A dynamic scheduling method, system and readable storage medium for power disaster relief and emergency repair

Abstract

The present invention relates to the technical field of power disaster relief emergency repair scheduling, and particularly relates to a dynamic scheduling method, system and readable storage medium for power disaster relief emergency repair. The method includes the following steps: Step S1, constructing a dynamic network topology model G of the disaster relief scenario; Step S2, constructing a dynamic power restoration model GP; Step S3, constructing a dynamic repair decision-making model: constructing a dynamic repair decision-making model through an objective function; Step S4, calculating the optimal repair strategy: according to the latest dynamic network topology model G and power restoration model GP, and the position of the power facility node where the rescue team is currently located, solving based on the Monte Carlo tree search algorithm to obtain the optimal repair strategy. The method in the present invention takes into account that the situation at the disaster relief site will change dynamically, and the repair strategy is dynamically adjusted according to the changes on site.

Description

Technical Field

[0001] The present invention relates to the technical field of power disaster relief emergency repair scheduling, and particularly relates to a dynamic scheduling method, system and readable storage medium for power disaster relief emergency repair. Background Art

[0002] The stable operation of the power grid plays a very important role in people's production and living standards. Once a major natural disaster such as an earthquake or flood causes a large-scale power outage, how to quickly and timely restore power supply to the power grid is of great significance for disaster relief.

[0003] Regarding the problem of post-disaster power grid fault repair and scheduling, it mainly analyzes the static power grid environment. However, in the actual disaster relief and repair scenario, along with secondary disasters and derivative disasters such as aftershocks, mudslides, and landslides, the existing environment will change dynamically. The originally damaged roads may gradually recover, and the originally passable roads may be damaged. The originally energized power grid may become a new damaged network, and even the repaired power grid segments may become damaged segments again. When dealing with the above situation where the power grid state further deteriorates, the existing methods often can only terminate the solution process and re-solve according to the changed power grid state, resulting in low decision-making efficiency. Summary of the Invention

[0004] Based on this, it is necessary for the present invention to provide a dynamic scheduling method, system and readable storage medium for power disaster relief emergency repair to solve at least one of the above technical problems.

[0005] To achieve the above object, a dynamic scheduling method for power disaster relief emergency repair includes the following steps:

[0006] Step S1, constructing a dynamic network topology model G of the disaster relief scenario: constructing the dynamic network topology model through an undirected graph G = {P, R}, where P is the set of all power facility nodes, , is the set of power facility nodes with the current state being normal, is the set of power facility nodes with the current state being damaged, R is the set of all road segments, , is the set of passable road segments with the current state being normal, is the set of damaged road segments with the current state being damaged; among them, the weight value of P is the time required to repair the power facility node, the weight value of is the time required to pass through this section of the road segment;

[0007] Step S2, constructing a dynamic power restoration model GP: the dynamic power restoration model GP is the set of power lines of the entire regional power grid, , is the set of normal power lines, is the set of damaged power lines;

[0008] Step S3, construct a repair dynamic decision-making model: Through the objective function: , construct a repair dynamic decision-making model according to the dynamic network topology model G and the dynamic power restoration model GP; where, represents the minimum time to repair the entire regional power grid at the current moment, is the time required to repair node i, is the time spent from the previous node to node i, represents the next time slice the maximum power restored by the entire regional power grid when it arrives, is the jth power line 's weight;

[0009] Step S4, calculate the best repair strategy: According to the latest dynamic network topology model G and the dynamic power restoration model GP, and the location of the power facility node where the rescue team is currently located, solve the objective function based on the Monte Carlo tree search algorithm to obtain the best repair strategy.

[0010] Further, the value is the number of all power facility nodes of the jth power line .

[0011] Further, after the step S4, it further includes: Step S5, at the beginning of each new time slice T, repeat the step S4 to calculate a new best repair strategy until all repairs are completed.

