A digital management method and system for power detection instrument

Abstract

The application provides a power detection instrument digital management method and system, belongs to the power detection instrument management technical field, and relates to the technical field of power detection instrument management. The method comprises the following steps: obtaining task attribute information of a current power plant power detection task, instrument state information of a target power detection instrument, and working environment information; obtaining qualification information of an associated worker based on the task attribute information; calculating an evaluation index of each power detection instrument through an instrument evaluation function based on the task attribute information, the instrument state information, the working environment information and the qualification information, determining an instrument identifier, generating a recommended list of the instrument and an instrument risk control strategy; matching the recommended list with internal inventory information of the power plant to generate a preliminary instrument deployment scheme; optimizing the preliminary instrument deployment scheme based on a preset optimization algorithm to generate a final instrument deployment scheme and verification guidance information; and generating comprehensive digital management instructions based on the final instrument deployment scheme, the risk control strategy and the verification guidance information.

Description

Technical Field

[0001] This application relates to the technical field of power testing instrument management, and in particular to a digital management method and system for power testing instruments. Background Technology

[0002] Traditional power testing instrument management relies on manual task allocation and instrument deployment. Staff select suitable instruments based on experience and general conditions, and then assign personnel to perform the testing. Instrument evaluation depends on simple visual inspection and basic performance testing. When developing instrument deployment plans, only inventory levels and basic task requirements are considered, lacking comprehensive consideration of factors such as the working environment and personnel qualifications. This lack of comprehensive consideration leads to instrument malfunctions or failure to meet testing requirements during use, affecting the accuracy of test results and work efficiency. Routine risk management and calibration guidance lack specificity and cannot respond promptly to complex and changing working environments and task requirements, failing to effectively ensure the smooth progress of power testing work.

[0003] Therefore, there is an urgent need for a digital management method and system for power testing instruments. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a digital management method and system for power testing instruments.

[0005] A first aspect of this application provides a digital management method for power testing instruments, comprising: Obtain task attribute information, instrument status information, and operating environment information for the current power plant power detection task; Based on the task attribute information, obtain the qualification information of the associated operators, including operational qualifications and skill levels; Based on the task attribute information, instrument status information, operating environment information, and qualification information, the evaluation index of each power testing instrument is calculated through the instrument evaluation function, and the instrument identifier is determined based on the evaluation index. Based on the instrument identifiers, a recommended list of instruments and associated instrument risk management strategies are generated. The recommended list is matched with the preset internal inventory information of the power plant to generate a preliminary instrument allocation plan; Based on a preset optimization algorithm, the preliminary instrument allocation plan is optimized to generate the final instrument allocation plan and verification guidance information. Based on the final instrument allocation plan, risk control strategy, and calibration guidance information, comprehensive digital management instructions are generated.

[0006] A second aspect of this application provides a digital management system for power testing instruments, comprising: The information acquisition module is used to acquire the task attribute information of the current power plant power detection task, the instrument status information of the target power detection instrument, and the operating environment information; The qualification matching module is used to obtain the qualification information of the associated operators based on the task attribute information, wherein the qualification information includes operational qualifications and skill levels; The instrument evaluation module is used to calculate the evaluation index of each power testing instrument based on the task attribute information, instrument status information, operating environment information and qualification information, and to determine the instrument identifier based on the evaluation index. The recommendation list module is used to generate a recommendation list of instruments and associated instrument risk management strategies based on the instrument identifier. The preliminary plan module is used to match the recommended list with the preset internal inventory information of the power plant to generate a preliminary instrument allocation plan. The final solution module is used to optimize the preliminary instrument allocation plan based on a preset optimization algorithm, and generate the final instrument allocation plan and verification guidance information. The digital management module is used to generate comprehensive digital management instructions based on the final instrument allocation plan, risk control strategy, and calibration guidance information.

[0007] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-described digital management method for power testing instruments.

[0008] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described digital management method for power testing instruments.

[0009] The beneficial effects of the digital management method and system for power testing instruments provided in this application are as follows: By acquiring task attributes, instrument status, operating environment, and personnel qualification information, this application can provide a comprehensive basis for instrument evaluation; by calculating evaluation indices and determining instrument identifiers, suitable instruments can be screened; by generating a recommendation list and risk control strategies, guidance can be provided for instrument allocation and risk control; by generating preliminary allocation plans based on inventory information, the rationality of instrument allocation can be improved; by using optimization algorithms to optimize the allocation plans, a better final plan and verification guidance information can be obtained; and finally, comprehensive digital management instructions are generated, realizing the digital management of power testing instruments and improving the scientific nature and efficiency of management. Attached Figure Description

[0010] Figure 1 A flowchart illustrating a digital management method for power testing instruments provided in an embodiment of this application; Figure 2 This is a structural block diagram of a digital management system for power testing instruments provided in an embodiment of this application; Figure 3 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0011] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0012] To make the purpose, technical solution, and advantages of this application clearer, the following will be described in conjunction with the appendix. Figure 1-3 The following is an explanation using specific examples.

[0013] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a digital management method for power testing instruments provided in an embodiment of this application. The method includes: S101: Obtain the task attribute information, instrument status information, and operating environment information of the current power plant power detection task.

[0014] In this embodiment, the current power plant power inspection task is the inspection work that the power plant needs to carry out within a specific time period to ensure the safe and stable operation of the power system, such as generator sets, transmission lines, and power distribution equipment. Examples include: insulation resistance testing of the stator winding of generator set #3 and leakage current testing of the 220kV bus arrester. Task attribute information is structured data describing the characteristics of the inspection task and is the basis for instrument matching and personnel allocation. It includes: basic task information: task number, initiation time, completion deadline, and responsible department; inspection object information: equipment model, installation location, years of operation, and historical fault records; inspection technical requirements: inspection items, accuracy standards, and execution specifications, such as referring to GB / T16927.1-2011, with insulation resistance measurement accuracy ±1%; and task priority: urgent / routine / planned, such as inspection during the unit shutdown maintenance window, with priority: urgent.

[0015] In this embodiment, the target power testing instrument is one or more specialized instruments and equipment used to complete this testing task. It needs to match the testing items of the task; for example, insulation resistance testing corresponds to a megohmmeter, and leakage current testing corresponds to a surge arrester leakage current tester. Instrument status information represents recent data indicating the current availability and performance status of the target instrument, including: basic status: in use, idle, under maintenance, calibrated; performance parameters: current accuracy and range matching, for example, a megohmmeter with a range of 0-10000MΩ, meeting the 1000-5000MΩ testing requirement; health data: most recent calibration time, remaining calibration validity period, and historical fault records, for example, calibration completed on August 15, 2025, valid until February 14, 2026; location information: current storage location and whether it is en route.

[0016] In this embodiment, the operating environment information refers to the environmental conditions at the testing site, which directly affects the instrument performance and the accuracy of the testing data. This includes: physical environment: temperature and humidity, for example, the temperature at the testing site is 32℃ and the humidity is 65%; electromagnetic environment: electromagnetic interference intensity, for example, near the #2 main transformer, the electromagnetic interference intensity is 0.8kV / m, which is less than the instrument's anti-interference threshold of 1.2kV / m; on-site safety conditions: whether there are flammable and explosive gases, the airtightness of the space, and the lighting conditions.

[0017] S102: Based on task attribute information, obtain the qualification information of the associated operators, including operational qualifications and skill levels.

[0018] In this embodiment, the task attribute information is based on the acquired detection task feature data to clarify the skill requirements of the operators. The detection object type, technical accuracy standards, execution specifications, and task priority in the task attribute information serve as a reference for matching personnel qualifications, avoiding the problem of assigning high-difficulty tasks to low-skilled personnel or wasting manpower due to an overabundance of qualified personnel. Associated operators are those directly related to this power detection task, whether as individuals or in teams, such as lead testers or auxiliary recorders. They must simultaneously meet two conditions: job affiliation matching (e.g., electrical maintenance rather than mechanical maintenance) and skill matching for the task. They are the main implementers of the task.

