Scheduling optimization method based on automatic detection of low-voltage metering box

Abstract

The invention provides a scheduling optimization method based on automatic detection of a low-voltage metering box, and belongs to the technical field of intelligent power grids, and the method comprises the following steps: S1, obtaining detection data, carrying out the preprocessing of the detection data, and carrying out the feature extraction based on the preprocessed detection data, and obtaining key feature data; s2, enhancing the key feature data based on a dynamic weight analysis enhancement algorithm, and optimizing the representation of data features by dynamically adjusting the weight; s3, identifying the current state of the low-voltage metering box based on an intelligent fusion scheduling algorithm, constructing an optimal distribution model, and intelligently distributing detection tasks; and S4, based on a multi-dimensional situation response scheduling algorithm, performing adaptive optimization on the distribution of the detection task to obtain the distribution of the optimal detection task. The method can ensure that skills of technicians are matched with task requirements, effectively improves the efficiency of automatic detection of the low-voltage metering box, ensures that tasks are distributed reasonably and efficiently, and reduces the cost and time waste at the same time.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart grids, and in particular relates to a dispatching optimization method based on automatic detection of a low-voltage meter box. Background Art

[0002] In automated detection systems, especially the monitoring and management of low-voltage metering boxes, how to effectively process and utilize large amounts of data while ensuring the accuracy and efficiency of detection tasks has always been a major challenge in the industry. Traditional detection systems usually rely on simple data processing techniques and static task scheduling methods, which often cannot fully adapt to rapidly changing environmental conditions or complex operational requirements.

[0003] In addition, task scheduling in traditional systems is usually pre-set and lacks flexibility. This static scheduling strategy fails to consider dynamic information such as the real-time availability of technicians, the urgency of tasks, and the current status of equipment, resulting in uneven resource utilization, long response times, and high costs.

[0004] In addition, existing systems often show low adaptability and robustness when encountering rapid changes in equipment status or fluctuations in the external environment. These scheduling algorithms lack consideration of multi-dimensional influencing factors, such as changes in environmental conditions, differences in technicians' skills, and historical equipment failure data. They have poor adaptability to environmental changes and have problems with low resource utilization and task scheduling efficiency. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, a scheduling optimization method based on automatic detection of low-voltage metering boxes is provided.

[0006] The technical solution adopted by the present invention to solve its technical problem is:

[0007] This technical solution proposes a scheduling optimization method based on automatic detection of low-voltage metering boxes, including the following steps:

[0008] S1: Based on the goal and / or functional requirements of the low-voltage metering box automated detection, the detection data is acquired, the detection data is preprocessed, and based on the preprocessed detection data, feature extraction is performed to obtain key feature data;

[0009] S2: Based on the dynamic weight analysis enhancement algorithm, key feature data is enhanced, and the representation of data features is optimized by dynamically adjusting the weights to obtain intelligently processed data;

[0010] S3: Based on the intelligent fusion scheduling algorithm, identify the current status of the low-voltage metering box, build an optimization allocation model, and intelligently allocate detection tasks;

[0011] S4: Based on the multi-dimensional situation response scheduling algorithm, the allocation of detection tasks is adaptively optimized to obtain the optimal allocation of detection tasks.

[0012] Preferably, in S1, the preprocessing of the detection data includes data cleaning, missing value processing, noise smoothing and data normalization, and the key feature data includes statistical features, trend features and frequency domain features.

[0013] Preferably, in S2, the dynamic weight analysis enhancement algorithm includes:

[0014] Initialize the weight matrix: Assign an initial weight to each data feature. The weight matrix is ​​represented by W and the formula is as follows:

[0015]

[0016] In the formula, w i is the initial weight of the i-th feature, and n is the number of features;

[0017] Calculate weighted eigenvalues: Use the weight matrix to weight the feature data X, each eigenvalue x i The corresponding weight w i Multiplying, the formula is as follows:

[0018]

[0019] In the formula, Z is the weighted eigenvector, which represents the comprehensive index of the eigenvalue after weight processing, x i is the original value of the i-th feature.

