Power grid line emergency repair scheduling distribution method for complex multi-constraint disaster scene

Abstract

The invention relates to a power grid line emergency repair scheduling distribution method for a complex multi-constraint disaster scene, and belongs to the technical field of power grid emergency management. The method comprises the following steps: decomposing a complex first-aid repair task into a plurality of quantifiable sub-tasks, and establishing a matrix description of task requirements and technical capability requirements; establishing a technical capability evaluation index system based on multiple dimensions, and dynamically quantifying the technical capability level of the emergency personnel through evaluation and scoring; s3, establishing an evaluation model based on a technical capability satisfaction rate, and estimating the first-aid repair time in combination with a Monte Carlo simulation method based on Latin hypercube sampling; s4, performing task allocation in the team by using a Hungary algorithm; and S5, optimizing task allocation of multiple teams by using an ant colony algorithm of multiple traveling salesmen in combination with first-aid repair working hour estimation. The method of the invention can effectively improve the efficiency and accuracy of emergency task allocation, improve the utilization rate of limited resources, and provide technical support for first-aid repair work in a disaster scene.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid emergency management, and particularly relates to a power grid line emergency repair scheduling and allocation method for complex multi-constraint disaster scenarios. Background Art

[0002] As an important infrastructure of the national economy, the safe and stable operation of the power grid is crucial for ensuring social and economic development and people's livelihood. However, natural disasters such as earthquakes, typhoons, floods, and human factors such as equipment aging and external force damage may all cause power grid failures and lead to power outages. Power outages not only cause economic losses but may also trigger social problems and affect people's lives.

[0003] When large-scale power grid failures occur, there are often a large number of fault points, and multiple emergency teams need to cooperate in emergency repairs. How to efficiently and reasonably allocate emergency repair tasks, shorten the power outage time, and minimize power losses to the greatest extent has become an important issue in power grid emergency management. Traditional emergency repair task allocation methods often adopt the principle of proximity or empirical allocation, lacking scientificity and rationality. For example, although the principle of proximity can shorten the time for the emergency repair team to reach the fault point, it ignores the different repair difficulties and required equipment at different fault points, which may lead to low emergency repair efficiency. Empirical allocation depends on the experience and intuition of emergency repair personnel, lacking objectivity and operability.

[0004] In recent years, some scholars have begun to apply optimization algorithms to emergency repair task allocation, such as genetic algorithms, ant colony algorithms, etc. However, most of these methods only consider the optimization of the emergency repair path and ignore factors such as the technical capabilities of emergency teams and emergency repair working hours, resulting in low task allocation efficiency and extended emergency repair time.

[0005] In response to the above problems, some scholars have proposed a repair task allocation method based on technical ability assessment. For example, GENG Zefei et al. proposed to establish an emergency demand assessment index and classification method for fault points (GENG Zefei, XUE Lirong, HU Yuejin. Scheduling and decision of power emergency team[J]. Power System and Clean Energy, 2011, 27(3): 38-41, 45.), and dispatched emergency teams of different levels according to the actual demand level of the disaster point, and first proposed the problem of different technical ability levels meeting different emergency needs. GUO Xiaoming et al. started from the priority division of the points to be repaired, established an emergency personnel and resource scheduling plan under the emergency resource guarantee rate (GUO Xiaoming, LIU Junyong. Research on power disaster-relief resources allocation schedule mode[J]. Power System Protection and Control, 2011, 39(20): 11-16.), and obtained the repair path. Zhu et al. (ZHU JM, LIU SY, GHOSH S. Model and algorithm of routes planning for emergency relief distribution in disaster management with disaster information update[J]. Journal of Combinatorial Optimization, 2019, 38(1): 208-223.) studied the optimization problem of rescue routes under the update of disaster information and proposed a path reliability metric.

