Modularized marshalling field life guarantee method and system

Abstract

The invention discloses a modular marshalling field life guarantee method and system, and relates to the technical field of resource management, and the method comprises the steps: collecting operation and environment adaptability data; the optimal configuration of resources is realized by constructing a vehicle configuration optimization model; designing and managing field life support system resources according to the dynamic resource allocation model; adapting to environment change based on an environment adaptability model; the deployment time is output in consideration of the automation level of the vehicle. According to the field life guarantee method capable of modular marshalling, optimal configuration of resources is achieved by building the vehicle configuration optimization model, task execution efficiency is improved, field life guarantee system resources are designed and managed according to the dynamic resource allocation model, the response capability and efficiency of the system are improved, and the service life of the system is prolonged. The method adapts to environment change based on the environment adaptability model, enhances the reliability and stability of the system, and achieves better effects in the aspects of resource utilization rate, efficiency and safety.

Description

Technical Field

[0001] The present invention relates to the technical field of resource management, and in particular to a method and system for ensuring outdoor life that can be modularly organized. Background Art

[0002] In the development of field life support systems, technological innovation has always been the core driving force for progress in this field. Early systems mainly relied on basic material equipment and manpower transportation, but with the development of technology, modern systems have begun to adopt highly integrated modules and automated solutions, such as pre-configured modular accommodation units and vehicles with integrated power supply and environmental control systems. These advances have greatly improved the deployment speed and system flexibility, enabling the rapid construction of survival and work infrastructure in complex environments. However, with the diversification of mission requirements and the increasing complexity of the execution environment, the shortcomings of existing technologies in dynamic resource management, environmental adaptability and automated deployment have gradually emerged. For example, existing systems often have limitations in quickly adapting to rapidly changing climate and terrain conditions, such as the inability to adjust power supply and heating facilities in real time to cope with sudden weather changes.

[0003] In addition, despite the modular design, resource allocation in existing systems is often based on static presets and lacks effective mechanisms to optimize and adjust based on actual operational data. This makes it difficult for the system to respond quickly when there are unforeseen changes in resource requirements, affecting overall operational efficiency and safety. For example, in unexpected high-demand situations, such as large-scale field activities or emergency rescue operations, the existing resource allocation strategy cannot be adjusted in a timely manner, which may lead to a shortage of critical resources. In addition, although the level of automation has improved, it has not yet been fully achieved in terms of rapid and accurate automated deployment of vehicles and equipment, especially in complex environments where terrain information is not fully understood. These technical deficiencies limit the effectiveness and reliability of field life support systems in a wider range of application scenarios. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the existing resource allocation method has the problems of low efficiency, low resource utilization, low security, and inability to cope with the diversity and complexity of tasks.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a modular field life support method, comprising collecting operation and environmental adaptability data; realizing optimal resource configuration by constructing a vehicle configuration optimization model; designing and managing field life support system resources according to a dynamic resource allocation model; adapting to environmental changes based on an environmental adaptability model; and outputting deployment time in consideration of the vehicle's automation level.

[0007] As a preferred solution of the modularized field life support method of the present invention, the collected operation and environmental adaptability data include vehicle cost data, vehicle capability data, mission requirement data, vehicle coordination and conflict data, resource consumption rate data, demand fluctuation data, resource supply capability data, environmental monitoring data, system response data, adaptability parameters, deployment time measured data, automation level data, and optimization and adjustment parameters;

[0008] The vehicle cost data includes the purchase and operating costs of each type of vehicle;

[0009] The vehicle capacity data includes the maximum passenger capacity and expansion capacity of each vehicle;

[0010] The mission requirement data includes specific vehicle configuration and functional requirements required for different mission types;

[0011] The vehicle cooperation and conflict data includes cooperation effects and potential conflict information between different vehicle types;

[0012] The resource consumption rate data includes the consumption rates of various resources under different conditions and time;

[0013] The demand fluctuation data includes data on changes in resource demand over time;

[0014] The resource supply capacity data includes the maximum capacity and start-up time of resources provided by the system;

[0015] The environmental monitoring data includes external environmental parameters;

[0016] The system response data includes performance response data under different environmental settings;

[0017] The adaptability parameters include statistical data of sensitivity and response time to different environmental factors;

[0018] The deployment time measured data includes the time required for different vehicle types and configurations to be deployed from arrival to completion;

[0019] The automation level data includes automation levels of various vehicles and equipment;

[0020] The optimization and adjustment parameters include data on factors and measures that influence deployment time under different conditions.

