Logistics support management system and method based on digital twinning

Abstract

The invention discloses a logistical support management system and method based on digital twinning, relates to the technical field of logistical support management, and solves the technical problems that in the prior art, flood disaster conditions cannot be simulated and predicted in advance, so that no enough time is provided for planning rescue measures, and the rescue efficiency is low. The method comprises the following steps: establishing a digital twinborn model of a target area; simulating the weather condition of the target area according to the meteorological data and the digital twinborn model; dividing the target area into a plurality of sub-areas with the same personnel density, acquiring environment information of the sub-areas, and evaluating flood disaster levels of the sub-areas; the rescue center collects and updates personnel information of the target area in real time; evaluating the rescue emergency level of the sub-region based on the flood disaster level and the personnel information; planning an optimal rescue path according to the rescue emergency level of the sub-region and the terrain; influences of flood disasters are effectively estimated, efficient rescue is achieved, and losses caused by natural disasters are reduced.

Description

Technical Field

[0001] The present invention belongs to the field of logistics support management, and specifically relates to a logistics support management system and method based on digital twins. Background Art

[0002] When encountering severe floods that exceed flood control standards, due to the rapid evolution of the floods and the wide inundation range, the floods will quickly submerge urban areas and cause serious disasters. Strong logistical support can not only effectively reduce the impact of the disaster, but also accelerate the post-disaster recovery process.

[0003] The invention patent application with publication number CN113780714A discloses an urban flood emergency response system, including an emergency response platform. The emergency response platform integrates all functions and tasks involved in the emergency response work, integrates and processes all information, and provides different functional usage terminals for different groups of people, including a platform working terminal, a rescue working terminal, and a public usage terminal.

[0004] The above-mentioned existing technology realizes real-time interaction of flood information between different smart terminals. The smart terminals plan rescue measures based on real-time data, but cannot simulate and predict flood conditions in advance, which will lead to insufficient time to plan rescue measures and low rescue efficiency. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a logistics support management system and method based on digital twins, which is used to solve the technical problem that the prior art cannot simulate and predict flood conditions in advance, which will lead to insufficient time to plan rescue measures and low rescue efficiency.

[0006] To achieve the above-mentioned purpose, the first aspect of the present invention provides a logistics support management system and method based on digital twins, including a regional digital twin module, and a disaster prediction module and a disaster rescue module connected thereto;

[0007] Regional digital twin module: used to establish a digital twin model of the target area; the digital twin model integrates the three-dimensional model and meteorological model of the target area; the target area is the area affected by flood disasters;

[0008] Disaster prediction module: used to simulate the weather conditions of the target area based on meteorological data and the digital twin model; divide the target area into several sub-areas, obtain the environmental information of the sub-areas, and evaluate the flood disaster level of the sub-areas; and,

[0009] The rescue center collects and updates personnel information in the target area in real time; evaluates the rescue emergency level of the sub-area based on the flood disaster level and personnel information;

[0010] Disaster Rescue Module: used to plan the optimal rescue route based on the rescue emergency level and terrain of the sub-area.

[0011] Preferably, the step of establishing a digital twin model of the target area includes:

[0012] Collect image data of the target area through drones to build a three-dimensional model of the target area; the image data includes terrain features and building features; and

[0013] The meteorological model simulates weather conditions according to the meteorological data; wherein the meteorological model includes a WRF model or an extended series of the WRF model;

[0014] By formulating dynamic interactions between models and data, a digital twin model of the target area is constructed.

[0015] The present invention combines the three-dimensional model with the results of meteorological simulation to realize dynamic interaction between the model and the real world. This involves mapping meteorological data onto the three-dimensional model to reflect changes in the target area under specific weather conditions. As meteorological conditions change, the meteorological parameters in the digital twin model are continuously updated to maintain the consistency of the digital twin model with the real world. Under severe weather conditions, the digital twin model can predict the occurrence of natural disasters such as floods and mudslides, and make response preparations in advance.

[0016] Preferably, the target area is divided into several sub-areas, including:

[0017] The target area is divided according to the density of people, and the density of people in each sub-area is the same.

[0018] The present invention divides the area according to the population density to ensure that the population density of each sub-area is the same, laying a foundation for the subsequent analysis of the degree of personnel crisis in the sub-areas; when an emergency occurs, resources can be quickly mobilized according to the division of population density, giving priority to densely populated areas and improving rescue efficiency.

