Emergency resource scheduling strategy generation method considering material demand urgency degree

Abstract

The invention relates to an emergency resource scheduling strategy generation method considering material demand urgency, and the method comprises the steps: representing all equipment on a target system through a GIS technology, obtaining heat supply network information, carrying out the coupling of the heat supply network information and traffic network information corresponding to the target system, and obtaining a geographic information model of a heat supply facility; based on a K-means clustering algorithm, determining an emergency resource scheduling task of the material distribution center according to the position information of the fault point on the geographic information model, the position information of the material distribution center and the load data of the first-aid repair vehicle in the material distribution center; calculating an optimal path of each emergency resource scheduling task by using a grey wolf algorithm to obtain an initial scheduling strategy; according to the method, the demand urgency degree of the fault point is obtained, the initial scheduling strategy is updated according to the demand urgency degree, the emergency resource scheduling strategy is obtained, efficient and accurate emergency resource scheduling can be achieved, and the scheduling efficiency and reliability of the system are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of emergency resource transportation technology, and in particular to a method for generating emergency resource allocation strategies that takes into account the urgency of resource demand. Background Technology In heating / gas supply systems, failures often lead to service interruptions, and in severe cases, can even disrupt users' normal lives and production. This is especially true for urban or large-scale heating / gas supply networks, where multiple failure points occur simultaneously, making emergency repairs more complex and urgent. Traditional emergency repair strategies typically rely on fixed priority rankings and manual dispatching, methods that have significant limitations when dealing with multi-failure scenarios. Taking heating systems as an example, with the continuous expansion of heating system scale and the increasing complexity of the operating environment, the number of failure points has increased dramatically, the demand for materials has become more unpredictable, and the dispatching of repair vehicles and materials faces significant challenges. Specifically, existing technologies suffer from the following main problems: Uncertainty in material demand: In the event of multiple failures, the demand for materials is often difficult to predict accurately. Traditional static scheduling methods can only estimate material demand based on historical data or hypothetical scenarios, but lack the ability to adjust in real time. This uncertainty may lead to insufficient or excessive supply of materials, thereby affecting the efficiency and effectiveness of emergency repair tasks.

[0002] Differences in fault point priority: Different fault points have varying degrees of impact on the system, therefore, reasonable prioritization and resource allocation are necessary based on their urgency and scope of impact. However, traditional prioritization methods are often based on fixed standards and fail to fully consider real-time changing factors, such as the specific extent of damage caused by the fault point, the number of affected users, and changes in weather conditions. This makes resource allocation potentially inflexible and unscientific.

[0003] The variability of road conditions: During emergency repairs, road conditions may change, such as traffic congestion, road damage, or inclement weather. These factors directly affect the efficiency of material transportation and the completion time of repair tasks. Traditional dispatching methods fail to effectively incorporate dynamic road and traffic factors, potentially leading to suboptimal routes for repair vehicles or even delays.

[0004] The static nature of resource scheduling: Current fault scheduling methods often rely on static models and scheduling schemes, lacking dynamic response mechanisms. Especially in complex and changing environments, traditional methods may not be able to quickly adapt to changes in fault conditions, leading to unreasonable resource allocation and low repair efficiency.

[0005] Existing fault handling methods often struggle to provide flexible and accurate resource scheduling solutions when faced with multiple faults and dynamic changes. Summary of the Invention

[0006] In response to the above-mentioned problems and technical requirements, this invention provides an emergency resource scheduling strategy method that takes into account the urgency of material needs, aiming to solve the technical problem that it is difficult to provide flexible and accurate resource scheduling strategies in the prior art.

