Plateau emergency material distribution method and device based on psychosomatic performance attenuation model

Abstract

This invention relates to the fields of emergency logistics optimization and transportation engineering, specifically to a method and device for allocating emergency supplies in high-altitude areas based on a physiological and psychological efficacy decay model. It includes: constructing a multi-objective optimization model for high-altitude emergency supply allocation; building a comprehensive impedance model of ground road sections based on road conditions and geological data and determining traffic status; introducing altitude parameters to correct for physiological and psychological efficacy decay in road section travel time based on driver physiological efficacy coefficients; determining rescue transportation modes and selecting safe transfer nodes based on comprehensive impedance and traffic status to form an air-ground coordinated transportation logic; optimizing the individual individuals in the algorithm population to obtain a legitimate population; using an improved multi-objective optimization algorithm to obtain a Pareto optimal solution set; and selecting and outputting a scheduling scheme based on real-time meteorological conditions. This invention is adapted to the high-altitude environment, improves the fairness of supply allocation, and enhances the overall efficiency of high-altitude emergency rescue.

Description

Technical Field

[0001] This invention relates to the fields of emergency logistics optimization and transportation engineering technology, and in particular to a method and device for allocating emergency supplies in high-altitude areas based on a physical and mental efficacy decay model. Background Technology

[0002] Plateau regions are vast, with high average altitudes and active geological structures, making them prone to natural disasters such as earthquakes, landslides, and mudslides. The timeliness and precision requirements for emergency rescue are far higher than in conventional scenarios. Compared to plains areas, the unique objective environment of plateaus—high altitude, low oxygen levels, and fragile road networks—imposes severe constraints on the transportation and distribution of emergency supplies. Existing general logistics scheduling models and conventional emergency rescue plans have not been adapted and optimized for the unique conditions of plateaus, and their direct application in this scenario suffers from strong environmental unsuitability and practical limitations.

[0003] High-altitude, low-oxygen environments have a significant nonlinear inhibitory effect on human-machine efficiency in transportation. Current technologies do not incorporate this attenuation effect into scheduling models, leading to severe distortions in transportation timeliness calculations. Physically, the increase in altitude causes a sharp drop in air density, resulting in a significant decrease in vehicle power output as altitude increases. Physiologically, the low-oxygen environment reduces the driver's blood oxygen saturation, leading to a decline in cognitive and reaction abilities. Under long-distance, high-intensity rescue driving, the decline in physical and mental efficiency is nonlinearly amplified. Using plain standards to calculate the estimated arrival time can result in an error of more than 20%, which can easily mislead rescue command decisions and delay the golden rescue opportunity. At the same time, plateau regions are vast and sparsely populated, with scattered towns, long rescue transportation distances, and scarce supply stations. Main roads are built along mountains with narrow roadbeds, and their special alignment characteristics lead to high driving risks. Moreover, the road network is mostly a single channel, and when geological disasters block the road, the existing pure ground transportation mode is easily paralyzed, making it impossible to guarantee the continuous delivery of supplies.

[0004] Traditional emergency distribution methods often use the Gini coefficient to measure the balance of supplies, pursuing only superficial quantitative fairness while completely ignoring the differences in living environments at different disaster-stricken areas on the plateau. The survival window for disaster victims in extremely cold and oxygen-deficient areas above 4,500 meters is much shorter than that in low-altitude areas. The logic of simply distributing supplies equally cannot prioritize the survival needs of high-risk groups, resulting in a substantial "injustice in life."

[0005] In summary, existing emergency material distribution technologies are ill-suited to the unique environment of high-altitude regions. They struggle to accurately measure timeliness deviations caused by the decline in human and machine performance, lack the collaborative transportation capabilities to withstand road network disruptions, and fail to achieve equitable distribution based on survival considerations. Consequently, they fall short of meeting the actual needs of emergency rescue efforts during natural disasters on high-altitude regions. There is an urgent need in this field for an air-ground collaborative emergency material distribution method that can accurately quantify the decline in physical and mental performance on high-altitude regions, adapt to the characteristics of fragile road networks, and ensure fairness in survival, in order to overcome the core pain points of existing technologies. Summary of the Invention

[0006] This invention addresses the technical problems in existing high-altitude emergency material distribution technologies, such as insufficient consideration of the impact of high-altitude and hypoxic environments on vehicle power and driver physiological efficiency, lack of a fair distribution mechanism for high-altitude and extremely cold scenarios, and the inability of a single transportation mode to adapt to extreme situations like road blockages. These problems lead to low material dispatch efficiency, insufficient distribution fairness, and delayed rescue response. The invention provides a high-altitude emergency material distribution method and device based on a psycho-efficacy attenuation model. This method optimizes the entire high-altitude emergency material distribution process, resulting in a precise dispatch plan adapted to the unique high-altitude scenarios. In high-altitude disaster relief scenarios, multi-objective optimization and transportation mode decisions can be made based on the altitude, temperature, and road network conditions of the disaster site. By constructing a psycho-efficacy-corrected impedance model, fairness indicators, and air-ground coordinated dispatch logic, the invention achieves efficient and fair material distribution, thereby improving emergency rescue effectiveness and ensuring the safety of disaster victims.

[0007] In a first aspect, embodiments of the present invention provide a method for allocating emergency supplies in high-altitude areas based on a physical and mental efficacy decline model, the method comprising:

[0008] S1. Construct a multi-objective optimization model for the allocation of emergency supplies in high-altitude areas based on basic data; S2. Based on the multi-objective optimization model, a comprehensive impedance model of the ground road segment is constructed by combining road condition and geological data, and the road segment traffic status is determined to obtain the comprehensive ground impedance and traffic status results of each road segment in the entire road network; and the estimated travel time of the road segment is corrected according to the altitude parameter and the driver's physiological efficiency coefficient to form the road network traffic parameters after the physiological and physiological efficiency decay correction. S3. Combining the ground comprehensive impedance, the road network traffic parameters corrected for physical and mental efficiency attenuation, and the traffic status results, determine the rescue transportation mode for the disaster-stricken points and screen safe transfer nodes; S4. Based on the rescue transportation mode and the information of the safe transfer node, and combined with the payload, endurance and traffic status of the UAV, optimize the population individuals to obtain legal population individuals that meet the transportation constraints; S5. Using an improved multi-objective optimization algorithm, based on the legal population individuals, the multi-objective optimization model, and the disaster evolution simulation parameters, the Pareto optimal solution set for the allocation of emergency supplies on the plateau is obtained; S6. Based on the real-time meteorological conditions in the disaster area, select a scheduling scheme from the Pareto optimal solution set, and output the final executable plateau emergency material allocation and transportation scheduling scheme.

[0009] Optionally, the basic data includes plateau road network data, vehicle data, basic information on disaster-stricken areas, and environmental parameters; The plateau road network data includes a set of road network nodes, a set of connected physical road segments, and road segment geometric parameters; The vehicle data includes basic performance parameters for trucks and drones; The basic information and environmental parameters of the disaster-stricken area include the total amount of supplies needed at the disaster-stricken area, altitude, and current ambient temperature. The construction of the multi-objective optimization model includes: constructing a multi-objective optimization model with the objectives of minimizing the total perceived pain value of the entire system, minimizing the unmet material demand rate, and minimizing the total comprehensive resistance risk of the transportation process, while setting time window constraints for physical and mental efficacy correction, air-ground collaborative load and range correction constraints, driver physiological limit constraints, and conventional logistics constraints.

