solving the model with the goal of maximizing the power supply reliability of the power equipment and minimizing the total life-cycle cost. This yields a global collaborative optimization scheme and identifies the core optimization nodes affecting overall efficiency, including: The results of each segment division and its corresponding value contribution are mapped to the corresponding segment variables in the pre-constructed system dynamics coupling model; Based on the aforementioned component variables, the system uses the maximization of power supply reliability and the minimization of total life-cycle cost as multi-objective optimization functions. Simulation calculations are performed in the system dynamic coupling model to obtain the optimal combination of component variable parameters under the preset constraints. The optimal combination of component variable parameters constitutes the global collaborative optimization scheme. The sensitivity of the changes in variables at each stage during the simulation calculation process to the results of the multi-objective optimization function is analyzed, and the stages whose sensitivity exceeds a preset threshold are identified as core optimization nodes affecting the overall efficiency.

3. The method for managing the renewal of power equipment according to claim 1, characterized in that, The step of inputting the operational data into a preset value assessment model to calculate the value contribution of each stage includes: Based on the aforementioned operational data, the accuracy assurance value coefficient, data traceability value coefficient, and compliance support value coefficient for each stage are calculated respectively. The value contribution of each stage is calculated by substituting the accuracy assurance value coefficient, the data traceability value coefficient, and the compliance support value coefficient into a preset stage value judgment formula.

4. The method for managing the renewal of power equipment according to claim 1, characterized in that, The process of dividing each stage based on the value contribution yields several stage division results, including: The value contribution is compared with a preset threshold range; If the value contribution exceeds the upper limit of the threshold range, it is determined to be a value-added process; If the value contribution is within the threshold range, it is determined to be a necessary non-value-added process; If the value contribution is less than the lower limit of the threshold range, it is determined to be a non-value-added process.

5. The method for managing the renewal of power equipment according to claim 1, characterized in that, The method of updating and managing power equipment based on the global collaborative optimization scheme and the core optimization node includes: Based on the optimization parameters for the core optimization node in the global collaborative optimization scheme, several target power devices located in the link corresponding to the core optimization node and whose evaluation results are that they need to be replaced or do not meet the preset performance requirements are selected. Based on the resource allocation plan determined in the global collaborative optimization scheme, adjustment instructions are generated to centrally replace each of the target power equipment in order to update and manage the power equipment.

6. The method for managing the renewal of power equipment according to any one of claims 1-5, characterized in that, The acquisition of operational data for power equipment at each stage of its entire life cycle includes: Acquire initial operational data for power equipment at each stage of its entire life cycle; Identify abnormal data in the initial operational data and remove the abnormal data from the initial operational data to obtain the processing result; The processing results are then converted into structured data to obtain operational data.

7. A power equipment update management system, characterized in that, include; The acquisition module is used to acquire operational data of power equipment at each stage of its entire life cycle, wherein the operational data includes operating load data, maintenance cost data, and performance status data. The segmentation module is used to input the operational data into a preset value assessment model to calculate the value contribution of each link, and to segment each link based on the value contribution to obtain several segmentation results. The solution module is used to input the division results of each link into a preset system dynamics coupling model, and solve the problem with the goal of maximizing the power supply reliability of the power equipment and minimizing the total cost of the whole life cycle, so as to obtain a global collaborative optimization scheme and identify the core optimization nodes that affect the global efficiency. The management module is used to update and manage power equipment based on the global collaborative optimization scheme and the core optimization node.

8. A terminal device, characterized in that, include: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the power equipment update management method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the steps of the power equipment update management method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the communication device, they implement the steps of the power equipment update management method as described in any one of claims 1 to 6.

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