[0006] However, most of the existing methods only consider the impact of a single factor on task allocation and lack comprehensive consideration of multiple factors. For example, the technical ability level of emergency personnel will affect the repair working hours, and thus affect the task allocation plan; the repair difficulties and required equipment of different fault points are different, and these also need to be considered in task allocation. In addition, the existing technical ability assessment methods often only consider the skill level of emergency personnel, while ignoring factors such as physical fitness, proficiency, and execution ability, resulting in incomplete and inaccurate assessment results.

[0007] Therefore, there is a need for a power grid line emergency repair scheduling and allocation method for complex multi-constraint disaster scenarios that comprehensively considers multiple factors to improve task allocation efficiency and repair efficiency and minimize power outage losses to the greatest extent. Summary of the Invention

[0008] The object of the present invention is to solve the problems of low efficiency in emergency task allocation, inaccurate estimation of repair working hours, and difficulty in optimizing task allocation for multiple teams, and to provide a power grid line emergency repair scheduling and allocation method for complex multi-constrained disaster scenarios, which can effectively improve the efficiency and accuracy of emergency task allocation, enhance the utilization rate of limited resources, and provide technical support for repair work in disaster scenarios.

[0009] To achieve the above object, the technical solution of the present invention is: a power grid line emergency repair scheduling and allocation method for complex multi-constrained disaster scenarios, including:

[0010] S1. For complex repair tasks, perform task decomposition, decompose them into multiple quantifiable sub-tasks, and establish a matrix description of repair task requirements and technical ability requirements;

[0011] S2. Establish a multi-dimensional technical ability evaluation index system, and dynamically quantify the technical ability level of emergency personnel through evaluation and scoring;

[0012] S3. Based on the evaluation model of technical ability satisfaction rate, and combined with the Monte Carlo simulation method based on Latin hypercube sampling to estimate the repair working hours;

[0013] S4. Use the Hungarian algorithm to perform task allocation within the team, achieve precise matching of technical ability and task requirements, thereby improving task allocation efficiency and shortening the repair time;

[0014] S5. Combining the estimation of repair working hours, use the ant colony algorithm of multiple traveling salesmen to optimize the task allocation of multiple teams, achieve collaborative repair of multiple teams, shorten the total time used, and thereby improve the emergency repair efficiency.

[0015] Preferably, in step S1, the complex repair task is decomposed into multiple quantifiable sub-tasks, and a matrix description of task requirements and technical ability requirements is established. The specific operation is as follows:

[0016] Through emergency task decomposition, it is changed into several work sub-tasks that are easy for emergency personnel to execute and achieve. The sub-task constraints include time, technical ability level, resources, etc. At the same time, the decomposed sub-tasks can be continuously statistically verified in terms of integrity, independence, and executability. The sub-tasks after the repair task decomposition refine the task technical ability requirements, establish a matrix description of repair task requirements and technical ability requirements, the emergency task is decomposed into several sub-tasks n, and a is the technical requirement required for a certain sub-task. Different demand scores are marked according to the difficulty of the sub-task, and the demand score is R na , the higher the requirement, the higher the score.

[0017] Technical ability requirements table

[0018]

[0019] Preferably, in step S2, a technical ability evaluation index system based on multiple dimensions is established, and the technical ability level of emergency personnel is dynamically quantified through evaluation and scoring. The specific operations are as follows:

[0020] The evaluation indicators can be refined into specific knowledge, behaviors, experience, technologies, and the resilience of emergency personnel in their own emergency states. Among the skill evaluation elements, the emergency technical ability should have the characteristics of dynamic and accurate statistics of technical ability, quantifiable by items, comprehensiveness, etc. Establish an inspection standard based on multiple dimensions such as the skill dimension, physical fitness dimension, proficiency, and execution ability dimension of emergency personnel, and continuously improve the inspection standard through dynamic updates. Rate the skill levels, and through the evaluation and scoring of the skills of emergency personnel, dynamically locate and quantify the technical ability level of emergency personnel, and form a statistics of the repair technical ability level as shown in the following table. p is the emergency personnel, and μ is the description of the technical ability level that the emergency personnel can provide, R pa is the specific technical ability score.

