A digital flood and drought prevention material dispatching and distribution method and system

Abstract

The application relates to the technical field of flood prevention and drought resistance emergency material management, and particularly discloses a digital flood prevention and drought resistance material dispatching and distribution method and system, which comprises the following steps: constructing a disaster response knowledge graph containing scene-task-unit-material hierarchical association; matching the graph scene after fusing and cleaning dynamic disaster information, disassembling the dynamic disaster information into minimum operation units and generating a customized material combination list; calculating the quantity matching degree of the supply point and the material, marking the matched and non-completely matched materials; differentiating and calculating the distribution time efficiency of the two types of materials, calculating the equivalent distribution time efficiency of the non-completely matched materials in combination with the space-time coordination degree; enumerating and verifying the global distribution combination to obtain a feasible scheme, screening an optimal scheme with the minimum overall completion time; and calculating the overall space-time coordination degree of the optimal scheme, and optimizing the supply point inventory or position after tracing the causes of the low coordination degree, so that the material dispatching is precise, efficient and collaborative, and the flood prevention and drought resistance emergency response capability is improved.

Description

Technical Field

[0001] This invention relates to the field of flood control and drought relief emergency material management technology, specifically to a digital flood control and drought relief material dispatching and distribution method and system. Background Technology

[0002] Flood control and drought relief are crucial for safeguarding people's lives and property and maintaining stable social and economic development. Material dispatch and distribution, as a core component of flood control and drought relief emergency response, directly determines the effectiveness of emergency measures through its efficiency and accuracy. Traditional flood control and drought relief material dispatch and distribution models rely heavily on manual experience and lack standardized and digitalized technical support, revealing numerous problems in practical applications. First, disaster information is fragmented and poorly structured, lacking professional knowledge maps, making it difficult to quickly translate into precise operational tasks and material requirements, easily leading to blind dispatching. Second, the matching of materials and supply points lacks quantitative standards, relying solely on manual judgment, easily resulting in material shortages, inventory redundancy, or disorderly piecing together. Third, delivery timeliness calculations are crude and one-size-fits-all, failing to consider the synergy of multiple supply points, leading to distorted timeliness calculations and an inability to quickly form operational capabilities after materials arrive. Fourth, the development of delivery plans lacks a systematic approach, failing to enumerate all combinations and lacking clear criteria for determining resource conflicts, resulting in poor feasibility of the plans. Fifth, the optimization of supply point inventory and location lacks data support, relying solely on experience adjustments, leading to low efficiency in overall resource allocation. These problems combine to result in low efficiency and slow emergency response of traditional dispatching, making it difficult to meet the emergency response needs of sudden floods and droughts.

[0003] The combination of these problems results in low efficiency in the dispatch and distribution of traditional flood control and drought relief materials, slow emergency response, and unreasonable resource allocation, making it difficult to meet the emergency response needs under sudden flood and drought disasters.

[0004] Therefore, the present invention provides a digital method and system for dispatching and distributing flood control and drought relief materials. Summary of the Invention

[0005] The purpose of this invention is to provide a digital method and system for dispatching and distributing flood control and drought relief materials to solve the aforementioned problems.

[0006] The objective of this invention can be achieved through the following technical solution: a digital method for dispatching and distributing flood control and drought relief materials, comprising the following steps: Construct a disaster response knowledge graph, deconstruct the current dynamic disaster information into multiple minimum operational units, and generate a material combination list for each minimum operational unit through matching analysis with the disaster knowledge graph; Based on the current real-time material inventory at multiple supply points, calculate the material matching degree between each supply point and each smallest work unit, and divide each material in the material combination list into matched materials and incompletely matched materials. Calculate the delivery time of each matched material and its corresponding candidate supply point, generate candidate combinations for materials that are not fully matched, and calculate the spatiotemporal coordination degree in the candidate combinations. Using the spatiotemporal coordination degree as the weight, calculate the equivalent delivery time of each candidate combination. Statistical analysis of the delivery time of all materials in the material combination list is performed to determine all feasible delivery plans, and the overall completion time of each feasible delivery plan is calculated to determine the optimal delivery plan. Calculate the overall spatiotemporal coordination degree of the optimal delivery plan, determine whether the supply points need to be optimized, and if so, determine the optimization direction by statistical analysis of matched and non-matched materials, and optimize each supply point.

[0007] Furthermore, the disaster response knowledge graph is constructed as follows: Classify disaster scenarios into Level 1 and Level 2, break down disaster tasks, and decompose disaster tasks into the smallest operational units, marking the core objectives of each unit; Match a standard material combination list to each smallest work unit, specifying the material categories and functional proportions; The hierarchical relationships and scheduling rules between scenarios, tasks, units, and materials are entered into the graph to form a visual network of relationships.