[0019] In this embodiment, qualification information refers to the professional credentials and competency ratings possessed by the operators, proving their ability to perform the corresponding testing tasks. It is crucial for ensuring compliance and data accuracy during the testing process and is divided into two categories: Operational Qualification: Legally recognized access certificates issued by official institutions (such as the Ministry of Emergency Management or the Electric Power Industry Association), which form the basis for operation. Examples include the Electrician's Network Access Permit (High Voltage) and the Electric Power Safety Tool Inspector Qualification Certificate. Personnel without the corresponding qualifications are strictly prohibited from participating in testing. Skill Level: A rating indicating the operator's proficiency and technical level, ensuring efficient and high-quality completion. It is divided into five levels: Elementary (Level 5), Intermediate (Level 4), Advanced (Level 3), Technician (Level 2), and Senior Technician (Level 1), such as Senior Technician (Level 1) for Electric Instrument Testing. High-priority, high-precision tasks require personnel with higher skill levels.

[0020] S103: Based on task attribute information, instrument status information, operating environment information, and qualification information, calculate the evaluation index of each power testing instrument through the instrument evaluation function, and determine the instrument identifier based on the evaluation index.

[0021] In this embodiment, the instrument evaluation function is a calculation model that integrates four types of information: task, instrument, environment, and personnel. By pre-setting weights, it transforms multi-dimensional qualitative / quantitative data into a single evaluation index, which is used to score the instrument and solve the problem of how to select the optimal instrument when there are multiple candidates. The form is a weighted summation formula, for example: Evaluation index = Task matching degree × weight 1 + Instrument health degree × weight 2 + Environmental adaptability × weight 3 + Operational adaptability × weight 4.

[0022] In this embodiment, the evaluation index is the output of the instrument evaluation function, representing the instrument's overall suitability for the task, ranging from 0-10 or 0-100. A higher score indicates better overall performance in terms of task matching, performance status, environmental tolerance, and operator capability. Instrument identification is a classification label based on the evaluation index, serving as the basis for generating a recommendation list and formulating control strategies. It is categorized into critical instrument labels (high suitability, priority allocation) and ordinary instrument labels (basic suitability, for backup).

[0023] S104: Based on instrument identification, generate a recommended list of instruments and associated instrument risk management strategies.

[0024] In this embodiment, the recommended list is a structured list of instruments generated based on instrument identification and sorted according to the comprehensive adaptability of the instruments to the task. It clearly presents the basic information, priority, and allocation suggestions of the instruments, serving as a direct reference for instrument allocation and solving the problem of which instrument to use first and which to reserve. The associated instrument risk management strategy is a proactive preventive measure and emergency plan formulated for the characteristics (such as status and performance shortcomings) and operating environment risks of each instrument in the recommended list. Its purpose is to avoid risks in advance, clarify the handling methods for anomalies, and ensure the safe and stable use of the instruments.

[0025] S105: Match the recommended list with the preset internal inventory information of the power plant to generate a preliminary instrument allocation plan.

[0026] In this embodiment, the preset internal inventory information of the power plant is an instrument asset database established and updated in advance by the power plant. It serves as the data foundation for instrument management and includes not only basic instrument parameters, such as model and accuracy consistent with the recommended list, but also a strong focus on inventory status information, including the current inventory location, available quantity, occupancy status (reserved / idle), inventory warning threshold (e.g., minimum stock level for a certain model), and contact information for the inventory administrator. This allows for quick verification of the actual availability of instruments. Matching involves comparing the key attributes of the instruments in the recommended list (model, accuracy requirements, required quantity, etc.) with the inventory information one by one. The purpose is to verify whether the recommended instruments are in stock and readily available, eliminating invalid recommendations that include instruments on the list but are already in stock, thus providing a realistic and feasible basis for the allocation plan.

[0027] In this embodiment, the preliminary instrument allocation plan is a phased execution plan generated after matching is completed. It serves as the basis for connecting instruments from inventory to the work site. The plan includes key information for the entire instrument allocation process, but it has not been optimized by algorithms. It mainly clarifies basic issues such as which instrument to use, who will pick it up, when to pick it up, and where to deliver it, providing a foundation for optimization.

[0028] S106: Based on the preset optimization algorithm, optimize the preliminary instrument allocation plan to generate the final instrument allocation plan and calibration guidance information.

[0029] In this embodiment, the preset optimization algorithm is a mathematical calculation model embedded in the power plant's digital management system to improve resource allocation efficiency. Its purpose is to address issues such as high cost, low efficiency, and potential risks in the initial allocation plan. In the power monitoring scenario, the algorithm used is the particle swarm optimization algorithm, which finds the optimal solution by simulating particle swarm collaboration. The logic transforms instrument allocation into a multi-objective optimization problem, with objectives including minimizing allocation time, minimizing labor costs, and minimizing risk coefficients. Furthermore, the algorithm parameters can be adjusted according to the task's risk level.

[0030] In this embodiment, the initialization settings of the particle swarm optimization algorithm include the following steps: Particle encoding, where each particle represents a candidate instrument allocation scheme, is a multi-dimensional vector, where each dimension corresponds to a decision variable. In the power detection instrument allocation scenario of this invention, the decision variables are defined as: instrument model selection (discrete variable, e.g., using integer encoding to represent different instrument numbers), instrument collection time window (continuous variable, e.g., the delay in minutes after the task begins), transportation route selection (discrete variable), etc. Through reasonable encoding design, the initial allocation scheme is mapped to the initial position of particles in the search space.

[0031] Population Initialization: Particle Number (Population Size N): This is set based on the complexity of the problem (e.g., the number of candidate instruments, the number of available time windows). The particle number can be set from 20 to 50. For a typical power plant inspection task involving 5-10 main instrument types, setting N=30 achieves a good balance between search efficiency and computational resources. The initialization method is to randomly initialize the initial positions and velocities of the particles within the boundaries of the solution space. The initial value of the velocity vector is set to zero or a small random value to ensure stable exploration in the initial stage.

[0032] Algorithm parameter initialization: Individual learning factor (c1) and social learning factor (c2): These are parameters of the algorithm, and their initial values ​​are set according to the overall risk level. For example, initial values ​​c1 and c2 are set before the task begins optimization. Inertia weight (ω): Used to balance global and local search capabilities. A linear decreasing strategy can be adopted, with an initial value of 0.9 and a final value of 0.4. Maximum number of iterations (MaxIter): Set according to the convergence difficulty and real-time requirements of the optimization problem. For this application scenario, setting 100 to 200 iterations is sufficient to stabilize the optimization scheme. Velocity limit (Vmax): To prevent the particle search step size from being too large and skipping the optimal solution, the velocity of each particle in each dimension is limited to 10%-20% of the range of position changes in that dimension. Fitness function definition: The fitness function is the objective of the particle swarm optimization algorithm, and its output value is used to evaluate the merits of each particle (i.e., each allocation scheme).

[0033] The input data for the particle swarm optimization algorithm is categorized and sourced from the real-time / historical database of the power plant's digital management system. It is functionally divided into three categories: target parameters, constraint parameters, and state parameters. Target parameters determine the optimization direction, constraint parameters define the optimization boundary, and state parameters represent the current state. Output data includes the final instrument allocation details, specifying the primary / backup instrument model, serial number, collection time, and executor. For example, the KEW3125 A01 instrument is prioritized for allocation, to be collected by Li Si from 9:00-9:20 in Area A on the 2nd floor of the maintenance building. The basis for this allocation is that the instrument's accuracy (±0.5%) meets the task requirements, its status is idle, and the primary tester, Zhang San, is proficient in its operation. Detailed timeframes are also provided, such as selecting the west fire lane as the transportation route, completing transportation between 9:20-9:28, and providing guidance on special verification. For the KEW3125, ZX is specified as the correct instrument. The accuracy and error of the 25 standard resistance box calibration must be less than or equal to ±0.5%. For the VC3650, the focus is on stability calibration and auxiliary decision-making data. For example, the risk coefficient is reduced from 0.12 to 0.03 after optimization. Sensitive factors indicate the potential occupation risk of the B03 instrument and corresponding suggestions. All output data are marked with the relevant basis, clearly reflecting the internal logic with the input parameters. This directly translates into on-site executable operation requirements and makes the particle swarm optimization process transparent and explainable, thus connecting the management decision-making and on-site execution of power testing instrument allocation.

[0034] In this embodiment, the preliminary instrument allocation plan serves as the foundation for optimization, but it suffers from shortcomings such as process redundancy and resource waste. For example, it fails to consider time conflicts between instrument retrieval and other tasks, and the transportation route does not avoid peak operating hours. Further optimization through algorithms is necessary. Optimization, aimed at cost reduction, efficiency improvement, and risk control, involves iteratively adjusting the entire process of the preliminary instrument allocation plan. This includes: resource conflict investigation (e.g., whether instrument retrieval time conflicts with inventory manager work schedules); path optimization (e.g., planning the shortest transportation route); streamlining redundant steps (e.g., merging instrument verification steps); and improving contingency plans (e.g., clarifying the rapid deployment process for backup instruments). Ultimately, this makes the plan more aligned with actual execution needs.