[0020] Preferably, dynamically adjust the weights: update the weight of each feature value according to its utility, as follows:

[0021]

[0022] In the formula, is the updated weight, w i is the current weight of the i-th feature, exp is an exponential function used to calculate the update scale of the weight, σ i is the standard deviation of the i-th feature, adjusting the sensitivity of weight update, s i and j are the utility scores of the i-th and j-th features respectively, and n is the total number of features;

[0023] Based on the data fusion algorithm, the enhanced feature data and the preprocessed data are fused to obtain intelligently processed data.

[0024] Preferably, in S3, the optimization allocation model construction method includes:

[0025] Define decision variables: Define decision variables y ijt It is expressed as whether the i-th technician is assigned to the j-th task at time t, y ijt =1 means assigned, y ijt =0 means not allocated;

[0026] Define the objective function; construct the objective function to maximize the weighted completion rate of task priorities and minimize the total cost, the formula is as follows:

[0027]

[0028] Where F is the objective function, p j is the priority weight of task j, c ijt is the cost of the i-th technician performing task j at time t, N is the total number of technicians, M is the total number of tasks that need to be assigned, and T is the time period.

[0029] Preferably, the constraints are: each task is assigned at least once within the specified time, and each technician can only perform one task at any time, the formula is as follows:

[0030]

[0031] Preferably, in S4, the multi-dimensional situation response scheduling algorithm includes:

[0032] Define multi-dimensional influencing factors, define influencing factors and quantification methods of influencing factors, and construct a matrix representation of quantitative variables. jt is the influencing factor vector of task j at time t, including environmental factors, operator skill level, and historical failure rate of equipment;

[0033] Weight adjustment of influencing factors: For each task, the weight is dynamically adjusted according to the task influencing factor vector. The formula is as follows:

[0034]

[0035] In the formula, w jt is the weight of the task at time, α k is the weight coefficient of the kth influencing factor, v jtk is the quantitative value of the kth influencing factor of the jth task at time t, β l is the weight coefficient of other influencing factors, u jtl is the quantitative value of other influencing factors.

[0036] Preferably, the objective function is optimized, and the formula is as follows:

[0037]

[0038] Where F' is the optimized objective function;

[0039] Constraint expansion: adding dynamic availability constraints, skill matching constraints, task completion requirements, continuous working time limits, and multi-task fairness constraints;

[0040] After optimizing the objective function and constraints, a heuristic algorithm combined with linear programming is used for optimization, a genetic algorithm is used to generate an initial solution, and then the initial solution is fine-tuned through linear programming to obtain the global optimal solution.

[0041] Preferably, based on the precision enhancement adjustment algorithm, the genetic algorithm is optimized to find a better solution in the global search space. The precision enhancement adjustment algorithm includes:

[0042] Introduction of local search: By introducing local search, the accuracy and ability of the adjustment algorithm to escape when encountering a local optimal solution are enhanced;

[0043] Implement local perturbation: In each generation of the genetic algorithm, a specified proportion of individuals are selected for local perturbation. The perturbation is in the form of applying periodic sinusoidal waveform changes to the gene r of the individual. The formula is as follows:

[0044]

[0045] In the formula, r old is the original gene value, δ is the disturbance amplitude, which controls the size of the change, λ controls the periodicity of the disturbance, and determines the frequency of the disturbance, r new is the new gene value after disturbance adjustment, and φ is the phase offset.

[0046] Preferably, the maintenance of genetic diversity: calculate the difference between individuals in the population, use the Hamming distance as a measure of the difference, and achieve diversity assessment;

[0047] When the population diversity is below the preset threshold, the diversity is increased by adding randomly generated new individuals or by producing more diverse offspring through crossover and mutation, as shown below:

[0048]

[0049] In the formula, H(r i ,r avg ) is individual r i The population average r avg Hamming distance, γ is a positive adjustment factor used to adjust the impact of diversity, G new is the diversity index of the new generation population, r i is the new gene value of the i-th individual, and Num is the number of individuals in the population.