[0019] Preferably, obtaining environmental information of the sub-region and predicting the flood disaster level of the sub-region includes:

[0020] The digital twin model of the target area simulates weather conditions and collects environmental information in the simulated environment;

[0021] Environmental information includes rainfall per unit time, wind speed, flood depth and flow rate, which are marked as YL, FS, HD, and LS respectively;

[0022] The flood disaster index ZHD of the sub-region is calculated by the formula ZHD = α × ln (YL) + β × ln (FS) + γ × ln (HD) + δ × ln (LS); where α, β, γ and δ are weight coefficients;

[0023] The flood disaster index is ratioed with the disaster index threshold to obtain the flood disaster level.

[0024] The present invention assesses the degree of flood disaster in a sub-region by incorporating multiple factors such as rainfall per unit time, wind speed, flood depth and flow rate into the assessment system, thereby ensuring the comprehensiveness of the assessment results; by performing ratio processing on the flood disaster index and the preset disaster index threshold, a clear flood disaster level can be obtained, providing quantitative indicators for relevant departments, and the quantitative disaster level division is helpful to adopt differentiated response measures, realize refined management, and improve rescue efficiency.

[0025] Preferably, the rescue emergency level of the sub-area is evaluated based on the flood disaster index and personnel information, including:

[0026] The rescue center collects and updates the personnel information in the target area in real time; the personnel information includes the age and health status of the personnel; the personnel are divided into two categories according to the personnel information: ordinary personnel and special personnel; the special personnel are the elderly, infants, pregnant women and sick people; the rest are ordinary personnel;

[0027] The rescue center counts the number of ordinary personnel and special personnel in each sub-area, calculates the ratio of special personnel to ordinary personnel, and obtains the personnel crisis index;

[0028] The rescue emergency level is the sum of the flood disaster level and the personnel crisis level.

[0029] By classifying personnel according to categories, the present invention can more accurately identify which areas need special attention, especially those areas with a large number of special personnel; the rescue center can reasonably allocate rescue resources according to the personnel crisis index, giving priority to ensuring the safety of special personnel; by calculating the ratio of special personnel to ordinary personnel, the personnel crisis index is obtained, which provides a quantitative basis for rescue decision-making. Combined with the method of determining the rescue emergency level according to the flood disaster level, it can significantly improve rescue efficiency, protect the rights and interests of vulnerable groups, support scientific decision-making, and enhance emergency response capabilities.

[0030] Preferably, the disaster index threshold is a critical value of the flood disaster index for floods occurring in the target area; and the crisis index threshold is a critical value of the crisis index for crises occurring to people.

[0031] Preferably, planning the optimal rescue route according to the rescue emergency level and terrain of the sub-area includes:

[0032] Planning a number of rescue routes according to the terrain of the sub-areas, and dividing each rescue route into a number of downstream sub-routes and upstream sub-routes according to the running conditions of the rescue equipment in downstream, upstream and still water;

[0033] The rescue time Ti required for the rescue path is calculated by the formula Ti=TSi+TNi+T0; wherein TSi is the time required for traveling in the downstream sub-path; TNi is the time required for traveling in the upstream sub-path; T0 is the time required for traveling in the still water path; i is the number of the rescue path, i=0, 1, ..., N; N is a positive integer;

[0034] In the case of limited rescue equipment, priority should be given to rescuing sub-areas with a high rescue emergency level;

[0035] The optimal rescue path is the path that takes the least time.

[0036] The present invention plans the rescue path and subdivides it into downstream sub-paths and upstream sub-paths, and calculates the total time required for each path to determine the optimal rescue path. The method can not only improve the rescue efficiency, but also reasonably allocate rescue resources, reduce rescue time, reduce labor costs, enhance decision support, and ensure personnel safety. This method has important application value in disaster relief, can effectively reduce the losses caused by disasters, and protect people's lives and property.

[0037] Preferably, the distance of the downstream sub-path is marked as LSi, the distance of the upstream sub-path is marked as LNi; the distance of the still water path is marked as LCi;

[0038] By the formula T0 = LCi / (λ×Vc); where Vc is the speed of the rescue equipment in still water;

[0039] The time required for the rescue equipment to travel in the downstream sub-path is calculated by the formula TSi=LSi / [λ×(Vc+Vs)]; where Vs is the downstream velocity of water; λ is the velocity attenuation coefficient;

[0040] The time required for the rescue equipment to travel in the downstream sub-path is calculated by the formula TNi=LNi / [λ×(Vc-Vn)], where Vn is the upstream flow velocity of water.

