Intelligent dispatching system for emergency supplies of supply chain based on meteorological short-term forecast

Abstract

This invention discloses an intelligent supply chain emergency material dispatching system based on short-term meteorological forecasts, relating to the fields of emergency management and material dispatching. The system comprises the following components: a short-term meteorological forecasting module, a road network resilience assessment module, an emergency material demand calculation module, a dynamic matching module, and a dispatching execution module. This invention establishes a multi-module collaborative intelligent dispatching system, integrates end-to-end data, accurately identifies the impact of meteorological risks, quantifies incremental material demand, solves traditional dispatching problems, and ensures stable and efficient data transmission. It achieves refined management and control of emergency material dispatching execution, optimizes transportation capacity allocation, synchronously issues instructions and collects status data in real time, forming a closed-loop optimization system that dynamically adapts to meteorological changes, improving the scientific nature of dispatching decisions, the efficiency of execution, and the level of supply chain intelligence.

Description

Technical Field

[0001] This invention relates to the field of emergency management and material dispatching, specifically to an intelligent dispatching system for emergency supplies in the supply chain based on short-term meteorological forecasts. Background Technology

[0002] Meteorological disasters significantly impact regional road network access, hindering the efficiency of emergency supply chain dispatch. Rapid and accurate delivery of emergency supplies has become a crucial link in disaster prevention and mitigation, and in ensuring people's livelihoods. Meteorological forecasts, as an important basis for emergency dispatch, are increasingly integrated with road network assessment, material demand calculation, and capacity matching. The demands for timeliness, accuracy, and dynamic adaptability in supply chain emergency supply dispatch are constantly rising. This requires leveraging multi-source meteorological data to achieve spatiotemporal risk identification, and combining this with road network status to intelligently match material demand with transportation resources, thus constructing a closed-loop dispatch system covering the entire process.

[0003] Existing supply chain emergency material dispatch technologies suffer from disconnects between various links. Meteorological data utilization is limited to basic forecasting, failing to identify and quantify meteorological risks in a spatiotemporal manner, thus hindering accurate mapping to road networks and sections. Road network capacity assessment lacks weighted analysis of multiple meteorological risk factors, and capacity attenuation calculations lack scientific algorithmic support. Material demand calculations are weakly correlated with road network traffic status, and gap calculations do not account for the increased material demand caused by road network attenuation, resulting in significant deviations in calculation results. Furthermore, capacity matching lacks precise demand constraints, and there is no closed-loop feedback mechanism after dispatch execution. Data transmission between modules suffers from delays and external data interference. Overall, the level of intelligence and dynamism in dispatch is insufficient, making it difficult to adapt to the emergency dispatch needs of dynamically changing meteorological risks.

[0004] Supply chain emergency material dispatch under weather influence is a complex system involving multi-stage collaboration. Existing technologies, lacking end-to-end data linkage and scientific algorithm support across weather, road networks, materials, and transportation capacity, cannot achieve intelligent and precise dispatch of emergency materials, resulting in emergency response efficiency that fails to meet actual needs. Against this backdrop, there is an urgent need to break through the limitations of traditional dispatch models and construct an intelligent supply chain emergency material dispatch system based on short-term weather forecasts. Through the orderly connection of various functional modules and one-way encrypted data transmission, this system can quantify weather risks, assess road network resilience, accurately calculate gaps, optimally match transportation capacity, and optimize the dispatch closed loop. This will enhance the system's adaptability to dynamic weather changes and ensure the efficiency and accuracy of emergency material dispatch. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an intelligent supply chain emergency material dispatching system based on short-term meteorological forecasts. It can accurately assess the road network capacity and quantify the incremental demand for materials through spatiotemporal processing of multi-source meteorological data; rely on algorithms to optimally adapt transportation resources, issue standardized dispatching instructions, and provide closed-loop feedback of execution results to the front-end module, forming a closed-loop optimization of the entire process and improving the intelligence and dynamic adaptability of emergency material dispatching.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an intelligent dispatching system for emergency supplies based on short-term meteorological forecasts, which includes the following components: a short-term meteorological forecasting module, a road network resilience assessment module, an emergency supplies demand calculation module, a dynamic matching module, and a dispatching execution module;

[0007] The short-term meteorological forecasting module collects multi-source meteorological monitoring and short-term numerical forecast data, performs spatiotemporal identification and quantification of meteorological risks in the region, and generates and outputs spatiotemporal meteorological risk data.

