Three-network emergency resource optimization method and system integrated with unmanned aerial vehicle communication relay

Abstract

The invention discloses a three-network emergency resource optimization method and system fused with an unmanned aerial vehicle communication relay, and belongs to the technical field of power system planning and disaster defense, and the method comprises the steps: building a communication power supply coupling model based on an unmanned aerial vehicle relay drive, and obtaining a communication recovery basic framework; constructing an outer layer optimization model taking the pre-disaster multi-type emergency resource layout scheme as a decision variable; an improved bald eagle optimization algorithm is adopted, constraint conditions of a communication recovery basic framework are embedded, an outer layer optimization model is iteratively solved, and a pre-disaster resource deployment scheme which is most robust in all scenes is determined; constructing an inner-layer model by adopting mixed integer second-order cone programming, performing refined solution on post-disaster emergency response, and optimizing a pre-disaster resource deployment scheme; and constructing a double-layer robust optimization model to realize three-network emergency resource optimization. According to the method, the cooperative scheduling capability of multiple types of resources in pre-disaster, in-disaster and post-disaster stages can be improved, the guarantee capability of key loads is enhanced, and the method has global search capability and engineering adaptability.

Description

Technical Field

[0001] This invention belongs to the field of power system planning and disaster prevention technology, specifically relating to a method and system for optimizing emergency resources of three networks by integrating UAV communication relay. Background Technology

[0002] With the increasing frequency of extreme natural disasters, sudden events such as floods, earthquakes, and landslides cause significant multi-network coupled damage to power distribution networks, power communication networks, and transportation networks. The problems caused by disasters, such as node inundation, line damage, road blockages, and communication base station downtime, exhibit strong synchronicity and correlation, making it difficult to maintain traditional post-disaster repair sequences and scheduling logic. Especially in situations where "communication is lost, roads are inaccessible, and power is interrupted" occur simultaneously, the load restoration range of the power distribution network continuously expands, and the power supply capacity of critical loads significantly decreases, placing higher demands on the system's resilience. Therefore, power distribution systems not only need to possess rapid post-disaster repair capabilities but also need to leverage scientific and collaborative resource pre-deployment strategies during the pre-disaster phase to enhance the system's survivability and recovery efficiency under disaster impacts.

[0003] In existing technologies, the planning and operation of distribution networks in disaster scenarios are mostly based on the single-network assumption, lacking system modeling of the cascading failure mechanisms between distribution networks, transportation networks, and communication networks; at the same time, the impact of communication link interruptions on processes such as emergency repair scheduling, network reconfiguration, and source-load control is generally ignored. Existing research has focused on post-disaster emergency repair team route planning, temporary power supply, drainage operations, or physical equipment reinforcement, while paying less attention to the coordinated deployment, accessibility analysis, and robustness assessment of various types of emergency resources before disasters (including mobile energy storage, distributed power sources, emergency repair teams, drainage equipment, and UAV communication relay platforms).

[0004] On the other hand, traditional intelligent optimization methods, such as particle swarm optimization and genetic algorithms, often suffer from drawbacks such as slow convergence speed, weak global search ability, and easy getting trapped in local optima when facing the high-dimensional, strongly constrained, strongly coupled, and multi-scenario decision space unique to disaster pre-disaster deployment problems. They are difficult to cope with complex optimization tasks with a large combination of disaster scenarios, diverse resource types, and constraint chains spanning multiple networks.

[0005] More importantly, when disasters cause communication network degradation or widespread outages, power distribution networks often cannot rely on traditional ground communication infrastructure for remote equipment control, dispatching commands, and collaborative operations. This limits critical actions such as repair route planning, mobile energy storage deployment, and emergency power supply access. Therefore, in disaster scenarios, unmanned aerial vehicle (UAV) communication relay platforms, as a rapidly deployable, mobile, and low-reliability emergency communication method, have become a key component in building the power dispatch chain during and after disasters. Restoring communication through UAV relays can significantly improve the controllability of power distribution equipment, the accuracy and accessibility of repair team dispatching, and thus enhance the overall efficiency of collaborative operations across the three networks (power distribution, telecommunications, and internet) in disaster situations.

[0006] In summary, pre-disaster planning urgently needs to introduce an integrated optimization method that takes into account the coordination of multiple types of emergency resources, multi-network coupling, multiple disaster scenarios, and multi-stage operational logic. Simultaneously, it should integrate the enhancement effect of UAV communication relay on the post-disaster emergency response chain, relying on more robust intelligent optimization algorithms and more refined three-network physical constraint models to achieve coordinated resource optimization and recovery scheduling across the pre-disaster, during-disaster, and post-disaster stages. However, currently, existing multi-type resource deployment and optimization algorithms are prone to getting trapped in local optima, have insufficient search capabilities and convergence performance, struggle to account for the uncertainties of multi-network coupling under flood disasters, and lack the ability to coordinate optimization throughout the entire pre-disaster, during-disaster, and post-disaster process. This results in insufficient survivability of the power system under flood impact and low recovery efficiency of critical loads. Summary of the Invention