[0012] Further, the step S4 specifically includes:

[0013] Step S41, taking the power facility node where the rescue team is currently located as the root node, and according to the dynamic network topology model G, listing all the power facility nodes that can be directly reached from this root node as optional child nodes;

[0014] Step S42, calculate the weight of each of the child nodes according to the UCB formula , and select the child node with the largest weight as the optimal child node;

[0015] Step S43, determine whether there are unrepaired power facility nodes. If so, enter step S44. If not, enter step S45;

[0016] Step S44, includes:

[0017] Step S441: Expand using the optimal child node selected in step S441 as the parent node, and list the power facility nodes reachable from this parent node as the optional child nodes;

[0018] Step S442: Randomly select an optional child node as the parent node for expansion, list the power facility nodes reachable from this parent node as the optional child nodes, and return to step S43;

[0019] Step S45: Update the parameters of each optional child node i through the backtracking update formula:

[0020] ,

[0021] ,

[0022] ,

[0023] where M represents the number of backtracking times, and represent the new value and the old value of the number of backtracking times respectively; Q(i) represents the weight function of the repair ability of child node i, and represent the new value and the old value of the weight function of the repair ability of child node i respectively; represents the weight function of the repair time of child node i, and represent the new value and the old value of the weight function of the repair time of child node i respectively; represents the change value of Q(i) caused by the expansion of the child node, represents the change value of T(i) caused by the expansion of the child node;

[0024] Step S46: Repeat steps S41 - S45 iteratively until after completing the preset total number of learning steps, output the final optimal solution.

[0025] Further, the UCB formula is: , where i_father represents the parent node of child node i, N represents the number of backtracking times of child node i, and are adjustment factors, taking empirical value constants.

[0026] Further, the calculation formula of Q(i) is:

[0027] ,

[0028] where, is the preset weight factor of the line corresponding to child node i, is the number of all power facility nodes in the line corresponding to child node i, is the number of unrepaired nodes in the line corresponding to child node i, is the level of child node i in the tree.

[0029] Furthermore, the calculation formula of T(i) is:

[0030]

[0031] wherein, is the total time spent before reaching child node i, is the time required to repair child node i, is the time spent from the previous node to reach child node i.

[0032] Furthermore, step S5 further includes immediately executing the steps of step S4 when the traffic state of the highway section changes.

[0033] The present invention also provides a power disaster relief and emergency repair dynamic scheduling system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the power disaster relief and emergency repair dynamic scheduling method described in any one of the above are implemented.

[0034] The present invention also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the power disaster relief and emergency repair dynamic scheduling method described in any one of the above are implemented.

[0035] The power disaster relief and emergency repair dynamic scheduling method of the present invention constructs a dynamic network topology model G of the disaster relief scenario through step S1; constructs a dynamic power restoration model GP through step S2; constructs a dynamic emergency repair decision-making model: through the objective function: , constructs a dynamic emergency repair decision-making model according to the dynamic network topology model G and the dynamic power restoration model GP; step S4, optimal emergency repair strategy calculation: based on the latest dynamic network topology model G, the dynamic power restoration model GP, and the position of the power facility node where the rescue team is currently located, solves through the Monte Carlo tree search algorithm to obtain the optimal emergency repair strategy. The method in the present invention takes into account the dynamic changes of the disaster relief site situation, and the emergency repair strategy will also be dynamically adjusted according to the changes of the site situation. Furthermore, through the Monte Carlo tree search algorithm, the optimal solution of the multi-objective function is solved to determine the optimal emergency repair strategy; at the same time, the UCB calculation formula of the child node weight is improved to avoid the problem that the classical Monte Carlo tree search algorithm is prone to local optimality. Description of the Drawings

[0036] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non - limiting embodiments read in conjunction with the accompanying drawings.

[0037] Figure 1 It is a schematic flowchart of a dynamic scheduling method for power disaster relief and emergency repair provided by the present invention.

[0038] Figure 2 It is a schematic hardware structure diagram of a system running the dynamic scheduling method for power disaster relief and emergency repair in an embodiment of the present invention.

[0039] The implementation, functional features, and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0040] The following clearly and completely describes the technical method of the present invention patent with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work belong to the scope of protection of the present invention.

[0041] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0042] It should be understood that although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly, the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed related items.

[0043] To achieve the above object, please refer to Figures 1 to 2 , the present invention provides a dynamic scheduling method for power disaster relief and emergency repair, including the following steps:

[0044] Step S1, constructing a dynamic network topology model G of the disaster relief scenario;

[0045] Step S2, constructing a dynamic power restoration model GP;

[0046] Step S3, construct a dynamic emergency repair decision-making model;

[0047] Step S4, calculate the optimal emergency repair strategy.