[0007] This application provides an emergency resource scheduling strategy method that considers the urgency of material needs. This method establishes a precise geographic information model of heating facilities to achieve spatial information sharing between heating pipe networks and transportation networks. It also combines K-means clustering algorithm to rationally divide material distribution tasks to optimize resource allocation. Simultaneously, it uses the Grey Wolf algorithm for intelligent planning of repair routes to improve transportation efficiency. Furthermore, it dynamically determines the weights of various indicators using the analytic hierarchy process (AHP) and entropy weight method, prioritizing tasks based on the urgency of fault points to ensure that the most urgent faults are addressed first. Compared to existing technologies, this invention comprehensively considers multiple dynamic factors and proposes a more flexible, intelligent, and efficient scheduling strategy generation method, capable of optimizing resource scheduling in complex emergency situations and improving the system's emergency response capabilities.

[0008] The technical means employed in this invention are as follows: A method for generating emergency resource allocation strategies that consider the urgency of material demand, comprising: By representing each device on the target system using GIS technology, heating network information is obtained. The heating network information is then coupled with the traffic network information corresponding to the target system to obtain a geographic information model of the heating facilities. Based on the K-means clustering algorithm, and according to the location information of the fault point on the geographic information model, the location information of the material distribution center, and the load data of the repair vehicles in the material distribution center, the emergency resource scheduling task of the material distribution center is determined. The optimal path for each emergency resource scheduling task is calculated using the Grey Wolf algorithm to obtain the initial scheduling strategy; The urgency of the demand at the fault point is obtained, and the initial scheduling strategy is updated according to the urgency of the demand to obtain an emergency resource scheduling strategy.

[0009] Furthermore, the step of representing each device on the target system using GIS technology to obtain heating network information, and coupling the heating network information with the traffic network information corresponding to the target system to obtain a geographic information model of the heating facilities, includes: By using GIS technology to locate each device in the target system, the location information of each device in the target system can be obtained; The SCADA system is used to acquire the operating data of each device on the target system, and the operating status of each device on the target system is determined based on the operating data; Determine the connection relationships between various devices in the target system based on the topology information of the target system; The location information of each device in the target system, the operating status of each device in the target system, and the connection relationship between each device in the target system are represented to obtain the heating network information; By coupling the heating network information with the traffic network information corresponding to the target system, a geographic information model of the heating facility is obtained.

[0010] Furthermore, the step of determining the emergency resource dispatching task of the material distribution center based on the K-means clustering algorithm, according to the location information of the fault point on the geographic information model, the location information of the material distribution center, and the load data of the repair vehicles within the material distribution center, includes: The device that is running abnormally on the target system is taken as the faulty device, and the location corresponding to the faulty device on the transportation network information in the geographic information model is taken as the fault point, so as to obtain the location information of the fault point on the geographic information model. Based on the location information of the fault point and the location information of the material distribution center on the geographic information model, the distance between the fault point and the material distribution center on the geographic information model is represented by at least one of Manhattan distance, Euclidean distance and diagonal distance. Based on the K-means clustering algorithm, the location information of the fault point on the geographic information model, the distance between the fault point on the geographic information model and the material distribution center, and the load data of the repair vehicles in the material distribution center are used to determine the emergency resource scheduling task of the material distribution center.

[0011] Furthermore, the step of using the Grey Wolf algorithm to calculate the optimal path for each emergency resource scheduling task and obtain the initial scheduling strategy includes: Assuming there are n fault points, using the Grey Wolf Algorithm, the objective function is obtained as follows:

[0012] In the formula: For the first The load value of each node; For the first The repair time required for each fault point; For the first The fault point and the first The vehicle travel time required to transfer between the fault points; Under a set of preset constraints, the objective function is solved to obtain the optimal path for each emergency resource scheduling task; The preset constraint set includes at least the following: The first constraint to ensure that emergency supply delivery vehicles must pass through each point of failure exactly once is:

[0013] The second constraint used to ensure that no loops are generated in the travel path:

[0014] The third constraint to ensure that all emergency supply delivery vehicles depart from and return to the distribution center:

[0015] The fourth constraint to ensure that the operating time of emergency supply delivery vehicles does not exceed the maximum working time:

[0016] in, For the site Estimated repair time, The time spent on the journey.