[0050] Preferably, the disturbance amplitude is adjusted dynamically: the disturbance amplitude is adjusted dynamically according to the progress of the search process, and the disturbance amplitude δ is adjusted according to the change of the optimal solution in consecutive generations;

[0051] If the optimal solution has not been improved for a time exceeding the set threshold, increase the disturbance amplitude and enhance the exploration area. If the optimal solution has not been improved for a time less than the set threshold, reduce the disturbance amplitude and refine the search in the current area. The formula is as follows:

[0052]

[0053] In the formula, δ old is the perturbation amplitude used in the previous generation, δ new is the newly calculated disturbance amplitude, which is used for the next generation of local search. Δr is the difference between the optimal fitness of the previous and next generations, which indicates the speed of algorithm optimization progress. Δt is the algebraic change, which indicates the progress of time. ρ is the adjustment coefficient, which is the parameter that controls the disturbance adjustment rate to adapt to the dynamically changing optimization environment.

[0054] Compared with the prior art, the present invention has the following advantages:

[0055] 1. This application effectively optimizes the representation of feature data and improves the effect of data processing by applying a dynamic weight analysis enhancement algorithm, making the data more reflective of the actual device status, thereby speeding up status identification and decision making.

[0056] 2. This application applies an intelligent fusion scheduling algorithm to dynamically allocate detection tasks according to the identified status of the meter box, thereby improving the efficiency of resource utilization and the timeliness of task execution. The optimization allocation model takes into account multiple factors, such as the urgency of the task and the availability of technicians, to ensure that task allocation is reasonable and efficient, while reducing costs and time waste.

[0057] 3. This application introduces multi-dimensional influencing factors, such as environmental conditions, technical personnel's skill level and historical failure rate of equipment. The multi-dimensional situation response scheduling algorithm can more accurately match task requirements and site conditions and optimize resource allocation. The dynamic weight adjustment mechanism ensures that changes in various influencing factors are reflected in real time during the task allocation process, making scheduling decisions more flexible and adaptable.

[0058] 4. This application adjusts task allocation by applying an optimized allocation model to ensure that the skills of technicians match task requirements and improve work efficiency. At the same time, it takes into account labor costs and resource allocation to reduce overall operating costs. Dynamic availability and multi-task fairness constraints ensure that technicians will not be overworked and maintain work quality and efficiency. The introduced precision enhancement adjustment algorithm enhances the adaptability to environmental changes and potential errors through periodic sinusoidal waveform changes and genetic diversity maintenance mechanisms. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0060] Figure 1 It is the overall flow chart of the present invention. DETAILED DESCRIPTION

[0061] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0062] like Figure 1 As shown, this embodiment proposes a scheduling optimization method based on automatic detection of low-voltage metering boxes, including the following steps:

[0063] S1: Based on the goal and / or functional requirements of the low-voltage metering box automated detection, the detection data is acquired, the detection data is preprocessed, and based on the preprocessed detection data, feature extraction is performed to obtain key feature data;

[0064] S2: Based on the dynamic weight analysis enhancement algorithm, key feature data is enhanced, and the representation of data features is optimized by dynamically adjusting the weights to obtain intelligently processed data;

[0065] S3: Based on the intelligent fusion scheduling algorithm, identify the current status of the low-voltage metering box, build an optimization allocation model, and intelligently allocate detection tasks;

[0066] S4: Based on the multi-dimensional situation response scheduling algorithm, the allocation of detection tasks is adaptively optimized to obtain the optimal allocation of detection tasks.

[0067] In S1, the goals and functional requirements of low-voltage meter box automation testing are determined based on experience. The goals include improving testing efficiency, reducing human errors, and enhancing data management and utilization. Functional requirements include automatically identifying meter box status, automatically recording test data, and automatically diagnosing faults. Based on the goals and functional requirements, sensors and data acquisition equipment are configured to obtain test data in real time. The test data includes operational data of voltage, current, and power, environmental data of temperature and humidity, and equipment status data;

[0068] The preprocessing of detection data includes data cleaning, missing value processing, noise smoothing and data normalization. The preprocessed data is subjected to feature extraction using feature engineering algorithms to obtain key feature data, which include statistical features, trend features and frequency domain features.

