A multifunctional shelter power supply method and system suitable for field operation

Abstract

The application provides a multifunctional shelter power supply method and system suitable for field operation. Through the deployment of environmental sensors, external environmental parameters are collected, combined with real-time power consumption data inside the shelter, and an operating condition report is generated. Based on the operating condition report, a differential evolution algorithm is used for multi-objective optimization analysis, a life cycle cost analysis technique is introduced, resource utilization is maximized, and an optimal energy combination is generated. Based on the optimal energy combination, a sliding mode variable structure control algorithm is used to dynamically adjust the working parameters, and through power demand side management technology, an energy conversion and distribution log is generated. Based on the energy conversion and distribution log, the operating state is continuously monitored, normal fault diagnosis is performed, operating data is recorded, and an optimized power supply scheme is generated. The technical scheme provided by the application can improve energy utilization efficiency and system stability, and ensure long-term reliable operation of the power supply system.

Description

Technical Field

[0001] This application relates to the field of mobile cabin power supply technology, and in particular to a multifunctional mobile cabin power supply method and system suitable for field operations. Background Technology

[0002] As field operation environments become increasingly complex and diverse, the demand for multifunctional modular shelters in various application scenarios continues to grow. To ensure the stable operation of the shelters in extreme environments, an efficient power supply system has become crucial.

[0003] Currently, the power supply systems of multi-functional modular shelters mainly rely on a single energy source, such as diesel generators or solar panels. While these systems can meet basic electricity needs to a certain extent, they have significant shortcomings in multi-energy collaborative management, resource optimization, and fault diagnosis. In addition, existing power supply systems often lack comprehensive monitoring of environmental parameters and real-time data analysis capabilities, resulting in low energy efficiency and high operating costs.

[0004] Existing solutions often rely on a single energy source, making it impossible to achieve coordinated management of multiple energy sources, which leads to unstable power supply in certain environments. The lack of multi-objective optimization analysis and life cycle cost analysis results in low resource utilization efficiency and high operating costs. The lack of real-time monitoring and routine fault diagnosis mechanisms makes it difficult to detect and resolve system faults in a timely manner, affecting the long-term stable operation of the system. Summary of the Invention

[0005] This application provides a multifunctional container power supply method and system suitable for field operations, in order to solve the problem of low energy utilization efficiency in the prior art.

[0006] In a first aspect, embodiments of this application provide a multifunctional modular power supply method suitable for field operations, comprising:

[0007] By deploying environmental sensors to collect external environmental parameters and combining them with real-time power consumption data inside the shelter, an operational status report is generated.

[0008] Based on the aforementioned operational status report, a differential evolution algorithm is employed to conduct multi-objective optimization analysis, taking into account multiple dimensions. Life cycle cost analysis technology is introduced to maximize resource utilization and generate the optimal energy combination.

[0009] Based on the optimal energy combination, a sliding mode variable structure control algorithm is adopted to dynamically adjust the operating parameters of each energy conversion device. Through power demand-side management technology, the power allocation strategy is precisely implemented and an energy conversion and allocation log is generated.

[0010] Based on the energy conversion and allocation log, the operating status of the energy supply system is continuously monitored, routine fault diagnosis is performed, energy supply system operating data is recorded, and optimized power supply schemes are generated.

[0011] Optionally, based on the operational status report, the step of employing a differential evolution algorithm to comprehensively consider multiple dimensions for multi-objective optimization analysis, introducing life cycle cost analysis technology, maximizing resource utilization, and generating an optimal energy combination includes:

[0012] Based on the operational status report, key operational data are extracted, the input parameters of the differential evolution algorithm are initialized, and an optimization objective function is generated.

[0013] Based on the aforementioned objective function, the differential evolution algorithm is used to search the space of feasible energy combinations, iteratively update the position of individuals in the population set to optimize the function value, and generate preliminary energy combinations.

[0014] Based on the aforementioned preliminary energy mix, life cycle cost analysis technology is introduced to assess the total cost over the usage period, and combined with energy conversion efficiency and environmental impact, a mix evaluation index is generated.

[0015] Based on the aforementioned combined evaluation indicators, the key indicators of the preliminary energy combination are quantified, and the overall performance is comprehensively compared and analyzed to generate the optimal energy combination.

[0016] Optionally, the step of using the differential evolution algorithm to search the feasible energy combination space based on the optimization objective function, iteratively updating the position of individuals in the population set to optimize the function value, and generating preliminary energy combinations includes:

[0017] Based on the optimization objective function, the differential evolution algorithm parameters are configured, and energy combinations are randomly sampled to generate an initial population set.