[0017] Furthermore, the step of determining the urgency of the need to identify the fault point includes: To address the factors influencing emergency repair decisions, a multi-level decision structure model is established based on the sub-indicators corresponding to these factors. Based on the multi-level decision structure model, the subjective weight of each sub-indicator is calculated using expert scoring and / or pairwise comparison, and the subjective weight value of each factor is obtained based on the subjective weight of each sub-indicator. The entropy value of each sub-indicator is calculated using the entropy weight method to quantify the uncertainty of each sub-indicator, and the objective weight of each sub-indicator is determined based on the entropy value. The subjective weight value is corrected based on the objective weight to obtain the target weight value for each sub-indicator; Based on the target weight value, the urgency of the fault point is calculated using a weighted summation method.

[0018] Furthermore, the step of updating the initial scheduling strategy according to the urgency of the demand to obtain an emergency resource scheduling strategy includes: The priority levels of each emergency resource scheduling task within the initial scheduling strategy are updated according to the urgency of the demand, and the emergency resource scheduling tasks are arranged in descending order of priority to obtain the emergency resource scheduling strategy.

[0019] Compared with the prior art, the present invention has the following advantages: By establishing a precise geographic information model of heating facilities, spatial information sharing between the heating pipeline network and the transportation network is achieved. Furthermore, the K-means clustering algorithm is used to rationally divide material distribution tasks to optimize resource allocation. Simultaneously, the Grey Wolf algorithm is employed for intelligent planning of emergency repair routes, improving transportation efficiency. The weights of various indicators are dynamically determined using the analytic hierarchy process and entropy weighting method, prioritizing tasks based on the urgency of fault points to ensure that the most urgent faults are addressed first. Compared to existing technologies, this invention comprehensively considers multiple dynamic factors and proposes a more flexible, intelligent, and efficient scheduling strategy generation method, capable of optimizing resource scheduling in complex emergency situations and improving the system's emergency response capabilities. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating an emergency resource allocation strategy generation method that considers the urgency of material demand, provided in an embodiment of this application; Figure 2 A geographic information model map is provided in an emergency resource scheduling strategy generation method that considers the urgency of material demand in an embodiment of this application. Figure 3 A path planning graph based on the Grey Wolf algorithm in an emergency resource scheduling strategy generation method that considers the urgency of material demand provided in an embodiment of this application; Figure 4 A multi-level decision structure model diagram is provided in the emergency resource scheduling strategy generation method that considers the urgency of material demand in the embodiments of this application. Figure 5 This is a schematic diagram of an emergency resource scheduling strategy generation device that considers the urgency of material demand, provided in an embodiment of this application. Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of a storage medium provided in an embodiment of this application. Detailed Implementation

[0022] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] like Figure 1 As shown, this application provides an emergency resource allocation strategy method that considers the urgency of material demand, including: S101. Represent each device on the target system using GIS technology to obtain heating network information, and couple the heating network information with the traffic network information corresponding to the target system to obtain a geographic information model of the heating facilities. In one possible embodiment, the step of representing each device on the target system using GIS technology to obtain heating network information, and coupling the heating network information with the traffic network information corresponding to the target system to obtain a geographic information model of the heating facilities, includes: By using GIS technology to locate each device in the target system, the location information of each device in the target system can be obtained; The SCADA system is used to acquire the operating data of each device on the target system, and the operating status of each device on the target system is determined based on the operating data; Determine the connection relationships between various devices in the target system based on the topology information of the target system; The location information of each device in the target system, the operating status of each device in the target system, and the connection relationship between each device in the target system are represented to obtain the heating network information; By coupling the heating network information with the traffic network information corresponding to the target system, a geographic information model of the heating facility is obtained.

[0024] For example, GIS technology is used to accurately locate heating facilities, including heating pipe networks, fault points, and user-end facilities. A geographic information system comprehensively displays the location, status, and interconnections of each facility (e.g., pipe network connection methods, and the connection between heating stations and the pipe network). Specifically, to ensure accurate location of each facility in geographic space, detailed coordinate information is set for all heating pipe network nodes.