[0041] The present invention calculates the travel speed of the rescue equipment in the downstream and upstream sub-paths respectively, and considers the influence of the rainfall and wind speed per unit time on the travel speed of the rescue equipment, accurately evaluates the travel speed of the rescue equipment in the downstream / reverse direction, and then obtains the accurate travel time. By calculating the time required for each path, the path with the shortest time can be found, thereby rationally allocating limited rescue resources.

[0042] Preferably, the speed attenuation coefficient includes:

[0043] The rainfall per unit time and wind speed in the target area are input into the speed attenuation assessment model, and the speed attenuation coefficient of the rescue equipment driving speed is output.

[0044] Preferably, the management method of the logistics support management system based on digital twins comprises the following steps:

[0045] Step 1: Establish a digital twin model of the target area; the digital twin model integrates the three-dimensional model and meteorological model of the target area; the target area is the area affected by flood disasters;

[0046] Step 2: Based on meteorological data, the digital twin model simulates the weather conditions in the target area; divides the target area according to the density of people, and the density of people in each sub-area is the same, obtains the environmental information of the sub-area, and evaluates the flood disaster level of the sub-area;

[0047] Step 3: The rescue center collects and updates the personnel information of the target area in real time; evaluates the rescue emergency level of the sub-area based on the flood disaster level and personnel information;

[0048] Step 4: Plan the optimal rescue route based on the rescue emergency level and terrain of the sub-area.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] 1. The present invention simulates the weather conditions of the target area through the digital twin model of the target area, divides the target area into several sub-areas, obtains its environmental information, evaluates the flood disaster level of each sub-area when a flood occurs, and analyzes the personnel crisis level of each sub-area; comprehensively evaluates the degree of disaster risk faced by the sub-area by combining the flood disaster level and the personnel crisis level, plans disaster logistics support based on the digital twin model platform, and optimizes resource allocation;

[0051] 2. The present invention plans several rescue paths according to the terrain of the sub-area, divides the rescue paths into downstream paths, upstream paths and still water paths, calculates the travel speed of the rescue equipment in the downstream / reverse / still water paths, further analyzes the travel time of the downstream / reverse / still water paths, sums the travel time of the downstream / reverse / still water paths in the rescue paths, and obtains the travel time of the rescue equipment in the rescue paths; and selects the path with the shortest travel time from the several rescue paths as the optimal rescue path, thereby reducing the rescue time and carrying out rescue in the severely affected areas in a timely manner;

[0052] 3. The present invention establishes a digital twin model of the target area; based on meteorological data, the digital twin model simulates the weather conditions of the target area; divides the target area into several sub-areas, obtains environmental information of the sub-areas, and evaluates the flood disaster level of the sub-areas; evaluates the rescue emergency level of the sub-area based on the flood disaster level and personnel information; plans the optimal rescue path according to the rescue emergency level and terrain of the sub-area; the present invention can provide early warning, prepare logistics support tasks, optimize resource allocation, reduce losses caused by disasters, and protect people’s lives and property. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 It is a schematic diagram of the system structure of the present invention;

[0055] Figure 2 It is a schematic diagram of the process of the optimal rescue route analysis method of the present invention;

[0056] Figure 3 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0057] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] See also Figure 1 , the first aspect of the present invention provides a logistics support management system and method based on digital twins, including a regional digital twin module, and a disaster prediction module and a disaster rescue module connected thereto;

[0059] The regional digital twin module establishes a digital twin model of the target area; the digital twin model integrates the three-dimensional model and meteorological model of the target area; the target area is the area that suffers from flood disasters;

[0060] The image data of the target area is collected by drones to construct a three-dimensional model of the target area; the image data includes terrain features and building features;

[0061] It should be noted that the terrain features and architectural features refer to the layout of mountains, lakes, houses, etc. in the target area.

[0062] The meteorological model simulates weather conditions according to the meteorological data; wherein the meteorological model includes a WRF model or an extended series of the WRF model;

[0063] By formulating dynamic interactions between models and data, a digital twin model of the target area is constructed.

[0064] It should be noted that the collected image data is corrected, denoised, enhanced and other processes to improve data quality, and computer vision technology is used to extract key features such as terrain undulations and building contours to construct a three-dimensional model of the target area.