[0007] This invention provides a method and system for optimizing emergency resources across three networks by integrating UAV communication relay. The aim is to address the problems in current resource deployment and optimization, such as the optimization algorithm being prone to getting trapped in local optima, insufficient search and convergence performance, difficulty in taking into account the uncertainties of multi-network coupling under flood disasters, and lack of collaborative optimization capabilities throughout the entire process before, during, and after disasters. These issues result in insufficient survivability of the power system under flood impacts and low efficiency of critical load recovery.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: This invention provides a method for optimizing emergency resources across three networks by integrating UAV communication relay, comprising the following steps: S1. Establish a communication power supply coupling model based on UAV relay drive to obtain the basic framework for communication recovery; S2. Based on the communication recovery framework and the set of uncertain fault scenarios caused by sudden disasters, construct an outer optimization model with pre-disaster multi-type emergency resource deployment schemes as decision variables. S3. Adopt the improved Bald Eagle optimization algorithm, embed the constraints of the communication recovery basic framework, iteratively solve the outer layer optimization model, and determine the most robust pre-disaster resource deployment scheme under all scenarios. S4. Based on the pre-disaster resource deployment plan, the basic framework for communication recovery, and the given disaster scenario, a mixed-integer second-order cone programming model is used to construct the inner layer model, which is used to solve the post-disaster emergency response in a refined manner to obtain the weighted load recovery level under different scenarios. The weighted load recovery level drives the iterative search of S3 in reverse to optimize the pre-disaster resource deployment plan. S5. By combining the pre-disaster deployment plan output by the outer optimization model with the scheduling results during and after the disaster output by the inner model, a two-layer robust optimization model is constructed to realize the full-cycle resource optimization of pre-disaster optimization and pre-deployment of multiple types of emergency resources in three networks and post-disaster coordinated mobilization.

[0009] In some implementations, in S1, the basic framework for communication recovery includes temporal reachability, link availability, information gating, and constraints related to UAV operation; The communication power supply coupling model is constructed based on the coverage matrix of topology hop count. Specifically, it includes: defining time-domain reachability, link availability and information gating, and coupling with UAV power and charging anchor point constraints to form a UAV relay communication framework. The UAV relay communication framework can construct air-to-ground multi-hop links, with connectivity reachability as a prerequisite constraint for power operation.

[0010] In some implementations, in S2, the set of uncertain fault scenarios is obtained by Monte Carlo simulation. Monte Carlo simulation is used to generate a combination of multiple fault scenarios covering node soaking, line damage, road blockage, and communication base station offline, providing scenario input for the outer optimization model.

[0011] In some implementations, in S3, the improved Bald Eagle optimization algorithm generates random coding keys and decodes them into multi-point location, capacity / organization deployment schemes for multiple types of emergency resources in candidate warehouses and forward outposts.

[0012] Furthermore, in S3, the improved solution process of the Bald Eagle optimization algorithm includes a random initialization phase, the addition of emergency team deployment constraints, the inclusion of UAV theory and related constraints, the introduction of the impact of rainstorms and floods on transportation and communication networks, the invocation of the inner MISOCP solution for each disaster scenario, and an evolutionary phase; among which: The random initialization phase includes: generating an initial coded representation solution space within the interval, while adding emergency team deployment constraints; The emergency team dispatch constraints describe the starting location, path continuity, access uniqueness, task completion requirements, and fault point handling sequence of emergency teams when performing emergency repair tasks after a disaster. A UAV relay-driven communication and power supply coupling model is established, incorporating UAV theory and related constraints, including mobility and temporal reachability, reachability index, coverage overlay and link availability, defining the coverage matrix, connectivity coverage and information gating, and UAV operation and charging anchor points; The impact of torrential rain and flooding on transportation and communication networks was introduced, as well as the impact of water depth on the speed of emergency rescue vehicles. For each disaster scenario, the inner MISOCP is invoked to solve the problem and obtain the response value of the solution under the scenario. The evaluation results of different scenarios are aggregated to form the robustness fitness of the candidate solution. Evolutionary stage: The selection, search, and perturbation strategies of the improved vulture optimization algorithm are implemented. The distribution of solutions is continuously improved through migration, pounce, and drift search mechanisms, so that it converges towards the robust optimal deployment. Through continuous iteration, the outer layer finally obtains the most robust pre-disaster resource deployment scheme in all scenarios.

[0013] In some implementations, in S4, an inner layer constructs a MISOCP model with the goal of maximizing weighted load recovery, which is used to simulate the emergency repair and power restoration process during and after a disaster.