[0008] Furthermore, the method for generating a material combination list for each smallest work unit is as follows: Multi-source dynamic disaster information is fused and cleaned to extract disaster-affected areas, disaster types, and disaster scale as disaster feature labels; The disaster feature labels are converted into quantitative vectors, the standard feature vectors of secondary scenarios in the disaster response knowledge graph are extracted, the cosine similarity is calculated, and the disaster scenario with the largest cosine similarity is selected to obtain the disaster scenario that matches the current disaster. Based on the matched disaster scenarios, the disaster tasks associated with the disaster response knowledge graph are retrieved, and the disaster tasks are broken down into the smallest operational units according to the operational boundaries and operational objectives. From the disaster response knowledge graph, retrieve the standard material combination list corresponding to each smallest operational unit, calculate the scale coefficient = actual disaster scale / standard unit matching scale, and adjust the quantity proportionally according to the scale coefficient to maintain the functional matching. Finally, determine the material combination list for each smallest work unit, and mark the required quantity, specifications, and functions of each material in the material combination list.

[0009] Furthermore, the calculation method for the material matching degree is as follows: Compile a list of supply points across the entire region, marking the types of supplies available at each supply point, real-time inventory, specifications, and location; For any given supply point, the material matching degree is calculated by proportionally matching the real-time supply quantity of the supply point with the material demand of each material in the material combination list.

[0010] Furthermore, the method for dividing the materials in the material combination list into matching materials and incompletely matching materials is as follows: For any one of the items in the resource combination list: If there is at least one supply point with a matching degree of 1, then it is marked as a matched material; If the matching degree between the material and all supply points is less than 1, meaning that no single supply point can meet the material demand, then it is marked as a partially matched material.

[0011] Furthermore, The equivalent delivery time calculation method for the candidate combination is as follows: For any non-perfectly matched resource: Select the available supply quantity from the candidate supply points, combine them to meet the total demand for materials, and generate all feasible candidate combinations. For any candidate combination: For each candidate supply point in the candidate combination, plan the route and calculate the delivery time of a single route. Calculate the coefficient of variation of all materials arriving at the target location in the assembly and normalize it to obtain the spatiotemporal synergy, i.e., spatiotemporal synergy = 1 - coefficient of variation; Using spatiotemporal coordination as the weight, the single-path delivery time of each candidate supply point within the patchwork combination is weighted and averaged to obtain the equivalent delivery time of the patchwork combination.

[0012] Furthermore, the optimal delivery plan is determined as follows: For each matching material in the material combination list, organize all candidate supply points that can meet the quantity requirements, form a matching material-candidate supply point, and record it as a matching material optional delivery unit; For each non-perfectly matched item in the material combination list, organize all feasible quantities of combinations to form a non-perfectly matched item-candidate combination, and record it as a non-perfectly matched item optional delivery unit. Using the material combination list as a unit, a candidate supply point is selected for each matching material, and a candidate combination is selected for each incompletely matching material. All selected combinations are superimposed to form a preliminary full-domain delivery combination. The delivery time of matched materials is taken as the single-path delivery time of the selected candidate supply points, the delivery time of non-fully matched materials is taken as the equivalent delivery time of the selected candidate combination, and the overall completion time of the plan is taken as the maximum value of the delivery time of all materials. The optimal delivery plan is the feasible delivery plan with the shortest overall completion time.

[0013] Furthermore, the method for determining whether supply points need to be optimized is as follows: Extract the expected arrival time of all materials: matched materials are the single-path delivery time, and non-fully matched materials are the single-path delivery time of each candidate supply point within its combination; Calculate the coefficient of variation for all expected arrival times; the overall spatiotemporal coordination of the scheme = 1 - coefficient of variation. Calculate the overall spatiotemporal coordination degree of the optimal delivery plan and all feasible delivery plans. If the overall spatiotemporal coordination degree of the plan does not meet the requirements, the supply points need to be optimized.

[0014] Furthermore, the optimization direction is determined as follows: Calculate and compare the proportions of matching and non-matching materials in the material combination list: If the proportion of non-fully matched materials in the material combination list is high, and the candidate combinations of non-fully matched materials all have a single-path timeliness variation coefficient that meets the requirements, while the timeliness distribution of matched materials does not meet the requirements, then the optimization direction is to optimize the inventory of supply points. If the proportion of matching materials in the material combination list is high, but the candidate supply points of different matching materials are scattered, or it is a combination of non-completely matching materials, the supply points are all in remote locations and unevenly distributed, then the optimization direction is to optimize the location of the supply points. If both exist, the optimization direction is a hybrid optimization of inventory and location.

[0015] A digital flood control and drought relief material dispatch and distribution system includes the following modules: Disaster Situation Deconstruction and Inventory Generation Module: Constructs a disaster response knowledge graph, deconstructs the current dynamic disaster information into multiple minimum operational units, and generates a material combination list for each minimum operational unit through matching analysis with the disaster knowledge graph; Material matching degree calculation and classification module: Based on the real-time material inventory of multiple supply points, calculate the material matching degree between each supply point and each smallest work unit, and classify each material in the material combination list into matched materials and incompletely matched materials. Delivery timeliness calculation module: calculates the delivery timeliness of each matched material and the corresponding candidate supply point, generates candidate piecing combinations for incompletely matched materials, calculates the spatiotemporal coordination degree in the candidate piecing combinations, and uses the spatiotemporal coordination degree as the weight to calculate the equivalent delivery timeliness of each candidate piecing combination. Delivery plan selection module: Statistical analysis of the delivery time of all materials in the material combination list, identification of all feasible delivery plans, calculation of the overall completion time of each feasible delivery plan, and determination of the optimal delivery plan; Supply point optimization module: Calculates the overall spatiotemporal coordination of the optimal delivery plan, determines whether the supply points need to be optimized, and if so, determines the optimization direction by statistical analysis of matched and non-matched materials, and optimizes each supply point.