[0021] Technical Ability Statistical Table

[0022]

[0023] Preferably, in step S3, an evaluation model based on the technical ability satisfaction rate is established, and the repair working hours are estimated by combining the Monte Carlo simulation method based on Latin hypercube sampling. The specific process is as follows:

[0024] It is assumed that the emergency fault task is completed by v emergency teams, and the team set K = {K1, K2,..., K v}, p represents the members within the emergency team; a certain task N consists of m subtasks, and the emergency task N = {n1, n2,..., n n ,..., n m}, where n represents the subtask in a certain task N, and n ∈ {1, 2,..., m}; the emergency skill set A = {a1, a2,...}, and the equipment set E = {e1, e2,...}.

[0025] L an is the skill value that the emergency personnel lack to complete subtask n; L en is the impact of the lack of emergency equipment e on the completion of subtask n; R na is the level of the emergency skill a required for subtask n; R pa is the level of the technical ability a that the candidate member p possesses; R ne is the quantity of the emergency equipment e required for subtask n; R ee is the quantity of the reserved emergency equipment e.

[0026] Cna Indicates the technical ability satisfaction rate. Technical ability includes the influence of personnel technical ability and equipment. Its meaning is a quantifiable measurement index established through skills (a) between the technical ability requirements of a certain task for emergency personnel (p) and equipment (e).

[0027] L an =max(0,R na -R pa ) (1)

[0028] L en =max(0,R ne -R ee ) (2)

[0029]

[0030] α is the influence coefficient of equipment satisfaction rate on technical ability execution, and γ is the non-linear influence parameter of skill gap.

[0031] The technical ability evaluation index formula (1) indicates the technical ability value lacking for emergency technical personnel to complete sub-task n. Formula (2) indicates the influence value of lacking equipment on completing sub-task n. Formula (3) is the technical ability evaluation value combining emergency personnel skills and equipment supply rate.

[0032] After calculation, a value between 0 and 1 is obtained. The closer the value is to 1, the more the technical supply capabilities of individuals and equipment can meet the task requirements. The closer the value is to 0, the smaller the ability of individuals and equipment to meet the task requirements.

[0033] Preferably, when estimating the repair time using the Monte Carlo simulation method based on Latin hypercube sampling, the emergency process will be affected by the satisfaction of different emergency personnel and equipment as well as environmental factors. Therefore, it is necessary to optimize and adjust the estimated repair task time under statistical data according to specific human, technical, equipment, and environmental conditions, combined with the main influencing attributes of specific execution. Monte Carlo analysis first confirms the statistical probability distribution of emergency task time. For example, the duration of test and detection activities conforms to the exponential distribution (Exponential Distribution, EX), and the repair duration for relatively simple and single faults conforms to the normal distribution (Normal Distribution, ND), etc. Further, combined with the probability distribution of repair time and the method of Latin hypercube sampling, the Monte Carlo analysis of the repair activity duration is realized to obtain the estimated repair times of different tasks within a certain confidence level.

[0034] Optimize the emergency working hours estimation method by deepening the task requirement analysis under emergency task decomposition, combining the technical ability supply of emergency personnel, the equipment satisfaction rate, and environmental factors. Establish an evaluation model for the impact of different technical abilities on the estimation of task working hours. And calibrate and proofread the parameters in the estimation method model based on the rich basic data of emergency rescue data and actual drill practice. First, determine the parameter mean μ and standard deviation σ of the basic distribution T- of the working hours of each subtask n. In case of extreme weather (such as heavy rain, strong wind), increase the mean and standard deviation of T-:

[0035] μ′ = μ·(1 + δ μ ), σ′ = σ·(1 + δ σ )(4)