[0048] Specifically, in the construction of the dynamic network topology model G of the disaster relief scenario in Step S1, the dynamic network topology model is constructed through the undirected graph G = {P, R}, where P is the set of all power facility nodes, , is the set of power facility nodes with the current status being normal, is the set of power facility nodes with the current status being damaged, and R is the set of all highway sections, , is the set of passable highway sections with the current status being normal, is the set of damaged highway sections. The weight value of P is the time required to repair the power facility node, The weight value of is the time required to pass through this section of the highway.

[0049] Specifically, in the construction of the dynamic power restoration model GP in Step S1, the dynamic power restoration model GP is the set of power lines of the entire regional power grid. Through the set of several lines of the entire regional power grid GP , PL represents an entire line, and a power line is represented as the set of several power facility nodes on this line , ={ }, is the set of normal power lines, is the set of damaged power lines.

[0050] In Step S3, through the objective function: , based on the dynamic network topology model G and the dynamic power restoration model GP, construct a dynamic emergency repair decision-making model; where represents the minimum time to repair the entire regional power grid at the current moment, is the time required to repair node i, is the time spent from the previous node to node i, represents the next time slice The maximum power restored by the entire regional power grid when it arrives, is the jth power line The weight of.

[0051] The total objective of the emergency repair is to find a strategy to repair the entire regional power with the minimum time under the current situation, that is, the first objective function To ensure that the emergency repair strategy can promptly adapt to the dynamic changes on site, a second objective is set. In the current environment, when the next time slice arrives, the power within the area can be restored to the maximum extent.

[0052] Furthermore, in this embodiment, the value is the number of all power facility nodes of the j-th power line .

[0053] Furthermore, in this embodiment, in step S4, the optimal emergency repair strategy is calculated: According to the latest dynamic network topology model G and dynamic power restoration model GP, as well as the position of the power facility node where the rescue team is currently located, the objective function is solved based on the Monte Carlo tree search algorithm to obtain the optimal emergency repair strategy.

[0054] Specifically, those skilled in the art can know that the Monte Carlo tree algorithm is a search method that applies the Upper Confidence Bound (UCB) algorithm to the game tree, used to solve optimization problems and calculate random numbers, including four steps: selection, expansion, simulation, and backpropagation. In a specific example of this embodiment, furthermore, step S4 specifically includes:

[0055] Step S41: Using the power facility node where the rescue team is currently located as the root node, according to the dynamic network topology model G, all power facility nodes that can be directly reached from this root node are listed as optional child nodes;

[0056] Step S42: Calculate the weight of each of the child nodes according to the UCB formula , and select the one with the maximum value as the optimal child node;

[0057] Step S43: Determine whether there are unrepaired power facility nodes. If so, enter step S44; if not, enter step S45;

[0058] Step S44 includes:

[0059] Step S441: Expand the optimal child node selected in step S42 as the parent node, and list all power facility nodes that can be reached from this parent node as optional child nodes;

[0060] Step S442: Randomly select an optional child node as the parent node for expansion, list all power facility nodes that can be reached from this parent node as optional child nodes, and return to step S43;

[0061] Step S45, update the parameters of each optional child node i through the backtracking update formula:

[0062] ,

[0063] ,

[0064] ,

[0065] where M represents the number of backtracking times, and represent the new value and the old value of the number of backtracking times respectively; Q(i) represents the weight function of the repair ability of child node i, and represent the new value and the old value of the weight function of the repair ability of child node i respectively; represents the weight function of the repair time of child node i, and represent the new value and the old value of the weight function of the repair time of child node i respectively; represents the change value of Q(i) caused by the expansion of child node i, represents the change value of T(i) caused by the expansion of child node i;

[0066] Step S46, repeat steps S41 - S45 iteratively until after completing the preset total number of learning steps, output the final optimal solution.

[0067] Furthermore, in this embodiment, an improved UCB formula is used to avoid the problem of local optimality that is prone to occur in the classical Monte Carlo tree search algorithm. Among them, the UCB formula is: , where i_father represents the parent node of child node i, N represents the number of backtracking times of child node i, and are adjustment factors, taking empirical value constants.