[0008] The road network resilience assessment module receives spatiotemporal data on meteorological risks, comprehensively assesses the carrying capacity of the road network structure, the traffic status of road sections, and the connectivity efficiency of nodes within the region, obtains the road network traffic capacity results under the influence of meteorology, and generates and outputs road network traffic capacity data.

[0009] The emergency supplies demand calculation module receives road network traffic capacity data, calculates the emergency supplies shortage using an emergency supplies shortage calculation algorithm, and generates and outputs emergency supplies shortage data.

[0010] The dynamic matching module receives emergency material shortage data, performs optimal matching of transportation resources based on the emergency material shortage data, and generates and outputs transportation capacity matching result data.

[0011] The scheduling execution module receives capacity matching result data, uses the capacity matching result data as the sole basis for execution, generates and issues standardized emergency dispatch instructions, and simultaneously feeds back the scheduling execution results to the short-term meteorological forecast module in a closed loop, thus completing the entire process of closed-loop scheduling.

[0012] Furthermore, the implementation process of the short-term weather forecast module is as follows: simultaneously collecting Doppler radar echo data, meteorological satellite remote sensing data, real-time observation data from ground automatic weather stations, and regional short-term weather forecast data; performing time synchronization and spatial registration on the collected meteorological data; performing gridding processing on the registered meteorological data to generate a gridded meteorological dataset; extracting four types of meteorological risk elements: precipitation intensity, wind force level, road surface temperature, and visibility; standardizing and quantifying each type of meteorological risk element to obtain the meteorological risk intensity parameters of the corresponding grid; integrating the risk intensity parameters and spatiotemporal coordinate information of all grids to generate and output meteorological risk spatiotemporal data.

[0013] Furthermore, the working steps of the road network resilience assessment module are as follows:

[0014] The first step is to receive the spatiotemporal data of meteorological risks and extract the meteorological risk element types, risk intensity parameters and spatiotemporal coordinate information of all grids within the target road network area;

[0015] The second step involves matching all road segments of the target road network with their corresponding spatial grids to determine the meteorological risk factors and risk intensity parameters for each road segment. Then, using the capacity attenuation coefficient calculation formula, the capacity attenuation coefficient for each road segment is obtained. The formula is:

[0016]

[0017] in, This is the traffic capacity attenuation coefficient of the target road section under the influence of meteorological risks, with a value ranging from 0 to 1. The smaller the value, the more severe the capacity reduction. This represents the total number of types of meteorological risk factors. For the first The preset weighting coefficients of meteorological risk factors meet the requirements. ; For the target road segment, the first grid cell within the corresponding grid cell The standardized risk intensity parameters for meteorological risk elements range from 0 to 1 and are derived from spatiotemporal meteorological risk data.

[0018] The third step is to design the traffic capacity based on the baseline of the target road segment. The actual traffic capacity of the target road segment is obtained through the traffic capacity calculation formula. The calculation formula is:

[0019]

[0020] in, The actual traffic capacity of the target road section under the influence of meteorological risks; The design baseline traffic capacity of the target road section under conditions of no weather risk;

[0021] The fourth step is to integrate the actual traffic capacity and traffic capacity attenuation coefficient of all road segments in the target road network, and generate and output the road network traffic capacity data.

[0022] Furthermore, the emergency supplies demand calculation module matches the received road network traffic capacity data with the basic support information and material reserve data of the target support area; and quantifies the incremental demand for materials by combining the degree of road network traffic capacity attenuation; and finally generates emergency supplies gap data that includes the types of materials, the total gap, and the timeliness of demand.

[0023] Furthermore, the calculation formula for the emergency material shortage calculation algorithm in the emergency material demand calculation module is as follows:

[0024]

[0025] in, For the first The shortage of designated types of emergency supplies in each protected area. ≤0 represents the first There is no shortage of supplies in any of the protected areas; The baseline demand for a specific type of emergency supplies per unit population per unit time; For the first The total number of people covered by the protection in each protection area; To ensure the effective duration of emergency support; For the first The weighted average capacity attenuation coefficient of the road network associated with each protected area; For the first The total available reserves of designated types of emergency supplies within each protected area.