[0010] Optionally, the road condition and geological data include road segment related parameters and geological disaster related data; the road segment related parameters include road segment geometric parameters and road segment length; the geological disaster related data is the probability of geological disaster occurrence; the road segment geometric parameters include road segment longitudinal slope, curve radius, and road width; The construction of the comprehensive impedance model includes: calculating the curve-slope combination value, road width constraint factor, and long-distance fatigue factor to quantify the danger level of sharp curves and steep slopes; obtaining a dynamic geometric risk term by combining the probability of geological disaster occurrence; integrating the actual driving time of ground vehicles after psycho-efficacy correction and the expected fuel cost of the road segment; and constructing a comprehensive impedance model of the ground road segment by assigning dimensionless weight coefficients to each component; the road segment traffic status includes normal traffic, reduced speed traffic, and physical blockage.

[0011] Optionally, based on the ground composite impedance, the road network traffic parameters corrected for physical and mental efficacy attenuation, the traffic status results, and survival-related parameters of the disaster-stricken area, the rescue transportation mode for the disaster-stricken area is determined and safe transfer nodes are selected; wherein: The survival-related parameters of the disaster-affected site include the altitude of the disaster-affected site, the current ambient temperature, and the baseline ideal survival tolerance time. The rescue transportation modes include direct ground mode and air-ground coordinated mode; the conditions for switching to air-ground coordinated mode include: the overall impedance of the ground path tends to infinity, or the estimated arrival time of pure ground transportation exceeds the survival time window. The safe transfer node is the node that is closest to the disaster site in a straight line, has a ground impedance lower than the safe impedance threshold, and meets the requirements of a flat take-off and landing site.

[0012] Optionally, optimizing the population includes: acquiring data related to pain perception at disaster sites and optimizing the population population based on algorithms; Population priority is initialized by sorting the initial pain perception values ​​of disaster-stricken areas from high to low, and highly adaptable drivers and high-performance vehicles are assigned to disaster-stricken areas with high pain perception values. Specifically, illegal individuals generated by algorithm crossover mutation are subject to drone endurance constraint checks. Individuals that violate the constraints are repaired by searching for a closer safe transit node in the preceding nodes or by flipping the rescue transportation mode to a ground direct mode, thus obtaining the legal individuals.

[0013] Optionally, S5 includes: performing a fast non-dominated sorting of the legal population individuals and completing Pareto stratification; dynamically updating the probability of geological disaster occurrence in the road network at a set frequency during the iteration of the multi-objective optimization algorithm based on the disaster evolution simulation parameters; removing infeasible population individuals; or triggering a mutation operation on the infeasible population individuals to switch them to the air-ground collaborative mode in the rescue transportation mode; and completing the solution by combining congestion distance calculation and elite retention strategy to maintain population diversity, thereby obtaining the Pareto optimal solution set.

[0014] Optionally, the scheduling scheme selected from the Pareto optimal solution set is the scheme with the minimum total comprehensive impedance risk of the transportation process under extreme weather conditions, and the scheme with the minimum total perceived pain value of the entire system in the early stage of the golden rescue period.

[0015] Optionally, the payload and endurance of the UAV are aerodynamically corrected based on the atmospheric density at the operating altitude, and the pilot's physiological efficiency coefficient is constrained based on the blood oxygen saturation at the operating altitude; the pilot's physiological efficiency coefficient includes the pilot's single continuous driving time.

[0016] Secondly, embodiments of the present invention provide a plateau emergency supplies distribution device based on a physical and mental efficacy decline model, the device comprising: The multi-source sensing and zoning module is used to collect plateau road network data, geological disaster data and disaster-affected point information, and to divide the area based on the access conditions of the disaster-affected points; The dynamic impedance modeling module is used to calculate the curve-slope combination value of road segments, construct a dynamic road network impedance model that includes geometric and environmental risks by combining the probability of geological disasters, determine the traffic status of road segments, and obtain the impedance value and traffic status results of each road segment in the entire road network. The efficiency correction calculation module is used to incorporate altitude parameters and, based on the vehicle power attenuation coefficient and the driver's physiological efficiency coefficient, correct the estimated travel time for each road segment. The fairness indicator construction module is used to construct fairness indicators with the perceived pain value of disaster-stricken areas as the core. The air-ground collaborative decision-making module is used to determine the rescue transportation mode, screen safe transfer nodes, and form relevant logic for air-ground collaborative transportation based on road network impedance, road section traffic status, and survival time window of disaster-stricken points. The multi-objective solution module is used to solve the optimal scheduling scheme for the allocation of emergency supplies in the plateau region by using an improved multi-objective optimization algorithm, combined with fairness index, dynamic road network impedance model and disaster evolution parameters. It also outputs the final executable allocation and transportation capacity scheduling results based on real-time meteorological conditions.

[0017] Optionally, the device includes a processor and a memory, the memory storing a computer program that, when executed by the processor, implements the plateau emergency supplies distribution method based on the physical and mental efficacy decay model as described in any one of claims 1 to 8.

[0018] The plateau emergency supplies distribution method and apparatus based on the psychophysical efficacy decay model according to the present invention have at least the following beneficial effects: The present invention provides a plateau emergency supplies allocation method based on a psychosocial performance decay model. First, it constructs a multi-objective optimization model for plateau emergency supplies allocation based on fundamental data, providing a scientific optimization benchmark for the entire allocation process. Existing technologies for plateau emergency supplies allocation often employ single-objective optimization without considering the plateau scenario, leading to chaotic allocation priorities. This method effectively solves this problem, laying a reasonable foundation for subsequent work.

[0019] Based on this multi-objective optimization model, and combined with road condition and geological data, a comprehensive impedance model for ground road sections is constructed, and the traffic status of road sections is determined. This accurately obtains the comprehensive ground impedance and traffic status results for each road section in the entire road network, providing reliable support for transportation decisions. Existing technologies neglect the characteristics of complex road conditions and frequent geological disasters in plateau areas, and fail to accurately analyze road section impedance and traffic status, which can easily lead to unreasonable route selection and transportation delays. This method effectively avoids the above-mentioned shortcomings by accurately constructing the model and determining the status.

[0020] Then, combining the ground comprehensive impedance and traffic status results, the disaster relief transportation mode is determined and safe transfer nodes are selected to achieve flexible adaptation of the transportation mode. Existing technologies mostly adopt a single ground transportation mode, which cannot adapt to scenarios where high-altitude road sections are blocked and difficult to pass. This method effectively solves the problem of poor adaptability of transportation modes through air-ground collaborative design.

[0021] Subsequently, based on the rescue transportation mode and safe transfer node information, an encoding and decoding system adapted to plateau air-ground coordination was designed. The algorithm population was optimized by combining the drone's endurance and traffic status results, ensuring the rationality and feasibility of the algorithm optimization. Existing algorithm optimization techniques are not adapted to plateau air-ground coordination scenarios and do not consider drone endurance constraints, resulting in unfeasible optimization results. This method effectively solves this disconnect problem.

[0022] Next, an improved multi-objective optimization algorithm is employed, based on legitimate population individuals, a multi-objective optimization model, and disaster evolution simulation parameters, to obtain a Pareto optimal solution set, achieving optimal solution selection under multi-objective equilibrium. Existing optimization algorithms have not been improved to address the dynamic evolution of disasters on plateaus, resulting in outdated solutions. This method combines disaster parameters with an improved algorithm to ensure the timeliness and adaptability of the solutions.