[0065] Collect historical meteorological data and input it into the WRF model for simulation training to obtain a high-precision meteorological model. The meteorological data includes temperature, humidity, wind direction, wind speed, precipitation and other elements.

[0066] The disaster prediction module uses the digital twin model to simulate the weather conditions in the target area based on meteorological data and collect environmental information in the simulated environment. The target area is divided according to the density of people, and the density of people in each sub-area is the same. The environmental information of the sub-area is obtained to evaluate the flood disaster level of the sub-area.

[0067] Environmental information includes rainfall per unit time, wind speed, flood depth and flow rate, which are marked as YL, FS, HD, and LS respectively; environmental information is extracted from the digital twin model of the target area;

[0068] The flood disaster index ZHD of the target area is calculated by the formula ZHD = α × ln (YL) + β × ln (FS) + γ × ln (HD) + δ × ln (LS); where α, β, γ and δ are weight coefficients;

[0069] It should be noted that the rainfall per unit time may be the rainfall every few minutes or every hour, such as the rainfall every 10 minutes or the rainfall every 1 hour.

[0070] The present invention evaluates the degree of flood disaster suffered by a sub-region through a flood disaster index; the degree of flood disaster is mainly closely related to the rainfall per unit time, wind speed, flood depth and flow velocity. If the rainfall per unit time, wind speed, flood depth and flow velocity are all large, it indicates that the degree of flood disaster is large, and the sub-region is seriously affected by the flood, that is, the flood disaster index is high.

[0071] The flood disaster index is ratioed to the disaster index threshold to obtain the flood disaster level, which is a relative measure of the flood disaster index to the disaster index threshold.

[0072] Among them, the disaster index threshold is the critical value of the flood disaster index for floods in the target area; that is, when the flood disaster index of the sub-area reaches or exceeds this specific value, it means that a serious flood disaster is about to occur or has occurred in the area.

[0073] The rescue center collects and updates personnel information in the target area in real time; evaluates the rescue emergency level of the sub-area based on the flood disaster level and personnel information;

[0074] Personnel information includes the age and health status of personnel; personnel are divided into two categories according to personnel information, namely ordinary personnel and special personnel; among them, special personnel are the elderly, infants, pregnant women and sick people; the rest of the personnel are ordinary personnel;

[0075] The rescue center counts the number of ordinary personnel and special personnel in each sub-area, calculates the ratio of special personnel to ordinary personnel, and obtains the personnel crisis index;

[0076] The present invention evaluates the danger level of personnel health in a sub-area through a personnel crisis index; the danger level of personnel health is mainly closely related to the number of special personnel and the number of ordinary personnel. If the number of special personnel is greater than the number of ordinary personnel, it indicates that there are many special personnel in the sub-area and rescue is required as soon as possible, that is, the personnel crisis index of the sub-area is large.

[0077] The personnel crisis index is compared with the crisis index threshold to obtain the personnel crisis level; that is, a relative measure of the personnel crisis index relative to the crisis index threshold.

[0078] The crisis index threshold is the critical value for the danger to the personnel in the sub-region; that is, when the crisis index of the personnel in the sub-region reaches or exceeds this specific value, it means that the crisis of the personnel in this region is more serious than that of the personnel in other sub-regions.

[0079] The rescue emergency level is the sum of the flood disaster level and the personnel crisis level.

[0080] The present invention evaluates the rescue priority of the sub-area by the rescue emergency level; the rescue priority is mainly closely related to the flood disaster level and the personnel crisis level of the sub-area. If the flood disaster level and the personnel crisis level of a sub-area are both large, it indicates that the sub-area faces a large flood disaster threat and needs to take rescue measures as soon as possible to avoid causing more serious dangerous situations, that is, the rescue emergency level is high.

[0081] See also Figure 2 ,The disaster rescue module plans the optimal rescue path according to the rescue level and terrain of the sub-area.

[0082] Planning a number of rescue paths according to the terrain of the sub-areas, and dividing each rescue path into a number of downstream sub-paths and upstream sub-paths according to the downstream and upstream conditions of the rescue equipment;

[0083] Among them, rescue equipment can be lifeboats, powered pontoon bridges and unmanned detection ships.