[0026] Furthermore, the dynamic matching module extracts the core constraint parameters of material demand from the received emergency material shortage data, traverses the available transportation capacity information in the transportation capacity resource pool, and completes the optimal matching of transportation capacity and material shortage based solely on the demand constraints, generating transportation capacity matching result data.

[0027] Furthermore, the process by which the scheduling execution module generates and issues standardized emergency dispatch instructions is as follows: receiving capacity matching result data, extracting four types of execution parameters from the capacity matching result data: delivery plan, transport vehicles, delivery time limit, and route planning; generating vehicle dispatch instructions, material loading instructions, route driving instructions, and delivery confirmation instructions based on the execution parameters; and synchronously issuing all generated instructions to the corresponding capacity execution terminal, warehouse management terminal, and on-site support terminal, and collecting status data of the entire instruction execution process in real time.

[0028] Furthermore, the process of the scheduling execution module to complete the full-process scheduling closed loop is as follows: the collected instruction execution deviation information and scheduling execution completion report are fed back to the short-term meteorological forecast module to optimize the weight calibration of meteorological risk elements and the accuracy of grid processing; after optimization, updated meteorological risk spatiotemporal data are generated, triggering full-process scheduling iteration to form a closed-loop optimization system.

[0029] Furthermore, the system's end-to-end data linkage meets the following requirements: one-way encrypted transmission is used between modules, with a transmission delay of ≤100ms; except for the short-term weather forecast module, the remaining modules only receive the single input from the preceding module and have no external data access; the data update frequency of all modules is synchronized with that of the short-term weather forecast module.

[0030] Compared with existing technologies, this intelligent supply chain emergency material dispatching system based on short-term weather forecasts has the following advantages:

[0031] I. This invention establishes a multi-module collaborative intelligent scheduling system to achieve deep integration across the entire chain of meteorological risk identification, road network resilience assessment, and material demand calculation. It leverages the spatiotemporal processing and standardized quantification of multi-source meteorological data to accurately uncover the actual impact of meteorological risks on the road network. Combined with scientific algorithms, it comprehensively determines the road network's traffic capacity and quantifies the incremental material demand based on the degree of road network attenuation, ensuring a high degree of alignment between material shortage calculations and the actual state of the regional meteorological and road network conditions. This solves the problems of insufficient utilization of meteorological data and the disconnect between material demand calculations and actual scenarios in traditional scheduling. Simultaneously, the modules employ a one-way encrypted linkage mode to ensure the stability and consistency of data transmission, with synchronized data updates across modules, making information transmission more efficient in all aspects of scheduling and providing accurate and reliable basic data support for emergency material dispatch.

[0032] Second, this invention achieves refined management and control of emergency material dispatching and execution through optimal matching of transportation resources and precise issuance of standardized dispatching instructions; it extracts core constraint parameters based on material shortage data to complete transportation capacity matching, ensuring a high degree of alignment between transportation resource allocation and material demand, thereby improving transportation capacity utilization efficiency; in the dispatching and execution phase, it integrates multiple types of execution parameters to generate dispatching instructions adapted to multiple terminals, enabling synchronous issuance of instructions and real-time collection of execution status; simultaneously, it feeds back the dispatching and execution results in a closed loop to the front-end module, optimizing parameters and accuracy related to meteorological risk handling, triggering iterative dispatching processes, and forming a closed-loop optimization system for the entire process; this allows the dispatching system to dynamically adapt to changes in meteorological risks, improving the scientific nature of dispatching decisions and the efficiency of execution, and enhancing the dynamic adaptability and intelligence level of emergency material dispatching in the supply chain.

[0033] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0035] Figure 1 A block diagram showing the modular components of an intelligent supply chain emergency material dispatching system based on short-term meteorological forecasts;

[0036] Figure 2 A flowchart illustrating the implementation of the short-term meteorological forecasting module in a supply chain emergency material intelligent dispatching system based on short-term meteorological forecasts;

[0037] Figure 3 This is a flowchart illustrating the closed-loop scheduling execution process of an intelligent supply chain emergency material dispatching system based on short-term meteorological forecasts. Detailed Implementation