[0021] As a preferred solution of the modularizable outdoor life support method of the present invention, the optimal configuration of resources by constructing a vehicle configuration optimization model includes configuring each vehicle, and when the functions provided by the vehicle can be provided at a lower cost, the vehicle configuration solution with the highest cost-effectiveness is output by constructing a vehicle configuration optimization model, and the global minimum or local minimum of the cost model is output by minimizing the cost mathematical model, and the model optimizes the overall resource configuration, which is expressed as:

[0022]

[0023]

[0024] Among them, x represents the number of vehicles of each type, c i represents the unit cost coefficient of the i-th type of vehicle, x i represents the number of vehicles of the i-th category, x j represents the number of vehicles of the jth type, i and j represent the index of the vehicle type, T represents the total time period considered, r represents the time discount rate, and A ij represents the elements of the demand and supply matrices, the contribution of the i-th vehicle to the j-th demand, b j represents the total amount of the jth demand;

[0025] Vehicle configuration directly affects the demand and supply of resources and provides a physical basis for the resource allocation model. The type and number of vehicles need to be optimized according to the environmental conditions faced. The vehicle configuration determines the time required for deployment.

[0026] As a preferred solution of the modularizable outdoor life support method of the present invention, wherein: the design and management of outdoor life support system resources according to the dynamic resource allocation model includes combining the demand mode and supply adjustment mode of each resource;

[0027] The demand pattern is represented as a Gaussian distribution;

[0028] The supply adjustment pattern is represented as a logistic growth function;

[0029] Dynamically adjust the resource allocation strategy to match the real-time demand, and adjust the resource supply strategy by predicting the resource demand at different time points, which is expressed as:

[0030]

[0031] Among them, d i (t) represents the demand for the i-th resource at time t, μ i represents the time shift of the peak demand, σ i represents the standard deviation of demand distribution and controls the concentration of demand, τi It represents the time constant of resource supply and affects the speed of resource supply;

[0032] Resource allocation requirements are fed back into vehicle configuration decisions to guide configuration to maximize resource utilization efficiency and response speed. Resource requirements and allocation strategies need to adapt to environmental changes.

[0033] As a preferred solution of the modularized outdoor life support method of the present invention, the adaptation to environmental changes based on the environmental adaptability model includes predicting and adjusting the system response by integrating historical data, so that the system preventively adapts to environmental changes, and adjusting the parameter λ k ,ζ k , and θ k Environmental adaptability parameters optimize the use and allocation of resources while maintaining operations in the face of extreme and unpredictable environmental conditions through the model's dynamic adjustment capabilities. The environmental adaptability model is expressed as:

[0034]

[0035] Where u(t,z) represents the system adaptive response at time t and given environmental factor z, λ k represents the influence weight of the kth environmental factor, z k represents the actual value of the kth environmental factor, ζ k , and θ k represents the parameter for adjusting the environmental response curve, α and β represent the attenuation and periodic adjustment parameters;

[0036] Environmental adaptability requirements guide the selection of vehicle type and quantity, while the data provided by the environmental adaptability model is used to adjust the resource allocation plan. Environmental conditions will affect the deployment process.

[0037] As a preferred solution of the modularizable outdoor life support method of the present invention, the output deployment time considering the automation level of the vehicle includes the deployment process of the vehicle in the outdoor life support system, considering the automation level of each vehicle, by understanding the time requirements for the deployment of each type of vehicle, the time consumption and dynamic characteristics of each stage of deployment, which can be expressed as:

[0038]

[0039] Where D represents the total deployment time of the system, ω i represents the deployment time weight of the i-th vehicle, L i represents the automation level of the i-th vehicle, Represents the discrete steps required for a complete deployment.