[0018] Based on the initial population set, the fitness of each individual is evaluated, the corresponding objective function value is calculated, and multiple dimensions are comprehensively considered to measure individual performance, thereby generating fitness evaluation results.

[0019] Based on the fitness evaluation results, the differential evolution algorithm is used to iteratively update the individual positions in the initial population set, adjust the proportion of each energy combination, optimize the objective function value, and generate an optimized population set.

[0020] Based on the optimized population set, an iterative process is performed with a set number of iterations to ensure high fitness and optimal objective function value, thereby generating an initial energy combination.

[0021] Optionally, based on the fitness evaluation results, a differential evolution algorithm is used to iteratively update the positions of individuals in the initial population set, adjust the proportions of each energy combination, optimize the objective function value, and generate an optimized population set, including:

[0022] Based on the fitness assessment results, three individuals are randomly selected from the initial population set as reference individuals to determine the direction of movement;

[0023] Introducing global information from the initial population set and setting relevant control parameters ensures the effectiveness of the optimization process, thereby generating individual locations;

[0024] The individual's location can be calculated using the following formula:

[0025]

[0026] Where V i,G+1 Let X be the position of the i-th individual in the G+1 generation; i,G X represents the position of the i-th individual in generation G; F is the scaling factor, typically ranging from [0,2]; r1,G ,X r2,G ,X r3,G X represents the positions of three distinct individuals randomly selected from the current population; α is a parameter controlling the strength of the nonlinear term; β is the frequency parameter of the nonlinear term; η is a parameter controlling the strength of the extra optimization term; λ is a parameter controlling the decay rate of the extra optimization term with the number of iterations; avg,G κ is the average position of all individuals in the population of generation G; G0 is the parameter controlling the rate of change of the fractional term; G0 is the number of central iterations of the fractional term; G max G represents the maximum number of iterations; G represents the current number of iterations.

[0027] Based on the individual's location, distribution characteristics are identified, key potential properties are extracted, and combined with the relative locations of other individuals, the degree of influence on the objective function is comprehensively considered to generate an individual fitness value.

[0028] The individual fitness value is calculated using the following formula:

[0029]

[0030] Where, f(X) i,G+1 f(X) represents the fitness value of the i-th individual in the (G+1)-th generation; i,G f(V) represents the fitness value of the i-th individual in the G-th generation; CR is the crossover probability, which typically ranges from [0,1]; f(V) represents the crossover probability. i,G+1 f(X) represents the fitness value of the i-th individual at its new position in generation G+1; γ is a parameter controlling the strength of the first nonlinear optimization term; ω is a frequency parameter controlling the first nonlinear optimization term; ... best,G) represents the fitness value of the individual with the best fitness in the population of generation G; μ is the parameter controlling the strength of the second nonlinear optimization term; v is the frequency parameter controlling the second nonlinear optimization term; ρ is the parameter controlling the rate of change of the fractional term; G1 is the number of central iterations of the fractional term; θ is the parameter controlling the strength of the third nonlinear optimization term; σ is the frequency parameter controlling the third nonlinear optimization term; τ is the parameter controlling the rate of change of the third fractional term; G2 is the number of central iterations of the third fractional term; G is the current iteration number.

[0031] Based on the individual's location and fitness value, the initial population set is updated, and multiple iterations are performed to reach the maximum number of iterations. Convergence is repeatedly evaluated to generate an optimized population set.

[0032] Optionally, based on the initial energy mix, life cycle cost analysis is introduced to assess the total cost over the usage period. Combined with energy conversion efficiency and environmental impact, a mix evaluation index is generated, including:

[0033] Based on the aforementioned preliminary energy mix, and combined with the complete life cycle, cost data for each energy type are collected and organized to generate a life cycle cost database.

[0034] Based on the life cycle cost database and combined with the conversion efficiency data of each energy type, the total cost of the initial energy combination during its usage cycle is comprehensively evaluated using life cycle cost analysis technology, and a cost assessment report is generated.

[0035] Based on the cost assessment report, the environmental impact of the preliminary energy combination is analyzed in depth. A multi-criteria decision analysis method is adopted to organically integrate the cost assessment report and the environmental impact to generate a comprehensive performance assessment report.

[0036] Based on the aforementioned comprehensive performance assessment report, to ensure long-term sustainability, the economic and environmental benefits of the preliminary energy combination are comprehensively evaluated, and combination evaluation indicators are generated.