[0038] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0039] Example 1

[0040] The short-term weather forecast module simultaneously collects Doppler radar echo data, meteorological satellite remote sensing data, real-time observation data from ground automatic weather stations, and regional short-term weather forecast data. First, it performs time synchronization and spatial registration on the collected meteorological data, enabling a unified analytical foundation across multiple meteorological sources in both time and space. Then, it performs gridded processing on the registered meteorological data to generate a gridded meteorological dataset. This allows meteorological risk analysis to be accurately applied to each grid unit within the region. From the dataset, four meteorological risk elements are extracted: precipitation intensity, wind speed, road surface temperature, and visibility. Each meteorological risk element is standardized and quantified to obtain the corresponding meteorological risk intensity parameters for each grid. This provides comparable and integrable quantitative standards for different types of meteorological risk elements. Finally, it integrates the risk intensity parameters and spatiotemporal coordinate information from all grids to generate and output spatiotemporal meteorological risk data. This data accurately captures the spatiotemporal distribution characteristics and intensity levels of heavy rainfall within the region, making the identification and quantification of meteorological risks more targeted. Precipitation intensity is the core meteorological risk element, while the risk intensities of the other three elements are at low levels.

[0041] The road network resilience assessment module receives spatiotemporal meteorological risk data output from the short-term meteorological forecast module and conducts road network resilience assessment. First, it extracts the meteorological risk element types, risk intensity parameters, and spatiotemporal coordinate information of all grids within the target road network area, establishing a precise data foundation for subsequent road segment risk matching. Then, it matches all road segments of the target road network with their corresponding spatial grids to determine the meteorological risk elements and risk intensity parameters corresponding to each road segment, ensuring that the risk assessment of road network segments accurately corresponds to the actual meteorological impact. Finally, it calculates the capacity attenuation coefficient for each road segment using the following formula: ,in, The traffic capacity attenuation coefficient of the target road section under the influence of meteorological risks; This represents the total number of types of meteorological risk factors. For the first Preset weighting coefficients for meteorological risk factors; For the target road segment, the first grid cell within the corresponding grid cell Standardized risk intensity parameters for meteorological risk factors; quantify the impact of meteorological risks on road segment capacity; then, combining the benchmark design capacity of the target road segment, obtain the actual capacity of the target road segment using the capacity calculation formula: ,in, The actual traffic capacity of the target road section under the influence of meteorological risks; It provides the design baseline traffic capacity for the target road segment under conditions without meteorological risks; it can accurately reflect the actual traffic level of each road segment under the influence of heavy rainfall; finally, it integrates the actual traffic capacity and traffic capacity attenuation coefficient of all road segments in the target road network, generates and outputs road network traffic capacity data, which can provide basic data that fits the actual road network status for subsequent material demand calculations. The data shows that the traffic capacity attenuation coefficient of road segments covered by heavy rainfall is significantly reduced, and the actual traffic capacity is greatly reduced.

[0042] The emergency supplies demand calculation module receives road network capacity data output from the road network resilience assessment module. It first matches this data with basic support information and supply reserve data for the target area, ensuring the supply gap calculation aligns with the population size and current reserves of the supported area. Then, it quantifies the incremental demand for supplies by combining the degree of road network capacity decay, fully considering the actual impact of road network obstruction on supply replenishment. Finally, it calculates the emergency supplies gap using an algorithm, the formula of which is: ,in, For the first The shortage of designated types of emergency supplies in each protected area; The baseline demand for a specific type of emergency supplies per unit population per unit time; For the first The total number of people covered by the protection in each protection area; To ensure the effective duration of emergency support; For the first The weighted average capacity attenuation coefficient of the road network associated with each protected area; For the first The total available reserves of designated types of emergency supplies within each protection area are calculated to make the process of calculating material shortages more scientific and accurate. The final result is emergency material shortage data that includes the types of supplies, the total shortage amount, and the timeliness of demand. This provides a clear and specific demand basis for subsequent capacity matching. Waterproof supplies, drainage equipment, emergency food, and drinking water are the main shortage supplies. In addition, the more severe the decline in road network capacity in the protection area, the higher the increase in the demand for supplies and the more urgent the timeliness of demand.