[0040] As a preferred solution of the modular field life support method described in the present invention, the deployment time output considering the vehicle's automation level includes feedback of the deployment time model to the vehicle configuration decision, guiding the optimization of the vehicle configuration to reduce the deployment time. At the same time, the deployment time model ensures that resources are quickly in place when needed and allocates resources. The optimization of the deployment time needs to consider the output content of the environmental adaptability model.

[0041] Another object of the present invention is to provide a modular field life support system, which can achieve optimal resource allocation by constructing a vehicle configuration optimization model, thereby solving the problem of low resource utilization in current resource allocation methods.

[0042] As a preferred solution of the modularizable outdoor life support system described in the present invention, it includes: an initialization module, a vehicle configuration module, a dynamic resource allocation module, an environmental adaptation module, and a time deployment module; the initialization module is used to collect operation and environmental adaptability data; the vehicle configuration module is used to achieve optimal configuration of resources; the dynamic resource allocation module is used to manage outdoor life support system resources; the environmental adaptation module is used to adapt to environmental changes; and the time deployment module is used to output deployment time.

[0043] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a method for supporting outdoor life in a modular manner.

[0044] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a modularly groupable outdoor life support method.

[0045] Beneficial effects of the present invention: The modularly groupable field life support method provided by the present invention realizes the optimal configuration of resources by constructing a vehicle configuration optimization model, improves the efficiency of task execution, ensures the maximum utilization efficiency of resources under budget constraints, designs and manages field life support system resources according to a dynamic resource allocation model, adjusts resource allocation in real time to ensure that resource supply always meets actual needs, improves the system's responsiveness and efficiency, adapts to environmental changes based on an environmental adaptability model, reduces risks caused by sudden environmental changes, enhances the reliability and stability of the system, considers the vehicle's automation level output deployment time, and increases the success rate and safety of operations. The present invention achieves better results in terms of resource utilization, efficiency and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0047] Figure 1 An overall flow chart of a modularly groupable outdoor life support method provided in the first embodiment of the present invention.

[0048] Figure 2 An overall flow chart of a modular outdoor life support system provided in accordance with the third embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0050] Example 1

[0051] Reference Figure 1 , which is an embodiment of the present invention, provides a method for supporting outdoor life in a modularized manner, comprising:

[0052] S1: Collect operation and environmental suitability data.

[0053] Furthermore, the collected operation and environmental adaptability data include vehicle cost data, vehicle capability data, mission requirement data, vehicle coordination and conflict data, resource consumption rate data, demand fluctuation data, resource supply capability data, environmental monitoring data, system response data, adaptability parameters, deployment time measured data, automation level data, and optimization and adjustment parameters.

[0054] 1. Vehicle configuration optimization model

[0055] Required data:

[0056] Vehicle cost data: The purchase and operating costs of each type of vehicle.

[0057] Vehicle Capacity Data: Maximum passenger capacity, expansion capabilities, and other relevant performance indicators for each vehicle.

[0058] Mission requirement data: specific vehicle configuration and functional requirements required for different mission types (such as scientific research, rescue, and exploration).

[0059] Vehicle cooperation and conflict data: Information on the cooperation and potential conflicts between different vehicle types.

[0060] 2. Dynamic resource allocation formula

[0061] Required data:

[0062] Resource consumption rate data: consumption rates of various resources (such as electricity and water) under different conditions and time.

[0063] Demand fluctuation data: data on changes in resource demand over time, including changes between day and night, changes in task phases, etc.

[0064] Resource supply capacity data: the maximum capacity and startup time of resources provided by the system.

[0065] 3. Environmental adaptability model

[0066] Required data:

[0067] Environmental monitoring data: external environmental parameters, such as temperature, humidity, altitude, etc.

[0068] System response data: system performance response data under different environmental settings.