[0035] In this embodiment, the final instrument allocation plan is an optimized, directly executable document. Compared to the initial instrument allocation plan, it features more precise timing, clearer responsibilities, more efficient processes, and more controllable risks, serving as the final execution basis for the entire instrument allocation process. The calibration guidance information, generated alongside the final instrument allocation plan, is a pre-use verification standard for the instrument. It clarifies how to verify whether the instrument meets the testing requirements, preventing data distortion due to instrument performance abnormalities. It includes calibration items, standard values, testing tools, and anomaly handling methods, representing a crucial preliminary step in ensuring testing accuracy.

[0036] S107: Generate comprehensive digital management instructions based on the final instrument allocation plan, risk control strategy, and calibration guidance information.

[0037] In this embodiment, the comprehensive digital management instruction is a standardized electronic instruction based on the final instrument allocation plan, risk control strategy, and calibration guidance information. It outlines the requirements for the entire task execution process and serves as a digital bridge connecting management decisions and on-site execution. It features unique instructions, structured content, and traceable execution, allowing direct import into the power plant's digital management system and simultaneous push notifications to the terminal devices of relevant personnel such as operators and inventory managers, replacing traditional paper instructions. The final instrument allocation plan is an optimized and finalized implementation plan, clearly defining the priority, executor, and timeframes for instrument allocation, forming the basis of the resource scheduling module in the management instruction. The risk control strategy outlines the risk prevention and emergency measures to be taken for the instruments, constituting the safety assurance module in the management instruction, enabling proactive risk avoidance and rapid anomaly handling. The calibration guidance information generates pre-use instrument verification standards, serving as the basis for the quality control module in the management instruction, ensuring that instrument performance meets testing requirements.

[0038] As can be seen from the above, this application can provide a comprehensive basis for instrument evaluation by obtaining information on task attributes, instrument status, operating environment, and personnel qualifications; it can screen suitable instruments by calculating evaluation indices and determining instrument identifiers; it can provide guidance for instrument allocation and risk control by generating a recommendation list and risk management strategies; it can improve the rationality of instrument allocation by generating preliminary allocation plans based on inventory information; it can obtain better final plans and verification guidance information by using optimization algorithms to optimize allocation plans; and it can finally generate comprehensive digital management instructions, realizing the digital management of power testing instruments and improving the scientific nature and efficiency of management.

[0039] In one embodiment of this application, based on task attribute information, associated operator and qualification information are obtained. The qualification information includes operational qualifications and skill levels, including: Obtain the task type and compliance requirements from the task attribute information; Establish a pre-defined rule base for mapping between task types, compliance requirements, and required operational qualifications and skill levels; The task attribute information of the current power plant power detection task is matched with the mapping rule base to determine the required target operation qualifications and target skill levels; Based on the target operational qualifications and target skill levels, the system queries the preset operator qualification database to obtain relevant operator and qualification information that meets the requirements.

[0040] In this embodiment, the task type is a specific classification of power plant electrical testing tasks, indicating the testing object and technical direction of the task. It serves as the basis for matching personnel qualifications. For example, generator stator winding insulation testing, transmission line infrared thermography, and high-voltage switchgear partial discharge testing all have significantly different requirements for personnel's operational capabilities. Compliance requirements are rigid standards that ensure the legal and standardized conduct of testing tasks. These include national / industry technical specifications, such as DL / T596-2021 "Preventive Testing Procedures for Power Equipment," power plant internal safety management regulations, and equipment manufacturer operating requirements. These directly determine the qualification threshold that personnel need to possess. The mapping rule base is a pre-built structured relational database. Its function is to establish the correspondence between task requirements and personnel capabilities, transforming abstract task requirements into explicit qualification standards. For example, high-voltage switchgear partial discharge testing corresponds to a high-voltage electrician's operating certificate + partial discharge testing special qualification + intermediate or higher skill level. The rule base can be updated according to new specifications and new task types.

[0041] In this embodiment, the target operational qualification is a legal / industry access certificate that personnel must hold, matched from the mapping rule base based on the current task requirements. It is a fundamental condition for participation in the task and is divided into two categories: general qualifications (e.g., electrician's grid access permit) and specialized qualifications (e.g., power equipment insulation testing qualification certificate). Those without the corresponding qualification are strictly prohibited from participating in the operation. The target skill level is the personnel's technical level rating corresponding to the task requirements, representing their operational proficiency and problem-solving ability. It is a guarantee for high-quality task completion and follows national vocational skill standards, divided into elementary (level 5), intermediate (level 4), advanced (level 3), technician (level 2), and senior technician (level 1).

[0042] In this embodiment, the operator qualification database is a digital archive that stores the qualification information of all operators in the power plant. The content includes basic personnel information, operating qualifications (certificate number, issuing agency, validity period), skill level (assessment time, level certificate), historical operation records, training and assessment information, etc. It supports quick filtering and querying by qualification type and skill level.

[0043] As can be seen from the above, this embodiment obtains the task type and compliance requirements from the task attribute information, establishes a mapping rule base, matches the task attribute information with the rule base to determine the target operation qualification and target skill level, and then queries the operator qualification database. This can accurately obtain the associated operator and qualification information that meet the requirements of the current power plant power testing task, providing a suitable basis for personnel qualification for instrument evaluation and allocation, and helping to improve the execution quality and efficiency of power testing tasks.

[0044] In one embodiment of this application, based on task attribute information, instrument status information, operating environment information, and qualification information, an evaluation index for each power testing instrument is calculated using an instrument evaluation function, and an instrument identifier is determined based on the evaluation index, including: Construct an instrument evaluation function, whose evaluation dimensions include task matching degree, instrument health degree, environmental adaptability and operational suitability; Based on task attribute information, instrument status information, operating environment information, and qualification information, the value of each evaluation dimension is calculated. The values ​​of each evaluation dimension are normalized and substituted into the instrument evaluation function to obtain the instrument's evaluation index. If the instrument's evaluation index is greater than or equal to the preset evaluation threshold, then the instrument's identifier is determined to be a critical instrument identifier. If the instrument's evaluation index is less than the preset evaluation threshold, the instrument's identification will be determined as a normal instrument identification.

[0045] In this embodiment, the instrument evaluation function is a computational model specifically designed for power testing scenarios. Its function is to integrate four types of dispersed information—task, instrument, environment, and personnel—into a single evaluation index. It achieves weighted calculations of multi-dimensional data through preset weights, in the form: Evaluation Index = Task Matching Degree × Weight 1 + Instrument Health Degree × Weight 2 + Environmental Adaptability × Weight 3 + Operational Suitability × Weight 4, providing an objective basis for instrument selection. The evaluation dimensions are the indicators that constitute the instrument evaluation function and are directly related to the needs of the scenario. Specifically, Task Matching Degree represents the degree to which the instrument's performance matches the task requirements, such as whether its accuracy and range meet testing standards; Instrument Health Degree represents the instrument's current availability, covering its status (idle / under maintenance), calibration validity period, and historical fault records; Environmental Adaptability assesses the instrument's tolerance to the work site, such as whether temperature, humidity, and electromagnetic interference are within the instrument's rated range; and Operational Suitability relates to the compatibility between the operator and the instrument, such as whether the operator is familiar with the instrument's operation and whether their skill level is appropriate.

[0046] In this embodiment, normalization is a standardized operation that transforms evaluation dimension data with different dimensions into a unified range (0-1 or 0-10 points), solving the problem that data such as accuracy (percentage) and validity period (days) cannot be directly calculated, so that the weights of each dimension take effect fairly.

[0047] In this embodiment, the evaluation index is the output of the instrument evaluation function and reflects the overall adaptability of the instrument. A higher value indicates that the instrument is more suitable for the current task requirements. The preset evaluation threshold is an instrument classification standard set by the power plant based on task priority and safety requirements. For example, the threshold for emergency tasks is set at 8.5 points, and for routine tasks at 7 points, used to classify instrument identification types. Instrument identification is an instrument classification label generated based on the evaluation index and serves as the basis for allocation priority. Critical instrument identification indicates high adaptability and is prioritized for allocation; ordinary instrument identification serves as a backup or supplement.