[0043] The dynamic matching module receives emergency material shortage data output by the emergency material demand calculation module. It first extracts the core constraint parameters of material demand to accurately grasp the key requirements for material allocation. Then, it traverses the available transportation capacity information in the transportation capacity resource pool to comprehensively understand the current available transportation capacity resources. Based solely on demand constraints, it completes the optimal matching of transportation capacity and material shortages, avoiding ineffective matching of transportation resources. It comprehensively considers the type, total amount, timeliness of demand, and road network accessibility of material shortages to match suitable transport vehicles, routes, and delivery teams for different support areas. It generates and outputs transportation capacity matching result data, enabling subsequent scheduling to obtain transportation capacity allocation plans that meet actual needs, improving the utilization efficiency of transportation resources and the accuracy of material delivery.

[0044] The scheduling execution module receives the capacity matching result data output by the dynamic matching module and uses this data as the sole basis for execution. This ensures that the generation and execution of scheduling instructions have a unified core basis. First, it extracts four types of execution parameters from the capacity matching result data: delivery plan, transport vehicles, delivery time limit, and route planning. This establishes a clear parameter foundation for the generation of standardized instructions. Then, based on these execution parameters, it generates vehicle scheduling instructions, material loading instructions, route driving instructions, and delivery confirmation instructions. This allows various execution terminals to obtain targeted and implementable operational requirements. Subsequently, all generated instructions are synchronously distributed to the corresponding capacity execution terminals and warehouses. The management terminal and field support terminal simultaneously collect real-time status data throughout the entire command execution process, enabling full monitoring of the dispatch command execution process and timely understanding of command implementation. After command execution is completed, the dispatch execution module feeds back the collected command execution deviation information and dispatch execution completion report to the short-term meteorological forecast module. Based on the feedback information, the short-term meteorological forecast module optimizes the weighting and gridding accuracy of meteorological risk elements, making the identification and quantification of meteorological risks more aligned with the needs of actual dispatch scenarios. After optimization, updated spatiotemporal data of meteorological risks are generated, triggering full-process dispatch iteration and forming a closed-loop optimization system, such as... Figure 1 As shown, this allows the system's overall scheduling capabilities to continuously improve with actual applications, completing the entire process of intelligent scheduling of emergency supplies for the supply chain under the meteorological conditions of this heavy rainfall.

[0045] This embodiment verifies the feasibility and adaptability of an intelligent supply chain emergency material dispatch system based on short-term meteorological forecasts under heavy rainfall conditions. The system's modules operate in an orderly manner according to predetermined logic, adhering to the end-to-end data requirements of unidirectional encrypted transmission, single-source input, and synchronous data updates, ensuring secure, real-time, and standardized data flow. From the short-term meteorological forecast module accurately identifying the spatiotemporal risk characteristics of heavy rainfall, to the road network resilience assessment module quantifying the attenuation of road segment capacity using a specific formula, and then to the demand calculation module deriving scientific material shortage data based on algorithms, each step is progressive and well-supported by data. The dynamic matching module achieves precise matching of transport capacity and material shortages, the dispatch execution module completes standardized instruction issuance and full-process monitoring, and optimizes meteorological module parameters through closed-loop feedback, ensuring the system's dispatch capabilities meet actual scenario requirements. The entire process achieves intelligent, precise, and efficient emergency material dispatch under heavy rainfall, providing a feasible implementation plan for supply chain emergency material dispatch under single severe weather risks.

[0046] Example 2

[0047] The short-term weather forecast module performs meteorological data acquisition and processing, simultaneously collecting Doppler radar echo data, meteorological satellite remote sensing data, real-time observation data from ground automatic weather stations, and regional short-term weather forecast data. This allows for comprehensive acquisition of multi-dimensional meteorological monitoring and forecasting information within the region. The module first performs time synchronization and spatial registration on the collected multi-source meteorological data, enabling a unified analytical system across different sources in both time and space. Then, it performs gridding processing to generate a gridded meteorological dataset, allowing meteorological risk analysis to accurately correspond to specific grids within the region. From this dataset, four meteorological risk elements are extracted: precipitation intensity, wind speed, road surface temperature, and visibility. Each meteorological risk element is standardized and quantified to obtain the corresponding meteorological risk intensity parameters for each grid. This allows for a unified quantitative standard for different types and intensities of meteorological risks, facilitating subsequent integration and analysis. Finally, the module integrates the risk intensity parameters and spatiotemporal coordinate information from all grids to generate and output spatiotemporal meteorological risk data. Figure 2 As shown, it can accurately capture the spatiotemporal distribution and intensity characteristics of strong winds accompanied by low visibility in the region, allowing the output data of meteorological risks to directly serve the subsequent road network resilience assessment. Among them, wind force level and visibility are the core high-risk elements, while precipitation intensity and road surface temperature risk intensity parameters are at the normal level.