[0037] Optionally, based on the optimal energy combination, a sliding mode variable structure control algorithm is used to dynamically adjust the operating parameters of each energy conversion device. Through power demand-side management technology, a precise power allocation strategy is implemented, and an energy conversion and allocation log is generated, including:

[0038] Based on the optimal energy combination, the specific contribution ratio of each energy type is determined, and an energy contribution ratio table is generated.

[0039] Based on the energy contribution ratio table, a sliding mode variable structure control algorithm is used to dynamically adjust the operating parameters of each energy conversion device to ensure that each energy conversion device operates efficiently under different environmental conditions and to generate optimized operating parameters.

[0040] Based on the optimized working parameters, the changes in power demand inside the container are monitored in real time through power demand-side management technology, the power allocation strategy is implemented accurately, the overall power utilization efficiency is optimized, and an efficient power allocation plan is generated.

[0041] Based on the aforementioned efficient power distribution plan, the operating data of each energy conversion device is recorded in real time during the control and scheduling process, and an energy conversion and distribution log is generated.

[0042] Optionally, based on the energy contribution ratio table, a sliding mode variable structure control algorithm is used to dynamically adjust the operating parameters of each energy conversion device to ensure that each energy conversion device operates efficiently under different environmental conditions, generating optimized operating parameters, including:

[0043] Based on the energy contribution ratio table, the specific role of each energy type is determined, the control strategy of each energy conversion device is guided, and a basic energy control scheme is generated.

[0044] Based on the aforementioned energy control scheme, a sliding mode variable structure control algorithm is adopted, and combined with real-time environmental conditions, the operating parameters of each energy conversion device are dynamically adjusted to generate preliminary adjustment parameters.

[0045] Based on the initial adjustment parameters, the operating effect of each energy conversion device is monitored in real time, and the operating parameters of each energy conversion device are optimized through feedback to ensure the overall stability of the system and generate intermediate optimization parameters.

[0046] Based on the intermediate optimization parameters, the operation of each energy conversion device is evaluated again in actual operation to ensure that each energy conversion device operates efficiently under different environmental conditions and to generate optimized operating parameters.

[0047] Optionally, based on the aforementioned energy control scheme, a sliding mode variable structure control algorithm is adopted, combined with real-time environmental conditions, to dynamically adjust the operating parameters of each energy conversion device and generate preliminary adjustment parameters, including:

[0048] Based on the aforementioned energy control scheme, the error values ​​of each energy conversion device are calculated.

[0049] Collect key parameter values ​​of each energy conversion device, analyze current environmental conditions, and formulate control strategies based on the energy contribution ratio table to generate new control parameters;

[0050] The new control parameters are calculated using the following formula:

[0051]

[0052] Among them, G i,G+1 P represents the new control parameters for the i-th energy conversion device in generation G+1; i,GK represents the current parameters of the i-th energy conversion device in generation G; p For proportional gain; K i K is the integral gain; d E is the differential gain; i,G E represents the error of the i-th energy conversion device in the G-th generation; i,G-1 P represents the error of the i-th energy conversion device in the (G-1)th generation; avg,G α is the average value of all energy conversion device parameters in the Gth generation; α is the parameter controlling the strength of the nonlinear term; β is the frequency parameter of the nonlinear term. This represents the cumulative error from generation 0 to generation G; Let G be the rate of change of error in the Gth generation; G is the number of iterations.

[0053] Based on the new control parameters, they are applied to each energy conversion device to monitor the operating performance of each energy conversion device in real time. Preliminary feedback adjustments are made based on the monitoring data to generate an environmental adaptability assessment value.

[0054] The environmental adaptability assessment value is calculated using the following formula:

[0055]

[0056] Among them, A i,G+1 C represents the environmental adaptability assessment value of the i-th energy conversion device in generation G+1; i,G+1 For the new control parameters of the i-th energy conversion device in generation G+1; C best,G γ represents the optimal value of the new control parameters for all energy conversion devices in Generation G; γ is the parameter representing the strength of the control fitness evaluation term; δ is the parameter representing the strength of the control cosine term; η is the frequency parameter of the control cosine term; E i,G E represents the error of the i-th energy conversion device in the G-th generation; avg,G Let be the average error of all energy conversion devices in the Gth generation; θ is the parameter controlling the strength of the logarithmic term; σ is the frequency parameter controlling the logarithmic term; μ is the parameter controlling the rate of change of the fractional term; G0 is the central iteration number of the fractional term; φ is the parameter controlling the strength of the tangent term; ψ is the frequency parameter controlling the tangent term; E min,G The minimum error of all energy conversion devices in the Gth generation; G is the iteration number;

[0057] Based on the environmental adaptability assessment values, the environmental adaptability assessment values ​​of each energy conversion device are sorted, the new control parameters are adjusted and optimized, the stability of the new control parameters is checked, the effect is applied to actual operation and evaluated again to generate preliminary adjustment parameters.