[0025] For example, real-time monitoring of heating facilities is integrated into a GIS platform. Data comes from a SCADA system, collecting operating parameters such as temperature, pressure, and flow rate in real time. Each monitoring point is connected to the data acquisition system via sensors, transmitting real-time data to the GIS platform for centralized management and analysis to determine the operating status of each heating facility. The GIS platform provides a data visualization interface, allowing operators to intuitively view the heating status of each area through a map display, thereby achieving convenient and accurate monitoring and management.

[0026] For example, traffic network information corresponding to the target system is obtained through the open-source mapping platform OpenStreetMap (OSM) and fused with heating network information. Traffic network information includes attributes such as road type, road grade, traffic flow, and traffic signals. By marking, filtering, and analyzing road network data, and spatially overlaying it with heating pipeline facility points and pipeline layout, a geographic information model is formed, such as... Figure 2 As shown.

[0027] Based on the pipeline network layout and actual fault conditions, the system intelligently plans the optimal route to ensure maintenance personnel can quickly and accurately reach the fault point, reducing unnecessary travel and time. Simultaneously, it optimizes the allocation of resources such as maintenance personnel, equipment, and materials according to the needs of the maintenance task, thereby improving maintenance efficiency. A visual interface facilitates operation and querying, and timely notifications are sent to users informing them of maintenance progress and results. Furthermore, the GIS platform can integrate with other relevant systems to achieve information sharing and data interaction, thereby improving the overall operational efficiency and informatization level of the heating system.

[0028] S102. Based on the K-means clustering algorithm, and according to the location information of the fault point on the geographic information model, the location information of the material distribution center, and the load data of the repair vehicles in the material distribution center, determine the emergency resource scheduling task of the material distribution center. In one possible embodiment, the step of determining the emergency resource dispatching task of the material distribution center based on the K-means clustering algorithm, according to the location information of the fault point on the geographic information model, the location information of the material distribution center, and the load data of the repair vehicles within the material distribution center, includes: The device that is running abnormally on the target system is taken as the faulty device, and the location corresponding to the faulty device on the transportation network information in the geographic information model is taken as the fault point, so as to obtain the location information of the fault point on the geographic information model. Based on the location information of the fault point and the location information of the material distribution center on the geographic information model, the distance between the fault point and the material distribution center on the geographic information model is represented by at least one of Manhattan distance, Euclidean distance and diagonal distance. Based on the K-means clustering algorithm, the location information of the fault point on the geographic information model, the distance between the fault point on the geographic information model and the material distribution center, and the load data of the repair vehicles in the material distribution center are used to determine the emergency resource scheduling task of the material distribution center.

[0029] For example, Manhattan distance, Euclidean distance, and diagonal distance are represented as follows:

[0030]

[0031] .

[0032] For example, the K-means clustering algorithm, during the clustering process, comprehensively considers factors such as the vehicle's load capacity, the shortest travel path from the distribution center to the fault point, the spatial distribution of the fault point and the weighted influence of its urgency, and real-time traffic information. By calculating the distance between fault points and the weight difference of the cluster centers, the clustering results are dynamically adjusted until the cluster centers converge, thereby forming multiple cluster regions with similar characteristics. The load data includes, but is not limited to, the number of vehicles and the standard load capacity of the vehicles.

[0033] S103. Use the Grey Wolf Algorithm to calculate the optimal path for each emergency resource scheduling task and obtain the initial scheduling strategy; In one possible embodiment, the step of using the Grey Wolf algorithm to calculate the optimal path for each emergency resource scheduling task and obtain the initial scheduling strategy includes: Assuming there are n fault points, using the Grey Wolf Algorithm, the objective function is obtained as follows:

[0034] In the formula: For the first The load value of each node; For the first The repair time required for each fault point; For the first The fault point and the first The vehicle travel time required to transfer between the fault points; Under a set of preset constraints, the objective function is solved to obtain the optimal path for each emergency resource scheduling task; The preset constraint set includes at least the following: The first constraint to ensure that emergency supply delivery vehicles must pass through each point of failure exactly once is:

[0035] The second constraint used to ensure that no loops are generated in the travel path:

[0036] The third constraint to ensure that all emergency supply delivery vehicles depart from and return to the distribution center:

[0037] The fourth constraint to ensure that the operating time of emergency supply delivery vehicles does not exceed the maximum working time:

[0038] in, For the site Estimated repair time, The time spent on the journey.