[0036] where δ μ and δ σ are correction factors based on environmental conditions. The optimized estimated working hours T y are:

[0037] T y = T-[1 + β(1 - C na )]+T c (5)

[0038] In the formula, β is the correction factor for the impact of the technical ability level of emergency personnel on the completion time, which is generally adjusted based on the historical statistical data of the completion time of a certain subtask by emergency personnel with different emergency ability levels in emergency drills and real emergencies. T c is the reserve time during the execution process. The reserve time can be adjusted and set according to different emergency tasks combined with the actual work statistics. Reserve some time in the task working hours to cope with the possible risks in task arrangements and increase the resilience of task allocation.

[0039] Use the LHS method to generate N sim sample points to ensure that the input parameters (task complexity, weather, equipment status) cover a wider range. For each simulation i = 1, 2, …, N sim , perform the following steps:

[0040] ① Randomly generate the basic working hours

[0041]

[0042] ② Correct the working hours according to the satisfaction rate C na :

[0043]

[0044] ③ Record the calculated average working hours generated by each simulation:

[0045]

[0046] Preferably, in step S4, the Hungarian algorithm is used for task allocation within the team to achieve an accurate match between technical capabilities and task requirements, thereby improving the task allocation efficiency and shortening the emergency repair time. The specific operations are as follows:

[0047] Preferably, in step S4, the Hungarian algorithm is used for task allocation within the team to achieve an accurate match between technical capabilities and task requirements, thereby improving the task allocation efficiency and shortening the emergency repair time. The specific operations are as follows:

[0048] T pn is the time taken for emergency responder p to complete subtask n, and the variable x pn indicates whether subtask n is completed by the p-th emergency responder. The modeling is as follows:

[0049] For the allocation goal of minimizing the total time for task completion within the team, the Hungarian algorithm is used for modeling as follows:

[0050]

[0051] The constraint conditions are:

[0052]

[0053] x pn = 0 or 1; p = 1, 2, …, q; n = 1, 2, …, m (12)

[0054] Z is the total time taken to complete all subtasks in task N. Equation (9) represents the total time taken for the team to complete task N as the objective function. Equations (10) to (12) constrain each emergency responder to be responsible for one task. Combining with the Hungarian method, a time-optimal task allocation plan can also be quickly generated when the number of tasks is equal to or different from the number of people.

[0055] Preferably, in step S5, in combination with the estimation of emergency repair man-hours, the ant colony algorithm for multiple traveling salesmen is used to optimize the task allocation of multiple teams, realizing the collaborative emergency repair of multiple teams, shortening the total time, and thus improving the emergency repair efficiency. The specific operations are as follows:

[0056] T ij is the travel time of the emergency team from i to j; T ki is the estimated man-hours for the emergency team to perform emergency repair at point j after completing the repair at point i; J = {1, 2, …, w} represents the set of emergency repair points to be repaired, where the repair points i, j ∈ J; X ij = 1 indicates that the emergency team continues to perform emergency repair at point j after completing the repair at point i, X ij= 0 represents others, where i and j are the emergency repair points awaiting repair. The objective function is to minimize the longest completion time among all teams:

[0057]

[0058] Constraints:

[0059] Each repair point can only be assigned to one team and can only be visited once:

[0060]

[0061] The path of each team is a closed loop (starting from point 0 and finally returning to point 0):

[0062]

[0063] The algorithm for optimizing the task allocation of multiple teams using the ant colony algorithm for multiple traveling salesmen is as follows:

[0064]

[0065]

[0066] The present invention also provides a computer-readable storage medium, on which computer program instructions that can be run by a processor are stored. When the processor runs the computer program instructions, the method steps as described in any of the above can be implemented.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] (1) By comprehensively considering factors such as the technical capabilities of emergency teams, repair working hours, and the locations of fault points, the present invention can allocate repair tasks more scientifically and reasonably, avoid resource waste, shorten the repair time, and improve the repair efficiency.