[0058] Optionally, the step of continuously monitoring the operating status of the energy supply system based on the energy conversion and allocation log, performing routine fault diagnosis, recording energy supply system operating data, and generating optimized power supply schemes includes:

[0059] Based on the energy conversion and allocation log, the operating status of the energy supply system is continuously monitored to ensure stable and efficient operation of the energy supply system and generate system operation monitoring data.

[0060] Based on the system operation monitoring data, fault diagnosis technology is used to perform routine fault diagnosis on the energy supply system, identify and warn of potential problems, and generate fault diagnosis reports.

[0061] Based on the fault diagnosis report, the system systematically analyzes the energy supply system operation data, outputs fault handling measures, and generates system operation records.

[0062] Based on the system operation records, statistical analysis is used to evaluate the efficiency and reliability of the current power supply scheme, and optimization is carried out in combination with improvement points to generate an optimized power supply scheme.

[0063] Secondly, embodiments of this application provide a multifunctional modular power supply system suitable for field operations, comprising:

[0064] The data acquisition module is used to collect external environmental parameters by deploying environmental sensors, and combine them with real-time power consumption data inside the container to generate an operational status report.

[0065] The analysis module is used to perform multi-objective optimization analysis based on the operation status report, using differential evolution algorithm, comprehensively considering multiple dimensions, introducing life cycle cost analysis technology, maximizing resource utilization, and generating the optimal energy combination.

[0066] The adjustment module is used to dynamically adjust the operating parameters of each energy conversion device based on the optimal energy combination and using a sliding mode variable structure control algorithm. Through power demand-side management technology, it accurately implements the power allocation strategy and generates an energy conversion and allocation log.

[0067] The monitoring module is used to continuously monitor the operating status of the energy supply system based on the energy conversion and distribution log, perform routine fault diagnosis, record the operating data of the energy supply system, and generate optimized power supply schemes.

[0068] Thirdly, embodiments of this application provide a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are to be invoked and executed by the processing component to implement a multi-functional modular power supply method suitable for field operations as described in the first aspect.

[0069] Fourthly, embodiments of this application provide a computer storage medium storing a computer program, which, when executed by a computer, implements a multifunctional container power supply method suitable for field operations as described in the first aspect.

[0070] In this embodiment, environmental sensors are deployed to collect external environmental parameters, which are then combined with real-time power consumption data inside the shelter to generate an operational status report. Based on this report, a differential evolutionary algorithm is used to perform multi-objective optimization analysis, taking into account multiple dimensions. Lifecycle cost analysis technology is introduced to maximize resource utilization and generate an optimal energy combination. Based on this optimal energy combination, a sliding mode variable structure control algorithm is used to dynamically adjust the operating parameters of each energy conversion device. Through power demand-side management technology, a precise power allocation strategy is implemented, generating an energy conversion and allocation log. Based on this log, the operational status of the energy supply system is continuously monitored, routine fault diagnosis is performed, energy supply system operational data is recorded, and an optimized power supply scheme is generated.

[0071] The technical solution of this application has the following beneficial effects:

[0072] This application utilizes environmental sensors to collect external environmental parameters and combines them with real-time power consumption data inside the shelter to generate an operational status report, achieving comprehensive monitoring of both the external environment and internal power consumption, thus improving data accuracy and real-time performance. Based on the operational status report, a differential evolution algorithm is employed to conduct multi-objective optimization analysis considering multiple dimensions, incorporating lifecycle cost analysis technology to maximize resource utilization and generate the optimal energy combination, ensuring the scientific and economical nature of energy allocation. Based on the optimal energy combination, a sliding mode variable structure control algorithm is used to dynamically adjust the operating parameters of each energy conversion device. Through power demand-side management technology, precise power allocation strategies are implemented, generating energy conversion and allocation logs, improving the system's response speed and allocation accuracy. Based on the energy conversion and allocation logs, the operating status of the energy supply system is continuously monitored, routine fault diagnosis is performed, energy supply system operating data is recorded, and optimized power supply schemes are generated, ensuring the long-term stable operation and efficient maintenance of the system.