[0023] Finally, based on real-time weather conditions in the disaster area, a scheduling scheme is selected from the Pareto optimal solution set, outputting the final executable material allocation and transportation scheduling scheme. Existing technologies do not consider the variable weather conditions at high altitudes, which can easily lead to safety hazards and transportation delays. This method effectively solves this problem. In summary, this method achieves precise, efficient, and adaptable allocation of emergency supplies at high altitudes, ensuring timely delivery of supplies and improving the overall effectiveness of emergency rescue at high altitudes.

[0024] The plateau emergency material distribution device based on a physical and mental efficacy decay model of this invention first collects various basic data and divides disaster-stricken areas through a multi-source sensing and zoning module. This solves the problem of incomplete data collection and unreasonable regional division in existing technologies, which leads to a lack of reliable data support for subsequent material distribution, thus laying a solid foundation for the operation of the entire device. The dynamic impedance modeling module calculates the combination value of road curves and slopes, constructs a dynamic road network impedance model by combining geological disaster risks, and determines the traffic status. This effectively avoids the shortcomings of existing technologies that ignore the complex road conditions and frequent geological disasters in plateau areas, resulting in inaccurate impedance calculations and incorrect traffic status determinations, thus providing accurate data for transportation decisions.

[0025] The efficiency correction calculation module incorporates altitude parameters, combining vehicle power attenuation coefficients and driver physiological efficiency coefficients to correct travel time. This addresses the shortcomings of existing technologies that fail to consider the impact of high-altitude environments on vehicles and personnel, leading to large deviations in travel time estimates and unreasonable transportation plans. This further improves the device's adaptability to high-altitude scenarios. The fairness index construction module uses the perceived suffering value at disaster sites as the core to construct fairness indicators. This solves the problems of chaotic priority allocation of resources and inability to consider the actual needs of disaster-stricken people in existing technologies, ensuring fair and reasonable resource allocation.

[0026] The air-ground collaborative decision-making module determines the transportation mode and selects safe transfer nodes based on road network impedance, traffic status, and the survival time window of disaster-stricken points. This effectively solves the problem of existing technologies having a single transportation mode and being unable to adapt to the blockage scenarios in high-altitude sections, resulting in the inability to deliver materials in a timely manner. It achieves flexible adaptation of air-ground collaborative transportation in high-altitude scenarios. The multi-objective solution module adopts an improved multi-objective optimization algorithm, combining various parameters to solve for the optimal scheduling scheme and outputting executable results based on real-time weather conditions. This avoids the shortcomings of existing technologies, such as lagging optimization schemes, failure to incorporate real-time weather conditions, resulting in schemes that cannot be implemented and potential safety hazards.

[0027] In summary, the device has clearly defined functions and a reasonable division of labor among its modules. It not only accurately covers the entire process of emergency material distribution in high-altitude areas, but also specifically addresses the technical problems of poor adaptability, ambiguous functions, and inability to meet the special conditions of high-altitude environments in existing devices. With its professional structure and stable operation, it provides reliable support for the implementation of emergency material distribution methods in high-altitude areas, ensuring that emergency materials are delivered to disaster-stricken areas in a timely, accurate, and equitable manner, and improving the overall efficiency of emergency rescue in high-altitude areas.

[0028] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. Attached Figure Description

[0029] Figure 1 This is a flowchart of the plateau emergency supplies allocation method based on the physical and mental efficacy decay model in Embodiment 1 of the present invention. Figure 2 This is a flowchart illustrating the steps of the plateau emergency supplies allocation method based on the physical and mental efficacy decay model in Embodiment 3 of the present invention. Figure 3 This is a structural block diagram of the plateau emergency supplies distribution device based on the physical and mental efficacy decay model in Embodiment 4 of the present invention. Detailed Implementation

[0030] The present invention will be further described in detail below with reference to experimental examples and specific embodiments. However, this should not be construed as limiting the scope of the above-mentioned subject matter of the present invention to the following embodiments. All technologies implemented based on the content of the present invention fall within the scope of protection of the present invention.

[0031] Unless otherwise specified, the use of terms such as "upper," "lower," "left," "right," "center," "inner," "outer," and "side" to indicate orientation or positional relationships in the description of specific embodiments of the present invention is based on the orientation or positional relationships shown in the accompanying drawings, or the orientation or positional relationship in which the product / equipment / device is typically placed during use. These terms are merely for the purpose of facilitating the description of the present invention or simplifying the description in specific embodiments, enabling those skilled in the art to quickly understand the solution, and do not indicate or imply that a particular device / component / element must have a specific orientation, or be constructed and operated in a specific positional relationship. Therefore, they should not be construed as limitations on the present invention.

[0032] In the description of the embodiments of this invention, technical terms such as "first" and "second" only distinguish one entity or operation from another, and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary or secondary relationship of the indicated technical features. In the description of the embodiments of this invention, "multiple" means two or more, unless otherwise explicitly defined.

[0033] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0034] Example 1 During their research, the inventors discovered that when using emergency material distribution methods for high-altitude rescue, achieving accurate and timely delivery of materials adapted to the unique high-altitude environment requires considering multiple factors, including road conditions, weather, personnel physiology, and transportation modes. This necessitates the use of specialized solutions tailored to the high-altitude environment. In addressing practical engineering problems, existing technologies are ill-suited to the complex road conditions, variable weather, and physiological decline inherent in high-altitude environments, failing to meet actual needs for efficient and safe high-altitude emergency material distribution. Therefore, after studying this issue, the inventors proposed a high-altitude emergency material distribution method based on a physical and mental efficacy decline model. Addressing the poor adaptability and low efficiency of high-altitude emergency material distribution, this method employs a multi-objective optimization model, a comprehensive impedance model, and a technical solution that determines air-ground coordinated transportation modes, optimizes algorithm populations, and selects scheduling schemes. This achieves precise and efficient high-altitude emergency material distribution, thereby improving the overall effectiveness of high-altitude emergency rescue.

[0035] Please refer to Figure 1 , Figure 1 This is a schematic diagram illustrating the steps of a plateau emergency supplies allocation method based on a psychosocial efficacy decay model, provided in an embodiment of the present invention. The method may include: S1. Construct a multi-objective optimization model for the allocation of emergency supplies in high-altitude areas based on basic data; S2. Based on the multi-objective optimization model, a comprehensive impedance model of the ground road segment is constructed by combining road condition and geological data, and the road segment traffic status is determined to obtain the comprehensive ground impedance and traffic status results of each road segment in the entire road network; and the estimated travel time of the road segment is corrected according to the altitude parameter and the driver's physiological efficiency coefficient to form the road network traffic parameters after the physiological and physiological efficiency decay correction. S3. Combining the ground comprehensive impedance, the road network traffic parameters corrected for physical and mental efficiency attenuation, and the traffic status results, determine the rescue transportation mode for the disaster-stricken points and screen safe transfer nodes; S4. Based on the rescue transportation mode and the information of the safe transfer node, and combined with the payload, endurance and traffic status of the UAV, optimize the population individuals to obtain legal population individuals that meet the transportation constraints; S5. Using an improved multi-objective optimization algorithm, based on the legal population individuals, the multi-objective optimization model, and the disaster evolution simulation parameters, the Pareto optimal solution set for the allocation of emergency supplies on the plateau is obtained; S6. Based on the real-time meteorological conditions in the disaster area, select a scheduling scheme from the Pareto optimal solution set, and output the final executable plateau emergency material allocation and transportation scheduling scheme.