[0016] The beneficial effects of this invention are as follows: Construct a disaster response knowledge graph that links scenarios, tasks, units, and materials; combine cosine similarity to achieve accurate matching of disaster scenarios; break down the smallest operational unit and generate a customized material list to avoid blind dispatch and ensure that material needs are highly adapted to the disaster situation. By quantifying the matching degree of materials, we can clearly distinguish between matching materials and incompletely matching materials, solve the problems of rough and unstandardized traditional material matching, and reduce material shortages, redundancy and disorderly piecing together. Differentiated delivery timeliness calculation methods are designed for the two types of materials. Spatiotemporal coordination is introduced to calculate the equivalent delivery timeliness of materials that are not perfectly matched, which meets the emergency needs of concentrated arrival of materials and rapid formation of operational capabilities, and solves the problem of timeliness calculation distortion. The system enumerates all delivery combinations, establishes clear criteria for resource conflict judgment, eliminates invalid combinations, and selects the feasible solution with the best overall completion time to avoid delivery delays and improve the feasibility of the solution. Using the overall spatiotemporal coordination of the solution as a quantitative indicator, we can trace the causes of low coordination and implement targeted optimizations of inventory, location, or hybrid systems to form a closed-loop management of scheduling, analysis, and optimization, thereby improving the efficiency of resource allocation across the entire domain. Attached Figure Description

[0017] The invention will now be further described with reference to the accompanying drawings.

[0018] Figure 1 This is a flowchart of a digital flood control and drought relief material dispatching and distribution method according to Embodiment 1 of the present invention; Figure 2 This is a logic diagram in Embodiment 1 of the present invention for determining whether the supply point needs to be optimized; Figure 3 This is a functional module diagram of a digital flood control and drought relief material dispatching and distribution system in Embodiment 2 of the present invention. Detailed Implementation