[0014] In some implementations, the decision variables of the inner model in S2 include: the location, start-up and shutdown scheduling, and temporary power supply sequence of MES and DG; the dispatch path, arrival time, and start and end time of operation of maintenance teams and drainage teams; the voltage status, power flow balance, branch capacity, and line commissioning of the distribution network; the cross-resource coordination logic of drainage, maintenance, temporary power supply, and network return; the single-point mutual exclusion operation relationship of DG / MES at the same node; and the connectivity gating of UAV relay communication.

[0015] In some implementations, the inner model constraint system in S4 includes: flow conservation, unique access constraint, and prohibition of self-loop constraint in team scheduling; arrival, start, and completion relationships in job timing; balance between drainage volume and job duration; power flow conservation and second-order cone capacity limit in the distribution network. The inner model ultimately returns the weighted load recovery level under the scenario, which is used for the fitness evaluation of the outer layer.

[0016] In some implementations, the objective function of the two-layer robust optimization model in S5 is as follows: ; in, For pre-disaster resource deployment plans, This represents the weighted load recovery level.

[0017] This invention also provides a three-network emergency resource optimization system integrating UAV communication relay, including a communication recovery module, an outer optimization module, a model solving module, an inner optimization module, and a two-layer robust optimization module, wherein: Communication recovery module: used to establish a communication power supply coupling model based on UAV relay drive, and obtain the basic framework for communication recovery; Outer Optimization Module: Based on the communication recovery framework and a set of uncertain fault scenarios caused by sudden disasters, this module constructs an outer optimization model with pre-disaster multi-type emergency resource deployment schemes as decision variables. Model Solving Module: Used to iteratively solve the outer-layer optimization model by employing the improved Bald Eagle optimization algorithm and embedding the constraints of the communication recovery framework, in order to determine the most robust pre-disaster resource deployment scheme under all scenarios; Inner optimization module: Based on the pre-disaster resource deployment plan, communication recovery framework and given disaster scenario, it uses mixed integer second-order cone programming to build an inner model, refines the solution for post-disaster emergency response, obtains the weighted load recovery level under different scenarios, and the weighted load recovery level drives the iterative search to optimize the pre-disaster resource deployment plan; The dual-layer robust optimization module is used to integrate the pre-disaster deployment plan output by the outer-layer optimization model with the scheduling results during and after the disaster output by the inner-layer model to construct a dual-layer robust optimization model, thereby realizing full-cycle resource optimization for pre-disaster optimization and pre-deployment of multiple types of emergency resources across three networks and post-disaster coordinated mobilization.

[0018] Compared with existing technologies, the present invention provides a three-network emergency resource optimization method and system that integrates UAV communication relay, which has the following advantages: This invention presents a three-network emergency resource optimization method integrating UAV communication relay. Addressing the coupled impact of "power outages – network outages – road outages" caused by floods, it constructs a resilience enhancement framework that coordinates the entire lifecycle of pre-disaster planning, in-disaster response, and post-disaster recovery. In the pre-disaster phase, this framework considers multiple uncertain inputs such as flood-induced loads, node flooding failures, road flooding, and communication interruptions. Through multi-point site selection and capacity configuration of various types of emergency resources (MES, DG, maintenance teams, drainage teams, UAV communication platforms, etc.), it achieves scientific, accessibility-constrained, and robust deployment of resources before a disaster, significantly improving survivability and early support capabilities for critical loads after a disaster.

[0019] This invention employs the Improved Eagle Search Algorithm (IBES) as its outer optimization engine, which boasts stronger global search capabilities, scenario robustness, and convergence stability compared to traditional intelligent algorithms. IBES's migration search, pounce search, and drift perturbation mechanisms effectively avoid local convergence problems in high-dimensional pre-disaster site selection spaces. In complex optimization contexts involving multiple disaster scenarios, resource types, and coupled constraints, it can quickly obtain engineering-feasible pre-deployment solutions. Furthermore, this invention combines IBES with a scenario-driven robust fitness aggregation mechanism, ensuring that the resulting solution maintains a high load recovery level even under the most unfavorable flood scenarios.

[0020] This invention tightly couples the hybrid integer second-order cone programming (MISOCP) in the post-disaster phase with pre-disaster deployment, constructing a coordinated scheduling model for electricity, roads, and communications throughout the entire process of "drainage, maintenance, temporary power supply, and grid return." This model simultaneously characterizes physical constraints such as drainage team operation time balance, reachable paths for maintenance teams, start-up and shutdown timing and energy constraints of DG / MES, connectivity gating for UAV relay communication, power flow and second-order cone capacity constraints in the distribution network, and voltage and current safety during islanded operation. This allows the post-disaster temporary power supply strategy and the pre-disaster deployment strategy to form a closed-loop optimization within the same decision-making system, thereby achieving the verifiability of pre-disaster configuration and the executability of post-disaster scheduling.