[0036] Specifically, this embodiment provides a method for allocating emergency supplies in high-altitude areas based on a physical and mental efficacy decline model. The implementation process revolves around the actual needs of emergency supply allocation in high-altitude areas and is adapted to the requirements of high-altitude scenarios, including: First, based on the basic data required for the distribution of emergency supplies in high-altitude areas, a multi-objective optimization model for emergency supply distribution adapted to high-altitude scenarios is constructed. This basic data includes high-altitude road network data, vehicle data, basic information on disaster-stricken areas, and environmental parameters. The high-altitude road network data includes a set of road network nodes, a set of connected physical road segments, and the geometric parameters of those segments. The vehicle data includes the basic performance parameters of trucks and drones. The basic information and environmental parameters of disaster-stricken areas include the total demand for supplies, altitude, and current ambient temperature. In constructing this multi-objective optimization model, the core optimization objectives are minimizing the total perceived suffering value of the entire system, minimizing the supply shortfall rate, and minimizing the total comprehensive resistance risk during transportation. Simultaneously, constraints are set for time windows for physical and mental efficiency correction, air-ground collaborative load and range correction, driver physiological limits, and conventional logistics. This results in a constrained multi-objective optimization model for high-altitude emergency supply distribution, providing a scientific optimization benchmark for subsequent supply distribution work.

[0037] After completing the construction of the multi-objective optimization model, a comprehensive impedance model for ground road sections is constructed by combining road condition and geological data, and the traffic status of each road section is determined, thereby obtaining the comprehensive impedance and traffic status results of each road section in the entire road network. The road condition and geological data include road section-related parameters and geological hazard-related data. Road section-related parameters include road section geometric parameters and road section length, while geological hazard-related data represents the probability of geological hazard occurrence. Road section geometric parameters include road section longitudinal slope, curve radius, and road width. When constructing the comprehensive impedance model for ground road sections, the curve-slope combination that quantifies the danger level of sharp bends and steep slopes is first calculated. The dynamic geometric risk term is obtained by combining the value, road width constraint factor, and long-distance fatigue factor with the probability of geological disaster occurrence. Then, the road network traffic parameters after correction for physical and mental efficiency decay are integrated, namely the actual travel time of ground vehicles on each road segment of the entire road network after double correction by vehicle power decay coefficient and driver physiological efficiency coefficient, as well as the expected fuel cost of the road segment. Dimensionless weight coefficients are assigned to each component to finally construct a comprehensive impedance model for ground road segments. At the same time, the road segment traffic status results are divided into three types: normal traffic, reduced-speed traffic, and physical blockage, providing data support for subsequent transportation mode selection.

[0038] Based on the obtained comprehensive ground impedance and traffic status results for each section of the entire road network, the rescue transportation mode for each disaster-stricken point is determined and safe transfer nodes are selected, thereby forming the relevant logic for air-ground coordinated transportation adapted to the plateau scenario. Before determining the rescue transportation mode, the survival-related parameters of the disaster-stricken point are input to calculate the survival time window of the disaster-stricken point, and the air-ground coordinated mode switching rules are set. The survival-related parameters of the disaster-stricken point include the altitude of the disaster-stricken point, the current ambient temperature, and the baseline ideal survival tolerance time. The air-ground coordinated mode switching rules are set to activate the air-ground coordinated transportation mode when the comprehensive ground impedance tends to infinity or the estimated arrival time of pure ground transportation exceeds the survival time window. The rescue transportation mode specifically includes two types: ground direct mode and air-ground coordinated mode. The safe transfer node is selected from the node with the closest straight-line distance to the disaster-stricken point, a comprehensive ground impedance lower than the safe impedance threshold, and a flat take-off and landing site that meets the requirements. The relevant logic for air-ground coordinated transportation covers the rescue transportation mode, safe transfer node information, and air-ground coordinated transportation time calculation logic to ensure the orderly development of air-ground coordinated transportation.

[0039] By combining the determined rescue transportation mode with the selected safe transit node information, an encoding and decoding system adapted to the air-ground collaborative material transportation in the plateau region is designed. At the same time, the algorithm population is optimized by combining the drone's endurance and the road conditions, and finally, a legal population of individuals that meet the transportation constraints is obtained. Before designing the encoding and decoding system, relevant encoding design data is input. This data includes a list of disaster-stricken areas, vehicle types, and road network topology data. The encoding and decoding system specifically adopts a three-layer hybrid encoding method, which includes the disaster-stricken area access order task sequence, rescue transportation mode selection, and vehicle and drone load allocation ratio. Decoding rules are also formulated to automatically split the path into truck trunk lines and drone branch lines when the mode selection is air-ground collaboration. When optimizing the population individuals, relevant population optimization data is input. This data includes disaster-stricken area pain perception data and algorithm population individuals. During the optimization process, the population priority is initialized by sorting the initial pain perception values ​​of disaster-stricken areas from high to low. Disaster-stricken areas with high pain perception values ​​are given priority in being assigned highly adaptable drivers and high-performance vehicles. Then, illegal population individuals generated by algorithm crossover mutation are checked for drone endurance constraints. For individuals that violate the constraints, gene repair is completed by searching for the nearest safe transfer node in the preceding nodes or by flipping the rescue transportation mode to the ground direct mode, thereby obtaining legal population individuals that meet the transportation constraints.

[0040] An improved multi-objective optimization algorithm is employed, based on the legal population individuals obtained above, the previously constructed multi-objective optimization model, and the disaster evolution simulation parameters, to obtain the Pareto optimal solution set for the allocation of emergency supplies on the plateau. The disaster evolution simulation parameters are the update frequency parameters of the probability of geological disasters occurring in the road network. The improved multi-objective optimization algorithm is specifically the NSGA-II algorithm, which introduces a dynamic environmental screening mechanism. During the solution process, the legal population individuals are first rapidly sorted using a non-dominated algorithm and Pareto stratification is completed. Then, based on the disaster evolution simulation parameters, the probability of geological disasters occurring in the road network is dynamically updated at a set frequency during algorithm iteration. Infeasible population individuals are eliminated, or mutation operations are triggered to switch them to the air-ground collaborative mode in the rescue transportation mode. Simultaneously, population diversity is maintained by combining congestion distance calculation and elite retention strategies, ultimately completing the solution and obtaining the Pareto optimal solution set.

[0041] Finally, based on real-time weather conditions in the disaster area, a suitable scheduling scheme was selected from the Pareto optimal solution set, resulting in a final executable plan for the distribution and transportation of emergency supplies in the high-altitude region. When selecting a scheduling scheme, under extreme weather conditions, the scheme with the lowest overall overall impedance risk during transportation was prioritized; in the early stages of the golden rescue period, the scheme with the lowest overall perceived suffering value was prioritized, ensuring the rationality and adaptability of the scheduling scheme. Simultaneously, throughout the entire supply distribution process, the payload and endurance of the drones were aerodynamically corrected based on the atmospheric density at the operating altitude, and the pilot's continuous flight time was physiologically constrained based on the blood oxygen saturation at the operating altitude, further adapting to the special needs of the high-altitude environment and ensuring the safe and efficient operation of the supply distribution work.