[0068] Furthermore, the calculation formula of the weight function Q(i) of the repair ability of child node i is:

[0069] ,

[0070] where is the preset weight factor of the line corresponding to child node i, is the number of all power facility nodes in the line corresponding to child node i, is the number of unrepaired nodes in the line corresponding to child node i, is the level number of child node i in the tree.

[0071] Furthermore, the calculation formula of the weight function T(i) of the repair time of child node i is:

[0072]

[0073] Among them, is the total time spent before reaching child node i, is the time taken to repair child node i, is the time taken to reach child node i from the previous node.

[0074] Furthermore, after the step S4, the following is further included: step S5, at the beginning of each new time slice T, repeat the step S4 to calculate a new optimal emergency repair strategy until all repairs are completed.

[0075] Preferably, the step S5 further includes immediately executing the steps of the step S4 when the traffic state of the highway section changes.

[0076] Since the situation at the disaster relief site will change dynamically, the emergency repair strategy needs to be adjusted dynamically according to the changes on site. Therefore, a time slice T is set, and every other time slice T, the numerical values of the dynamic network topology model G are refreshed, and the optimal emergency repair strategy is solved again.

[0077] The dynamic scheduling method for power disaster relief emergency repair of the present invention includes, through step S1, constructing a dynamic network topology model G of the disaster relief scenario; step S2, constructing a dynamic power restoration model GP; step S3, constructing a dynamic emergency repair decision-making model: through the objective function: , according to the dynamic network topology model G and the dynamic power restoration model GP, construct a dynamic emergency repair decision-making model; step S4, optimal emergency repair strategy calculation: according to the latest dynamic network topology model G and the dynamic power restoration model GP, and the position of the power facility node where the rescue team is currently located, solve based on the Monte Carlo tree search algorithm to obtain the optimal emergency repair strategy. The method in the present invention takes into account the dynamic changes in the situation at the disaster relief site, and the emergency repair strategy can be adjusted dynamically according to the changes on site. Furthermore, through the Monte Carlo tree search algorithm to solve the optimal solution of the multi-objective function, the determination of the optimal emergency repair strategy is realized; at the same time, the UCB calculation formula of the child node weight is improved to avoid the problem that the classical Monte Carlo tree search algorithm is prone to local optimality.

[0078] The present invention also provides a dynamic scheduling system for power disaster relief emergency repair. The system runs on the basis of a computer system, and specifically includes a memory 61, a processor 62, and a computer program 63 stored in the memory 61 and operable on the processor 62. When the processor 62 executes the computer program 63, the steps of the dynamic scheduling method for power disaster relief emergency repair as described in any one of the above are implemented.

[0079] Exemplarily, the computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the computer.

[0080] The so-called processor may be a central processing unit (CPU), or may also 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 the processor may also be any conventional processor, etc.

[0081] The memory may be an internal storage unit, such as a hard disk or memory; the memory may also be an external storage device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory may also include both an internal storage unit and an external storage device. The memory is used to store the computer program and other programs and data required by the terminal device. The memory may also be used to temporarily store data that has been output or is to be output.

[0082] The present invention also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the power disaster relief and emergency repair dynamic scheduling method described in any one of the above are implemented.

[0083] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0084] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0085] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0086] In the embodiments provided by the present invention, it should be understood that the disclosed device / system and method can be implemented in other ways. For example, the device / terminal device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.