[0048] As can be seen from the above, this embodiment constructs an instrument evaluation function that includes evaluation dimensions such as task matching degree, instrument health, environmental adaptability, and operational suitability. Based on task attribute information, instrument status information, operating environment information, and qualification information, the values ​​of each dimension are calculated and normalized to calculate the instrument evaluation index. This enables a more comprehensive and accurate evaluation of power testing instruments. The evaluation index determines the identification of key or ordinary instruments, which helps to generate a recommended list of instruments and risk management strategies, and achieves more reasonable allocation and management of power testing instruments.

[0049] In one embodiment of this application, before matching the recommended list with preset power plant internal inventory information to generate a preliminary instrument allocation plan, the method further includes: By monitoring the operating environment information, the instantaneous values ​​of temperature change rate and electromagnetic interference intensity are calculated; If the temperature change rate is greater than the preset temperature change threshold or the instantaneous value of the electromagnetic interference intensity is greater than the preset intensity threshold, the evaluation index of the power testing instrument is recalculated based on the value of the environmental adaptability weight increased by the first weight step size. Based on the recalculated evaluation index, the instrument identification and recommended list of instruments will be updated.

[0050] In this embodiment, the monitoring of the working environment information involves collecting environmental parameters at the power plant site through sensors deployed on-site (such as temperature and humidity sensors and electromagnetic interference detectors) and supplemented by manual inspections. This process focuses on matching environmental stability with instrument tolerance to avoid sudden environmental changes affecting instrument performance and detection accuracy. The temperature change rate is the amplitude of temperature change at the working site per unit time, calculated as: (current temperature - temperature of the previous monitoring cycle) / monitoring time interval. For example, if the temperature rises from 28℃ to 33℃ within 5 minutes, the change rate is 1℃ / minute, indicating the severity of ambient temperature fluctuations. Excessive fluctuations can easily cause performance drift of internal instrument components. The instantaneous electromagnetic interference intensity is the instantaneous peak value (unit: kV / m) of electromagnetic interference at the working site at a specific moment. Interference at the power testing site mainly comes from nearby operating equipment, such as transformers and reactors. Exceeding the instantaneous value can cause distortion of the instrument's detection signal, affecting data accuracy.

[0051] In this embodiment, the preset temperature change threshold and preset intensity threshold are the rated operating parameters and historical fault data of the power plant's power detection instrument, along with pre-set environmental safety boundary values. For example, the temperature change rate threshold is set to 0.5℃ / minute, and the electromagnetic interference intensity threshold is set to 1.0kV / m. Values ​​exceeding these thresholds indicate a potential environmental risk. The first weight step size is the adjustment range of the environmental adaptability weight, a parameter used to optimize the weights of the evaluation dimensions. When environmental risks occur, the environmental adaptability weight is increased through the first weight step size, highlighting the importance of the instrument's resistance to environmental interference. The environmental adaptability weight is the proportion of the environmental adaptability dimension in the instrument's evaluation function. For example, the initial weight is 15%. The higher the weight, the greater the impact of the instrument's performance in that dimension on the evaluation index. Adjusting the weight can make the evaluation results more closely reflect environmental risks.

[0052] Specifically, the first weighting step size is determined as follows: First, calculate the environmental risk impact factor F1, converting the environmental risk parameters into a calculable impact factor. The formula is: F1 = Comprehensive Environmental Risk Coefficient × 0.5 + Risk Diffusion Correction Coefficient. Wherein, the comprehensive environmental risk coefficient = Exceedance Amplitude Coefficient × Risk Duration Coefficient (range 0-2, the larger the coefficient, the higher the risk); the risk diffusion correction coefficient is adjusted according to the enclosure of the working environment. For enclosed spaces such as switchgear rooms, add 0.3; for open spaces such as power transmission lines, subtract 0.2. The value ranges from 0.1 to 0.5, indicating whether the risk is easily accumulated. Example: Electromagnetic interference exceedance amplitude coefficient 1.2, duration coefficient 0.6, environment is an enclosed switchgear room (correction coefficient 0.3), then F1 = (1.2 × 0.6) × 0.5 + 0.3 = 0.66.

[0053] Next, calculate the instrument sensitivity adaptation factor F2, which represents the instrument's tolerance to environmental risks. The formula is: F2 = 1 - (Instrument environmental sensitivity coefficient / 2). Where, the instrument environmental sensitivity coefficient = (1 - Environmental tolerance margin / Rated value) × Historical fault correlation (value range 0-1, the larger the coefficient, the more sensitive the instrument). Example: If the instrument's interference immunity margin is 0.5kV / m, rated value is 1.5kV / m, and historical fault correlation is 15%, then the instrument environmental sensitivity coefficient = (1 - 0.5 / 1.5) × 0.15 = 0.1, F2 = 1 - (0.1 / 2) = 0.95.

[0054] Finally, the first weight step size S is calculated using the formula: S = Initial Environmental Weight × F1 × F2 × Adjustment Gain Coefficient. The adjustment gain coefficient is set according to task priority: 1.2 for urgent tasks, 1.0 for routine tasks, and 0.8 for planned tasks, making high-priority tasks more sensitive to risk. After calculation, verification is required: if the initial environmental weight + S exceeds the total upper limit of weight adjustment, then the total upper limit minus the initial weight is taken as the final first weight step size to avoid assessment imbalance. Example: Initial environmental weight 15% (0.15), F1 = 0.66, F2 = 0.95, task is a routine task (gain 1.0), then S = 0.15 × 0.66 × 0.95 × 1.0 ≈ 0.094, rounded to 0.1 (i.e., 10 percentage points), and 0.15 + 0.1 = 0.25 ≤ 0.4 (total upper limit), finally determining the first weight step size as 0.1.

[0055] As can be seen from the above, this embodiment monitors the working environment information, calculates the temperature change rate and the instantaneous value of electromagnetic interference intensity. When the temperature change rate or the instantaneous value of electromagnetic interference intensity exceeds the threshold, the environmental adaptability weight is increased, the evaluation index is recalculated, and the instrument identification and recommendation list are updated. This makes the instrument evaluation more in line with the actual working environment, improves the accuracy and applicability of the recommendation list, and helps to generate a more reasonable instrument allocation plan.

[0056] In one embodiment of this application, the preset optimization algorithm is a particle swarm optimization algorithm. Before optimizing the preliminary instrument allocation plan based on the preset optimization algorithm and generating the final instrument allocation plan and calibration guidance information, the method further includes: Based on risk management strategies and operational environment information, determine the overall risk level of power plant power testing tasks; Adjust the individual learning factor and social learning factor of the particle swarm optimization algorithm according to the overall risk level. If the overall risk level is greater than the preset risk threshold, the value of the individual learning factor will be increased based on the first step length, and the value of the social learning factor will be decreased based on the second step length. If the overall risk level is less than the preset risk threshold, the value of the individual learning factor is reduced based on the third step, and the value of the social learning factor is increased based on the fourth step.

[0057] In this embodiment, the comprehensive risk level is a rating that integrates task risk, environmental risk, and instrument risk, ranging from 1 to 5, with level 5 being the highest. This level serves as the basis for adjusting the parameters of the particle swarm optimization algorithm. In power system testing, the risk level is calculated based on the task's criticality, environmental severity, and the inherent risk of the instrument. For example, the risk level for testing high-voltage equipment connected to the main power grid is higher than that for auxiliary equipment. The preset risk threshold is a risk classification line set by the power plant based on safety management regulations, for example, set to level 3. This threshold is used to determine whether the task risk triggers parameter adjustments in the particle swarm optimization algorithm. A risk level greater than the preset risk threshold indicates a high task risk, requiring adjustments to the particle swarm optimization algorithm's logic reinforcement scheme; a risk level less than the preset risk threshold allows for the reuse of global experience.

[0058] In this embodiment, the individual learning factor, denoted as c1, is a parameter in the particle swarm optimization algorithm that guides particles to iterate towards their historical best position, representing the particle's autonomous exploration capability. In the power detection scenario, the larger the individual learning factor, the more the particle swarm optimization algorithm focuses on optimizing the solution based on the unique needs of this task (e.g., specific instrument accuracy, environmental risks), enhancing the solution's relevance. The social learning factor, denoted as c2, is a parameter that guides particles to iterate towards the group's historical best position, reflecting the particle's ability to learn from global experience. In power detection, the larger the social learning factor, the more the algorithm tends to reuse mature allocation experience from similar tasks in power plants, improving the solution's stability. The first step length, second step length, third step length, and fourth step length represent the adjustment range of the particle swarm optimization algorithm parameters. Different step lengths correspond to the adjustment needs of different risk scenarios, ensuring that parameter changes accurately respond to risks while avoiding excessive fluctuations. For example, a larger step length is used to increase the individual learning factor in high-risk scenarios. The first step corresponds to the increase in the individual learning factor under high risk, the second step corresponds to the decrease in the social learning factor under high risk, the third step corresponds to the decrease in the individual learning factor under low risk, and the fourth step corresponds to the increase in the social learning factor under low risk, so that the parameter changes accurately match the risk level.