[0048] After receiving the spatiotemporal data of meteorological risks, the road network resilience assessment module initiates the road network traffic capacity assessment process. First, it extracts the meteorological risk element types, risk intensity parameters, and spatiotemporal coordinate information of all grids within the target road network area, providing data support for accurate matching of road segments with meteorological risks. Then, it matches all road segments of the target road network with their corresponding spatial grids, clarifying the meteorological risk elements and risk intensity parameters corresponding to each road segment. This ensures that the risk assessment of each road segment aligns with the actual meteorological conditions of the area. The traffic capacity attenuation coefficient for each road segment is calculated using the traffic capacity attenuation coefficient calculation formula, accurately quantifying the impact of strong winds and low visibility on the traffic capacity of road segments. Combined with the baseline design traffic capacity of the target road segment, the actual traffic capacity of the target road segment is obtained using the traffic capacity calculation formula, objectively reflecting the traffic level of each road segment under the influence of superimposed meteorological risks. Finally, it integrates the actual traffic capacity and traffic capacity attenuation coefficient of all road segments in the target road network, generating and outputting road network traffic capacity data. It can provide a realistic road network condition basis for subsequent emergency material demand calculation. Data shows that the traffic capacity attenuation coefficient of road sections affected by the combined effects of strong winds and low visibility has been greatly reduced, and the actual traffic capacity of some trunk roads has been reduced to a lower level than the benchmark design traffic capacity.

[0049] After receiving road network capacity data, the emergency supplies demand calculation module first matches the basic support information and material reserve data of the target support area. This allows the material shortage calculation to fully consider the basic conditions such as the population size and current reserve status of the support area. Then, it quantifies the incremental demand for materials by combining the degree of road network capacity attenuation. This fully considers the difficulties caused by road network obstruction to material replenishment. Through the emergency supplies shortage calculation algorithm, the module accurately calculates the emergency supplies shortage, making the calculation results more scientific and reasonable, and closely matching the actual emergency needs of the support area. Finally, it generates emergency supplies shortage data that includes the types of materials, the total shortage, and the timeliness of demand. This provides clear and specific demand guidance for subsequent dynamic matching of transportation capacity. Emergency lighting equipment, windproof materials, traffic warning materials, and emergency medical supplies are the main shortage materials. The lower the road network capacity attenuation coefficient of the support area, the larger the total material shortage and the higher the timeliness requirement of demand.

[0050] After receiving emergency supply shortage data, the dynamic matching module first extracts the core constraint parameters of supply demand from the data. This accurately identifies the key requirements and limitations in the capacity matching process. Then, it comprehensively traverses the available capacity information in the capacity resource pool, gaining a complete understanding of all currently available capacity resources. Based solely on demand constraints, and considering the characteristics of supply shortages in each support area, road network conditions, and timeliness of demand, it achieves optimal matching between capacity and supply shortages. This ensures that the capacity allocation plan perfectly aligns with the actual supply demand, avoiding waste and mismatch of capacity resources. It matches corresponding transportation vehicles for different types of shortage supplies, plans suitable delivery routes for different support areas, and generates and outputs capacity matching result data. This provides subsequent scheduling and execution stages with directly implementable capacity allocation plans, improving the efficiency and accuracy of emergency supply delivery.