[0021] This invention, by employing a two-layer robust optimization and a three-network coupled scenario-driven collaborative approach, significantly improves the resilience of the distribution network to flood disasters compared to traditional strategies that rely solely on static reinforcement or linear programming, and has certain practical significance. Attached Figure Description

[0022] The accompanying drawings are provided to further understand the invention and constitute a part of this invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0023] Figure 1 This is a flowchart illustrating a three-network emergency resource optimization method integrating UAV communication relay according to the present invention. Figure 2 This is a schematic diagram of the improved Bald Eagle optimization algorithm in the three-network emergency resource optimization method integrating UAV communication relay of the present invention; Figure 3 This is a schematic diagram illustrating the deployment of faulty nodes and various distributed resources after an actual disaster occurs in the three-network emergency resource optimization method integrating UAV communication relay of the present invention. Figure 4 This is a schematic diagram of the post-disaster active power recovery process that takes into account node load weight in the three-network emergency resource optimization method integrating UAV communication relay of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0025] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0026] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0027] It should be noted that the apparatus and methods disclosed in the embodiments herein can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments herein. In this regard, each block in a flowchart or block diagram may represent a module, program, or part of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system to perform the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0028] In addition, the functional modules in the various embodiments of this article can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0029] like Figure 1 As shown, this invention provides a method for optimizing emergency resources across three networks by integrating UAV communication relay, comprising the following steps: S1. Establish a communication power supply coupling model based on UAV relay drive to obtain the basic framework for communication recovery; S2. Based on the communication recovery framework and the set of uncertain fault scenarios caused by sudden disasters, construct an outer optimization model with pre-disaster multi-type emergency resource deployment schemes as decision variables. S3. Adopt the improved Bald Eagle optimization algorithm, embed the constraints of the communication recovery basic framework, iteratively solve the outer layer optimization model, and determine the most robust pre-disaster resource deployment scheme under all scenarios. S4. Based on the pre-disaster resource deployment plan, the basic framework for communication recovery, and the given disaster scenario, a mixed-integer second-order cone programming model is used to construct the inner layer model, which is used to solve the post-disaster emergency response in a refined manner to obtain the weighted load recovery level under different scenarios. The weighted load recovery level drives the iterative search of S3 in reverse to optimize the pre-disaster resource deployment plan. S5. By combining the pre-disaster deployment plan output by the outer optimization model with the scheduling results during and after the disaster output by the inner model, a two-layer robust optimization model is constructed to realize the full-cycle resource optimization of pre-disaster optimization and pre-deployment of multiple types of emergency resources in three networks and post-disaster coordinated mobilization.

[0030] This invention aims to improve the survivability, emergency resilience, and rapid post-disaster recovery capabilities of power distribution networks before floods. First, it constructs a multi-scenario uncertainty model based on the flood-causing mechanism, comprehensively considering the nonlinear impact of node submersion depth on equipment failure probability, power flow inaccessibility caused by branch flooding, constraints on emergency resource accessibility due to road flooding, and limitations on scheduling feasibility due to communication link interruptions, forming a set of scenarios covering the most ideal to the most unfavorable disaster conditions. Based on this, the invention constructs a two-layer robust optimization framework integrating "pre-disaster resource allocation and post-disaster emergency scheduling": the outer layer uses the deployment location and capacity configuration of various types of distributed power sources (including mobile energy storage MES, diesel generator DG, vehicle-mounted power supplies, etc.) as decision variables, employing an improved Bald Eagle Search algorithm (IBES) to achieve robust deployment optimization across scenarios; the inner layer, based on disaster scenarios and the deployment results of the outer layer, constructs an emergency scheduling model that integrates traffic network flooding constraints, repair team arrival timing, temporary power supply start-up and shutdown logic, and mixed integer second-order cone power flow constraints of the power distribution network, to optimize the reachability paths of distributed power sources and the post-disaster load recovery process. Ultimately, through iterative two-layer structure, a pre-disaster pre-allocation plan with optimal robustness and feasibility across multiple disaster scenarios is obtained.

[0031] This invention can significantly improve the power supply continuity and critical load guarantee capability of distribution networks after floods, achieving high efficiency in resource deployment, accessibility of path scheduling, and safety in operation recovery. Simulation verification shows that this invention has excellent global search capabilities and engineering adaptability in complex scenarios, and can be widely applied to enhance the disaster resilience and optimize the allocation of emergency resources in various types of urban and rural distribution networks.

[0032] Specifically, the method of the present invention includes the following steps: S1. A communication power supply coupling model is established based on UAV relay drive to quickly restore communication, while considering the failure of ground communication base stations and link interruption caused by floods, so as to ensure the schedulability of emergency resources in the post-disaster stage.

[0033] S2. Based on the set of uncertain fault scenarios caused by sudden disasters, an outer optimization model is constructed with pre-disaster multi-type emergency resource deployment schemes as decision variables, and the constraints of road water accumulation, node flooding and branch failure on resource accessibility and power supply capacity are comprehensively considered.