[0069] In S2, the dynamic weight analysis enhancement algorithm includes:

[0070] Initialize the weight matrix: Assign an initial weight to each data feature. The weight matrix is ​​represented by W. The weight initialization can be pre-set based on the empirical method and the importance of the feature. The formula is as follows:

[0071]

[0072] In the formula, w i is the initial weight of the i-th feature, and n is the number of features;

[0073] Calculate weighted eigenvalues: Use the weight matrix to weight the feature data X to enhance the influence of important features. Each eigenvalue x i The corresponding weight w i Multiplying, the formula is as follows:

[0074]

[0075] In the formula, Z is the weighted eigenvector, which represents the comprehensive index of the eigenvalue after weight processing, x i is the original value of the i-th feature.

[0076] Dynamically adjust weights: Update the weight of each feature value based on its utility, as follows:

[0077]

[0078] In the formula, is the updated weight, w i is the current weight of the i-th feature, exp is an exponential function used to calculate the update scale of the weight, σ i is the standard deviation of the i-th feature, adjusting the sensitivity of weight update, s i and j are the utility scores of the i-th and j-th features, respectively, reflecting the contribution of each feature to the model, and n is the total number of features;

[0079] Based on the data fusion algorithm, the enhanced feature data and the preprocessed data are fused to obtain intelligently processed data.

[0080] In S3, a machine learning model such as a support vector machine is used to identify the status of the meter box based on the intelligently processed data. The status includes normal, maintenance required, and faulty. An optimized allocation model is built based on the identified status of the meter box to intelligently allocate detection tasks.

[0081] The optimization allocation model can dynamically adjust the task allocation according to the status of the meter box, the urgency of the task and the availability of the technician. The construction method of the optimization allocation model includes:

[0082] Define decision variables: Define decision variables y ijt It is expressed as whether the i-th technician is assigned to the j-th task at time t, y ijt =1 means assigned, y ijt =0 means not allocated;

[0083] Define the objective function; construct the objective function to maximize the weighted completion rate of task priorities and minimize the total cost, the formula is as follows:

[0084]

[0085] Where F is the objective function, p j is the priority weight of task j, which reflects the urgency and importance of the task. The priority is set based on the impact scope, risk level or criticality of the task according to the empirical method. ijt is the cost for the i-th technician to perform task j at time t, which may include labor cost, travel time, cost of required resources, etc. N is the total number of technicians, M is the total number of tasks that need to be assigned, and T is the time period, which reflects the time dimension of task scheduling.

[0086] Constraints: Each task must be assigned at least once within the specified time, and each technician can only perform one task at any time. The formula is as follows:

[0087]

[0088] By applying the dynamic weight analysis enhancement algorithm, the representation of feature data is effectively optimized, the effect of data processing is improved, and the data is made to better reflect the actual equipment status, thereby speeding up status identification and decision making.

[0089] By applying an intelligent fusion scheduling algorithm, detection tasks are dynamically allocated according to the identified status of the meter box, which improves the efficiency of resource utilization and the timeliness of task execution. The optimization allocation model takes into account multiple factors, such as the urgency of the task and the availability of technicians, to ensure that task allocation is reasonable and efficient, while reducing costs and time waste.

[0090] In S4, the multi-dimensional situation response scheduling algorithm includes:

[0091] Define multi-dimensional influencing factors, define influencing factors and quantification methods of influencing factors, and construct a matrix representation of quantitative variables. jtis the influencing factor vector of task j at time t, including environmental factors, operator skill level, and historical failure rate of equipment;

[0092] Weight adjustment of influencing factors: For each task, the weight is dynamically adjusted according to the task influencing factor vector. The formula is as follows:

[0093]

[0094] In the formula, w jt is the weight of the task at time, α k is the weight coefficient of the kth influencing factor, v jtk is the quantitative value of the kth influencing factor of the jth task at time t, β l is the weight coefficient of other influencing factors, u jtl is the quantitative value of other influencing factors.