[0069] (2) The present invention can perform precise matching according to the technical capabilities of emergency teams and the requirements of fault points, avoid situations of "using a sledgehammer to crack a nut" or "using a small tool for a big job", optimize resource allocation, and improve resource utilization rate.

[0070] (3) The present invention can quickly and accurately evaluate the emergency requirements of fault points and the technical capabilities of emergency teams, and perform reasonable task allocation, thereby improving the emergency response ability of the power grid and minimizing power outage losses to the greatest extent. Description of the Drawings

[0071] Figure 1 is the flowchart of the method of the present invention;

[0072] Figure 2 is the final scheduling result of Example 1. Detailed Embodiments

[0073] The technical solution of the present invention will be specifically described below in conjunction with the accompanying drawings.

[0074] The present invention will be further described below in conjunction with embodiments. It should be noted that these are only examples and explanations of the concept of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the specific embodiments described, or use similar methods for substitution, as long as they do not deviate from the concept of the invention or exceed the scope defined by the claims of the present invention, they should be regarded as falling within the protection scope of the present invention.

[0075] Embodiment 1:

[0076] Province Y is a province where natural disasters occur frequently, such as earthquakes, typhoons, and debris flow disasters. This has put strict requirements on the emergency repair capabilities of the local power grid. There is an emergency repair center in Province Y, which is composed of different emergency personnel. The emergency repair center is the starting point for the emergency team to participate in the repair work, and it allocates emergency tasks based on the capabilities of the emergency team. The task scheduling problem of the emergency team is solved based on the above conditions.

[0077] According to the power grid geographical location information system, automation system, intelligent diagnosis system, or through on-site inspections by personnel in the fault area, information such as the location of the points to be repaired, the types of faults, and the degree of damage is obtained. The location coordinates and fault types of 20 fault points that need to be repaired after importance assessment are shown in Table 1.

[0078] Table 1 Statistics of Fault Points in the Marked Power Outage Area

[0079]

[0080] Based on the longitude and latitude positions of the fault points, in actual emergency work, real-time transportation routes and their congestion data can be obtained by calling precise location indicators (APIs) similar to Baidu Maps, etc., and the estimated travel time can be calculated. For the convenience of calculation, an average vehicle speed of 30 km / h is adopted, and the travel times between the fault points are shown in Table 2.

[0081] Table 2 Travel Times (min) between Fault Points

[0082]

[0083]

[0084] Figure 1 It is a flowchart of the method. As Figure 1 shown, a power grid line emergency repair scheduling and allocation method for complex multi-constraint disaster scenarios in this example includes the following steps:

[0085] S1. Decompose complex emergency repair tasks into multiple quantifiable subtasks, and establish a matrix description of the task requirements and technical capability requirements. Table 3 shows the technical capability requirements for the emergency repair execution process of the fault point numbered 15 in Table 1, which are the technical capabilities required for the emergency repair work of the UHV DC converter station.

[0086] Table 3 Technical Capability Requirements for Emergency Work

[0087]

[0088] S2. Establish a multi-dimensional technical capability evaluation index system, and dynamically quantify the technical capability level of emergency personnel through evaluation and scoring. Taking the results of the technical capability evaluation of emergency personnel in Team 2 as an example, according to the skill statistics table of candidate members within the team, retrieve the summary table of the supply of emergency personnel's technical capabilities, as shown in Table 4.

[0089] Table 4 Statistics of Emergency Personnel's Technical Capabilities (Partial)

[0090]

[0091] Generally, the demand for emergency equipment is related to the types and quantities of technical outputs used in the task execution process and the requirements of the tasks to be repaired. For example, the output of high-altitude operation capabilities requires equipment such as cranes (forklifts) to support. According to the task technical capability requirements and task requirements, generate a corresponding list of emergency equipment requirements, as shown in Table 5.