[0069] Adaptability parameters: Statistics of sensitivity and response time to different environmental factors.

[0070] 4. Advanced Automation Deployment Time Model

[0071] Required data:

[0072] Measured deployment time data: Actual measurements of the time required for different vehicle types and configurations to be deployed from arrival to completion.

[0073] Automation level data: Automation level and operational complexity of various vehicles and equipment.

[0074] Optimization and tuning parameters: Data on factors affecting deployment time and improvement measures under different conditions.

[0075] Data collection methods and tools

[0076] Field measurements and experiments: Conduct field tests on vehicle performance, resource consumption, and environmental adaptability to collect detailed operational data.

[0077] Historical Record Analysis: Analyze records of past missions to extract data on vehicle configuration, resource usage, and environmental responses.

[0078] Sensors and monitoring systems: Use sensor networks to collect real-time data about the environment, resource consumption, and system status.

[0079] User Feedback and Reporting: Gather feedback from operators and mission participants on system performance and possible areas for improvement.

[0080] Simulation and modeling: Use computer models to simulate different operating scenarios and verify the consistency of the data and the predictive power of the models.

[0081] S2: Optimal allocation of resources is achieved by building a vehicle configuration optimization model.

[0082] Furthermore, each vehicle is configured. When the functions provided by the vehicle can be provided at a lower cost, the vehicle configuration optimization model is constructed to output the most cost-effective vehicle configuration solution. The global minimum or local minimum of the cost model is output through the minimization of the cost mathematical model. At the same time, the model optimizes the overall resource configuration, which is expressed as:

[0083]

[0084] Among them, x represents the number of vehicles of each type, c i represents the unit cost coefficient of the i-th type of vehicle, x i represents the number of vehicles of the i-th category, x j represents the number of vehicles of the jth type, i and j represent the index of the vehicle type, T represents the total time period considered, r represents the time discount rate, and A ij represents the elements of the demand and supply matrices, the contribution of the i-th vehicle to the j-th demand, b j represents the total amount of the jth demand;

[0085] Vehicle configuration directly affects the demand and supply of resources and provides a physical basis for the resource allocation model. The type and number of vehicles need to be optimized according to the environmental conditions faced. The vehicle configuration determines the time required for deployment.

[0086] It should be noted that in the vehicle configuration optimization model, each type of vehicle x i are assigned a cost coefficient c i Here The expression reflects the phenomenon that the cost increases superlinearly with the number of vehicles. The use of this quadratic term is often used to express the accelerated growth of costs when the scale increases, such as factors such as management complexity and increased maintenance costs due to scale expansion.

[0087] Cross-effect terms: Cross-terms in formulas The interaction between different types of vehicles is considered. Here (ij) 2Indicates the difference between vehicle types. The greater the difference, the larger the denominator value and the smaller the mutual influence. This item reflects the synergy between vehicles (such as different vehicles working together to improve efficiency) and potential conflicts (such as decreased efficiency due to resource sharing).

[0088] Balancing cost and efficiency: by minimizing the cost function of the entire system The model aims to find the most cost-effective vehicle configuration, taking into account not only the impact of individual vehicle costs, but also the complex interactions between vehicles that affect overall system performance.

[0089] Application of optimization algorithms: The model usually needs to be solved by a mathematical optimization algorithm, such as gradient descent, Newton's method, or more advanced global optimization techniques to find the global minimum or an acceptable local minimum of the cost function.

[0090] Functional requirements are met: In the field life support system, each vehicle has a specific function, such as camping, food supply or medical support. The right vehicle configuration can ensure that all these functional requirements are met without over-investing in a certain type of vehicle, thus maintaining cost-effectiveness.

[0091] Optimal resource allocation: Through this model, optimal resource allocation can be achieved. For example, if the functions provided by a certain type of vehicle can be provided by other types of vehicles at a lower cost, the model will tend to recommend vehicles with lower configuration costs, thereby optimizing the overall resource allocation.