[0042] The plateau emergency supplies allocation method based on a physical and mental efficacy decay model provided in this invention can be applied to many technical fields, such as plateau emergency rescue, plateau disaster prevention and control, and plateau supplies allocation, including supply allocation scenarios after plateau earthquakes, snowstorms, and mudslides. In the above implementation, when accurately allocating various supplies in plateau disaster areas, model parameters can be adjusted according to the characteristics of different disaster scenarios. Transportation modes can be flexibly switched, and efficient supplies allocation can be achieved by constructing an optimized model and collaborative transportation logic adapted to the scenario, thereby improving disaster relief efficiency and ensuring the needs of affected people.

[0043] Example 2 As a further optimization of the previous embodiment, this embodiment addresses the issues of decreased human-machine efficiency due to the high-altitude, cold, and hypoxic environment and road blockages caused by frequent geological disasters. First, a dynamic risk impedance model is constructed, including curve-slope combination values ​​for quantifying the danger of sharp bends and steep slopes, road width constraints, and long-distance fatigue factors. A physical-psychological efficiency decay model coupled with altitude, blood oxygen, and reaction time is established to correct the time window constraints of ground transportation. A physiological risk-weighted fairness index is introduced to quantify the urgency of survival at disaster sites. An air-ground collaborative triggering mechanism based on a "survival time window" is constructed, automatically switching to a "truck + drone" relay mode when ground access is unavailable or timeouts occur. Finally, an improved NSGA-II algorithm incorporating an environmental screening mechanism is used to solve the problem. The specific implementation provided in this embodiment effectively overcomes the failure of general models in high-altitude scenarios, enabling substantial optimization of rescue plans in the life dimension. The steps include: Step 1: Establish a multi-objective constrained optimization model for the allocation of emergency supplies in high-altitude areas based on the decline of physical and mental efficacy. Step 1.1: Establish the objective function of the multi-objective optimization model: The objective function of the resource allocation model considering the unique characteristics of the plateau environment is to minimize the total perceived suffering value of the entire system. Minimize the rate of unmet material requirements and minimizing the overall impedance risk during the transportation process. composition:

[0044] The variables involved in the objective function and their definitions are as follows: Let be the set of all nodes in the road network. A collection of disaster-stricken points ( ); It is the set of all connected physical road segments (arcs) in the road network; A collection of rescue vehicles (including trucks and drones); A collection of emergency supplies; Disaster-stricken areas The total demand for materials; Supplies delivered to disaster-stricken areas The time.

[0045] For transport vehicles To the disaster-stricken areas Transported supplies Quantity; For 0-1 decision variables, if the vehicle From node Drive to the node If the value is 1, then the value is 1; otherwise, the value is 0. Among them, disaster-stricken areas Pain perception value The (physiological risk-weighted fairness index) is defined as follows:

[0046] To ensure supplies reach the disaster area Time; Disaster-stricken areas altitude; The baseline elevation for plains (generally taken as) ); Disaster-stricken areas The current ambient temperature (degrees Celsius); This is a correction term for the absolute value of temperature (taken as 273.15), ensuring that the denominator is not zero and conforms to the laws of thermodynamics; The time sensitivity coefficient represents the psychological tolerance of disaster-stricken people for waiting time. This is used as an environmental risk weighting coefficient to quantify the amplification factor of survival risks caused by high altitude and low temperature.

[0047] Among them, the survival time window of the disaster-stricken point serves as an implicit constraint on transportation feasibility. By influencing the calculation results of the arrival time of supplies and the perceived value of suffering, it indirectly affects the optimization direction of the objective function and is used as a key criterion in the subsequent air-ground collaborative triggering mechanism.

[0048] Step 1.2: Set the constraints for the model: (1) Time window constraints for physical and mental efficacy modification: ground vehicles On the road section Actual driving time It must be corrected to:

[0049] in: node With nodes The physical path length between them; Design speed based on plains benchmark; The vehicle power attenuation coefficient is denoted as , where Let be the altitude power loss constant. The upper limit of altitude for performance degradation; , Driver physiological efficiency coefficient; This is a high-altitude adaptation factor used to distinguish between local (smaller values) and visiting (larger values) drivers; The rate constant of physiological response decay characterizes the severity of hypoxia response with increasing altitude; This is the critical altitude for the physiological transition to hypoxia.

[0050] Among them, the plateau adaptation factor The rule for determining the value is: for drivers who are stationed on the plateau for extended periods, For support drivers on the plains, .

[0051] (2) Constraints on air-ground coordinated load and range correction: For drones Its maximum load and maximum range Aerodynamic corrections are required:

[0052] in At the atmospheric density at the operating altitude, This refers to the atmospheric density at sea level. and These are the aerodynamic attenuation indices for load capacity and range, respectively.

[0053] (3) Driver's physiological limits: Single continuous driving time The blood oxygen saturation level must not exceed Maximum tolerance duration:

[0054] in, The maximum continuous driving time based on the plains benchmark. altitude Predicted blood oxygen saturation value at the location, This represents normal blood oxygen saturation at sea level.

[0055] (4) Conventional logistics constraints: These include flow balance constraints, vehicle capacity constraints, and demand coverage constraints.

[0056] Step 2: Construct a dynamic risk impedance model and calculation formula adapted to the characteristics of plateau road conditions: Define the vehicle (Specifically referring to ground vehicle) road section Comprehensive impedance as follows:

[0057] in, The actual travel time after correction in step 1.2. Expected fuel costs for the route; It is a dimensionless impedance component weighting coefficient used to balance the dimensional differences between time, cost, and risk. The dynamic geometric risk term is calculated using the following formula:

[0058] in: Values ​​for the curve-slope combination: ,in The longitudinal slope, The radius of the curve; Road width constraint factor: when road width hour, Otherwise, it is 0; Long-distance fatigue factor: ;in For the length of the road segment, The threshold for a single continuous driving distance that triggers driver fatigue.

[0059] Probability of geological disaster occurrence (debris flow / landslide); Blockage determination rule: when and At that time, set (That is, physical blockage, vehicles cannot pass); These are the curve-slope sensitivity, road width sensitivity, and fatigue sensitivity coefficients in the geometric risk combination, respectively.

[0060] Step 3: Establish air-to-ground collaborative triggering and calculation logic based on the "survival time window": For any disaster-stricken point Calculate its survival limit time window :

[0061] in: The baseline ideal survival tolerance time; The altitude tolerance attenuation coefficient (characterizing the effect at altitudes above 3000m). The low-temperature tolerance attenuation coefficient; The elevation of the disaster site. This refers to the current ambient temperature.

[0062] Define mode switching function :

[0063] in: This represents the estimated arrival time when using a purely ground transportation option.

[0064] when At that time, activate air-ground collaborative computing at the disaster site. Total arrival time for:

[0065] in: This serves as the departure point for supplies; A safe transit node must meet the following conditions: distance from the disaster site The spatial straight-line distance is the shortest, and the ground composite impedance of this node itself is the shortest. ( (The safety impedance threshold set for the system); the safety relay node The selection must meet the following condition: the elevation of the node. And possesses greater than A flat take-off and landing area.

[0066] Preparation time for unloading, assembling, and taking off the drone; and These represent the truck's travel time on the road and the drone's flight time, respectively.