[0073] Furthermore, key operational data are extracted, input parameters for the differential evolution algorithm are initialized, and an optimization objective function is generated. Using the differential evolution algorithm, a feasible energy combination space is searched, and the position of individuals in the population set is iteratively updated to optimize the function value, generating a preliminary energy combination. Life cycle cost analysis technology is introduced to evaluate the total cost over the usage period, and combined with energy conversion efficiency and environmental impact, combination evaluation indicators are generated. The key indicators of the energy combination are quantified, and the overall performance is comprehensively compared and analyzed to generate the optimal energy combination. By employing a differential evolutionary algorithm and comprehensively considering multiple dimensions for multi-objective optimization analysis, the scientific and flexible nature of energy allocation is ensured, improving resource utilization. Life cycle cost analysis technology is introduced to assess the total cost over the usage period. Combining energy conversion efficiency and environmental impact, a combined evaluation index is generated, reducing overall operating costs and enhancing economic benefits. Quantifying key indicators of the energy combination and comprehensively comparing and analyzing overall performance generates the optimal energy combination, ensuring efficient system operation and environmental friendliness. Iteratively updating the position of individuals in the population set and dynamically adjusting the energy combination improves the system's adaptability and response speed, ensuring stable operation under different environmental conditions. Based on the generated optimal energy combination, the operating status of the energy supply system is continuously monitored, routine fault diagnosis is performed, operating data is recorded, and optimized power supply schemes are generated, ensuring long-term system stability and efficient management.

[0074] Furthermore, the specific contribution ratio of each energy type is determined, generating an energy contribution ratio table; a sliding mode variable structure control algorithm is adopted to dynamically adjust the operating parameters of each energy conversion device, generating optimized operating parameters; through power demand-side management technology, changes in power demand inside the container are monitored in real time, and power allocation strategies are precisely implemented to generate an efficient power allocation plan; during the control and scheduling process, the operating data of each energy conversion device is recorded in real time, generating an energy conversion and allocation log; the sliding mode variable structure control algorithm is adopted to dynamically adjust the operating parameters of each energy conversion device, ensuring efficient operation under different environmental conditions, improving the system's response speed and adaptability; through power demand-side management technology, changes in power demand inside the container are monitored in real time, and power allocation strategies are precisely implemented to optimize overall power utilization efficiency and reduce energy waste; an energy contribution ratio table is generated, clarifying the contribution ratio of each energy type, ensuring the rational allocation and utilization of resources, and improving the overall performance of the system; during the control and scheduling process, the operating data of each energy conversion device is recorded in real time, generating an energy conversion and allocation log, facilitating subsequent fault diagnosis and performance optimization, ensuring the long-term stable operation of the system; through optimized operating parameters and an efficient power allocation plan, the overall operating efficiency and reliability of the system are improved, maintenance costs are reduced, and equipment lifespan is extended.

[0075] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0076] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0077] Figure 1 A flowchart illustrating a multifunctional modular power supply method suitable for field operations, provided in this application embodiment;

[0078] Figure 2 A schematic diagram of a multifunctional modular power supply system suitable for field operations is provided in this application embodiment;

[0079] Figure 3 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application. Detailed Implementation

[0080] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0081] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0082] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0083] Figure 1 A flowchart illustrating a power supply method for a multifunctional modular container suitable for field operations is provided in this application embodiment. Figure 1 As shown, the method includes:

[0084] 101. By deploying environmental sensors, external environmental parameters are collected, and combined with real-time power consumption data inside the container, an operational status report is generated.

[0085] In this step, an environmental sensor is a device that can detect and measure parameters of the surrounding environment, including temperature, humidity, light intensity, wind speed, and air pressure. This data is used to assess the impact of the external environment on the energy supply system.

[0086] External environmental parameters include temperature, humidity, light intensity, wind speed, and air pressure. These parameters have a direct impact on the performance and efficiency of energy conversion devices. For example, excessively high or low temperatures can affect the charging and discharging efficiency of batteries, while light intensity affects the power generation efficiency of solar panels.

[0087] Real-time power consumption data inside the makeshift hospital refers to the real-time power consumption of various electrical devices inside the makeshift hospital, including air conditioning, lighting, and communication equipment. It is used to assess the power demand inside the makeshift hospital and ensure the rational allocation of the power supply system.

[0088] The operation status report is a comprehensive report generated based on external environmental parameters and internal power consumption data. It is used to assess the current operating status of the power supply system, including energy utilization efficiency, equipment operating status, and fault warnings.