[0034] S3. The improved Bald Eagle Search (IBES) algorithm is used to iteratively solve the outer-layer optimization model to determine the most robust pre-disaster resource deployment scheme under all scenarios, thereby improving the robustness of the scheme under the most unfavorable scenario.

[0035] S4. Based on the pre-disaster resource deployment plan and the given disaster scenario, the inner model uses mixed integer second-order cone programming (MISOCP) to refine the solution of the post-disaster emergency response, so as to evaluate the weighted load recovery level under different scenarios and simultaneously characterize the coordinated scheduling process of multiple resources such as drainage, maintenance, and temporary supply.

[0036] S5. By integrating the outer layer of pre-disaster deployment and the inner layer of in-disaster and post-disaster scheduling, a two-layer robust optimization model is constructed to achieve full-cycle resource optimization and obtain the optimal pre-disaster pre-allocation scheme that is both feasible and stable in multiple scenarios.

[0037] This invention also provides a three-network emergency resource optimization system integrating UAV communication relay, including a communication recovery module, an outer optimization module, a model solving module, an inner optimization module, and a two-layer robust optimization module, wherein: Communication recovery module: used to establish a communication power supply coupling model based on UAV relay drive, and obtain the basic framework for communication recovery; Outer Optimization Module: Based on the communication recovery framework and a set of uncertain fault scenarios caused by sudden disasters, this module constructs an outer optimization model with pre-disaster multi-type emergency resource deployment schemes as decision variables. Model Solving Module: Used to iteratively solve the outer-layer optimization model by employing the improved Bald Eagle optimization algorithm and embedding the constraints of the communication recovery framework, in order to determine the most robust pre-disaster resource deployment scheme under all scenarios; Inner optimization module: Based on the pre-disaster resource deployment plan, communication recovery framework and given disaster scenario, it uses mixed integer second-order cone programming to build an inner model, refines the solution for post-disaster emergency response, obtains the weighted load recovery level under different scenarios, and the weighted load recovery level drives the iterative search to optimize the pre-disaster resource deployment plan; The dual-layer robust optimization module is used to integrate the pre-disaster deployment plan output by the outer-layer optimization model with the scheduling results during and after the disaster output by the inner-layer model to construct a dual-layer robust optimization model, thereby realizing full-cycle resource optimization for pre-disaster optimization and pre-deployment of multiple types of emergency resources across three networks and post-disaster coordinated mobilization.

[0038] The following detailed description of the present invention, a method and system for optimizing emergency resources across three networks by integrating unmanned aerial vehicle (UAV) communication relay, is provided through specific embodiments.

[0039] like Figures 2-4 As shown, Figure 3 In this context, Power Flow represents the physical power flow network of the distribution network, Information Flow represents the communication network or information exchange network, Traffic Flow represents the movement path of emergency resources in the transportation network, Fault Node represents a fault node, Relay Drone represents a communication relay drone, DG represents distributed power generation, MES represents a mobile energy storage system, Maintenance team represents a repair team, and Drainage team represents a drainage team. Figure 4 In this context, Recovered active power (HW) represents the active power recovered, and Recovery ratio represents the recovery rate.

[0040] Step 1: Construct an outer-layer optimization model with pre-disaster multi-type emergency resource deployment plans as decision variables; For each disaster scenario, the inner MISOCP layer is invoked to solve the problem and obtain the response value of the solution under the scenario. The evaluation results from different scenarios are aggregated to form the robustness fitness of candidate solutions, reflecting the recovery performance of the solutions under the most unfavorable scenarios. ; Constraints on joining emergency response teams: ; ; ; in, The warehouse serves as the starting point for emergency response teams; Deploy 0-1 variables for emergency response teams. =1 indicates emergency response team Deployed on nodes .

[0041] This constraint means that each emergency response team is deployed at one starting warehouse and cannot be deployed at non-warehouse nodes; the same type of emergency response team cannot be deployed at the same warehouse.

[0042] Restrictions on joining emergency response teams: ; ; ; ; ; ; ; in, The final warehouse for emergency response teams; Assign 0-1 variables to emergency response teams. =1 indicates emergency response team From node Move to node ; For the set of faulty nodes, Assign 0-1 variables to emergency response teams. =1 represents emergency response teams Reaching the fault node ; The order of emergency response teams is a 0-1 variable; =1 indicates emergency response team From the fault node To the fault node The processing order.

[0043] This set of constraints systematically describes the starting location, path continuity, access uniqueness, task completion requirements, and fault point handling sequence of emergency teams when performing emergency repair tasks after a disaster, in order to ensure the physical feasibility of team deployment and the logical consistency of path scheduling.