[0087] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0088] In addition, in each embodiment of the present invention, the functional units may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0089] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above embodiment methods of the present invention, it may also be completed by a computer program instructing the relevant hardware. The computer program may be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0090] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to cover all changes falling within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0091] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A dynamic scheduling method for power disaster relief and emergency repair, characterized in that It includes the following steps: Step S1, construct a dynamic network topology model G for the disaster relief scenario: construct the dynamic network topology model through an undirected graph G = {P, R}, where P is the set of all power facility nodes, , is the set of power facility nodes with a normal current state, is the set of power facility nodes with a damaged current state, and R is the set of all highway sections, , is the set of passable highway sections with a normal current state, is the set of damaged highway sections; among them, the weight value of P is the time required to repair the power facility node, the weight value of is the time required to pass through this section of the highway section; Step S2, construct the dynamic power restoration model GP: The dynamic power restoration model GP is a set of power lines of the entire regional power grid, , is a set of normal power lines, is a set of damaged power lines; Step S3, construct a dynamic emergency repair decision-making model: Through the objective function: , construct a dynamic emergency repair decision-making model according to the dynamic network topology model G and the dynamic power restoration model GP; where represents the minimum time required to repair the entire regional power grid at the current moment, is the time required to repair node i, is the time spent from the previous node to node i, represents the next time slice is the maximum power restored by the entire regional power grid when arriving at the next time slice, is the j-th power line of the weight; Step S4, optimal emergency repair strategy calculation: According to the latest dynamic network topology model G and the dynamic power restoration model GP, and the location of the power facility node where the rescue team is currently located, the objective function is solved based on the Monte Carlo tree search algorithm to obtain the optimal emergency repair strategy.

2. The dynamic scheduling method for power disaster relief emergency repair according to claim 1, characterized in that, The said value is the number of all power facility nodes of the j-th power line .

3. The dynamic scheduling method for power disaster relief and emergency repair according to claim 1, wherein After the step S4, it further includes: Step S5, at the beginning of each new time slice T, repeat the step S4 to calculate a new optimal emergency repair strategy until all repairs are completed.

4. The dynamic dispatching method for power disaster relief and emergency repair according to claim 1, characterized in that The step S4 specifically includes: Step S41, taking the power facility node where the rescue team is currently located as the root node, and according to the dynamic network topology model G, all power facility nodes that can be directly reached from this root node are listed as optional child nodes; Step S42, calculate the weights of each of the child nodes according to the UCB formula , select the weight with the maximum value as the optimal child node; where UCB represents the Upper Confidence Bound algorithm; Step S43, determine whether there are unrepaired power facility nodes. If so, enter step S44; if not, enter step S45; Step S44 includes: Step S441, expand the optimal child node selected in step S42 as the parent node, and list the power facility nodes that can be reached from this parent node as optional child nodes; Step S442, randomly select an optional child node as the parent node for expansion, list the power facility nodes that can be reached from this parent node as optional child nodes, and return to step S43; Step S45, update the parameters of each optional child node i through the backtracking update formula: ， ， ， Where M represents the number of backtracking times, and represent the new value and the old value of the number of backtracking times respectively; Q(i) represents the weight function of the repair ability of child node i, and represent the new value and the old value of the weight function of the repair ability of child node i respectively; represents the weight function of the repair time of child node i, and represent the new value and the old value of the weight function of the repair time of child node i respectively; represents the change value of Q(i) caused by the expansion of the child node, represents the change value of T(i) caused by the expansion of the child node; Step S46, repeat and iterate steps S41 - S45 until after the preset total number of learning steps, output the final optimal solution.

5. The dynamic dispatching method for power disaster relief and emergency repair according to claim 4, characterized in that The UCB formula is as follows: , where i_father represents the parent node of child node i, N represents the number of backtracks of child node i, and are adjustment factors and take empirical value constants.

6. The dynamic dispatching method for power disaster relief and emergency repair according to claim 5, characterized in that The calculation formula of the Q(i) is: ， Among them, is the preset weight factor of the line corresponding to child node i, is the number of all power facility nodes in the line corresponding to child node i, is the number of unrepaired nodes in the line corresponding to child node i, is the level number of child node i in the tree.

7. The dynamic dispatching method for power disaster relief and emergency repair according to claim 6, characterized in that The calculation formula of the T(i) is: Among them, is the total time spent before reaching child node i, is the time taken to repair child node i, is the time taken to reach child node i from the previous node.

8. The dynamic scheduling method for power disaster relief and emergency repair according to claim 3, wherein, The step S5 further includes that when the traffic status of the road section changes, immediately execute the steps of the step S4.

9. A dynamic dispatching system for power disaster relief and emergency repair, characterized in that It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the computer program, it implements the steps of the power disaster relief emergency repair dynamic scheduling method according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the power disaster relief emergency repair dynamic scheduling method according to any one of claims 1 to 8.

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