[0092] Adaptability to different scales and conditions: The model is also flexible to adapt to different scales of tasks and different operating conditions. By adjusting model parameters (such as vehicle costs and interaction effects), the vehicle configuration can be optimized for specific tasks or environmental conditions.

[0093] S3: Design and manage field life support system resources based on a dynamic resource allocation model.

[0094] Furthermore, by combining the demand pattern and supply adjustment pattern of each resource;

[0095] The demand pattern is represented as a Gaussian distribution;

[0096] The supply adjustment pattern is represented as a logistic growth function;

[0097] Dynamically adjust the resource allocation strategy to match the real-time demand, and adjust the resource supply strategy by predicting the resource demand at different time points, which is expressed as:

[0098]

[0099] Among them, di (t) represents the demand for the i-th resource at time t, μ i represents the time shift of the peak demand, σ i represents the standard deviation of demand distribution and controls the concentration of demand, τ i It represents the time constant of resource supply and affects the speed of resource supply;

[0100] Resource allocation requirements are fed back into vehicle configuration decisions to guide configuration to maximize resource utilization efficiency and response speed. Resource requirements and allocation strategies need to adapt to environmental changes.

[0101] It should be noted that.

[0102] S4: Adapt to environmental changes based on the environmental adaptability model.

[0103] Furthermore, by integrating historical data to predict and adjust system responses, the system can preventively adapt to environmental changes by adjusting the parameter λ k ,ζ k , and θ k Environmental adaptability parameters optimize the use and allocation of resources while maintaining operations in the face of extreme and unpredictable environmental conditions through the model's dynamic adjustment capabilities. The environmental adaptability model is expressed as:

[0104]

[0105] Where u(t,z) represents the system adaptive response at time t and given environmental factor z, λ k represents the influence weight of the kth environmental factor, z k represents the actual value of the kth environmental factor, ζ k , and θ k represents the parameter for adjusting the environmental response curve, α and β represent the attenuation and periodic adjustment parameters;

[0106] Environmental adaptability requirements guide the selection of vehicle type and quantity, while the data provided by the environmental adaptability model is used to adjust the resource allocation plan. Environmental conditions will affect the deployment process.

[0107] It should be noted that.

[0108] S5: Consider the vehicle's automation level output deployment time.

[0109] Furthermore, the deployment process of vehicles in the field life support system, considering the automation level of each vehicle, is expressed as follows by understanding the time requirements for the deployment of each type of vehicle, the time consumption and dynamic characteristics of each stage of deployment:

[0110]

[0111] Where D represents the total deployment time of the system, ω i represents the deployment time weight of the i-th vehicle, L i represents the automation level of the i-th vehicle, Represents the discrete steps required for a complete deployment.

[0112] It should be noted that the deployment time model feeds back to the vehicle configuration decision, guiding the optimization of vehicle configuration to reduce deployment time. At the same time, the deployment time model ensures that resources are in place quickly when needed and allocates resources. The optimization of deployment time needs to consider the output content of the environmental adaptability model.

[0113] Example 2

[0114] An embodiment of the present invention provides a method for supporting outdoor life in a modular manner. To verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0115] First, the experiment selected two similar field locations as test environments, and deployed a system at each location: one using the invented model and the other using existing technology. The goal of the experiment was to evaluate the performance of each system in terms of resource allocation efficiency, response time, resource consumption rate, and security.

[0116] The experimental preparation phase includes the following steps:

[0117] Site selection and condition simulation: Select two field locations with similar geographical and climatic conditions for testing. Each location simulates different environmental conditions, such as temperature fluctuations, rainfall, and wind speed, to test the system's environmental adaptability.

[0118] System deployment: A life support system is deployed at each location. The system applied by the invention (system A) adopts an automated advanced vehicle and a dynamic resource allocation model, while the control system (system B) adopts the standard configuration of the existing technology.

[0119] Data acquisition device installation: Install sensors and data recording equipment in each system to collect vehicle operation data, environmental adaptability data, resource consumption rate and other information in real time.