[0019] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0020] Example 1: Please refer to Figure 1 As shown in the figure, the digital flood control and drought relief material dispatching and distribution method according to an embodiment of the present invention specifically includes the following steps: Step 1: Construct a disaster response knowledge graph, deconstruct the current dynamic disaster information into multiple minimum operational units, and generate a material combination list for each minimum operational unit through matching analysis with the disaster knowledge graph; In step one, the process of constructing the disaster response knowledge graph includes: The first point to clarify is that the disaster scenarios are divided as follows: The primary scenarios are divided into flood and drought categories. The secondary scenarios are further divided into river embankment breach and urban and rural waterlogging under the flood category, and into agricultural drought and drinking water shortage for people and livestock under the drought category. Each secondary scenario is labeled with a feature tag (e.g., urban and rural waterlogging: urban area, water depth ≥30cm, road network interruption, residents trapped), forming a scenario feature library. Secondly, it should be noted that the disaster relief task is specifically as follows: Based on industry standards and historical cases, the macro tasks of each secondary scenario are broken down into specific executable tasks (such as urban and rural flooding being broken down into water drainage, evacuation of trapped people, and temporary resettlement and support). The execution conditions and time requirements are marked for each specific task (such as water drainage requiring a water area of ​​≥1000㎡ and activation within 4 hours). Thirdly, it should be noted that the smallest unit of work is defined as follows: Based on the principles of independent execution, clear boundaries, and standardization of materials / personnel, disaster tasks are broken down into the smallest operational units (e.g., flood drainage is broken down into a mobile water pump unit), and the core objectives of each unit are marked (e.g., a single unit can drain ≥50m³ per hour). Fourthly, it should be noted that the matching standard material list is as follows: Based on industry configuration standards and case reviews, a standard material list is matched for each smallest work unit, specifying the material categories, functional ratios (e.g., 1 15kw unit = water pump: 20L diesel: 50m water pipe = 1:2:5), key performance parameters (e.g., water pump head ≥10m, diesel grade 0#), and personnel configuration (e.g., 1 certified operator + 1 maintenance worker). The hierarchical relationships between scenarios, tasks, units, and materials, as well as scheduling rules (such as prioritizing the matching of portable water pump units in mountainous flood areas), are entered into the map to form a visual network of relationships. In step one, the process of generating a material combination list for each smallest work unit includes: Acquire multi-source data, including: Dynamic disaster information: meteorological remote sensing data, real-time monitoring data from hydrological stations, data reported manually by grassroots personnel, IoT sensor data (water accumulation / soil moisture / road network), and emergency response level notifications (clearly specifying the urgency of the disaster). Basic supporting data: Disaster response knowledge graph (including association rules between disaster scenarios, specific tasks, minimum work units, and material combinations, definition standards for minimum work units, and templates for material combination lists), and a historical disaster response case database; Multi-source dynamic disaster information is fused and cleaned to extract core features: disaster-affected area, disaster type (flood / drought), disaster scale (number of affected people / operation area / risk level), road traffic status, emergency response level, and urgency of demand as disaster feature labels; The disaster feature labels are converted into quantitative vectors. The standard feature vectors of the secondary scenarios in the disaster response knowledge graph are extracted. The cosine similarity is calculated (formula: similarity = disaster vector ・ standard vector / |disaster vector|×|standard vector|). The disaster scenario with the highest cosine similarity is selected to obtain the disaster scenario that matches the current disaster. Based on the matched disaster scenario, specific tasks associated with the disaster response knowledge graph are retrieved, and the specific tasks are broken down into the smallest operational units according to the operational boundaries and operational objectives. From the disaster response knowledge graph, retrieve the standard material combination list corresponding to each smallest operational unit, calculate the scale coefficient K = actual disaster scale / standard unit adaptation scale, and adjust the quantity proportionally according to K to maintain the functional matching. Finally, determine the material combination list for each smallest work unit, and mark the required quantity, specifications, and functions of each material in the material combination list; It should be noted that the role of constructing a disaster response knowledge graph and generating customized material combination lists based on dynamic disaster situations is to: establish a standardized system for flood control and drought relief scheduling, solve the problem that traditional scheduling lacks a unified basis and relies entirely on manual experience, provide a standardized template for the deconstruction of all disaster situations, and provide a unique and accurate core basis for demand-side matching, timeliness calculation, and scheme selection in the future; Step 2: Based on the current real-time material inventory of multiple supply points, calculate the material matching degree between each supply point and each smallest work unit, and divide each material in the material combination list into matched materials and incompletely matched materials. In step two, the calculation process for the material matching degree includes: Compile a list of supply points across the entire region, marking the types of supplies available at each supply point, real-time inventory, specifications, and location; Clearly define the details of the material combination list for each smallest work unit, categorized by core materials / general materials / consumables; For any given supply point, the material matching degree is calculated by proportionally matching the real-time supply quantity of the supply point with the material demand of each material in the material combination list. It is understandable that the physical meaning of the material matching degree is: a material matching degree of 1 means that the inventory quantity at the supply point meets the demand, a value greater than 1 means that the inventory is redundant, and a value less than 1 means that there is a shortage in the inventory quantity. The smaller the value, the larger the shortage. In step two, the process of dividing the materials in the material combination list into matching materials and partially matching materials includes: Based on whether the material matching degree at a single supply point equals 1, materials are divided into matched materials and partially matched materials, specifically: For any one of the items in the resource combination list: If there is at least one supply point with a material matching degree of 1, then it is marked as a matching material, and the candidate supply point number, outbound efficiency, and location of all materials with a matching degree of 1 are recorded. If the matching degree between the material and all the materials at the supply points is less than 1, that is, no single supply point can meet the material demand, then it is marked as a non-perfectly matched material, and the number, supply quantity and location of all candidate supply points are recorded. It should be noted that the purpose of calculating the material matching degree and marking matched and non-matched materials is to: connect the key link between the material demand side and the resource side of the supply point, to quantify the matching of supply and demand, and to define clear scenario boundaries for subsequent differentiated delivery timeliness calculations. Step 3: Calculate the delivery time of each matched material and the corresponding candidate supply point, generate candidate piecing combinations for materials that are not fully matched, and calculate the spatiotemporal coordination degree in the candidate piecing combinations. Using the spatiotemporal coordination degree as the weight, calculate the equivalent delivery time of each candidate piecing combination. In step three, the calculation process for the delivery time of each matched material and its corresponding candidate supply point includes: For any matching material, calculate the delivery time of all candidate supply points that can meet the material demand; In step three, the process of generating candidate combinations of incompletely matched materials includes: For any non-perfectly matched resource: Select the available supply quantity from the candidate supply points, combine them to meet the total demand for materials, and generate all feasible candidate combinations. For any candidate combination: For each candidate supply point in the candidate combination, plan the route and calculate the delivery time of a single route. Calculate the coefficient of variation of all materials arriving at the target location in the assembly and normalize it to obtain the spatiotemporal synergy, i.e., spatiotemporal synergy = 1 - coefficient of variation; Using spatiotemporal coordination as the weight, the single-path delivery time of each candidate supply point within the patchwork combination is weighted and averaged to obtain the equivalent delivery time of the patchwork combination. It is understandable that the physical meaning of equivalent delivery timeliness is: in scenarios where multiple supply points are combined for delivery, considering the concentration of time when goods arrive at the destination, the comprehensive timeliness value that reflects the actual effective delivery capacity of the combined delivery system, specifically including: Effectiveness of timeliness: In the dispatch of flood control and drought relief materials, the core value of multi-material splicing and distribution is to form an operational capacity that can be put into use immediately. If the arrival time of materials at each supply point is scattered (low spatiotemporal coordination), even if the single-path timeliness is short, the actual start time of operation will be greatly delayed due to the idleness of early-arriving materials and the slowdown of late-arriving materials. The equivalent delivery timeliness is weighted by the spatiotemporal coordination, which weakens the timeliness value of scattered arrivals and strengthens the timeliness value of concentrated arrivals, and is more in line with the actual dispatching needs of quickly forming operational capacity after the arrival of materials. The guiding role of weighting: the spatiotemporal coordination degree (1-coefficient of variation) takes the value of 0-1. The more concentrated the arrival time (the smaller the coefficient of variation), the higher the spatiotemporal coordination degree. The corresponding single-path timeliness of the supply point has a larger weight in the weighting. The final equivalent delivery timeliness is closer to the fast timeliness of the core supply point. Conversely, the more dispersed the arrival time, the lower the spatiotemporal coordination degree. The weight is diluted evenly, and the equivalent delivery timeliness will be higher than the arithmetic mean timeliness. This directly reflects the delivery efficiency loss caused by poor coordination of the patchwork combination. It should be noted that the role of differentiated calculation of the optimal delivery time for matched / incompletely matched materials is to complete the transformation from supply point resources to delivery time data, and to provide core quantitative basis for the generation of subsequent delivery plans; Step 4: Statistically analyze the delivery time of all materials in the material combination list, determine all feasible delivery plans, calculate the overall completion time of each feasible delivery plan, and determine the optimal delivery plan; In step four, the process of generating the feasible delivery plan includes: For each matching material in the material combination list, organize all candidate supply points that can meet the quantity requirements to form a matching material-candidate supply point, record it as a matching material optional delivery unit, and record the transportation capacity limit, outbound batch and material categories that can be delivered at the same time for each candidate supply point. For each incompletely matched material in the material combination list, organize all feasible quantities of the combination to form an incompletely matched material-candidate combination, which is recorded as an optional delivery unit for incompletely matched materials. Also record the equivalent delivery time, overall spatiotemporal system degree, coding / single-path time of each supply point in the combination, as well as the capacity limit, outbound batch, and material categories that can be delivered simultaneously for all supply points in the combination. Define the criteria for determining resource conflicts at supply points. If the same supply point (regardless of whether it belongs to a combination or not) exhibits the following conditions, it is considered a resource conflict, and the corresponding combination is deemed invalid: When the same supply point provides delivery services for multiple goods at the same time, it exceeds its transportation capacity limit (e.g., it can only deliver 2 types of goods at a time, but delivers 3 types of goods at the same time). When the same supply point provides delivery services for multiple materials at the same time, there may be a conflict in the outbound batches (such as when only one batch of materials can be outbound at the same time, but two batches of materials need to be sorted and loaded at the same time). The same supply point is a combination of multiple non-perfectly matched materials, and the delivery times of each combination overlap, resulting in the repeated use of inventory / transport capacity. Using the material combination list as a unit, perform a global Cartesian product combination on all the optional delivery units of matching materials and optional delivery units of incompletely matching materials. That is, select a candidate supply point for each matching material and a candidate combination for each incompletely matching material. All selected combinations are superimposed to form a preliminary global delivery combination. The core rules include: Single item unique selection: Each item in the list can only select one of the available delivery units (matching items select one candidate supply point, and non-matching items select one candidate combination) to avoid duplicate delivery of the same item; Full coverage: The enumeration must cover all possible unit combinations, without omitting any single material's optional delivery unit, to ensure the completeness of the subsequent screening plan; Initial identification: Each initial global delivery combination is uniquely coded, and the correspondence between all materials and delivery units contained in the combination is recorded; For all preliminary global delivery combinations, each one is verified according to the resource conflict judgment criteria. Invalid combinations with resource conflicts are eliminated, and combinations without resource conflicts are retained, which are all feasible delivery plans in the material combination list. In step four, the calculation process for the overall completion time of each feasible delivery plan includes: For any feasible delivery plan, calculate the overall completion time of the plan, that is, the delivery time of the last batch of materials in the material combination list to arrive at the target location under the current feasible delivery plan, specifically: The delivery time of matched materials is taken as the single-path delivery time of the selected candidate supply points, the delivery time of non-fully matched materials is taken as the equivalent delivery time of the selected candidate combination, and the overall completion time of the plan is taken as the maximum value of the delivery time of all materials. In step four, the optimal delivery plan is the feasible delivery plan with the shortest overall completion time; It should be noted that the purpose of generating feasible delivery plans and selecting the optimal plan based on the overall completion time is to provide a clear, executable, and resource-free optimal delivery plan for actual flood control and drought relief work by integrating data on the timeliness of individual materials into the overall scheduling plan. Step 5: Calculate the overall spatiotemporal coordination degree of the optimal delivery plan, determine whether the supply points need to be optimized, and if so, perform statistical analysis on the matching and non-matching materials in the material combination list, determine the optimization direction based on the analysis results, and optimize each supply point. Please see Figure 2 As shown, in step five, the calculation process of the overall spatiotemporal coordination degree of the optimal delivery plan includes: The overall spatiotemporal coordination of the plan, i.e., the degree of time concentration in which all materials arrive at the target location under the current feasible delivery plan, is specifically as follows: Extract the expected arrival time of all materials: matched materials are the single-path delivery time, and non-fully matched materials are the single-path delivery time of each candidate supply point within its combination; Calculate the coefficient of variation for all expected arrival times; the overall spatiotemporal coordination of the scheme = 1 - coefficient of variation. In step five, the process of determining whether the supply point needs optimization includes: Calculate the overall spatiotemporal coordination degree of the optimal delivery plan and all feasible delivery plans, and compare it with the threshold of the corresponding disaster level to determine whether it is lower than the threshold of the corresponding disaster level. If it is lower, then the supply points need to be optimized; In step five, the process of determining the optimization direction includes: Calculate and compare the proportions of matching and non-matching materials in the material combination list: If the proportion of non-fully matched materials in the material combination list is high, and the candidate combinations of non-fully matched materials all have a single-path timeliness variation coefficient greater than the preset variation coefficient for each supply point, while the timeliness distribution of matched materials is less than or equal to the preset variation coefficient. The reason for the incomplete matching of materials is that the inventory of supply points across the entire region is insufficient (no single supply point has enough inventory to meet the quantity requirements, rather than it is a matter of specifications or quality). If replenishing the inventory of these partially matched materials to a single supply point can meet the quantity requirements (achieving a shift from piecemeal delivery to single-supply-point matching delivery), the delivery time deviation of the materials can be reduced. Historical data from the same scenario shows that when this type of material is a matching material, the timeliness fluctuation of its single supply point delivery is small, and its contribution to the overall spatiotemporal coordination of the solution is positive. The optimization direction is then to optimize supply point inventory; If the proportion of matching materials in the material combination list is greater than the preset proportion, but the candidate supply points of different matching materials are scattered, resulting in a time difference of matching materials that is greater than the preset difference, or if it is a combination of non-completely matching materials, the supply points are all located in remote areas and are unevenly distributed, rather than due to insufficient inventory. The timeliness gap of matching materials is caused by the large difference in road network distance between the supply point and the target point (not due to temporary factors such as road network congestion or differences in transportation vehicles), and the supply point is the only / optimal candidate point for the material (there are no other nearby supply points to choose from). The combination of non-perfectly matched materials may have sufficient supply from the supply points, but the timeliness difference is large due to the dispersed location. Even if the inventory is adjusted, the combination cannot be reduced (such as the inventory distribution being highly tied to the location distribution). Historical data from the same scenario shows that the distribution of supply points in this area has consistently resulted in large differences in delivery timeliness, and the overall spatiotemporal coordination of the solution has been consistently low, regardless of the amount of inventory. The optimization direction is then to optimize the location of the supply point; If both types of materials have a significant impact on low coordination, then the optimization direction is a hybrid optimization of inventory and location. In step five, the process of optimizing each supply point includes: If the optimization direction is to optimize the inventory at the supply points, replenish the inventory at the core supply points for materials that are not fully matched, and achieve single-supply-point matching to reduce piecing together; adjust the regional inventory distribution, and gather scattered inventory to suburban / high-quality supply points to improve the single-supply-point matching rate; increase the reserve quota for high-frequency materials that are not fully matched to avoid piecing together due to insufficient inventory. If the optimization direction is to optimize the location of supply points, transfer materials across supply points, and transfer core materials from suburban supply points to high-quality supply points in the suburbs / around the target point; add temporary emergency supply points around the target point and reserve frequently used matching materials to shorten delivery time; long-term optimization can adjust the regional supply point layout, add fixed supply points in high-risk areas, and achieve precise matching of inventory and location. If it is a mixed problem, first reduce the number of incompletely matched materials by replenishing / consolidating inventory to reduce the timeliness gap caused by piecing together materials from the root; then optimize the location of supply points for the remaining timeliness gap by means of material allocation, adding emergency supply points, etc. It should be noted that the purpose of calculating the overall spatiotemporal coordination and implementing targeted supply point optimization is to: formulate specific inventory optimization, location optimization, or hybrid optimization strategies for different causes, fundamentally solve the problem of low coordination in material arrival, not only ensure the coordination and efficiency of this dispatch, but also improve the material matching rate, delivery timeliness, and coordination of subsequent flood control and drought relief dispatch through supply point optimization, forming a closed-loop management of dispatch-analysis-optimization-redispatch, allowing the dispatch system to continuously iterate and upgrade with actual application, solving the pain point of traditional dispatch that only completes a single dispatch without subsequent optimization, and the recurrence of similar problems.