[0039] For example, such as Figure 3 As shown, in the path planning step of the Gray Wolf algorithm, preliminary path data from the material distribution center to each fault point is first input, including road network, traffic flow, road conditions, and other information. The Gray Wolf algorithm simulates the cooperative and competitive behavior of a wolf pack during hunting, iteratively updating the optimal solution for each path. By simulating the wolf's pursuit process, the Gray Wolf algorithm uses a combination of global and local search mechanisms to find the shortest and most time-efficient path, effectively avoiding congestion or other obstacles, ensuring that each repair vehicle can reach its destination in the shortest possible time.

[0040] For example, considering that each material distribution center can dispatch multiple repair vehicles, the Grey Wolf algorithm optimizes the route selection of each vehicle to achieve reasonable scheduling and coordination among vehicles, avoiding duplicate travel or traffic conflicts between multiple vehicles on the same route. Specifically, the Grey Wolf algorithm intelligently allocates the departure time and travel route of each vehicle based on its starting location, destination, and the transportation load of each workshop, to ensure a balanced distribution of repair tasks and maximize overall response speed.

[0041] The Grey Wolf algorithm is capable of adjusting routes in real time to address potential changes in traffic conditions or temporary obstacles. Once traffic congestion, road closures, or other emergencies are detected, the system recalculates and adjusts the optimal route for vehicles. Through multiple iterations and local optimization mechanisms, it ensures that repair vehicles maintain high emergency response efficiency even in complex and dynamic environments.

[0042] S104. Obtain the urgency of the demand for the fault point, and update the initial scheduling strategy according to the urgency of the demand to obtain an emergency resource scheduling strategy.

[0043] In one possible embodiment, the step of determining the urgency of the need to identify the fault location includes: To address the factors influencing emergency repair decisions, a multi-level decision structure model is established based on the sub-indicators corresponding to these factors. Based on the multi-level decision structure model, the subjective weight of each sub-indicator is calculated using expert scoring and / or pairwise comparison, and the subjective weight value of each factor is obtained based on the subjective weight of each sub-indicator. The entropy value of each sub-indicator is calculated using the entropy weight method to quantify the uncertainty of each sub-indicator, and the objective weight of each sub-indicator is determined based on the entropy value. The subjective weight value is corrected based on the objective weight to obtain the target weight value for each sub-indicator; Based on the target weight value, the urgency of the fault point is calculated using a weighted summation method.

[0044] Exemplary, multi-level decision structure models, such as Figure 4 As shown, the top layer is set as "Repair Resource Allocation Decision," the next layer consists of the main factors influencing the repair decision, including fault severity, user characteristics, repair difficulty and time, and material requirements. The next layer consists of specific sub-indicators (impact range of the faulty site, system redundancy, key user needs, number of user complaints, repair complexity, repair time window, material requirement matching, and the impact of missing key materials). The relative importance of each factor is assessed using expert scoring or pairwise comparison methods to calculate the weight of each indicator, thus forming the weight value for each factor.

[0045] For example, the entropy weight method is used to further optimize the weights of each sub-indicator. The entropy weight method quantifies the uncertainty of each sub-indicator by calculating its entropy value, thereby determining the objective weight of each sub-indicator. The smaller the entropy value, the richer the information provided by the indicator, the higher its importance, and the greater its weight. By calculating and standardizing the information entropy of each indicator, a set of objective weight values ​​is obtained. Combining the results of the analytic hierarchy process (AHP) and the entropy weight method ensures that the weight allocation considers both the subjective judgment of experts and reflects the actual impact of each sub-indicator.