[0120] Operator Training: Operators will be trained on the use of the system to ensure they understand how to operate both systems and can record data accurately.

[0121] Experiment monitoring: Throughout the experiment, the monitoring team will remotely monitor system operations to ensure accurate data collection and evaluate system responses and adjustments.

[0122] During the experiment, both System A and System B will perform a series of standard operating tasks, including facility deployment, routine maintenance and emergency response drills, to test the actual performance of each system.

[0123] Table 1 Experimental results

[0124]

[0125]

[0126] From the above table data, it can be observed that system A is significantly better than system B in terms of deployment time and resource consumption rate. For example, at site 1, the deployment time of system A is 2 hours, while system B needs 4 hours, indicating that the automation level and optimized vehicle configuration of system A can significantly speed up the deployment speed. At the same time, the resource consumption rate of system A is lower, with a consumption rate of 50 units of resources per hour at site 1, compared with 75 units for system B, indicating that the dynamic resource allocation model of system A performs better in improving resource utilization efficiency.

[0127] In terms of environmental adaptability, the number of environmental adaptability adjustments of system A in the two sites was significantly less than that of system B. This data reflects that system A can more effectively respond to environmental changes through the prediction and adjustment mechanism based on the environmental adaptability model, reducing the need for environmental adaptability adjustments, thereby improving the stability and safety of the system.

[0128] In summary, the present invention achieves better results in terms of resource utilization, efficiency and safety.

[0129] Example 3

[0130] Reference Figure 2 , as an embodiment of the present invention, provides a modularizable field life support system, including an initialization module, a vehicle configuration module, a dynamic resource allocation module, an environment adaptation module, and a time deployment module.

[0131] The initialization module is used to collect operation and environmental adaptability data, the vehicle configuration module is used to achieve optimal resource configuration, the dynamic resource allocation module is used to manage the resources of the field life support system, the environmental adaptation module is used to adapt to environmental changes, and the time deployment module is used to output the deployment time.

[0132] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0133] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0134] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0135] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A modular field life support method, characterized in that: include: Collect operational and environmental suitability data; Realize optimal allocation of resources by building a vehicle configuration optimization model; Design and manage field life support system resources based on a dynamic resource allocation model; Adapt to environmental changes based on environmental adaptability models; Consider the vehicle's automation level output deployment time.

2. The modular field life support method according to claim 1, characterized in that: The collected operation and environmental adaptability data include vehicle cost data, vehicle capability data, mission requirement data, vehicle coordination and conflict data, resource consumption rate data, demand fluctuation data, resource supply capability data, environmental monitoring data, system response data, adaptability parameters, deployment time measured data, automation level data, and optimization and adjustment parameters; The vehicle cost data includes the purchase and operating costs of each type of vehicle; The vehicle capacity data includes the maximum passenger capacity and expansion capacity of each vehicle; The mission requirement data includes specific vehicle configuration and functional requirements required for different mission types; The vehicle cooperation and conflict data includes cooperation effects and potential conflict information between different vehicle types; The resource consumption rate data includes the consumption rates of various resources under different conditions and time; The demand fluctuation data includes data on changes in resource demand over time; The resource supply capacity data includes the maximum capacity and start-up time of resources provided by the system; The environmental monitoring data includes external environmental parameters; The system response data includes performance response data under different environmental settings; The adaptability parameters include statistical data of sensitivity and response time to different environmental factors; The deployment time measured data includes the time required for different vehicle types and configurations to be deployed from arrival to completion; The automation level data includes automation levels of various vehicles and equipment; The optimization and adjustment parameters include data on factors and measures that influence deployment time under different conditions.