[0051] The scheduling and execution module uses the received capacity matching results as the sole basis for execution, ensuring the uniformity and standardization of the entire scheduling and execution process. It first extracts four types of execution parameters: delivery plan, transport vehicles, delivery time limit, and route planning. This provides a clear core basis for generating standardized scheduling instructions. Based on these parameters, it generates vehicle scheduling instructions, material loading instructions, route driving instructions, and delivery confirmation instructions, enabling various terminals such as capacity, warehousing, and on-site support to receive targeted operational instructions, ensuring the orderly operation of each link. Subsequently, all instructions are synchronously sent to the corresponding capacity execution terminals, warehouse management terminals, and on-site support terminals, while simultaneously collecting real-time data on the entire instruction execution process. The status data enables real-time monitoring of the entire process of dispatching instructions being implemented, allowing for timely detection and understanding of various situations during execution. After all dispatching instructions have been executed, the dispatching execution module will collect the instruction execution deviation information and dispatching execution completion report, providing closed-loop feedback to the short-term meteorological forecasting module. Based on the feedback information, the short-term meteorological forecasting module will optimize and adjust the weighting of meteorological risk elements and the accuracy of grid processing, making the data analysis and output of the short-term meteorological forecasting module more closely aligned with actual dispatching application scenarios, improving the accuracy of meteorological risk identification, and generating updated spatiotemporal data of meteorological risks after optimization. This triggers a full-process dispatching iteration, forming a closed-loop optimization system, such as... Figure 3 As shown, this allows the system's overall emergency material dispatching capabilities to continuously iterate and upgrade in practical applications, enabling intelligent dispatching of emergency supplies for the supply chain under these conditions of strong winds and low visibility.

[0052] This embodiment verifies the actual operational effectiveness of the intelligent dispatch system under combined meteorological conditions of strong winds and low visibility. The system's modules are activated sequentially, controlling data transmission delays and methods. The single-source input mode, free from external data interference, makes data flow more controllable, and synchronized data updates across all modules ensure the real-time nature of dispatch decisions. The short-term meteorological forecast module accurately captures the core risk elements and spatiotemporal distribution of combined meteorological conditions. The road network resilience assessment module uses a series of formulas to quantitatively assess the traffic capacity of road sections. The demand calculation module, combined with the degree of road network attenuation, uses algorithms to derive realistic material shortage data. The dynamic matching module achieves optimal capacity matching based on demand, the dispatch execution module completes the issuance and execution monitoring of standardized instructions from multiple terminals, and closed-loop feedback further optimizes the analytical accuracy of the meteorological module, driving iterative upgrades of the system dispatch. The overall process efficiently adapts to emergency dispatch needs under combined meteorological risks, providing a practical reference for intelligent dispatch of emergency supplies in supply chains under scenarios with overlapping meteorological risks.

[0053] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A supply chain emergency material intelligent dispatching system based on short-term meteorological forecasts, characterized in that: The system comprises the following components: a short-term weather forecast module, a road network resilience assessment module, an emergency supplies demand calculation module, a dynamic matching module, and a dispatch execution module; The short-term meteorological forecasting module collects multi-source meteorological monitoring and short-term numerical forecast data, performs spatiotemporal identification and quantification of meteorological risks in the region, and generates and outputs spatiotemporal meteorological risk data. The road network resilience assessment module receives spatiotemporal data on meteorological risks, comprehensively assesses the carrying capacity of the road network structure, the traffic status of road sections, and the connectivity efficiency of nodes within the region, obtains the road network traffic capacity results under the influence of meteorology, and generates and outputs road network traffic capacity data. The emergency supplies demand calculation module receives road network traffic capacity data, calculates the emergency supplies shortage using an emergency supplies shortage calculation algorithm, and generates and outputs emergency supplies shortage data. The dynamic matching module receives emergency material shortage data, performs optimal matching of transportation resources based on the emergency material shortage data, and generates and outputs transportation capacity matching result data. The scheduling execution module receives capacity matching result data, uses the capacity matching result data as the sole basis for execution, generates and issues standardized emergency dispatch instructions, and simultaneously feeds back the scheduling execution results to the short-term meteorological forecast module in a closed loop, thus completing the entire process of closed-loop scheduling.

2. The intelligent supply chain emergency material dispatching system based on short-term meteorological forecasting according to claim 1, characterized in that, The implementation process of the short-term weather forecast module is as follows: synchronously collect Doppler radar echo data, meteorological satellite remote sensing data, real-time observation data from ground automatic weather stations, and regional short-term weather forecast data, and perform time synchronization and spatial registration on the collected meteorological data; The registered meteorological data is processed into a grid to generate a gridded meteorological dataset, and four meteorological risk factors are extracted: precipitation intensity, wind force level, road surface temperature, and visibility. Each type of meteorological risk element is standardized and quantified to obtain the meteorological risk intensity parameters of the corresponding grid. The risk intensity parameters and spatiotemporal coordinate information of all grids are integrated to generate and output the spatiotemporal data of meteorological risks.