[0059] Specifically, the first, second, third, and fourth step lengths are all calculated based on a unified basic formula, and then adjusted according to the adjustment direction (increase / decrease) and risk scenario (high / low). The formula is as follows: Basic step length = Benchmark adjustment range × Parameter sensitivity coefficient × Risk level difference × Task type weight. Wherein, the benchmark adjustment range is the basic unit for parameter adjustment, set based on historical power plant data, and fixed at 0.2 (corresponding to a 20% parameter change ratio). The parameter sensitivity coefficient represents the degree of impact of parameter changes on the scheme's effectiveness: the individual learning factor (c1) has high sensitivity, with a coefficient of 1.5; the social learning factor (c2) has medium sensitivity, with a coefficient of 1.0. The risk level difference is the absolute value of the difference between the comprehensive risk level and the preset risk threshold, calculated as |Comprehensive Risk Level - Preset Risk Threshold|, with a value range of 1-2 (calculated as 2 when the level difference ≥ 2). The task type weight is set according to the task's criticality: 1.2 for main grid equipment detection, 1.0 for auxiliary equipment detection, and 0.8 for routine inspection.

[0060] The first step length (the extent to which c1 is increased in high-risk situations) is applicable when the overall risk level exceeds a preset risk threshold, requiring an increase in the individual learning factor to enhance autonomous exploration capabilities. The calculation steps are as follows: First, calculate the base step length using the basic formula; second, because the adjustment effect needs to be amplified due to high risk, multiply by a risk correction coefficient of 1.2; finally, round the result to one decimal place. The calculation formula is: First Step Length = Base Step Length × c1 Sensitivity Coefficient × Risk Level Difference × Task Type Weight × 1.2.

[0061] The second step size (the extent to which c2 is reduced in high-risk situations) is applicable when the overall risk level exceeds a preset risk threshold, requiring a reduction in the social learning factor to decrease reliance on experience. The calculation steps are as follows: First, calculate the base step size using the basic formula; second, to avoid excessively low c2 affecting stability, multiply by a conservative correction coefficient of 0.9; finally, round the result to one decimal place. The calculation formula is: Second step size = Base step size × c2 sensitivity coefficient × Risk level difference × Task type weight × 0.9.

[0062] The third step (the reduction in c1 for low-risk situations) is applicable when the overall risk level is less than a preset risk threshold, requiring a reduction in the individual learning factor to decrease overexploration. The calculation steps are as follows: First, calculate the base step using the basic formula; second, for low-risk situations, the adjustment range needs to be reduced by multiplying by a correction factor of 0.8; finally, round the result to one decimal place, ensuring it is not less than 0.1. The calculation formula is: Third Step = Base Step × c1 Sensitivity Coefficient × Risk Level Difference × Task Type Weight × 0.8.

[0063] The fourth step (the increase in c2 for low-risk situations) is applicable when the overall risk level is less than a preset risk threshold, requiring an increase in the social learning factor to enhance experience reuse. The calculation steps are as follows: First, calculate the basic step size using the basic formula; second, for low-risk situations, the experience weight can be appropriately increased by multiplying it by a correction coefficient of 1.1; finally, round the result to one decimal place. The calculation formula is: Fourth Step Size = Basic Step Size × c2 Sensitivity Coefficient × Risk Level Difference × Task Type Weight × 1.1.

[0064] For example, given a comprehensive risk level of 4, a preset risk threshold of 3, a task type of main network device detection (weight 1.2), and a risk level difference of 1, substituting these values ​​into the formula, the first step length is 0.2 × 1.5 × 1 × 1.2 × 1.2 = 0.432; the second step length is 0.2 × 1.0 × 1 × 1.2 × 0.9 = 0.216.

[0065] As can be seen from the above, this embodiment first obtains the task attributes, instrument status, operating environment, and operator qualification information of the power plant power testing task; calculates the power testing instrument evaluation index and determines the instrument identification; generates a recommended list and risk control strategy; matches inventory to generate a preliminary instrument allocation plan; then determines the comprehensive risk level based on the risk control strategy and operating environment information; adjusts the individual learning factor and social learning factor of the particle swarm optimization algorithm based on the comprehensive risk level, making the optimization algorithm more in line with the actual task risk situation; optimizes the preliminary instrument allocation plan; and generates a more reasonable final instrument allocation plan and verification guidance information, thereby improving the scientificity and rationality of power testing instrument allocation and reducing task risk.

[0066] In one embodiment of this application, the comprehensive risk level of a power plant power monitoring task is determined based on risk management strategies and operational environment information, including: Based on the task type in the task attribute information, query the preset task risk database based on historical accident statistics to determine the task criticality coefficient; Based on the temperature and electromagnetic interference intensity values ​​in the operating environment information, the percentage of exceedance relative to the instrument's rated operating range is calculated, and the environmental severity coefficient is obtained by weighting them. The inherent risk coefficient of the instrument is calculated based on the instrument risk items identified in the risk management strategy and the health score in the instrument status information. The mission criticality coefficient, environmental severity coefficient, and instrument inherent risk coefficient are input into the risk level assessment model, and a comprehensive risk value is obtained through weighted calculation. The overall risk level of the power plant power monitoring task is determined based on the numerical range of the overall risk value.

[0067] In this embodiment, task attribute information is a data set describing the characteristics of power testing tasks, including task type (e.g., partial discharge detection of high-voltage switchgear), detection accuracy requirements (e.g., error less than or equal to 5%), and associated equipment level (e.g., 110kV main grid equipment). This serves as the basis for determining the importance of the task. Risk management strategy is a generated risk prevention plan that identifies key risk points requiring control in the task (e.g., insufficient instrument anti-interference, excessive ambient temperature and humidity) and preliminary control measures, such as configuring backup instruments and activating ventilation equipment. The instrument risk item is a key input for calculating the inherent risk of the instrument. Operating environment information consists of environmental parameter data from the testing site, including temperature, humidity, electromagnetic interference intensity, and dust concentration. These parameters directly affect the instrument's operational stability and the accuracy of the detection data. Instrument status information represents the current availability data of the power testing instrument, including a health score (e.g., 95 out of 100), calibration validity period (e.g., until May 2026), and current status (idle / under maintenance / in transit). The health score is directly related to the instrument's reliability.

[0068] In this embodiment, the task risk database is a structured database built by the power plant based on historical accident and fault data from the past 5-10 years. It stores the correlation between task type, historical accident probability, impact scope, and processing cost. For example, under the item of partial discharge detection of high-voltage switchgear, data such as historical accident probability of 2.1%, average number of affected users of 50,000, and fault processing time of 3 hours are recorded to match the basic risk level of the task. The task criticality coefficient is an indicator of the degree of impact of the detection task on the safe operation of the power plant, with a value range of 0.1-1.0, corresponding to 1-10 points. The larger the coefficient, the more critical the task. It is calculated as the power supply load ratio of the equipment associated with the task multiplied by the impact weight of historical accidents. Due to the high load and wide impact of the main grid equipment, the task criticality coefficient is greater than or equal to 0.8 (8 points), while that of auxiliary equipment is equal to or equal to 0.5 (5 points).

[0069] In this embodiment, the percentage exceeding the limit is the proportion of the operating environment parameters that exceed the instrument's rated operating range. It is calculated only when the actual value is greater than the rated upper limit, and the formula is: Percentage exceeding the limit = (Actual value - Rated upper limit value) / Rated upper limit value × 100%. For example, if the instrument's rated anti-interference upper limit is 1.0 kV / m and the actual detected value is 1.2 kV / m, then the percentage exceeding the limit = (1.2 - 1.0) / 1.0 × 100% = 20%. When the limit is not exceeded, this value is 0.