[0021] The technical solution and advantages of this application embodiment are as follows: A disaster response knowledge graph is constructed, current dynamic disaster information is deconstructed into multiple minimum operational units, and a material combination list is generated for each minimum operational unit through matching analysis with the disaster knowledge graph; based on the real-time material inventory of multiple supply points, the material matching degree between each supply point and each minimum operational unit is calculated, and each material in the material combination list is divided into matched materials and partially matched materials; the delivery time of each matched material and its corresponding candidate supply point is calculated, candidate combinations are generated for partially matched materials, and the spatiotemporal coordination degree in the candidate combinations is calculated. The equivalent delivery time of each candidate combination is obtained by weighting the spatiotemporal coordination degree; the delivery time of all materials in the material combination list is statistically analyzed to determine all feasible delivery schemes, and the overall completion time of each feasible delivery scheme is calculated to determine the optimal delivery scheme; the overall spatiotemporal coordination degree of the optimal delivery scheme is calculated to determine whether the supply points need to be optimized. If so, the optimization direction is determined by statistical analysis of matched and partially matched materials, and each supply point is optimized. This invention constructs a disaster response knowledge graph with hierarchical associations of scenarios, tasks, units, and materials; it integrates and cleans dynamic disaster information, matches it to the graph scenarios, breaks it down into the smallest operational units, and generates a customized material combination list; it calculates the quantity matching degree between supply points and materials, marking matching / incompletely matching materials; it differentiates the delivery time of the two types of materials, and calculates the equivalent delivery time of incompletely matching materials based on spatiotemporal coordination; it enumerates and verifies all-domain delivery combinations to obtain feasible solutions, and selects the optimal solution by minimizing the overall completion time; it calculates the overall spatiotemporal coordination degree of the optimal solution, traces the causes of low coordination degree, and optimizes the inventory or location of supply points accordingly, thus achieving precise, efficient, and coordinated material scheduling and improving the emergency response capabilities for flood control and drought relief.