3. The intelligent supply chain emergency material dispatching system based on short-term meteorological forecasting according to claim 1, characterized in that, The working steps of the road network resilience assessment module are as follows: The first step is to receive the spatiotemporal data of meteorological risks and extract the meteorological risk element types, risk intensity parameters and spatiotemporal coordinate information of all grids within the target road network area; The second step involves matching all road segments of the target road network with their corresponding spatial grids to determine the meteorological risk factors and risk intensity parameters for each road segment. Then, using the capacity attenuation coefficient calculation formula, the capacity attenuation coefficient for each road segment is obtained. The formula is: ； in, The traffic capacity attenuation coefficient of the target road section under the influence of meteorological risks; This represents the total number of types of meteorological risk factors. For the first Preset weighting coefficients for meteorological risk factors; For the target road segment, the first grid cell within the corresponding grid cell Standardized risk intensity parameters for meteorological risk factors; The third step is to design the traffic capacity based on the baseline of the target road segment. The actual traffic capacity of the target road segment is obtained through the traffic capacity calculation formula. The calculation formula is: ； in, The actual traffic capacity of the target road section under the influence of meteorological risks; The design baseline traffic capacity of the target road section under conditions of no weather risk; The fourth step is to integrate the actual traffic capacity and traffic capacity attenuation coefficient of all road segments in the target road network, and generate and output the road network traffic capacity data.

4. The intelligent supply chain emergency material dispatching system based on short-term meteorological forecasting according to claim 1, characterized in that, The emergency supplies demand calculation module receives road network traffic capacity data, matches it with basic support information and material reserve data for the target support area, and quantifies the incremental demand for materials based on the degree of road network traffic capacity attenuation. Finally, it generates emergency supplies gap data that includes the types of materials, the total gap, and the timeliness of demand.

5. The intelligent supply chain emergency material dispatching system based on short-term meteorological forecasting according to claim 1, characterized in that, The calculation formula for the emergency material shortage calculation algorithm in the emergency material demand calculation module is as follows: ； in, For the first The shortage of designated types of emergency supplies in each protected area; The baseline demand for a specific type of emergency supplies per unit population per unit time; For the first The total number of people covered by the protection in each protection area; To ensure the effective duration of emergency support; For the first The weighted average capacity attenuation coefficient of the road network associated with each protected area; For the first The total available reserves of designated types of emergency supplies within each protected area.

6. The intelligent supply chain emergency material dispatching system based on short-term meteorological forecasting according to claim 1, characterized in that, The dynamic matching module extracts the core constraint parameters of material demand from the received emergency material shortage data, traverses the available transportation capacity information in the transportation capacity resource pool, and completes the optimal matching of transportation capacity and material shortage based solely on the demand constraint, generating transportation capacity matching result data.

7. The intelligent supply chain emergency material dispatching system based on short-term meteorological forecasting according to claim 1, characterized in that, The process by which the scheduling execution module generates and issues standardized emergency dispatch instructions is as follows: receiving capacity matching result data, extracting four types of execution parameters from the capacity matching result data: delivery plan, transport vehicles, delivery time limit, and route planning; generating vehicle dispatch instructions, material loading instructions, route driving instructions, and delivery confirmation instructions based on the execution parameters; and synchronously issuing all generated instructions to the corresponding capacity execution terminal, warehouse management terminal, and on-site support terminal, and collecting status data of the entire instruction execution process in real time.

8. The intelligent supply chain emergency material dispatching system based on short-term meteorological forecasting according to claim 1, characterized in that, The process of the scheduling execution module to complete the full-process scheduling closed loop is as follows: the collected instruction execution deviation information and scheduling execution completion report are fed back to the short-term meteorological forecast module to optimize the accuracy of meteorological risk element weight calibration and grid processing; After optimization, updated spatiotemporal meteorological risk data is generated, triggering full-process scheduling iteration and forming a closed-loop optimization system.

9. The intelligent supply chain emergency material dispatching system based on short-term meteorological forecasting according to claim 1, characterized in that, The system's end-to-end data linkage meets the following requirements: one-way encrypted transmission is used between modules, with a transmission delay of ≤100ms; except for the short-term weather forecast module, the remaining modules only receive the single input from the preceding module and have no external data access; the data update frequency of all modules is synchronized with that of the short-term weather forecast module.

Images