[0070] In this embodiment, the environmental severity coefficient is an indicator that comprehensively considers the exceedance of multiple environmental parameters, with a value range of 0.1-1.0, corresponding to a score of 1-10. It is calculated by weighting the percentage of each parameter exceeding the standard. The weights are set according to the degree of influence of the parameters on the instrument. For example, electromagnetic interference has the greatest impact on partial discharge detection, so its weight is set at 70%, temperature at 30%, and the risk accumulation coefficient of 1.2 is applied to enclosed spaces.

[0071] In this embodiment, the instrument's inherent risk coefficient represents an indicator of the instrument's reliability, ranging from 0.1 to 1.0, corresponding to a score of 1 to 10. It integrates the number of risk items and the health score of the instrument. The calculation is: (number of risk items × 2 + (100 - health score) / 10) / 4. The more risk items and the lower the health score, the higher the coefficient, indicating the inherent risk difference of the instrument. The risk level assessment model is a standardized calculation model that transforms the task criticality coefficient, environmental severity coefficient, and instrument inherent risk coefficient into a comprehensive risk value. The weights are set based on the principles of prioritizing power plant safety and core tasks, with the following weights: task criticality coefficient 40%, environmental severity coefficient 30%, and instrument inherent risk coefficient 30%. The output is a comprehensive risk value of 0-10.

[0072] In this embodiment, the comprehensive risk level is a risk level (level 1-5) based on the comprehensive risk value, corresponding to a preset numerical range, for example, 0-2 points = level 1 (low risk), 2-4 points = level 2 (lower risk), 4-6 points = level 3 (medium risk), 6-8 points = level 4 (higher risk), 8-10 points = level 5 (high risk).

[0073] As can be seen from the above, this embodiment determines the task criticality coefficient, environmental severity coefficient, and instrument inherent risk coefficient by using the task type in the task attribute information, the temperature value and electromagnetic interference intensity value in the working environment information, the instrument risk items identified in the risk control strategy, and the health score in the instrument status information. These coefficients are then input into the risk level assessment model to obtain a comprehensive risk value, thereby determining the comprehensive risk level of the power plant power testing task. This approach can more comprehensively and accurately assess the risk level of the power testing task, providing a basis for adjusting and optimizing algorithm parameters and other operations. It helps improve the rationality and safety of the power testing instrument allocation plan and ensures the smooth progress of the power testing task.

[0074] In one embodiment of this application, after generating and outputting comprehensive digital management instructions based on the final instrument allocation plan, risk control strategy, and calibration guidance information, the method further includes: Establish and bind a unique digital identity to each power testing instrument; the digital identity is used to associate and store the instrument's full lifecycle information; By scanning digital identity tokens, information files of power testing instruments can be updated and obtained, including historical usage records, maintenance and calibration records, and performance evolution data; Based on information archives, count the number of times the same type of fault occurs repeatedly for a specific fault code; If the number of times the same type of fault occurs repeatedly exceeds the preset fault count threshold, the preset instrument performance evaluation model will be invoked to calculate the instrument's usage efficiency, maintenance costs, and reliability indicators based on historical usage data. If the reliability index is less than the preset threshold, an update and replacement instruction for the power testing instrument will be generated, triggering the instrument procurement or replacement process.

[0075] In this embodiment, the digital identity is a unique electronic identifier assigned to each power testing instrument. It takes the form of a QR code, RFID chip, etc., and is non-replicable, essentially serving as the instrument's electronic ID card. This identifier is used to link and bind the instrument's entire lifecycle data. The instrument's lifecycle information covers the complete data chain from purchase to disposal, including purchase information such as purchase time, price, and supplier; usage records such as each testing task number and operation time; maintenance and calibration records such as calibration date, maintenance content, and performance evolution data (e.g., accuracy change trend); and disposal information such as reason for disposal and time.

[0076] In this embodiment, the information archive is an instrument database built based on digital identity identifiers. It updates information such as the instrument's historical usage records, maintenance and calibration records, and performance evolution data, supporting data traceability and trend analysis, and serving as the basis for fault statistics and performance evaluation. The fault codes are standardized codes set by power plants for common faults of power testing instruments (e.g., E01 indicates accuracy drift, E02 indicates anti-interference module fault), facilitating rapid identification and statistical classification of fault types.

[0077] In this embodiment, the preset fault frequency threshold is a fault warning value set by the power plant based on the instrument type, usage frequency, and operation and maintenance experience. For example, if the same type of fault occurs 3 times within one month, it is determined that the instrument has potential reliability problems and an performance evaluation needs to be initiated. The instrument performance evaluation model is a calculation model of the instrument's comprehensive performance. The input is historical usage data, and the output consists of three main parameters: usage efficiency, maintenance cost, and reliability index. The weights of the instrument performance evaluation model are set based on the principle of prioritizing power plant reliability and secondarily considering economic efficiency.

[0078] In this embodiment, the core performance indicators include: Usage efficiency: the ratio of actual instrument operation time to available time, for example, monthly usage efficiency = monthly operation time / (30 days × 8 hours / day); Maintenance cost: the total cost of instrument maintenance and calibration per unit time, for example, annual maintenance cost = annual maintenance cost / annual usage time; Reliability indicator: the probability that the instrument will operate without failure within a specified time, with a value of 0-1. The closer to 1, the higher the reliability. The calculation logic is 1 - (number of failures / total number of operations).

[0079] In this embodiment, the preset index threshold is the minimum acceptable standard for instrument reliability, such as 0.85. A value less than the preset index threshold indicates that the instrument reliability cannot meet the testing requirements, and the update / retirement process needs to be initiated. The update / retirement instruction includes standardized instructions containing information such as the instrument number, current status, reason for retirement, and recommended replacement model. Once triggered, it is automatically pushed to the equipment management department to initiate the procurement or replacement process.

[0080] As can be seen from the above, this embodiment establishes a unique digital identity for the power testing instrument, which can be associated with and stored throughout its entire lifecycle. Scanning the identity can update and obtain information files, count the number of times the same type of fault occurs repeatedly for a specific fault code, and calculate the instrument's usage efficiency, maintenance cost, and reliability indicators when the number of repetitions exceeds a preset fault count threshold. If the reliability indicator is less than the preset indicator threshold, an update and replacement instruction is generated and the procurement or replacement process is triggered, thereby achieving effective management and timely update and replacement of the power testing instrument throughout its entire lifecycle and ensuring the smooth progress of the testing work.

[0081] In one embodiment of this application, it further includes: Based on the execution effect information of the final instrument allocation plan, the individual learning factor and social learning factor in the preset optimization algorithm are adjusted. The actual performance score of the power plant power detection task is obtained based on the calculation of task completion efficiency, data accuracy and compliance deviation. The actual performance score is compared with the preset expected performance threshold; If the actual performance score is less than the expected performance threshold, the individual learning factor is increased based on the fifth step, and the social learning factor is decreased based on the sixth step.

[0082] In this embodiment, the execution effect information refers to the full-process data collected after the task is completed, including task completion efficiency (e.g., actual time consumption), data accuracy (e.g., detection data error), and compliance deviation (e.g., whether it complies with safety regulations). This information serves as the basis for evaluating the merits of the allocation plan and optimizing the algorithm. Task completion efficiency is an indicator of task execution speed, calculated as: (Planned completion time - Actual completion time) / Planned completion time × 100%. A positive value indicates early completion, and a negative value indicates delay, reflecting the reasonableness of instrument allocation time, such as the timeliness of instrument arrival. Data accuracy is an indicator of the reliability of detection data, calculated as: 1 - (Actual detection data error / Allowable error range) × 100%. For example, if the allowable error for partial discharge detection is less than or equal to 5%, and the actual error is 3%, the accuracy rate is 94%, indicating the suitability of the instrument for the task. Compliance deviation is the degree of deviation from safety / technical specifications during task execution, quantified by the number and severity of violations. For example, failure to perform instrument calibration is a serious violation, deducting 20 points; incomplete records are a minor violation, deducting 5 points, reflecting the compliance assurance capability of the allocation plan.

[0083] In this embodiment, the actual execution performance score is a comprehensive quantitative score (0-100 points) integrating completion efficiency, data accuracy, and compliance deviation, calculated through weighted average. A higher score indicates a better execution performance of the allocation plan and a more suitable fit between the current parameters and the task requirements. The expected execution performance threshold is the minimum acceptable standard for task execution performance set by the power plant, for example, 80 points. A score below the expected execution performance threshold indicates that the allocation plan generated by the current parameters has significant defects, requiring the initiation of a parameter adjustment process.