[0046] In one possible embodiment, the step of updating the initial scheduling strategy according to the urgency of the demand to obtain an emergency resource scheduling strategy includes: The priority levels of each emergency resource scheduling task within the initial scheduling strategy are updated based on the urgency of the demand, and the emergency resource scheduling tasks are arranged in descending order of priority to obtain the emergency resource scheduling strategy. For example, by combining real-time fault data and external environmental factors, dynamic evaluation and ranking are achieved, ensuring that resource allocation is more scientific and reasonable.

[0047] like Figure 5 As shown in the figure, this application provides an emergency resource scheduling strategy generation device that considers the urgency of material demand. The device includes: The data integration module 201 is used to represent each device on the target system using GIS technology, obtain heating network information, and couple the heating network information with the traffic network information corresponding to the target system to obtain a geographic information model of the heating facilities. Clustering module 201 is used to determine the emergency resource scheduling task of the material distribution center based on the K-means clustering algorithm, according to the location information of the fault point on the geographic information model, the location information of the material distribution center, and the load data of the repair vehicles in the material distribution center. The path planning module 201 is used to calculate the optimal path for each emergency resource scheduling task using the Grey Wolf algorithm, and to obtain the initial scheduling strategy. The strategy generation module 201 is used to obtain the urgency of the demand at the fault point and update the initial scheduling strategy according to the urgency of the demand to obtain an emergency resource scheduling strategy.

[0048] Specific limitations regarding the emergency resource allocation strategy device that considers the urgency of material needs can be found in the limitations of the emergency resource allocation strategy method that considers the urgency of material needs described above, and will not be repeated here. Each module in the aforementioned joint task implementation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0049] In one embodiment, a computer device is provided, which can be a server or a client. The computer device includes a processor, memory, a network interface, and a database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external user terminals via a network connection. When the computer program is executed by the processor, it implements server-side functions or steps of an emergency resource allocation strategy method that considers the urgency of material needs.

[0050] In one possible implementation, such as Figure 6 As shown, this application embodiment provides an electronic device 300, including: a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, it performs the following steps: representing each device on the target system using GIS technology to obtain heating network information, and coupling the heating network information with the traffic network information corresponding to the target system to obtain a geographic information model of the heating facilities; determining the emergency resource scheduling tasks of the material distribution center based on the K-means clustering algorithm, according to the location information of the fault point on the geographic information model, the location information of the material distribution center, and the load data of the repair vehicles in the material distribution center; calculating the optimal path for each emergency resource scheduling task using the Grey Wolf algorithm to obtain an initial scheduling strategy; obtaining the urgency of the demand for the fault point, and updating the initial scheduling strategy according to the urgency of the demand to obtain an emergency resource scheduling strategy.

[0051] In one possible implementation, such as Figure 7 As shown, this application embodiment provides a computer-readable storage medium 400, on which a computer program 411 is stored. When the computer program 411 is executed by a processor, it implements the following steps: representing each device on the target system using GIS technology to obtain heating network information, and coupling the heating network information with the traffic network information corresponding to the target system to obtain a geographic information model of the heating facilities; determining the emergency resource scheduling tasks of the material distribution center based on the K-means clustering algorithm, according to the location information of the fault point on the geographic information model, the location information of the material distribution center, and the load data of the repair vehicles in the material distribution center; calculating the optimal path for each emergency resource scheduling task using the Grey Wolf algorithm to obtain an initial scheduling strategy; obtaining the urgency of the demand for the fault point, and updating the initial scheduling strategy according to the urgency of the demand to obtain an emergency resource scheduling strategy.