[0022] Example 2: Please refer to Figure 3 As shown in the figure, a digital flood control and drought relief material dispatching and distribution system according to an embodiment of the present invention includes the following modules: Disaster Situation Deconstruction and Inventory Generation Module: Constructs a disaster response knowledge graph, deconstructs the current dynamic disaster information into multiple minimum operational units, and generates a material combination list for each minimum operational unit through matching analysis with the disaster knowledge graph; Material matching degree calculation and classification module: Based on the real-time material inventory of multiple supply points, calculate the material matching degree between each supply point and each smallest work unit, and classify each material in the material combination list into matched materials and incompletely matched materials. Delivery timeliness calculation module: calculates the delivery timeliness of each matched material and the corresponding candidate supply point, generates candidate piecing combinations for incompletely matched materials, calculates the spatiotemporal coordination degree in the candidate piecing combinations, and uses the spatiotemporal coordination degree as the weight to calculate the equivalent delivery timeliness of each candidate piecing combination. Delivery plan selection module: Statistical analysis of the delivery time of all materials in the material combination list, identification of all feasible delivery plans, calculation of the overall completion time of each feasible delivery plan, and determination of the optimal delivery plan; Supply point optimization module: Calculates the overall spatiotemporal coordination of the optimal delivery plan, determines whether the supply points need to be optimized, and if so, determines the optimization direction by statistical analysis of matched and non-matched materials, and optimizes each supply point.