[0095] Optimize the objective function, the formula is as follows:

[0096]

[0097] Where, F' is the optimized objective function;

[0098] Constraint extension: Add dynamic availability constraints to ensure that technicians are assigned to appropriate tasks based on their real-time availability and qualifications; skill matching constraints to ensure that technicians assigned to specific tasks have the skill level required to perform the task; task completion requirements to ensure that each task is assigned a sufficient number of times within its required time window to meet its completion criteria; continuous working time restrictions to limit the continuous working time of technicians to prevent overwork; multi-task fairness constraints to ensure that task allocation is as fair as possible among technicians.

[0099] After optimizing the objective function and constraints, a heuristic algorithm combined with linear programming is used for optimization, and the initial solution is generated using a genetic algorithm. Then, the initial solution is fine-tuned through linear programming to ensure global optimality and obtain the global optimal solution. The optimal solution obtained can achieve the allocation of optimal detection tasks and realize scheduling optimization based on automatic detection of low-voltage metering boxes.

[0100] In the process of implementing the genetic algorithm, in order to avoid the problem of local optimal solutions instead of global optimal solutions, the precision enhancement adjustment algorithm is introduced to optimize the genetic algorithm to ensure that the algorithm can effectively avoid local optimal solutions and find better solutions in the global search space. The precision enhancement adjustment algorithm includes:

[0101] Introduction of local search: By introducing local search, the accuracy and ability of the adjustment algorithm to escape when encountering a local optimal solution are enhanced, thereby exploring a larger search space;

[0102] Implement local perturbation: In each generation of the genetic algorithm, a specified proportion of individuals are selected empirically for local perturbation. The perturbation is in the form of applying periodic sinusoidal waveform changes to the gene r of the individual to increase the randomness and coverage of the search. The formula is as follows:

[0103]

[0104] In the formula, r old is the original gene value, δ is the disturbance amplitude, which controls the size of the change, λ controls the periodicity of the disturbance, and determines the frequency of the disturbance, r new is the new gene value after disturbance adjustment, φ is the phase offset, which is introduced to increase the randomness of the disturbance.

[0105] Maintaining genetic diversity: Maintaining the genetic diversity of the population is a key measure to avoid premature convergence of the genetic algorithm. It can enhance the global search capability, calculate the difference between individuals in the population, use the Hamming distance as a measure of difference, and achieve diversity evaluation;

[0106] When the population diversity is below the preset threshold, the diversity is increased by adding randomly generated new individuals or by producing more diverse offspring through crossover and mutation, as shown below:

[0107]

[0108] In the formula, H(r i ,r avg ) is individual r i The population average r avg Hamming distance, γ is a positive adjustment factor used to adjust the impact of diversity, G new is the diversity index of the new generation population, r i is the new gene value of the i-th individual, and Num is the number of individuals in the population.

[0109] Dynamically adjust the perturbation amplitude: dynamically adjust the perturbation amplitude according to the progress of the search process, maintain a balance between exploration and development, and adjust the perturbation amplitude δ according to the changes in the optimal solution in consecutive generations; if the optimal solution has not been improved for a period of time exceeding the set threshold, increase the perturbation amplitude and enhance the exploration area; if the optimal solution has not been improved for a period of time less than the set threshold, reduce the perturbation amplitude and refine the search in the current area. The formula is as follows:

[0110]

[0111] In the formula, δ old is the perturbation amplitude used in the previous generation, δ newis the newly calculated disturbance amplitude, which is used for the next generation of local search. Δr is the difference between the optimal fitness of the previous and next generations, which indicates the speed of algorithm optimization progress. Δt is the algebraic change, which indicates the progress of time. ρ is the adjustment coefficient, which is the parameter that controls the disturbance adjustment rate to adapt to the dynamically changing optimization environment.

[0112] The above process can effectively help the genetic algorithm escape from the local optimum and maintain the effectiveness and efficiency of the algorithm in the global search process, thereby increasing the probability of the algorithm finding the global optimal solution and enhancing the accuracy and reliability of the solution.