[0044] Incorporate UAV theory and related constraints; Mobility and temporal reachability; Assume the continuous time after the disaster is discretized into steps with size . >0. The drone follows a predetermined route map or road network. G Moving in a three-dimensional space on any side Let the side length be . UAVs take time-varying values ​​based on the wind speed or mission status of the day. The travel time of the UAV along the border is then: ; For any two nodes Record the shortest travel time of the UAV as (Based on edge weights) (Obtained using the shortest path algorithm). To ensure the executability of the schedule, a reachability metric is defined: ; Coverage overlay and link availability; Define the covering matrix: ; in, Let be the shortest hop count in the air-to-ground relay network between the UAV residing at j and the ground node i (including direct connections = 1; for diagonal connections i = j, let ). (To represent local direct access). Let the number of UAVs residing at each node in step t be denoted as . .

[0045] Multiple UAVs residing within the topological neighborhood form the aerial coverage of node i: ; set up For the static availability of ground base stations, there are 0-1 variables. =1 indicates that ground base station i is statically available at time t. Therefore, the local coverage state is defined as follows: ; Link availability is categorized into two types and updated over time. Ground links require both ends to simultaneously meet certain conditions. When =1, it is available; the capacity is denoted as... >0. The air link is available when there is at least one drone stationed at each end of a ground node pair located within the coverage radius, and its capacity is denoted as [capacity]. >0.

[0046] Connectivity Coverage and Information Gating: The connectivity of a communication network is described using the single-commodity flow method: Let... for The node is associated with the matrix. For edge flow, For 0-1 variables, =1 indicates that the link is available; For link capacity, take it as . or .make As a unit supply and demand vector, only when The value at target node i is non-zero and satisfies: =-1, =1; Then node conservation and connectivity cover can be defined as: ; ; Among them, variables Used as a gating variable for information accessibility. During the post-disaster phase, temporary power supply and mains grid restoration, as well as maintenance and drainage operations, are only used for... Nodes with a value of 1 are considered executable. To accelerate the decision-making process, this study maintains an "available edge spanning tree" for each gated node i, performing only local updates when link states change; if some links are potentially available (waiting for UAV arrival), they are treated as zero-capacity edges and wait for... After the update, the capacity is recalculated and the connectivity status is updated.

[0047] Drone operation and charging anchor points: Let the battery level of the u-th drone at time t be . The equivalent power of hovering and parking is The energy balance of the drone is then: ; in ≥0 indicates that the drone replenishes energy at node i. The location where the drone is allowed to charge is limited to nodes such as those deployed with distributed power sources, and the drone can only replenish energy while stationed at that node. The device limits and initial values ​​satisfy: ; ; Simultaneously incorporate MES and DG operation constraints and distribution network constraints: ; ; ; ; in, for Working status 0-1 variable, =1 means exist Time as a node Temporary power supply; for Working status 0-1 variable, =1 means exist Time as a node Temporary power supply; The 0-1 variables are used to complete the tasks for the emergency response team. =1 indicates a faulty node. k At any moment t The water has been drained or the components have been repaired; , These represent the minimum and maximum states of charge of the MES, respectively. The remaining energy of the MES is a continuous variable; This is the upper limit for MES energy storage; Self-discharge rate; For discharge efficiency; , These represent the discharge and charging power of the MES at node i, respectively; ; ; ; ; ; in, , These represent the active and reactive power flows of line l at time t, respectively. , These are the per-unit values ​​of the resistance and reactance of line l, respectively; The line status is a 0-1 variable. =1 indicates that line l is put into operation at time t; The squared value of the voltage at node i; Let i be the square of the current flowing through line l, and let i be the positive direction; Let be the virtual feed flow of the root node at time t; Let t be the energy flow on line l. This represents the total flow required by distribution network nodes based on load weight. The power supply status of the two nodes of line l. =1 indicates a node i It is in the load recovery state.

[0048] The above two types of constraints together ensure that: 1) the scheduling, energy evolution and power output of MES / DG conform to the actual operating laws of energy equipment; 2) the post-disaster distribution network meets electrical constraints such as power flow, voltage and line capacity during temporary power supply and load restoration, thereby ensuring the physical feasibility and operational safety of the entire post-disaster recovery process.

[0049] Step 2: As Figure 2 As shown, Figure 2 Individuals are displayed According to its stage ( Global search or Local search), updated in different directions and with different perturbation methods. The process. The bald eagle symbolizes a search strategy, distance... The direction of movement is determined, and the perturbation factor F determines the exploration range; The improved Eagle Optimization Algorithm (IBES) is used to solve the upper-level optimization model by calling the commercial Gurobi solver. Step 3: Map each component of the distribution network to a node in the transportation network, and establish a water accumulation model for the transportation network, specifically including: The impact of torrential rain and flooding on transportation and communication networks is introduced, primarily focusing on the effect of water depth on the speed of emergency rescue vehicles. If the transportation network is modeled as an undirected graph: ; in, For transportation network model; As a node in the transportation network; Let the road network be the set of roads. The impact of torrential rain and flooding on the speed of emergency resource vehicles within the transportation network can be expressed as: ; For emergency resource vehicles on the road r The actual driving speed on the road For roads r Baseline driving speed under normal conditions, This is the critical threshold parameter for velocity decay. b Parameters used to control the smoothness of speed descent.