[0023] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A digital method for dispatching and distributing flood control and drought relief supplies, characterized in that: Includes the following steps: Construct a disaster response knowledge graph, deconstruct the current dynamic disaster information into multiple minimum operational units, and generate a material combination list for each minimum operational unit through matching analysis with the disaster knowledge graph; Based on the current real-time material inventory at multiple supply points, calculate the material matching degree between each supply point and each smallest work unit, and divide each material in the material combination list into matched materials and incompletely matched materials. Calculate the delivery time of each matched material and its corresponding candidate supply point, generate candidate combinations for materials that are not fully matched, and calculate the spatiotemporal coordination degree in the candidate combinations. Using the spatiotemporal coordination degree as the weight, calculate the equivalent delivery time of each candidate combination. Statistical analysis of the delivery time of all materials in the material combination list is performed to determine all feasible delivery plans, and the overall completion time of each feasible delivery plan is calculated to determine the optimal delivery plan. Calculate the overall spatiotemporal coordination degree of the optimal delivery plan, determine whether the supply points need to be optimized, and if so, determine the optimization direction by statistical analysis of matched and non-matched materials, and optimize each supply point.

2. The digital flood control and drought relief material dispatching and distribution method according to claim 1, characterized in that: The disaster response knowledge graph is constructed as follows: Classify disaster scenarios into Level 1 and Level 2, break down disaster tasks, and decompose disaster tasks into the smallest operational units, marking the core objectives of each unit; Match a standard material combination list to each smallest work unit, specifying the material categories and functional proportions; The hierarchical relationships and scheduling rules between scenarios, tasks, units, and materials are entered into the graph to form a visual network of relationships.

3. The digital flood control and drought relief material dispatching and distribution method according to claim 2, characterized in that: The method for generating a material combination list for each smallest work unit is as follows: Multi-source dynamic disaster information is fused and cleaned to extract disaster-affected areas, disaster types, and disaster scale as disaster feature labels; The disaster feature labels are converted into quantitative vectors, the standard feature vectors of secondary scenarios in the disaster response knowledge graph are extracted, the cosine similarity is calculated, and the disaster scenario with the largest cosine similarity is selected to obtain the disaster scenario that matches the current disaster. Based on the matched disaster scenarios, the disaster tasks associated with the disaster response knowledge graph are retrieved, and the disaster tasks are broken down into the smallest operational units according to the operational boundaries and operational objectives. From the disaster response knowledge graph, retrieve the standard material combination list corresponding to each smallest operational unit, calculate the scale coefficient = actual disaster scale / standard unit matching scale, and adjust the quantity proportionally according to the scale coefficient to maintain the functional matching. Finally, determine the material combination list for each smallest work unit, and mark the required quantity, specifications, and functions of each material in the material combination list.