[0084] In this embodiment, the fifth step length and the sixth step length represent the adjustment range of the algorithm parameters. The fifth step length corresponds to increasing c1 when the execution effect is poor, and the sixth step length corresponds to decreasing c2 when the execution effect is poor, so that the parameter changes accurately respond to the execution problem.

[0085] Specifically, the base step size = baseline adjustment coefficient × parameter sensitivity coefficient, where the individual learning factor c1 has high sensitivity, so the parameter sensitivity coefficient is set to 1.2; the social learning factor c2 has moderate sensitivity, so the parameter sensitivity coefficient is set to 1.0. The final step size = base step size × risk correction coefficient × historical fit coefficient. The risk correction coefficient = 1 + (overall risk level - 3) × 0.1, where 3 represents a medium risk level. The correction coefficient for high-risk levels is greater than 1, and for low-risk levels it is less than 1. The historical fit coefficient is set based on the historical adjustment effects of similar tasks; a coefficient of 1.0 is used if the adjusted performance meets the target, and 0.9 is used if it does not, to avoid repeated and ineffective adjustments.

[0086] For example, the overall risk level of the #5 switchgear task is level 4 (higher risk), the baseline adjustment coefficient is 0.15, and the fifth step length is calculated as follows: basic step length = 0.15 × 1.2 = 0.18; risk correction coefficient = 1 + (4-3) × 0.1 = 1.1; historical adaptation coefficient 1.0; final fifth step length = 0.18 × 1.1 × 1.0 ≈ 0.2 (consistent with the preset value).

[0087] As can be seen from the above, this embodiment can make the algorithm parameters more closely match the actual task situation by adjusting the individual learning factor and social learning factor in the preset optimization algorithm according to the execution effect information of the final instrument allocation plan; the actual execution effect score of the power plant power detection task can be calculated and compared with the preset expected execution effect threshold to evaluate the task execution; when the actual execution effect score is less than the expected threshold, adjusting the individual learning factor and social learning factor can optimize the algorithm performance and improve the rationality of the instrument allocation plan and the task execution effect.

[0088] In one embodiment of this application, a score for the actual performance of a power plant power monitoring task is obtained based on task completion efficiency, data accuracy, and compliance deviation calculations, including: Obtain the actual and planned time of power plant power testing tasks, testing data and standard reference data, and actual and standard operating procedures; The task completion efficiency score is obtained based on the ratio function of actual task time to planned task time. The data accuracy score is obtained based on the error function between the test data and the standard reference data; A compliance deviation score is obtained based on the deviation function between the actual operation process and the standard operation procedure. Based on the preset first, second, and third weights, the task completion efficiency score, data accuracy score, and compliance deviation score are weighted and summed to obtain the actual execution effect score.

[0089] In this embodiment, the actual task time is the total time consumed by the power testing task from instrument arrival and commissioning to data upload and archiving, accurate to the minute. It includes the time spent on key stages such as instrument preparation, on-site testing, data verification, and report generation. For example, the actual testing time for switchgear #5 was 5 hours and 20 minutes. The planned task time is the standard completion time preset by the power plant based on task complexity, instrument performance, and historical data. A reasonable redundancy of 10%-20% needs to be reserved. For example, the planned testing time for switchgear #5 is 4 hours, serving as a benchmark for efficiency evaluation. The ratio function represents the task completion efficiency, logically representing the ratio of planned time to actual time. The standardized formula is: Efficiency Ratio = Planned Task Time ÷ Actual Task Time. A ratio closer to 1 indicates higher efficiency; a ratio greater than 1 indicates early completion; and a ratio less than 1 indicates delayed completion. These ratios are then converted into an efficiency score of 0-100 points using preset rules.

[0090] In this embodiment, the detection data consists of raw data collected by on-site testing instruments and the processed results. The core of power testing includes partial discharge (pC), insulation resistance (MΩ), and dielectric loss (%), which serve as the basis for judging the equipment's operating status. The standard reference data is a reference range set by the power plant's integrated equipment manufacturer standards and industry specifications (e.g., GB50150-2016) plus historical equipment health data. For example, the standard reference value for partial discharge of switchgear #5 is less than or equal to 500pC; a value greater than this indicates a defect. The error function represents the reliability of the detection data. In power testing, the relative error function is preferred, with the formula: Relative Error = |Detection Data - Standard Reference Data| ÷ Standard Reference Data × 100%. A smaller error indicates more accurate data. Considering the characteristics of power equipment testing, a rule is set to significantly deduct points if the error exceeds the standard, avoiding invalid data from affecting the evaluation.

[0091] In this embodiment, the actual operation process is a record of the real steps taken by on-site personnel to perform their tasks. This record is stored in real time through a work recorder and a digital management system, covering details such as instrument calibration procedures, safety precautions, and data recording methods. The standard operating procedure (SOP) is a standardized document for power plant testing, clearly defining the operational requirements for each step. For example, instruments must undergo three-point calibration and meet safety regulations before testing; warning barriers and quality control points must be set up in high-voltage areas. These serve as the red line for compliance assessment. The deviation function is a function for calculating the compliance of the operation process. It is quantified by weighting the levels of violations, with the formula: Deviation = Σ (Violation weight × Number of violations). The power plant presets violation levels: a weight of 3 for serious violations, 2 for general violations, and 1 for minor violations. A lower deviation indicates better compliance, corresponding to a higher compliance deviation score.

[0092] In this embodiment, the first weight, second weight, and third weight are scoring weights set by the power plant based on the management principles of safety priority, data core, and efficiency assurance, corresponding to the three scores of task completion efficiency, data accuracy, and compliance deviation, respectively. The weights are 30%, 40%, and 30%, respectively. The main grid equipment detection can increase the data accuracy weight to 50%, reflecting the veto characteristic of data quality.

[0093] In this embodiment, the task completion efficiency score is a 0-100 point quantitative result obtained through a ratio function, representing the time control capability of task execution and serving as an important indicator for evaluating the rationality of instrument adjustment and the potential for process optimization. The data accuracy score is a 0-100 point result calculated based on an error function, representing the reliability of the detection data and serving as a standard for judging the effectiveness of the detection task. The compliance deviation score is a 0-100 point result obtained through a deviation function, representing the standardization of operator procedures and relating to the safety risks and process standardization level of power detection.

[0094] In this embodiment, the actual performance score is a comprehensive score (0-100 points) obtained by weighted summation. The formula is: Comprehensive score = Efficiency score × First weight + Accuracy score × Second weight + Compliance score × Third weight. It is the final basis for evaluating the overall quality of the task and the adaptability of algorithm parameters.

[0095] As can be seen from the above, this embodiment obtains the actual and planned task time, detection data and standard reference data, actual operation process and standard operation procedure, and obtains the task completion efficiency score, data accuracy score and compliance deviation score through ratio function, error function and deviation function respectively. Then, the actual execution effect score is obtained by weighted summation. This can accurately represent the actual execution effect of the power plant power detection task, provide a basis for adjusting the individual learning factor and social learning factor of the preset optimization algorithm, and improve the rationality of the power detection instrument allocation scheme and the quality of task execution.

[0096] Corresponding to the digital management method for power testing instruments in the above embodiment, Figure 2 This is a structural block diagram of a digital management system for power testing instruments provided according to an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 2 The digital management system 20 for power testing instruments includes: an information acquisition module 21, a qualification matching module 22, an instrument evaluation module 23, a recommendation list module 24, a preliminary solution module 25, a final solution module 26, and a digital management module 27.

[0097] in, The information acquisition module 21 is used to acquire the task attribute information of the current power plant power detection task, the instrument status information of the target power detection instrument, and the operating environment information; The qualification matching module 22 is used to obtain the qualification information of associated operators based on task attribute information. The qualification information includes operational qualifications and skill levels. The instrument evaluation module 23 is used to calculate the evaluation index of each power testing instrument based on task attribute information, instrument status information, operating environment information and qualification information, and determine the instrument identifier based on the evaluation index. The recommendation list module 24 is used to generate a recommendation list of instruments and associated instrument risk management strategies based on instrument identification. The preliminary plan module 25 is used to match the recommended list with the preset internal inventory information of the power plant to generate a preliminary instrument allocation plan. The final solution module 26 is used to optimize the preliminary instrument allocation plan based on a preset optimization algorithm, and generate the final instrument allocation plan and verification guidance information. The digital management module 27 is used to generate comprehensive digital management instructions based on the final instrument allocation plan, risk control strategy and calibration guidance information.