[0050] This will affect the shortest path time matrix based on vehicle speed, thereby affecting the scheduling sequence of various resources.

[0051] Step 4: Construct an inner layer MISOCP model with the goal of maximizing weighted load recovery to simulate the emergency repair and power restoration process during and after a disaster.

[0052] Step 5: Construct a two-layer robust optimization model with the objective function as follows: ; in, This indicates the pre-disaster resource deployment plan.

[0053] The bi-level optimization model established in this invention can be regarded as a mixed integer second-order cone programming (MISOCP) model, which can be solved by relaxing the second-order cone constraints and calling Gurobi.

[0054] In summary, this invention presents a method and system for optimizing emergency resources across three networks (networks, power grids, and communications) by integrating UAV (unmanned aerial vehicle) communication relay. Addressing the problem of multi-network collaborative failures caused by floods, it constructs a full-cycle resilience enhancement framework integrating communication recovery, power deployment, and road accessibility. By introducing UAV relays to build a communication-power supply coupling model, it achieves rapid recovery of early-stage post-disaster communication capabilities. Simultaneously, based on the uncertainties caused by floods, it generates multi-scenario fault sets and constructs an outer optimization model centered on pre-disaster deployment of emergency resources. An improved Bald Eagle Optimization Algorithm (IBES) is used to search for robust deployment schemes across different scenarios. The inner model of this invention utilizes Mixed Integer Second-Order Cone Programming (MISOCP) to finely characterize the coordinated scheduling process of drainage, maintenance, temporary power supply, and power distribution network flow, evaluating the weighted load recovery level of different schemes after a disaster. Through the iterative linkage of outer deployment and inner scheduling, integrated optimization is achieved throughout the entire process from pre-disaster to during-disaster to post-disaster, ensuring that distributed power sources, mobile energy storage, maintenance teams, drainage teams, and communication relay resources all possess accessibility, controllability, and high recovery efficiency in multi-disaster scenarios. This invention can significantly improve the critical load guarantee capacity and resource scheduling resilience of the power distribution network under the impact of floods, and provides technical support with engineering feasibility for pre-disaster deployment planning and post-disaster recovery decision-making in actual flood disasters, and has certain application value.

[0055] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Anyone skilled in the art can readily implement the present invention according to the description and above. Any modifications, alterations, or equivalent variations made using the technical content disclosed above are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, or variations made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the present invention.

Claims

1. A method for optimizing emergency resources across three networks by integrating UAV communication relay, characterized in that, Includes the following steps: S1. Establish a communication power supply coupling model based on UAV relay drive to obtain the basic framework for communication recovery; S2. Based on the communication recovery framework and the set of uncertain fault scenarios caused by sudden disasters, construct an outer optimization model with pre-disaster multi-type emergency resource deployment schemes as decision variables. S3. Adopt the improved Bald Eagle optimization algorithm, embed the constraints of the communication recovery basic framework, iteratively solve the outer layer optimization model, and determine the most robust pre-disaster resource deployment scheme under all scenarios. S4. Based on the pre-disaster resource deployment plan, the basic framework for communication recovery, and the given disaster scenario, a mixed-integer second-order cone programming model is used to construct the inner layer model, which is used to solve the post-disaster emergency response in a refined manner to obtain the weighted load recovery level under different scenarios. The weighted load recovery level drives the iterative search of S3 in reverse to optimize the pre-disaster resource deployment plan. S5. By combining the pre-disaster deployment plan output by the outer optimization model with the scheduling results during and after the disaster output by the inner model, a two-layer robust optimization model is constructed to realize the full-cycle resource optimization of pre-disaster optimization and pre-deployment of multiple types of emergency resources in three networks and post-disaster coordinated mobilization.

2. The method for optimizing emergency resources across three networks by integrating UAV communication relay as described in claim 1, characterized in that, In S1, the basic framework for communication recovery includes temporal reachability, link availability, information gating, and constraints related to UAV operation. The communication power supply coupling model is constructed based on the coverage matrix of topology hop count. Specifically, it includes: defining time-domain reachability, link availability and information gating, and coupling with UAV power and charging anchor point constraints to form a UAV relay communication framework. The UAV relay communication framework can construct air-to-ground multi-hop links, with connectivity reachability as a prerequisite constraint for power operation.

3. The method for optimizing emergency resources across three networks using integrated UAV communication relay as described in claim 1, characterized in that, In S2, the set of uncertain fault scenarios is obtained through Monte Carlo simulation. Monte Carlo simulation is used to generate a combination of multiple fault scenarios covering node soaking, line damage, road blockage, and communication base station offline, providing scenario input for the outer optimization model.

4. The method for optimizing emergency resources across three networks using integrated UAV communication relay as described in claim 1, characterized in that, In S3, the improved Bald Eagle optimization algorithm generates random coding keys and decodes them into multi-site location, capacity / organization deployment schemes for multiple types of emergency resources in candidate warehouses and forward outposts.