[0092] Table 5 Emergency Equipment Requirements

[0093]

[0094] S3. Establish an evaluation model based on the technical capability satisfaction rate, and combine the Monte Carlo simulation method based on Latin hypercube sampling to estimate the emergency repair working hours. Confirm the skills and equipment required for the task. According to the technical capability satisfaction rate evaluation model in Equation (3), obtain the technical capability evaluation in Table 6.

[0095] Use the Monte Carlo simulation method based on Latin hypercube sampling to estimate the emergency repair working hours. According to Equations (7) and (8), calculate the estimated average completion time of different emergency personnel for different subtasks as shown in Table 7.

[0096] Table 6 Technical Capability Evaluation

[0097]

[0098] Table 7 Monte Carlo Time Estimation Based on Different Emergency Personnel (h)

[0099]

[0100]

[0101] S4. Use the Hungarian algorithm to perform task allocation within the team, achieve an accurate match between technical capabilities and task requirements, thereby improving task allocation efficiency and shortening the repair time. n1 to n7 are parallel subtask sets, and one task is assigned to one emergency responder. The estimated repair man-hours for the task is min(maxT) = 6.62h = 397min. The above is the estimated man-hours for the emergency task arrangement of a team to complete one task. At the same time, by applying this method, the estimated completion time of tasks for different emergency teams at different fault points can be obtained, providing basic data for the optimization of task allocation between teams in the next step. Through the in-team optimization allocation of the fault point repaired by Team 2, the estimated emergency execution time for Team 2 to handle Fault Point 15 is 397min. Similarly, the estimated man-hours required for different teams to repair different emergency fault points are obtained, as shown in Table 8.

[0102] Table 8 Estimated repair man-hours (min) for different teams at different points to be repaired

[0103]

[0104] S5. Combining the estimated repair man-hours, use the ant colony algorithm for multiple traveling salesmen to optimize the task allocation of multiple teams, achieve collaborative repair by multiple teams, shorten the total time, and thus improve the emergency repair efficiency. After obtaining the travel time between team points and the estimated man-hours to be repaired at each point, multiple emergency teams start from the emergency repair center point 1 and repair the entire multi-task concurrent fault task points. Based on the goal of the shortest repair time, use the ant colony algorithm to perform task allocation for the emergency teams, and the emergency teams traverse each fault point to complete the repair task in the shortest time. Table 9 is the statistical table of the repair time obtained by using the ant colony algorithm for multiple traveling salesmen.

[0105] Table 9 Statistical table of repair time

[0106]

[0107]

[0108] The method of the present invention uses task decomposition technology to establish task requirement analysis and emergency responder technical ability assessment, quantitatively analyze the types and levels of technical capabilities possessed by emergency responders, and establish a technical ability matching model under task requirement assessment. And in combination with technical ability assessment, the estimated repair man-hours are optimized, improving the task allocation ability between emergency repair teams and improving the impact of the time used at different repair points by different teams on task allocation during the task allocation process. Based on the Hungarian algorithm and the multiple traveling salesman problem, the task allocation is optimized and solved, and the reliability and effectiveness of the model and algorithm are verified through simulation analysis.

[0109] The present invention also provides a computer-readable storage medium, on which computer program instructions capable of being run by a processor are stored. When the processor runs the computer program instructions, the method steps as described in any one of the above can be implemented.

[0110] The above are the preferred embodiments of the present invention. All changes made according to the technical solution of the present invention, when the functions and effects produced do not exceed the scope of the technical solution of the present invention, fall within the protection scope of the present invention.