[0052] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0053] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0054] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0055] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0056] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0057] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

[0058] The above description discloses only preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. Therefore, equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for generating emergency resource allocation strategies that considers the urgency of material needs, characterized in that: By representing each device on the target system using GIS technology, heating network information is obtained. The heating network information is then coupled with the traffic network information corresponding to the target system to obtain a geographic information model of the heating facilities. The steps of representing each device on the target system using GIS technology to obtain heating network information, and coupling the heating network information with the traffic network information corresponding to the target system to obtain a geographic information model of the heating facilities include: By using GIS technology to locate each device in the target system, the location information of each device in the target system can be obtained; The SCADA system is used to acquire the operating data of each device on the target system, and the operating status of each device on the target system is determined based on the operating data; Determine the connection relationships between various devices in the target system based on the topology information of the target system; The location information of each device in the target system, the operating status of each device in the target system, and the connection relationship between each device in the target system are represented to obtain the heating network information; The heating network information and the traffic network information corresponding to the target system are coupled to obtain a geographic information model of the heating facilities; Based on the K-means clustering algorithm, and according to the location information of the fault point on the geographic information model, the location information of the material distribution center, and the load data of the repair vehicles in the material distribution center, the emergency resource scheduling task of the material distribution center is determined. The optimal path for each emergency resource scheduling task is calculated using the Grey Wolf algorithm to obtain the initial scheduling strategy; The urgency of the demand at the fault point is obtained, and the initial scheduling strategy is updated according to the urgency of the demand to obtain an emergency resource scheduling strategy. The step of determining the urgency of the fault location includes: To address the factors influencing emergency repair decisions, a multi-level decision structure model is established based on the sub-indicators corresponding to these factors. Based on the multi-level decision structure model, the subjective weight of each sub-indicator is calculated using expert scoring and / or pairwise comparison, and the subjective weight value of each factor is obtained based on the subjective weight of each sub-indicator. The entropy value of each sub-indicator is calculated using the entropy weight method to quantify the uncertainty of each sub-indicator, and the objective weight of each sub-indicator is determined based on the entropy value. The subjective weight value is corrected based on the objective weight to obtain the target weight value for each sub-indicator; Based on the target weight value, the urgency of the demand for the fault point is calculated by weighted summation method; The step of updating the initial scheduling strategy according to the urgency of the demand to obtain an emergency resource scheduling strategy includes: The priority levels of each emergency resource scheduling task within the initial scheduling strategy are updated according to the urgency of the demand, and the emergency resource scheduling tasks are arranged in descending order of priority to obtain the emergency resource scheduling strategy.

2. The method for generating an emergency resource allocation strategy considering the urgency of material demand according to claim 1, characterized in that, The steps for determining the emergency resource dispatching task of the material distribution center based on the K-means clustering algorithm, according to the location information of the fault point on the geographic information model, the location information of the material distribution center, and the load data of the repair vehicles within the material distribution center, include: The device that is running abnormally on the target system is taken as the faulty device, and the location corresponding to the faulty device on the transportation network information in the geographic information model is taken as the fault point, so as to obtain the location information of the fault point on the geographic information model. Based on the location information of the fault point and the location information of the material distribution center on the geographic information model, the distance between the fault point and the material distribution center on the geographic information model is represented by at least one of Manhattan distance, Euclidean distance and diagonal distance. Based on the K-means clustering algorithm, the location information of the fault point on the geographic information model, the distance between the fault point on the geographic information model and the material distribution center, and the load data of the repair vehicles in the material distribution center are used to determine the emergency resource scheduling task of the material distribution center.

3. The method for generating an emergency resource allocation strategy considering the urgency of material demand according to claim 1, characterized in that, The step of using the Grey Wolf algorithm to calculate the optimal path for each emergency resource scheduling task and obtain the initial scheduling strategy includes: Assuming there are n fault points, using the Grey Wolf Algorithm, the objective function is obtained as follows: In the formula: For the first The load value of each node; For the first The repair time required for each fault point; For the first The fault point and the first The vehicle travel time required to transfer between the fault points; Under a set of preset constraints, the objective function is solved to obtain the optimal path for each emergency resource scheduling task; The preset constraint set includes at least the following: The first constraint to ensure that emergency supply delivery vehicles must pass through each point of failure exactly once is: The second constraint used to ensure that no loops are generated in the travel path: The third constraint to ensure that all emergency supply delivery vehicles depart from and return to the distribution center: The fourth constraint to ensure that the operating time of emergency supply delivery vehicles does not exceed the maximum working time: in, For the site Estimated repair time, The time spent on the journey.

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