[0098] See Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 3 The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of the modules in the aforementioned device embodiments, for example... Figure 2 The functions of the information acquisition module 21, qualification matching module 22, instrument evaluation module 23, recommendation list module 24, preliminary plan module 25, final plan module 26, and digital management module 27 are shown.

[0099] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), or it may be 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. The general-purpose processor may be a microprocessor or any conventional processor.

[0100] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0101] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.

[0102] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation methods described in any embodiment of the digital management method for power testing instruments provided in the embodiments of this application, or they can execute the implementation methods of the electronic devices described in the embodiments of this application, which will not be repeated here.

[0103] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. 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 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.

[0104] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0105] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0106] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0107] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or units, or it may be an electrical, mechanical, or other form of connection.

[0108] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0109] Furthermore, the functional units in the various embodiments of this application 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 as a software functional unit.

[0110] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A digital management method for power testing instruments, characterized in that, include: Obtain task attribute information, instrument status information, and operating environment information for the current power plant power detection task; Based on the task attribute information, obtain the qualification information of the associated operators, including operational qualifications and skill levels; Based on the task attribute information, instrument status information, operating environment information, and qualification information, the evaluation index of each power testing instrument is calculated through the instrument evaluation function, and the instrument identifier is determined based on the evaluation index. Based on the instrument identifiers, a recommended list of instruments and associated instrument risk management strategies are generated. The recommended list is matched with the preset internal inventory information of the power plant to generate a preliminary instrument allocation plan; Based on a preset optimization algorithm, the preliminary instrument allocation plan is optimized to generate the final instrument allocation plan and verification guidance information. Based on the final instrument allocation plan, risk control strategy, and calibration guidance information, comprehensive digital management instructions are generated.

2. The digital management method for power testing instruments according to claim 1, characterized in that, Based on the task attribute information, the associated operator and qualification information are obtained. The qualification information includes operational qualifications and skill levels, including: Obtain the task type and compliance requirements from the task attribute information; Establish a pre-defined rule base for mapping between task types, compliance requirements, and required operational qualifications and skill levels; The task attribute information of the current power plant power detection task is matched with the mapping rule base to determine the required target operation qualification and target skill level; Based on the target operational qualifications and target skill levels, a preset database of operator qualifications is queried to obtain the relevant operator and qualification information that meets the requirements.

3. The digital management method for power testing instruments according to claim 1, characterized in that, The process of calculating an evaluation index for each power testing instrument based on the task attribute information, instrument status information, operating environment information, and qualification information, and determining the instrument identifier based on the evaluation index, includes: Construct an instrument evaluation function, the evaluation dimensions of which include task matching degree, instrument health degree, environmental adaptability and operational suitability; Based on the task attribute information, instrument status information, operating environment information, and qualification information, the value of each evaluation dimension is calculated; The values ​​of each evaluation dimension are normalized and substituted into the instrument evaluation function to obtain the instrument's evaluation index. If the evaluation index of the instrument is greater than or equal to the preset evaluation threshold, then the instrument identifier of the instrument is determined to be a key instrument identifier. If the evaluation index of the instrument is less than the preset evaluation threshold, the instrument is identified as a normal instrument.

4. The digital management method for power testing instruments according to claim 3, characterized in that, Before matching the recommended list with preset power plant internal inventory information to generate a preliminary instrument allocation plan, the process also includes: By monitoring the operating environment information, the instantaneous values ​​of temperature change rate and electromagnetic interference intensity are calculated; If the temperature change rate is greater than a preset temperature change threshold or the instantaneous value of the electromagnetic interference intensity is greater than a preset intensity threshold, the environmental adaptability weight is increased based on the first weight step size, and the evaluation index of the power detection instrument is recalculated. Based on the recalculated evaluation index, the instrument identification and the recommended list of instruments are updated.

5. The digital management method for power testing instruments according to claim 1, characterized in that, The preset optimization algorithm is a particle swarm optimization algorithm. Before optimizing the preliminary instrument allocation plan based on the preset optimization algorithm to generate the final instrument allocation plan and calibration guidance information, the following steps are also included: Based on the risk management strategy and the operating environment information, determine the comprehensive risk level of the power plant power monitoring task; Based on the comprehensive risk level, adjust the individual learning factor and social learning factor of the particle swarm optimization algorithm; If the overall risk level is greater than the preset risk threshold, the value of the individual learning factor is increased based on the first step, and the value of the social learning factor is decreased based on the second step. If the overall risk level is less than the preset risk threshold, the value of the individual learning factor is reduced based on the third step, and the value of the social learning factor is increased based on the fourth step.

6. The digital management method for power testing instruments according to claim 5, characterized in that, The determination of the comprehensive risk level of the power plant power monitoring task based on the risk management strategy and the operating environment information includes: Based on the task type in the task attribute information, a preset task risk database based on historical accident statistics is queried to determine the task criticality coefficient. Based on the temperature and electromagnetic interference intensity values ​​in the operating environment information, the percentage of exceedance relative to the instrument's rated operating range is calculated, and the environmental severity coefficient is obtained by weighting. Based on the instrument risk items identified in the risk management strategy and the health score in the instrument status information, the inherent risk coefficient of the instrument is calculated. The task criticality coefficient, environmental severity coefficient, and instrument inherent risk coefficient are input into the risk level assessment model, and a comprehensive risk value is obtained through weighted calculation. The overall risk level of the power plant power monitoring task is determined based on the numerical range of the overall risk value.

7. The digital management method for power testing instruments according to claim 1, characterized in that, After generating and outputting comprehensive digital management instructions based on the final instrument allocation plan, risk control strategy, and calibration guidance information, the process also includes: A unique digital identity is established and bound to each power testing instrument; the digital identity is used to associate and store the instrument's full lifecycle information. By scanning the digital identity, the information file of the power testing instrument is updated and obtained, including historical usage records, maintenance and calibration records, and performance evolution data; Based on the information archive, the number of times the same type of fault occurs repeatedly for a specific fault code is counted; If the number of times the same type of fault occurs repeatedly exceeds the preset fault count threshold, the preset instrument performance evaluation model is invoked to calculate the instrument's usage efficiency, maintenance cost, and reliability indicators based on historical usage data. If the reliability index is less than the preset threshold, an update and replacement instruction for the power testing instrument is generated, and the instrument procurement or replacement process is triggered.

8. The digital management method for power testing instruments according to claim 5, characterized in that, Also includes: Based on the execution effect information of the final instrument allocation scheme, the individual learning factor and social learning factor in the preset optimization algorithm are adjusted. The actual performance score of the power plant power detection task is obtained based on the calculation of task completion efficiency, data accuracy and compliance deviation. The actual performance score is compared with the preset expected performance threshold. If the actual performance score is less than the expected performance threshold, then the value of the individual learning factor is increased based on the fifth step, and the value of the social learning factor is decreased based on the sixth step.

9. A digital management method for power testing instruments according to claim 8, characterized in that, The actual performance score of the power plant power monitoring task is obtained by calculating based on task completion efficiency, data accuracy, and compliance deviation, including: Obtain the actual and planned task time, detection data and standard reference data, and actual and standard operating procedures for the power plant power detection task. The task completion efficiency score is obtained based on the ratio function of actual task time to planned task time. The data accuracy score is obtained based on the error function between the test data and the standard reference data; A compliance deviation score is obtained based on the deviation function between the actual operation process and the standard operation procedure. Based on the preset first weight, second weight, and third weight, the task completion efficiency score, data accuracy score, and compliance deviation score are weighted and summed to obtain the actual execution effect score.

10. A digital management system for power testing instruments, characterized in that, include: The information acquisition module is used to acquire the task attribute information of the current power plant power detection task, the instrument status information of the target power detection instrument, and the operating environment information; The qualification matching module is used to obtain the qualification information of the associated operators based on the task attribute information, wherein the qualification information includes operational qualifications and skill levels; The instrument evaluation module is used to calculate the evaluation index of each power testing instrument based on the task attribute information, instrument status information, operating environment information and qualification information, and to determine the instrument identifier based on the evaluation index. The recommendation list module is used to generate a recommendation list of instruments and associated instrument risk management strategies based on the instrument identifier. The preliminary plan module is used to match the recommended list with the preset internal inventory information of the power plant to generate a preliminary instrument allocation plan. The final solution module is used to optimize the preliminary instrument allocation plan based on a preset optimization algorithm, and generate the final instrument allocation plan and verification guidance information. The digital management module is used to generate comprehensive digital management instructions based on the final instrument allocation plan, risk control strategy, and calibration guidance information.

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