[0067] Step 4: Construct an air-ground collaborative hybrid coding and decoding method: Employing a three-layer hybrid real number coding: First layer (task sequence): The order of natural numbers in which disaster-stricken points are visited; Second layer (mode selection): 0 / 1 binary encoding, corresponding to... The calculation results (0 for trucks, 1 for drone collaboration). The third layer (capacity allocation): a real number between 0 and 1, representing the vehicle / drone load allocation ratio. Decoding rule: When the second layer gene is 1, the decoder automatically searches the road network topology, locks the optimal transfer node (Hub), and splits the path into two segments: "main line + branch line".

[0068] Step 5: Design population initialization and repair operators based on differences in pain perception: Step 5.1 Priority Initialization: Instead of using completely random generation, the initial suffering value of all disaster-stricken points is calculated. ,according to Arrange in descending order, prioritizing the allocation of highly adaptable drivers and high-performance vehicles to nodes with high pain values.

[0069] Step 5.2 Gene Repair: After new individuals are generated through crossover mutation, check whether the drone's endurance constraint is met.

[0070] If a violation occurs, a forward search is performed to fix it: the nearest node is found in the preceding nodes of the path. Alternatively, the gene locus may be forcibly flipped to 0 (if the road surface is not blocked).

[0071] Step 6: Solve using the improved NSGA-II algorithm with a dynamic environment filtering mechanism: Step 6.1 Fast Non-Dominated Sort: Calculate the individual in the population The values ​​are used to perform Pareto stratification.

[0072] Step 6.2 Dynamic Environment Screening: During the iteration process, the evolution of the disaster is simulated, and every... Randomly updated road network Value. If a path becomes infeasible (impedance infinite) due to a disaster update, the individual is either removed or a mutation operation is triggered to switch it to cooperative mode.

[0073] Step 6.3 Crowding Distance Calculation and Elite Preservation: Maintain population diversity and output the final Pareto optimal solution set.

[0074] Step 7: Make decisions based on the scheduling plan obtained in Step 6: The command center selects the priority from the Pareto solution set based on real-time weather conditions. In extreme weather conditions, choose The option with the lowest (risk); In the early stages of the golden rescue period, choose The option with the lowest (pain level) should prioritize rescuing high-altitude nodes.

[0075] In summary, the specific implementation method provided in this embodiment effectively overcomes the failure problem of the general model in plateau scenarios, and can achieve substantial optimization of the rescue plan in the dimension of life.

[0076] Example 3 The specific embodiments of the present invention are further described below with reference to the accompanying drawings and technical solutions. An emergency resource allocation method for high-altitude emergency supplies based on a physical and mental efficacy decay model disclosed in this invention is used for emergency resource scheduling. Please refer to [the accompanying drawings / documents]. Figure 2 , Figure 2 The following is a flowchart of the steps of the plateau emergency supplies allocation method based on the physical and mental efficacy decay model. The execution details and technical principles of each step are explained in detail through a complete simulation example.

[0077] Example scenario: A 6.2 magnitude earthquake is simulated in a high-altitude region of the Qinghai-Tibet Plateau in my country. The rescue command center (rescue point D1) is located in a county town at a relatively low altitude and needs to urgently deliver emergency supplies (medicines, blood plasma, tents) to three typical disaster-stricken areas in the surrounding area (J1, J2, J3).

[0078] Capacity configuration: 2 heavy-duty trucks (Truck-A, Truck-B), with a standard design speed for flat terrain. One UAV group with a maximum payload of 20kg and a maximum range of 30km on plains.

[0079] Personnel configuration: Drivers are configured as support personnel urgently dispatched from low-altitude plains areas. According to the model described above, their high-altitude acclimatization factor is set. (A large parameter indicates that it is more sensitive to hypoxic environments and its physiological efficacy is significantly reduced.)

[0080] Step 1: Obtain scheduling data and disaster partitioning Please refer to Table 1, which is the basic data and partition table of the rescue network nodes. The system first collects the basic attribute data of each node.

[0081] Technical explanation: The core of this step lies in identifying "rescue priorities." Traditional methods only consider the quantity of supplies needed. This invention introduces the concept of a "Survival Time Window." For high-altitude, extremely cold locations, this window is very short and is a key criterion for determining the "air-ground coordination zone."

[0082] Table 1. Basic Data and Partition Table of Rescue Network Nodes

[0083] Special Note (Regarding Node J3): Disaster point J3 simulates the typical characteristics of a "high-altitude transportation vulnerable area" on the plateau. This point is at an altitude of nearly 5000 meters and experiences extremely low temperatures (-20℃). Once supplies are cut off, injured personnel are highly susceptible to hypothermia and death; therefore, its survival window is only 6 hours. Such nodes typically have only one external access route and are prone to becoming "physical islands" after a disaster, making them a key target for the air-ground coordination mechanism of this invention. The "extremely high-risk island" described in this article refers to a disaster-stricken node that is inaccessible by ground transportation due to physical road blockages or transportation time exceeding the survival window under high-altitude and extremely low-temperature conditions.

[0084] Step 2: Construct a dynamic risk impedance model system to collect road network data, based on the formula in the claims. Calculate the combined value of the bend and slope, and take into account the geological risk. Determine the traffic status. Please refer to Table 2 for the calculation process. Table 2 is the dynamic impedance calculation table for the plateau road network.

[0085] Table 2 Calculation of Dynamic Impedance of Plateau Road Network

[0086] Data Analysis: Road Sections The curve and slope combination value reached as high as 200 (far exceeding the safety threshold of 50), and the road width was only 3.5 meters (single lane), with a geological risk of 0.9 (a high probability that a mudslide has already occurred). Based on this, the system triggered a "hard constraint blockage," determining that ground vehicles could not pass. Technical effect: This step effectively avoids the erroneous path planned by traditional navigation software that is "theoretically passable but actually a death trap."

[0087] Step 3: Establish a transportation time prediction model based on the decline of physical and mental efficacy. The vehicle power attenuation coefficient is consistent with the meaning of the vehicle power attenuation coefficient in step 1.2 of claim 1, and is used to characterize the degree of decrease in vehicle output power under high altitude and low air pressure environment; The driver physiological efficiency coefficient, consistent with that described in step 1.2 of claim 1, is used to characterize the decline in a driver's reaction time and safe driving ability under hypoxic conditions. To obtain an accurate estimated time of arrival (ETA), the system uses a dual-decay model for correction:

[0088] Please refer to Table 3 for a comparison of the correction results. Table 3 is a comparison table of physical and mental efficacy correction and time consumption.

[0089] Table 3 Comparison of Physical and Mental Efficacy Improvement and Time Consumption

[0090] Detailed explanation: In The elevation rises to 3800m on this section of the road. For drivers from low-lying areas (…), this is particularly challenging. Its physiological efficacy coefficient A drop to 0.45 means that reaction speed and safe driving ability have decreased by more than half. Combined with the vehicle's power reduction, the actual speed is only 18.1 km / h. Significance of the result: The corrected time (3.6h) is more than three times that of the traditional model (1.08h). This data not only better reflects the actual conditions at high altitudes, but also directly affects the subsequent determination of whether "air-ground coordination" is triggered.

[0091] Step 4: Construct a physiological risk-weighted fairness evaluation index and calculate the perceived pain value at each disaster-stricken location. Please refer to Table 4, which compares the perceived pain values ​​under the basic gap and the punishment of high-altitude hypoxia, and introduces the high-altitude hypoxia punishment factor. Set the sensitivity coefficient .