4. The digital flood control and drought relief material dispatching and distribution method according to claim 1, characterized in that: The method for calculating the material matching degree is as follows: Compile a list of supply points across the entire region, marking the types of supplies available at each supply point, real-time inventory, specifications, and location; For any given supply point, the material matching degree is calculated by proportionally matching the real-time supply quantity of the supply point with the material demand of each material in the material combination list.

5. The digital flood control and drought relief material dispatching and distribution method according to claim 4, characterized in that: The method for classifying the materials in the material combination list into matching materials and partially matching materials is as follows: For any one of the items in the resource combination list: If there is at least one supply point with a matching degree of 1, then it is marked as a matched material; If the matching degree between the material and all supply points is less than 1, meaning that no single supply point can meet the material demand, then it is marked as a partially matched material.

6. The digital flood control and drought relief material dispatching and distribution method according to claim 1, characterized in that: The equivalent delivery time calculation method for the candidate combination is as follows: For any non-perfectly matched resource: Select the available supply quantity from the candidate supply points, combine them to meet the total demand for materials, and generate all feasible candidate combinations. For any candidate combination: For each candidate supply point in the candidate combination, plan the route and calculate the delivery time of a single route. Calculate the coefficient of variation of all materials arriving at the target location in the assembly and normalize it to obtain the spatiotemporal synergy, i.e., spatiotemporal synergy = 1 - coefficient of variation; Using spatiotemporal coordination as the weight, the single-path delivery time of each candidate supply point within the patchwork combination is weighted and averaged to obtain the equivalent delivery time of the patchwork combination.

7. The digital flood control and drought relief material dispatching and distribution method according to claim 1, characterized in that: The optimal delivery plan is determined as follows: For each matching material in the material combination list, organize all candidate supply points that can meet the quantity requirements, form a matching material-candidate supply point, and record it as a matching material optional delivery unit; For each non-perfectly matched item in the material combination list, organize all feasible quantities of combinations to form a non-perfectly matched item-candidate combination, and record it as a non-perfectly matched item optional delivery unit. Using the material combination list as a unit, a candidate supply point is selected for each matching material, and a candidate combination is selected for each incompletely matching material. All selected combinations are superimposed to form a preliminary full-domain delivery combination. The delivery time of matched materials is taken as the single-path delivery time of the selected candidate supply points, the delivery time of non-fully matched materials is taken as the equivalent delivery time of the selected candidate combination, and the overall completion time of the plan is taken as the maximum value of the delivery time of all materials. The optimal delivery plan is the feasible delivery plan with the shortest overall completion time.

8. The digital flood control and drought relief material dispatching and distribution method according to claim 1, characterized in that: The method for determining whether supply points need optimization is as follows: Extract the expected arrival time of all materials: matched materials are the single-path delivery time, and non-fully matched materials are the single-path delivery time of each candidate supply point within its combination; Calculate the coefficient of variation for all expected arrival times; the overall spatiotemporal coordination of the scheme = 1 - coefficient of variation. Calculate the overall spatiotemporal coordination degree of the optimal delivery plan and all feasible delivery plans. If the overall spatiotemporal coordination degree of the plan does not meet the requirements, the supply points need to be optimized.

9. A digital flood control and drought relief material dispatching and distribution method according to claim 8, characterized in that: The optimization direction is determined as follows: Calculate and compare the proportions of matching and non-matching materials in the material combination list: If the proportion of non-fully matched materials in the material combination list is high, and the candidate combinations of non-fully matched materials all have a single-path timeliness variation coefficient that meets the requirements, while the timeliness distribution of matched materials does not meet the requirements, then the optimization direction is to optimize the inventory of supply points. If the proportion of matching materials in the material combination list is high, but the candidate supply points of different matching materials are scattered, or it is a combination of non-completely matching materials, the supply points are all in remote locations and unevenly distributed, then the optimization direction is to optimize the location of the supply points. If both exist, the optimization direction is a hybrid optimization of inventory and location.

10. A digital flood control and drought relief material dispatch and distribution system, characterized in that: Includes the following modules: Disaster Situation Deconstruction and Inventory Generation Module: Constructs a disaster response knowledge graph, deconstructs the current dynamic disaster information into multiple minimum operational units, and generates a material combination list for each minimum operational unit through matching analysis with the disaster knowledge graph; Material matching degree calculation and classification module: Based on the real-time material inventory of multiple supply points, calculate the material matching degree between each supply point and each smallest work unit, and classify each material in the material combination list into matched materials and incompletely matched materials. Delivery timeliness calculation module: calculates the delivery timeliness of each matched material and the corresponding candidate supply point, generates candidate piecing combinations for incompletely matched materials, calculates the spatiotemporal coordination degree in the candidate piecing combinations, and uses the spatiotemporal coordination degree as the weight to calculate the equivalent delivery timeliness of each candidate piecing combination. Delivery plan selection module: Statistical analysis of the delivery time of all materials in the material combination list, identification of all feasible delivery plans, calculation of the overall completion time of each feasible delivery plan, and determination of the optimal delivery plan; Supply point optimization module: Calculates the overall spatiotemporal coordination of the optimal delivery plan, determines whether the supply points need to be optimized, and if so, determines the optimization direction by statistical analysis of matched and non-matched materials, and optimizes each supply point.

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