[0113] By introducing multi-dimensional influencing factors, such as environmental conditions, technicians' skill levels, and historical equipment failure rates, the multi-dimensional situation response scheduling algorithm can more accurately match task requirements and site conditions and optimize resource allocation. The dynamic weight adjustment mechanism ensures that changes in various influencing factors are reflected in real time during task allocation, making scheduling decisions more flexible and adaptable.

[0114] By applying the optimization allocation model and adjusting the task allocation, we can ensure that the technicians' skills match the task requirements and improve work efficiency. At the same time, we take into account the labor cost and resource allocation and reduce the overall operating cost. The dynamic availability and multi-task fairness constraints ensure that the technicians will not be overworked and maintain the work quality and efficiency. The introduced precision enhancement adjustment algorithm enhances the adaptability to environmental changes and potential errors through periodic sinusoidal waveform changes and genetic diversity maintenance mechanisms.

[0115] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A scheduling optimization method based on automatic detection of low-voltage metering boxes, characterized in that: The following steps are involved: S1: Based on the goal and / or functional requirements of the low-voltage metering box automated detection, the detection data is acquired, the detection data is preprocessed, and based on the preprocessed detection data, feature extraction is performed to obtain key feature data; S2: Based on the dynamic weight analysis enhancement algorithm, key feature data is enhanced, and the representation of data features is optimized by dynamically adjusting the weights to obtain intelligently processed data; S3: Based on the intelligent fusion scheduling algorithm, identify the current status of the low-voltage metering box, build an optimization allocation model, and intelligently allocate detection tasks; S4: Based on the multi-dimensional situation response scheduling algorithm, the allocation of detection tasks is adaptively optimized to obtain the optimal allocation of detection tasks.

2. A scheduling optimization method based on automatic detection of low-voltage metering boxes according to claim 1, characterized in that: In S1, the preprocessing of the detection data includes data cleaning, missing value processing, noise smoothing and data normalization, and the key feature data includes statistical features, trend features and frequency domain features.

3. The scheduling optimization method based on automatic detection of low-voltage metering boxes according to claim 1 is characterized in that: In S2, the dynamic weight analysis enhancement algorithm includes: Initialize the weight matrix: Assign an initial weight to each data feature. The weight matrix is ​​represented by W and the formula is as follows: In the formula, w i is the initial weight of the i-th feature, and n is the number of features; Calculate weighted eigenvalues: Use the weight matrix to weight the feature data X, each eigenvalue x i The corresponding weight w i Multiplying, the formula is as follows: In the formula, Z is the weighted eigenvector, which represents the comprehensive index of the eigenvalue after weight processing, x i is the original value of the i-th feature.

4. A scheduling optimization method based on automatic detection of low-voltage metering boxes according to claim 3, characterized in that: Dynamically adjust weights: Update the weight of each feature value based on its utility, as follows: In the formula, is the updated weight, w i is the current weight of the i-th feature, exp is an exponential function used to calculate the update scale of the weight, σ i is the standard deviation of the i-th feature, adjusting the sensitivity of weight update, s i and j are the utility scores of the i-th and j-th features respectively, and n is the total number of features; Based on the data fusion algorithm, the enhanced feature data and the preprocessed data are fused to obtain intelligently processed data.

5. The scheduling optimization method based on automatic detection of low-voltage metering boxes according to claim 1 is characterized in that: In S3, the optimization allocation model construction method includes: Define decision variables: Define decision variables y ijt It is expressed as whether the i-th technician is assigned to the j-th task at time t, y ijt =1 means assigned, y ijt =0 means not allocated; Define the objective function; construct the objective function to maximize the weighted completion rate of task priorities and minimize the total cost, the formula is as follows: Where F is the objective function, p j is the priority weight of task j, c ijt is the cost of the i-th technician performing task j at time t, N is the total number of technicians, M is the total number of tasks that need to be assigned, and T is the time period.