[0092] Table 4 Comparison of Basic Gap and Perceived Pain Values ​​under High-Altitude Hypoxia Penalty.

[0093] Explanation of the principle: Although the gaps J1 and J3 are the same, the pain value of J3 (9.02) is orders of magnitude greater. Mathematically, this forces the optimization algorithm to prioritize J3, achieving the substantive fairness of "saving the most dangerous one first".

[0094] Step 5: Predictive Air-Ground Collaborative Planning For node J3, the system executes the following logical chain: ① Path determination: Ground Physical blockage (see step 2).

[0095] ②Scheme generation: Select J2 (the highest point accessible by vehicles) as the mobile hub.

[0096] ③ Load correction: Indicates altitude The atmospheric density at an altitude of 4200m can be calculated based on international standard atmospheric models or empirical approximation models. This invention does not limit its specific analytical form, but only requires that it decreases monotonically with increasing altitude, for proportional correction of the UAV's payload and endurance. According to the formula described in the claims, the air density at an altitude of 4200m is... The drone's payload has been corrected to... .

[0097] ④ Time chain verification: Truck travel time ( ): .

[0098] Assembly preparation time: .

[0099] Drone flight time ( ): .

[0100] Total time: .

[0101] in conclusion: (Survival window) The solution is feasible.

[0102] Detailed Explanation: Execution Details of the Improved NSGA-II Algorithm To obtain the scheduling scheme shown in Table 5, the specific execution logic of the algorithm in this embodiment is as follows: Mixed encoding example: For the three disaster-stricken points J1, J2, and J3, the algorithm generates a typical individual code as follows: Task layer: [2,1,3] (representing the access order: ); Mode layer: [0,0,1] (corresponding to J2, J1 are ground modes, J3 is cooperative mode); Transit layer: [0,0,2] (The corresponding UAV takeoff point index for J3 is node 2, i.e., J2).

[0103] Pain perception initialization: During the first generation of population generation, due to J3's pain perception value Far exceeding other nodes, the algorithm forces the J3-related gene bits to be set as the "priority service" sequence with an 80% probability in the initialization function, and activates its cooperative mode bit.

[0104] Gene repair execution: In the 15th generation evolution, the crossover operator produced an illegal solution: the drone attempted to fly directly from D1 to J3 (a distance of 80km).

[0105] Repair operator intervention: detected (Endurance limit) The operator automatically searches in the preceding nodes and finds that J2 (55km from J3) also does not meet the requirement. Finally, the operator forcibly flips the pattern layer of this gene locus to 0 (ground transportation), but then finds that the road resistance between J2 and J3 is infinite. This individual is eventually eliminated in the dynamic environment selection step due to its extremely low fitness, thus ensuring that the population converges to the only feasible solution of "transferring through J2".

[0106] Step 6: Multi-objective solution results and scheme analysis. The improved NSGA-II algorithm is used to solve the problem and output the final emergency material dispatch scheme table, as shown in Table 5.

[0107] Table 5 Final Emergency Material Dispatch Plan

[0108] This embodiment clearly demonstrates how this method successfully solves an extremely high-risk island rescue problem that traditional methods cannot handle by identifying blockages (step 2), correcting time (step 3), establishing priorities (step 4), and implementing air-to-ground relay (step 5). Data proves that this method has extremely high feasibility and application value in extreme high-altitude environments.

[0109] It should be noted that the above embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art can make several improvements and modifications without departing from the principles of the present invention (for example, replacing the psychosomatic efficacy decay function with other equivalent physiological fitting formulas, or replacing the NSGA-II algorithm with other multi-objective evolutionary algorithms such as MOEA / D), and these improvements and modifications should also be considered within the scope of protection of the present invention.

[0110] Example 4 This embodiment provides a high-altitude emergency supplies distribution device based on a psychosocial efficacy decay model, used to implement the aforementioned high-altitude emergency supplies distribution method. Please refer to [link / reference]. Figure 3 It includes: a multi-source sensing and partitioning module, a dynamic impedance modeling module, an efficiency correction calculation module, a fairness index construction module, an air-ground collaborative decision-making module, a multi-objective solution module, as well as a processor and memory.

[0111] Among them, the multi-source sensing and zoning module is responsible for collecting plateau road network data, geological disaster data and disaster-affected point information. After the collection is completed, the disaster-affected points are divided into ground direct access areas and air-ground collaborative areas according to the access conditions of the disaster-affected points, providing a basis for subsequent transportation mode selection. The dynamic impedance modeling module receives road network data and geological disaster data collected by the multi-source sensing and zoning module. It first calculates the curve-slope combination value of the road segment to quantify the danger of sharp curves and steep slopes. Then, it combines the probability of geological disaster occurrence to construct a dynamic road network impedance model that includes geometric risk and environmental risk. At the same time, it determines the traffic status of each road segment and outputs the impedance value and traffic status results of each road segment in the entire road network. The efficiency correction calculation module incorporates plateau altitude parameters and combines vehicle power attenuation coefficient and driver physiological efficiency coefficient to correct the estimated travel time of each road segment output by the dynamic impedance modeling module, ensuring that the travel time prediction is consistent with the actual plateau environment. The fairness indicator construction module constructs a fairness indicator with the perceived suffering value of the disaster-stricken areas as the core based on the basic information of the disaster-stricken areas, providing a basis for determining the priority of material allocation; The air-ground collaborative decision-making module receives road network impedance, road segment traffic status, and disaster point survival time window related data from the dynamic impedance modeling module. It determines the rescue transportation mode for each disaster point, selects safe transfer nodes with suitable distance and conditions, and forms air-ground collaborative transportation logic adapted to plateau scenarios. The multi-objective solution module uses an improved multi-objective optimization algorithm, combining the fairness index output by the fairness index construction module, the dynamic road network impedance model constructed by the dynamic impedance modeling module, and relevant parameters of disaster evolution to obtain the optimal scheduling scheme for the distribution of emergency supplies in the plateau. Then, combined with the real-time meteorological conditions in the disaster area, the most feasible scheme is selected and the final material distribution and transportation scheduling results are output.

[0112] In addition, the processor and memory provide hardware support for the stable operation of the device. The memory stores the corresponding computer program. When the computer program is executed by the processor, the above-mentioned plateau emergency material distribution method based on the physical and mental efficacy decay model can be realized, ensuring that the various modules of the device work together efficiently and stably to complete the entire process of plateau emergency material distribution.

[0113] It should be understood that the various modules of the plateau emergency material distribution device based on the physical and mental efficacy decay model provided in the above embodiments are only illustrated by the division of each functional module in the above description when distributing materials. In actual applications, the above functional allocation can be completed by different functional modules as needed. That is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0114] The functional modules in the above embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of the embodiments of the present invention.

[0115] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects.