Claims

1. A power grid line emergency repair scheduling and allocation method for complex multi-constraint disaster scenarios, characterized in that Including: S1. For complex emergency repair tasks, decompose the tasks into multiple quantifiable subtasks, and establish a matrix description of the emergency repair task requirements and technical capacity requirements. S2. Establish a multi-dimensional technical capacity evaluation index system, and dynamically quantify the technical capacity level of emergency personnel through evaluation and scoring. S3. Based on the evaluation model of technical capacity satisfaction rate, and combined with the Monte Carlo simulation method based on Latin hypercube sampling to estimate the emergency repair working hours. S4. Use the Hungarian algorithm for task allocation within the team to achieve an accurate match between technical capacity and task requirements, thereby improving the task allocation efficiency and shortening the emergency repair time. S5. Combining the estimation of emergency repair working hours, use the ant colony algorithm of multiple traveling salesmen to optimize the task allocation of multiple teams, realize the collaborative emergency repair of multiple teams, shorten the total time used, and thus improve the emergency repair efficiency.

2. The emergency repair scheduling and allocation method for power grid lines facing complex multi-constraint disaster scenarios according to claim 1, wherein In step S1, decompose the complex emergency repair tasks into multiple quantifiable subtasks, and establish a matrix description of the emergency repair task requirements and technical capacity requirements, specifically as follows: Complex emergency repair tasks are decomposed into several sub-tasks n. Let a be the technical requirements for a certain sub-task. Different technical ability requirement scores are marked according to the difficulty level of the sub-task, denoted as requirement score R na , see the following table for the relationship between the technical requirements and technical ability requirements of the task: The higher the technical capacity requirement, the higher the requirement score.

3. The emergency repair scheduling and allocation method for power grid lines facing complex multi-constraint disaster scenarios according to claim 2, characterized in that In step S2, the multi-dimensional technical capacity evaluation index system includes multiple dimensions such as the emergency personnel skill dimension, physical fitness dimension, proficiency and execution ability dimension.

4. The emergency repair scheduling and allocation method for power grid lines facing complex multi-constraint disaster scenarios according to claim 3, characterized in that, In step S2, the implementation method of dynamically quantifying the technical capacity level of emergency personnel through evaluation and scoring is as follows: Rate the skill level, and through the evaluation and scoring of the technical capacity of emergency personnel, dynamically locate and quantify the technical capacity level of emergency personnel, and form the technical capacity statistical table as follows: p is the emergency responder, μ is the description of the technical ability level that the emergency responder can provide, and R pa is the specific technical ability score.

5. The emergency repair scheduling and allocation method for power grid lines facing complex multi-constraint disaster scenarios according to claim 4, characterized in that, Step S3 is specifically implemented as follows: The technical capacity evaluation index formula (1) represents the technical capacity value that emergency technical personnel lack to complete subtask n, formula (2) represents the influence value of the lack of equipment on the completion of subtask n, and formula (3) is the technical capacity evaluation value combining the skills of emergency personnel and the equipment supply rate. L an = max(0, R na - R pa ) (1) L en = max(0, R ne - R ee ) (2) L an It means that the emergency personnel lack the skill value to complete subtask n; L en It means the impact of the lack of emergency equipment e on completing subtask n; R na It means the level of the emergency skill a required for subtask n; R pa It means the level of the technical ability a possessed by candidate member p; R ne It means the quantity of the emergency equipment e required for subtask n; R ee It means the quantity of the reserved emergency equipment e; C na It represents the technical ability satisfaction rate. The technical ability includes the influence of personnel technical ability and equipment influence. Its meaning is a quantifiable measurement index established between the technical ability requirements of a certain task for emergency personnel (p) and equipment (e) through skills (a); α is the influence coefficient of the equipment satisfaction rate on the technical ability execution force, and γ is the non-linear influence parameter of the skill gap; After calculation, a value between 0 and 1 is obtained. The closer the value is to 1, the more the technical supply capacity of individuals and equipment can meet the task requirements, and the closer the value is to 0, the smaller the ability of individuals and equipment to meet the task requirements. Optimize the emergency working hours estimation method by deepening the task requirement analysis under the breakdown of emergency tasks, combining the technical ability supply of emergency personnel and the equipment satisfaction rate, and establish an evaluation model for the influence of different technical abilities on the estimation of operation task time; and calibrate and proofread the parameters in the estimation method model based on the rich basic data of emergency rescue data and actual drill practice. The optimized estimated working hours T y is as follows: T y = T - [1 + β(1 - C na )] + T c (4) where β is the correction coefficient based on the influence of the technical ability level of emergency personnel on the completion time; T- is the estimated emergency task time of a certain fault sub-task based on Monte Carlo analysis of Latin hypercube sampling; T c is the reserve time during the execution process.