[0084] The distance of the downstream sub-path is marked as LSi, the distance of the upstream sub-path is marked as LNi; the distance of the still water path is marked as LCi;

[0085] By the formula T0 = LCi / (λ×Vc); where Vc is the speed of the rescue equipment in still water;

[0086] The time required for the rescue equipment to travel in the downstream sub-path is calculated by the formula TSi=LSi / [λ×(Vc+Vs)]; where Vs is the downstream velocity of water; λ is the velocity attenuation coefficient;

[0087] The time required for the rescue equipment to travel in the downstream sub-path is calculated by the formula TNi=LNi / [λ×(Vc-Vn)], where Vn is the upstream flow velocity of water.

[0088] Among them, the same type of rescue equipment has the same driving speed in still water; the rainfall per unit time and wind speed in the target area are input into the speed attenuation evaluation model, and the speed attenuation coefficient of the rescue equipment's driving speed is output.

[0089] It should be noted that the rainfall per unit time and wind speed per unit time of the target area are the average values ​​of the rainfall per unit time and wind speed per unit time of each sub-area, respectively.

[0090] Several model training data are extracted from historical data, and the model training data include rainfall per unit time, wind speed, and the corresponding speed attenuation coefficient; the rainfall per unit time and wind speed are used as input data of the artificial intelligence model, and the speed attenuation coefficient is used as output data of the artificial intelligence model. The artificial intelligence model is repeatedly trained through input data and output data to obtain a speed attenuation evaluation model through training.

[0091] The travel speed of the rescue equipment of the present invention is mainly closely related to the length of the upstream / downstream path, the upstream / downstream flow velocity of water, and the speed attenuation coefficient. If the downstream flow velocity is larger or the upstream flow velocity is smaller, and the speed attenuation coefficient is smaller, the travel speed of the rescue equipment is faster; if the downstream flow velocity is smaller or the upstream flow velocity is larger, and the speed attenuation coefficient is larger, the travel speed of the rescue equipment is slower.

[0092] The rescue time Ti required for the rescue path is calculated by the formula Ti=TSi+TNi+T0; wherein TSi is the time required for traveling in the downstream sub-path; TNi is the time required for traveling in the upstream sub-path; T0 is the time required for traveling in the still water path; i is the number of the rescue path, i=0, 1, ..., N; N is a positive integer;

[0093] Among them, when rescue equipment is limited, priority will be given to rescuing sub-areas with a high rescue emergency level;

[0094] The optimal rescue path is the path that takes the least time.

[0095] It should be noted that the terrain of the target area includes low-lying areas and high-lying areas, so the rescue path to the sub-area includes both low-lying sections and high-lying sections. The rescue driving path includes both downstream and upstream, and the driving speed of the rescue equipment will also change according to the downstream or upstream situation.

[0096] See also Figure 3 The second aspect of the present invention proposes a management method for a logistics support management system based on digital twins, comprising the following steps:

[0097] Step 1: Establish a digital twin model of the target area; the digital twin model integrates the three-dimensional model and meteorological model of the target area; the target area is the area affected by flood disasters;

[0098] Step 2: Based on meteorological data, the digital twin model simulates the weather conditions in the target area; divides the target area according to the density of people, and the density of people in each sub-area is the same, obtains the environmental information of the sub-area, and evaluates the flood disaster level of the sub-area;

[0099] Step 3: The rescue center collects and updates the personnel information of the target area in real time; evaluates the rescue emergency level of the sub-area based on the flood disaster level and personnel information;

[0100] Step 4: Plan the optimal rescue route based on the rescue emergency level and terrain of the sub-area.

[0101] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0102] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A logistics support management system and method based on digital twins, characterized in that: It includes a regional digital twin module, as well as a disaster prediction module and a disaster relief module connected to it; Regional digital twin module: used to establish a digital twin model of the target area; the digital twin model integrates the three-dimensional model and meteorological model of the target area; the target area is the area affected by flood disasters; Disaster prediction module: used to simulate the weather conditions of the target area based on meteorological data and the digital twin model; divide the target area into several sub-areas, obtain the environmental information of the sub-areas, and evaluate the flood disaster level of the sub-areas; and, The rescue center collects and updates personnel information in the target area in real time; evaluates the rescue emergency level of the sub-area based on the flood disaster level and personnel information; Disaster Rescue Module: used to plan the optimal rescue route based on the rescue emergency level and terrain of the sub-area.

2. The digital twin-based logistics support management system and method according to claim 1 is characterized in that: The establishing of the digital twin model of the target area includes: Collect image data of the target area through drones to build a three-dimensional model of the target area; the image data includes terrain features and building features; and The meteorological model simulates weather conditions according to the meteorological data; wherein the meteorological model includes a WRF model or an extended series of the WRF model; By formulating dynamic interactions between models and data, a digital twin model of the target area is constructed.