[0116] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0117] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for allocating emergency supplies in high-altitude areas based on a physical and mental efficacy decay model, characterized in that, include: S1. Construct a multi-objective optimization model for the allocation of emergency supplies in high-altitude areas based on basic data; S2. Based on the multi-objective optimization model, a comprehensive impedance model of the ground road segment is constructed by combining road condition and geological data, and the road segment traffic status is determined to obtain the comprehensive ground impedance and traffic status results of each road segment in the entire road network; and the estimated travel time of the road segment is corrected according to the altitude parameter and the driver's physiological efficiency coefficient to form the road network traffic parameters after the physiological and physiological efficiency decay correction. S3. Combining the ground comprehensive impedance, the road network traffic parameters corrected for physical and mental efficiency attenuation, and the traffic status results, determine the rescue transportation mode for the disaster-stricken points and screen safe transfer nodes; S4. Based on the rescue transportation mode and the information of the safe transfer node, and combined with the payload, endurance and traffic status of the UAV, optimize the population individuals to obtain legal population individuals that meet the transportation constraints; S5. Using an improved multi-objective optimization algorithm, based on the legal population individuals, the multi-objective optimization model, and the disaster evolution simulation parameters, the Pareto optimal solution set for the allocation of emergency supplies on the plateau is obtained; S6. Based on the real-time meteorological conditions in the disaster area, select a scheduling scheme from the Pareto optimal solution set, and output the final executable plateau emergency material allocation and transportation scheduling scheme.

2. The plateau emergency supplies distribution method based on the physical and mental efficacy decay model according to claim 1, characterized in that, The basic data includes plateau road network data, vehicle data, basic information on disaster-stricken areas, and environmental parameters; The plateau road network data includes a set of road network nodes, a set of connected physical road segments, and road segment geometric parameters; The vehicle data includes basic performance parameters for trucks and drones; The basic information and environmental parameters of the disaster-stricken area include the total amount of supplies needed at the disaster-stricken area, altitude, and current ambient temperature. The construction of the multi-objective optimization model includes: constructing a multi-objective optimization model with the objectives of minimizing the total perceived pain value of the entire system, minimizing the unmet material demand rate, and minimizing the total comprehensive resistance risk of the transportation process, while setting time window constraints for physical and mental efficacy correction, air-ground collaborative load and range correction constraints, driver physiological limit constraints, and conventional logistics constraints.

3. The plateau emergency supplies distribution method based on the physical and mental efficacy decay model according to claim 1, characterized in that, The road condition and geological data include road segment-related parameters and geological hazard-related data; the road segment-related parameters include road segment geometric parameters and road segment length; the geological hazard-related data is the probability of geological hazard occurrence. The geometric parameters of the road segment include the longitudinal slope, curve radius, and road width. The construction of the comprehensive impedance model includes: calculating the curve-slope combination value, road width constraint factor, and long-distance fatigue factor to quantify the danger level of sharp curves and steep slopes; obtaining a dynamic geometric risk term by combining the probability of geological disaster occurrence; integrating the actual driving time of ground vehicles after psycho-efficacy correction and the expected fuel cost of the road segment; and constructing a comprehensive impedance model of the ground road segment by assigning dimensionless weight coefficients to each component; the road segment traffic status includes normal traffic, reduced speed traffic, and physical blockage.

4. The plateau emergency supplies distribution method based on the physical and mental efficacy decay model according to claim 1, characterized in that, Based on the ground comprehensive impedance, the road network traffic parameters corrected for physical and mental efficacy attenuation, the traffic status results, and survival-related parameters at the disaster-stricken points, the rescue transportation mode for the disaster-stricken points is determined and safe transfer nodes are selected; wherein: The survival-related parameters of the disaster-affected site include the altitude of the disaster-affected site, the current ambient temperature, and the baseline ideal survival tolerance time. The rescue transportation modes include direct ground mode and air-ground coordinated mode; the conditions for switching to air-ground coordinated mode include: the overall impedance of the ground path tends to infinity, or the estimated arrival time of pure ground transportation exceeds the survival time window. The safe transfer node is the node that is closest to the disaster site in a straight line, has a ground impedance lower than the safe impedance threshold, and meets the requirements of a flat take-off and landing site.

5. The plateau emergency supplies distribution method based on a psychosomatic efficacy decay model according to claim 1, characterized in that, Optimizing the population includes: acquiring data related to pain perception at disaster sites and optimizing the population population based on algorithms; Population priority is initialized by sorting the initial pain perception values ​​of disaster-stricken areas from high to low, and highly adaptable drivers and high-performance vehicles are assigned to disaster-stricken areas with high pain perception values. Specifically, illegal individuals generated by algorithm crossover mutation are subject to drone endurance constraint checks. Individuals that violate the constraints are repaired by searching for a closer safe transit node in the preceding nodes or by flipping the rescue transportation mode to a ground direct mode, thus obtaining the legal individuals.

6. The plateau emergency supplies distribution method based on the physical and mental efficacy decay model according to claim 1, characterized in that, S5 includes: performing fast non-dominated sorting on the legal population individuals and completing Pareto stratification; dynamically updating the probability of geological disaster occurrence in the road network at a set frequency during the multi-objective optimization algorithm iteration process based on the disaster evolution simulation parameters; removing infeasible population individuals; or triggering mutation operations on the infeasible population individuals to switch the infeasible population individuals to the air-ground collaborative mode in the rescue transportation mode; and completing the solution after combining congestion distance calculation and elite retention strategy to maintain population diversity, thereby obtaining the Pareto optimal solution set.

7. The plateau emergency supplies distribution method based on a psychosocial efficacy decay model according to claim 1, characterized in that, The scheduling scheme selected from the Pareto optimal solution set is the scheme with the minimum overall comprehensive impedance risk in the transportation process under extreme weather conditions, and the scheme with the minimum total perceived pain value of the entire system in the early stage of the golden rescue period.

8. The plateau emergency supplies distribution method based on the physical and mental efficacy decay model according to claim 1, characterized in that, The payload and endurance of the drone are aerodynamically corrected based on the atmospheric density at the operating altitude, and the pilot's physiological efficiency coefficient is constrained by the physiological limits based on the blood oxygen saturation at the operating altitude. The driver physiological efficiency coefficient includes the driver's single continuous driving time.

9. A plateau emergency supplies distribution device based on a psychosocial efficacy decay model, characterized in that, include: The multi-source sensing and zoning module is used to collect plateau road network data, geological disaster data and disaster-affected point information, and to divide the area based on the access conditions of the disaster-affected points; The dynamic impedance modeling module is used to calculate the curve-slope combination value of road segments, construct a dynamic road network impedance model that includes geometric and environmental risks by combining the probability of geological disasters, determine the traffic status of road segments, and obtain the impedance value and traffic status results of each road segment in the entire road network. The efficiency correction calculation module is used to incorporate altitude parameters and, based on the vehicle power attenuation coefficient and the driver's physiological efficiency coefficient, correct the estimated travel time for each road segment. The fairness indicator construction module is used to construct fairness indicators with the perceived pain value of disaster-stricken areas as the core. The air-ground collaborative decision-making module is used to determine the rescue transportation mode, screen safe transfer nodes, and form relevant logic for air-ground collaborative transportation based on road network impedance, road section traffic status, and survival time window of disaster-stricken points. The multi-objective solution module is used to solve the optimal scheduling scheme for the allocation of emergency supplies in the plateau region by using an improved multi-objective optimization algorithm, combined with fairness index, dynamic road network impedance model and disaster evolution parameters. It also outputs the final executable allocation and transportation capacity scheduling results based on real-time meteorological conditions.

10. The plateau emergency supplies distribution device based on a psychosocial efficacy decay model according to claim 9, characterized in that, The device includes a processor and a memory, the memory storing a computer program that, when executed by the processor, implements the plateau emergency supplies distribution method based on the physical and mental efficacy decay model as described in any one of claims 1 to 8.

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