6. The method for emergency repair scheduling and allocation of power grid lines for complex multi-constraint disaster scenarios according to claim 5, characterized in that Step S4 is specifically implemented as follows: For the allocation goal of minimizing the total task completion time within the team, use the Hungarian algorithm to model as follows: The constraint conditions are: x pn = 0 or 1; p = 1, 2, …, q; n = 1, 2, …, m (8) Z is the total time taken to complete all subtasks in task N; T pn is the time taken by emergency responder p to complete subtask n; variable x pn indicates whether task n is completed by the p-th emergency responder; Equation (5) represents the objective function that the total time taken for the team to complete task N is minimized; Equations (6)-(8) are to ensure that each emergency responder is responsible for one task, and combined with the Hungarian method, it is also possible to quickly generate an optimal-time task allocation plan in the case of equal or unequal numbers of tasks and people.

7. The method for emergency repair scheduling and allocation of power grid lines for complex multi-constraint disaster scenarios according to claim 6, characterized in that, In step S5, before using the ant colony algorithm of multiple traveling salesmen to optimize the task allocation of multiple teams, it is necessary to establish an objective function to minimize the longest completion time among all teams, as follows: Among them, T ij is the travel time of the emergency team from i to j; T ki is the estimated working hours for the emergency team to rush to repair at point j after completing the repair at point i; J = {1, 2, …, w} represents the set of emergency repair points to be repaired, where the repair points i, j ∈ J; X ij = 1 indicates that the emergency team continues to rush to repair point j after completing the repair at point i, and X ij = 0 indicates otherwise, and i, j are the fault repair points to be repaired.

8. The emergency repair scheduling and allocation method for power grid lines facing complex multi-constraint disaster scenarios according to claim 7, characterized in that The constraint conditions of the objective function to minimize the longest completion time among all teams are as follows: Each emergency repair point can only be assigned to one team and can only be visited once: The path of each team is a closed loop:

9. The emergency repair scheduling and allocation method for power grid lines facing complex multi-constraint disaster scenarios according to claim 8, characterized in that In step S5, the specific method of using the ant colony algorithm of multiple traveling salesmen to optimize the task allocation of multiple teams is as follows: (1) Input parameters, including: n: number of fault points; m: number of repair teams; T ij : path time from point i to j; T ki : time required for team k to repair fault point i; l k : task load limit of team k; α, β, ρ, Q: swarm algorithm parameters; maximum number of iterations max_iterations; (2) Initialize the pheromone matrix τ ij = τ0; Initialize the heuristic factor matrix Set the global optimal time T best = ∞; (3) From iteration number 1 to max_iterations, perform the following steps: (3.1) Randomly allocate n - 1 task points to m teams, with no more than l tasks for each team k , and each team starts from the starting point, i.e., the emergency center, and constructs a path according to the probability P ij Construct a path: (3.2) Record the path of each team and calculate the longest emergency repair time T k : Take the maximum value T max = max k (T k ) (3.3) Update the global optimal solution: if T max <T best : T best = T max (14) Save the current path as Best_Path; (3.4) Local pheromone update: Update the pheromone for all paths: (4) Global information table update: Enhance the pheromone according to the global optimal path (5) Output Best_Path and T best .

10. A computer-readable storage medium storing computer program instructions executable by a processor, which, when executed by the processor, can implement the method steps as described in any one of claims 1-9.

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