3. The modular field life support method according to claim 2, characterized in that: The method of realizing optimal resource configuration by constructing a vehicle configuration optimization model includes configuring each vehicle. When the functions provided by the vehicle can be provided at a lower cost, the vehicle configuration scheme with the highest cost-effectiveness is output by constructing a vehicle configuration optimization model. The global minimum or local minimum of the cost model is output by minimizing the cost mathematical model. At the same time, the model optimizes the overall resource configuration, which is expressed as: Among them, x represents the number of vehicles of each type, c i represents the unit cost coefficient of the i-th type of vehicle, x i represents the number of vehicles of the i-th category, x j represents the number of vehicles of the jth type, i and j represent the index of the vehicle type, T represents the total time period considered, r represents the time discount rate, and A ij represents the elements of the demand and supply matrices, the contribution of the i-th vehicle to the j-th demand, b j represents the total amount of the jth demand; Vehicle configuration directly affects the demand and supply of resources and provides a physical basis for the resource allocation model. The type and number of vehicles need to be optimized according to the environmental conditions faced. The vehicle configuration determines the time required for deployment.

4. The modular field life support method according to claim 3, characterized in that: The design and management of field life support system resources according to the dynamic resource allocation model includes combining the demand mode and supply adjustment mode of each resource; The demand pattern is represented as a Gaussian distribution; The supply adjustment pattern is represented as a logistic growth function; Dynamically adjust the resource allocation strategy to match the real-time demand, and adjust the resource supply strategy by predicting the resource demand at different time points, which is expressed as: Among them, d i (t) represents the demand for the i-th resource at time t, μ i represents the time shift of the peak demand, σ i represents the standard deviation of demand distribution and controls the concentration of demand, τ i It represents the time constant of resource supply and affects the speed of resource supply; Resource allocation requirements are fed back into vehicle configuration decisions to guide configuration to maximize resource utilization efficiency and response speed. Resource requirements and allocation strategies need to adapt to environmental changes.

5. The modular field life support method according to claim 4, characterized in that: The adaptation to environmental changes based on the environmental adaptability model includes predicting and adjusting the system response by integrating historical data, so that the system can preventively adapt to environmental changes, and adjusting the parameter λ k ,ζ k , and θ k Environmental adaptability parameters optimize the use and allocation of resources while maintaining operations in the face of extreme and unpredictable environmental conditions through the model's dynamic adjustment capabilities. The environmental adaptability model is expressed as: Where u(t,z) represents the system adaptive response at time t and given environmental factor z, λ k represents the influence weight of the kth environmental factor, z k represents the actual value of the kth environmental factor, ζ k , and θ k represents the parameter for adjusting the environmental response curve, α and β represent the attenuation and periodic adjustment parameters; Environmental adaptability requirements guide the selection of vehicle type and quantity, while the data provided by the environmental adaptability model is used to adjust the resource allocation plan. Environmental conditions will affect the deployment process.

6. The modular field life support method according to claim 5, characterized in that: The output deployment time considering the automation level of the vehicle includes the deployment process of the vehicle in the field life support system. Considering the automation level of each vehicle, by understanding the time requirements for the deployment of each type of vehicle, the time consumption and dynamic characteristics of each stage of deployment, it is expressed as: Where D represents the total deployment time of the system, ω i represents the deployment time weight of the i-th vehicle, L i represents the automation level of the i-th vehicle, Represents the discrete steps required for a complete deployment.

7. The modular field life support method according to claim 6, characterized in that: The output deployment time considering the automation level of the vehicle includes feedback of the deployment time model to the vehicle configuration decision, guiding the optimization of the vehicle configuration to reduce the deployment time. At the same time, the deployment time model ensures that resources are quickly in place when needed and allocates resources. The optimization of the deployment time needs to consider the output content of the environmental adaptability model.

8. A system using the modularizable outdoor life support method according to any one of claims 1 to 7, characterized in that: It includes initialization module, vehicle configuration module, dynamic resource allocation module, environment adaptation module and time deployment module; The initialization module is used to collect operation and environmental adaptability data; The vehicle configuration module is used to achieve optimal configuration of resources; The dynamic resource allocation module is used to manage the resources of the field life support system; The environmental adaptation module is used to adapt to environmental changes; The time deployment module is used to output the deployment time.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the modular field life support method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the modularly groupable outdoor life support method described in any one of claims 1 to 7 are implemented.

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