3. The digital twin-based logistics support management system and method according to claim 1 is characterized in that: The target area is divided into several sub-areas, including: The target area is divided according to the density of people, and the density of people in each sub-area is the same.

4. The digital twin-based logistics support management system and method according to claim 1 is characterized in that: The obtaining of environmental information of the sub-region and predicting the flood disaster level of the sub-region includes: The digital twin model of the target area simulates weather conditions and collects environmental information in the simulated environment; Environmental information includes rainfall per unit time, wind speed, flood depth and flow rate, which are marked as YL, FS, HD, and LS respectively; The flood disaster index ZHD of the sub-region is calculated by the formula ZHD = α × ln (YL) + β × ln (FS) + γ × ln (HD) + δ × ln (LS); where α, β, γ and δ are weight coefficients; The flood disaster index is ratioed with the disaster index threshold to obtain the flood disaster level.

5. The digital twin-based logistics support management system and method according to claim 4 is characterized in that: The rescue emergency level of the sub-region is evaluated based on the flood disaster index and personnel information, including: The rescue center collects and updates the personnel information in the target area in real time; the personnel information includes the age and health status of the personnel; the personnel are divided into two categories according to the personnel information: ordinary personnel and special personnel; the special personnel are the elderly, infants, pregnant women and sick people; the rest are ordinary personnel; The rescue center counts the number of ordinary personnel and special personnel in each sub-area, calculates the ratio of special personnel to ordinary personnel, and obtains the personnel crisis index; The rescue emergency level is the sum of the flood disaster level and the personnel crisis level.

6. The digital twin-based logistics support management system and method according to claim 5 is characterized in that: The disaster index threshold is the critical value of the flood disaster index for floods in the target area; the crisis index threshold is the critical value of the crisis index for personnel crises.

7. The digital twin-based logistics support management system and method according to claim 6 is characterized in that: The planning of the optimal rescue path according to the rescue emergency level and terrain of the sub-area includes: Planning a number of rescue routes according to the terrain of the sub-areas, and dividing each rescue route into a number of downstream sub-routes and upstream sub-routes according to the running conditions of the rescue equipment in downstream, upstream and still water; The rescue time Ti required for the rescue path is calculated by the formula Ti=TSi+TNi+T0; wherein TSi is the time required for traveling in the downstream sub-path; TNi is the time required for traveling in the upstream sub-path; T0 is the time required for traveling in the still water path; i is the number of the rescue path, i=0, 1, ..., N; N is a positive integer; In the case of limited rescue equipment, priority should be given to rescuing sub-areas with a high rescue emergency level; The optimal rescue path is the path that takes the least time.

8. The digital twin-based logistics support management system and method according to claim 7 is characterized in that: The distance of the downstream sub-path is marked as LSi, the distance of the upstream sub-path is marked as LNi; the distance of the still water path is marked as LCi; By the formula T0 = LCi / (λ×Vc); where Vc is the speed of the rescue equipment in still water; The time required for the rescue equipment to travel in the downstream sub-path is calculated by the formula TSi=LSi / [λ×(Vc+Vs)]; where Vs is the downstream velocity of water; λ is the velocity attenuation coefficient; The time required for the rescue equipment to travel in the downstream sub-path is calculated by the formula TNi=LNi / [λ×(Vc-Vn)], where Vn is the upstream flow velocity of water.

9. The digital twin-based logistics support management system and method according to claim 8, characterized in that: The speed attenuation coefficient includes: The rainfall per unit time and wind speed in the target area are input into the speed attenuation assessment model, and the speed attenuation coefficient of the rescue equipment driving speed is output.

10. A management method for a logistics support management system based on digital twins, characterized in that: The following steps are involved: Step 1: Establish a digital twin model of the target area; the digital twin model integrates the three-dimensional model and meteorological model of the target area; the target area is the area affected by flood disasters; Step 2: Based on meteorological data, the digital twin model simulates the weather conditions in the target area; divides the target area according to the density of people, and the density of people in each sub-area is the same, obtains the environmental information of the sub-area, and evaluates the flood disaster level of the sub-area; Step 3: The rescue center collects and updates the personnel information of the target area in real time; evaluates the rescue emergency level of the sub-area based on the flood disaster level and personnel information; Step 4: Plan the optimal rescue route based on the rescue emergency level and terrain of the sub-area.

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