5. The method for optimizing emergency resources across three networks using integrated UAV communication relay as described in claim 4, characterized in that, In S3, the solution process of the improved Bald Eagle optimization algorithm includes a random initialization stage, adding emergency team deployment constraints, adding UAV theory and related constraints, introducing the impact of rainstorms and floods on transportation and communication networks, calling the inner MISOCP solution for each disaster scenario, and an evolutionary stage; wherein: The random initialization phase includes: generating an initial coded representation solution space within the interval, while adding emergency team deployment constraints; The emergency team dispatch constraints describe the starting location, path continuity, access uniqueness, task completion requirements, and fault point handling sequence of emergency teams when performing emergency repair tasks after a disaster. A UAV relay-driven communication and power supply coupling model is established, incorporating UAV theory and related constraints, including mobility and temporal reachability, reachability index, coverage overlay and link availability, defining the coverage matrix, connectivity coverage and information gating, and UAV operation and charging anchor points; The impact of torrential rain and flooding on transportation and communication networks was introduced, as well as the impact of water depth on the speed of emergency rescue vehicles. For each disaster scenario, the inner MISOCP is invoked to solve the problem and obtain the response value of the solution under the scenario. The evaluation results of different scenarios are aggregated to form the robustness fitness of the candidate solution. Evolutionary stage: The selection, search, and perturbation strategies of the improved vulture optimization algorithm are implemented. The distribution of solutions is continuously improved through migration, pounce, and drift search mechanisms, so that it converges towards the robust optimal deployment. Through continuous iteration, the outer layer finally obtains the most robust pre-disaster resource deployment scheme in all scenarios.

6. The method for optimizing emergency resources across three networks using integrated UAV communication relay as described in claim 1, characterized in that, In S4, the inner layer constructs a MISOCP model with the goal of maximizing weighted load recovery, which is used to simulate the emergency repair and power restoration process during and after a disaster.

7. The method for optimizing emergency resources across three networks using integrated UAV communication relay as described in claim 1, characterized in that, In S2, the decision variables of the inner model include: the location, start-up and shutdown scheduling, and temporary power supply sequence of MES and DG; the dispatch path, arrival time, and start and end time of operation of maintenance team and drainage team; the voltage status, power flow balance, branch capacity and line commissioning of distribution network; the cross-resource coordination logic of drainage, maintenance, temporary power supply and network return; the single-point mutual exclusion operation relationship of DG / MES at the same node; and the connectivity gating of UAV relay communication.

8. The method for optimizing emergency resources across three networks using integrated UAV communication relay as described in claim 1, characterized in that, In S4, the inner model constraint system includes: flow conservation, unique access constraint, and prohibition of self-loop constraint in team scheduling; arrival, start, and completion relationship in operation sequence; balance between drainage volume and operation duration; power flow conservation and second-order cone capacity limit in distribution network; The inner model ultimately returns the weighted load recovery level under the scenario, which is used for the fitness evaluation of the outer layer.

9. The method for optimizing emergency resources across three networks using integrated UAV communication relay as described in claim 1, characterized in that, In S5, the objective function of the two-layer robust optimization model is as follows: ； in, For pre-disaster resource deployment plans, This represents the weighted load recovery level.

10. A three-network emergency resource optimization system integrating UAV communication relay, used to implement the three-network emergency resource optimization method integrating UAV communication relay as described in any one of claims 1-9, characterized in that, It includes a communication recovery module, an outer optimization module, a model solving module, an inner optimization module, and a two-layer robust optimization module, among which: Communication recovery module: used to establish a communication power supply coupling model based on UAV relay drive, and obtain the basic framework for communication recovery; Outer Optimization Module: Based on the communication recovery framework and a set of uncertain fault scenarios caused by sudden disasters, this module constructs an outer optimization model with pre-disaster multi-type emergency resource deployment schemes as decision variables. Model Solving Module: Used to iteratively solve the outer-layer optimization model by employing the improved Bald Eagle optimization algorithm and embedding the constraints of the communication recovery framework, in order to determine the most robust pre-disaster resource deployment scheme under all scenarios; Inner optimization module: Based on the pre-disaster resource deployment plan, communication recovery framework and given disaster scenario, it uses mixed integer second-order cone programming to build an inner model, refines the solution for post-disaster emergency response, obtains the weighted load recovery level under different scenarios, and the weighted load recovery level drives the iterative search to optimize the pre-disaster resource deployment plan; The dual-layer robust optimization module is used to integrate the pre-disaster deployment plan output by the outer-layer optimization model with the scheduling results during and after the disaster output by the inner-layer model to construct a dual-layer robust optimization model, thereby realizing full-cycle resource optimization for pre-disaster optimization and pre-deployment of multiple types of emergency resources across three networks and post-disaster coordinated mobilization.

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