6. A scheduling optimization method based on automatic detection of low-voltage metering boxes according to claim 5, characterized in that: Constraints: Each task must be assigned at least once within the specified time, and each technician can only perform one task at any time. The formula is as follows:

7. The scheduling optimization method based on automatic detection of low-voltage metering boxes according to claim 1 is characterized in that: In S4, the multi-dimensional situation response scheduling algorithm includes: Define multi-dimensional influencing factors, define influencing factors and quantification methods of influencing factors, and construct a matrix representation of quantitative variables. jt is the influencing factor vector of task j at time t, including environmental factors, operator skill level, and historical failure rate of equipment; Weight adjustment of influencing factors: For each task, the weight is dynamically adjusted according to the task influencing factor vector. The formula is as follows: In the formula, w jt is the weight of the task at time, α k is the weight coefficient of the kth influencing factor, v jtk is the quantitative value of the kth influencing factor of the jth task at time t, β l is the weight coefficient of other influencing factors, u jtl is the quantitative value of other influencing factors.

8. The scheduling optimization method based on automatic detection of low-voltage metering boxes according to claim 7 is characterized in that: Optimize the objective function, the formula is as follows: Where, F' is the optimized objective function; Constraint expansion: adding dynamic availability constraints, skill matching constraints, task completion requirements, continuous working time limits, and multi-task fairness constraints; After optimizing the objective function and constraints, a heuristic algorithm combined with linear programming is used for optimization, a genetic algorithm is used to generate an initial solution, and then the initial solution is fine-tuned through linear programming to obtain the global optimal solution.

9. A scheduling optimization method based on automatic detection of low-voltage metering boxes according to claim 8, characterized in that: Based on the precision enhancement adjustment algorithm, the genetic algorithm is optimized to find a better solution in the global search space. The precision enhancement adjustment algorithm includes: Introduction of local search: By introducing local search, the accuracy and ability of the adjustment algorithm to escape when encountering a local optimal solution are enhanced; Implement local perturbation: In each generation of the genetic algorithm, a specified proportion of individuals are selected for local perturbation. The perturbation is in the form of applying periodic sinusoidal waveform changes to the gene r of the individual. The formula is as follows: In the formula, r old is the original gene value, δ is the disturbance amplitude, which controls the size of the change, λ controls the periodicity of the disturbance, and determines the frequency of the disturbance, r new is the new gene value after disturbance adjustment, and φ is the phase offset.

10. A scheduling optimization method based on automatic detection of low-voltage metering boxes according to claim 9, characterized in that: Maintaining genetic diversity: Calculate the differences between individuals in a population, use the Hamming distance as a measure of differences, and achieve diversity assessment; When the population diversity is below the preset threshold, the diversity is increased by adding randomly generated new individuals or by producing more diverse offspring through crossover and mutation, as shown below: In the formula, H(r i ,r avg ) is individual r i The population average r avg Hamming distance, γ is a positive adjustment factor used to adjust the impact of diversity, G new is the diversity index of the new generation population, r i is the new gene value of the i-th individual, and Num is the number of individuals in the population.

11. The scheduling optimization method based on automatic detection of low-voltage metering boxes according to claim 9 is characterized in that: Dynamically adjust the perturbation amplitude: dynamically adjust the perturbation amplitude according to the progress of the search process, and adjust the perturbation amplitude δ according to the changes in the optimal solution in consecutive generations; If the optimal solution has not been improved for a time exceeding the set threshold, increase the disturbance amplitude and enhance the exploration area. If the optimal solution has not been improved for a time less than the set threshold, reduce the disturbance amplitude and refine the search in the current area. The formula is as follows: In the formula, δ old is the perturbation amplitude used in the previous generation, δ new is the newly calculated disturbance amplitude, which is used for the next generation of local search. Δr is the difference between the optimal fitness of the previous and next generations, which indicates the speed of algorithm optimization progress. Δt is the algebraic change, which indicates the progress of time. ρ is the adjustment coefficient, which is the parameter that controls the disturbance adjustment rate to adapt to the dynamically changing optimization environment.

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