[0089] In this embodiment, multiple environmental sensors are deployed at key locations both outside and inside the shelter to collect external environmental parameters and internal power consumption data in real time. The environmental sensors transmit the collected data to the central control system via wireless or wired means. The central control system processes and analyzes the received data to generate an operational status report. The report includes current environmental conditions, power consumption, and equipment operating status. Based on the processing and analysis results, a detailed operational status report is generated, providing a basis for subsequent optimization and management.

[0090] Suppose that in a field research container, it is necessary to ensure the stable operation of the power supply system;

[0091] Temperature, humidity, light intensity, and wind speed sensors are deployed on the exterior of the shelter to monitor external environmental conditions in real time. Current and voltage sensors are deployed at key locations inside the shelter (such as air conditioning, lighting, and communication equipment) to monitor power consumption data in real time. Temperature sensors collect data every 5 minutes, humidity sensors every 10 minutes, and light intensity and wind speed sensors every 15 minutes. Internal power consumption data is collected every minute and transmitted to the central control system via wired connection. The central control system processes the received external environmental parameters and internal power consumption data in real time, calculates the impact of current environmental conditions on the equipment inside the shelter, analyzes the power consumption inside the shelter, identifies high-energy-consuming equipment and time periods, and generates a preliminary operational status report. Based on the processing and analysis results, and considering the current external environmental conditions and the power consumption inside the shelter, combined with equipment operating status and energy efficiency assessments, a detailed operational status report is generated.

[0092] Through the above steps, the mobile hospital management personnel can monitor the system's operating status in real time, adjust energy configuration and equipment operating parameters in a timely manner, and ensure the efficient and stable operation of the power supply system.

[0093] 102. Based on the aforementioned operational status report, a differential evolution algorithm is adopted to conduct multi-objective optimization analysis by comprehensively considering multiple dimensions, and life cycle cost analysis technology is introduced to maximize resource utilization and generate the optimal energy combination.

[0094] In this step, the Differential Evolutionary Algorithm (DEA) is a highly efficient global search algorithm, particularly well-suited for solving complex multi-objective optimization problems. In this scenario, it is used to find the optimal energy combination to adapt to varying external environments and internal power demands.

[0095] Multi-objective optimization analysis considers multiple conflicting objectives, such as lowest cost, highest efficiency, and least environmental impact, with the aim of finding a balance.

[0096] Lifecycle cost analysis is a method for assessing the total cost of a product or system throughout its entire lifecycle, including purchase cost, operating cost, maintenance cost, etc., which helps to make more economical and reasonable decisions.

[0097] The optimal energy mix refers to the best energy supply plan that meets all objectives.

[0098] In this embodiment, key external environmental parameters and internal power consumption data are extracted from the operation status report. Based on the extracted data, the input parameters of the differential evolution algorithm are initialized to generate an optimization objective function. The differential evolution algorithm is used to search for feasible energy combination spaces, iteratively updating the positions of individuals in the population set to optimize the function value and generate a preliminary energy combination. Life cycle cost analysis technology is introduced to evaluate the total cost over the usage period, and combined with energy conversion efficiency and environmental impact, a combination evaluation index is generated. Based on the combination evaluation index, the key indicators of the energy combination are quantified, and the overall performance is comprehensively compared and analyzed to generate the optimal energy combination.

[0099] Optionally, step 102, which involves using the differential evolution algorithm based on the operational status report to perform multi-objective optimization analysis considering multiple dimensions and introducing life cycle cost analysis technology to maximize resource utilization and generate the optimal energy combination, includes: extracting key operational data based on the operational status report, initializing the input parameters of the differential evolution algorithm, and generating an optimization objective function; using the differential evolution algorithm based on the optimization objective function to search the feasible energy combination space, iteratively updating the position of individuals in the population set to optimize the function value, and generating a preliminary energy combination; introducing life cycle cost analysis technology based on the preliminary energy combination to evaluate the total cost within the usage period, and generating a combination evaluation index by combining energy conversion efficiency and environmental impact; and quantifying the key indicators of the preliminary energy combination based on the combination evaluation index, comprehensively comparing and analyzing the overall performance, and generating the optimal energy combination.

[0100] In this embodiment, key external environmental parameters and internal power consumption data are extracted from the operation status report to initialize the input parameters of the differential evolution algorithm. Based on the extracted data, an optimization objective function is defined, which comprehensively considers multiple objectives, such as energy utilization efficiency, cost, and environmental impact. The differential evolution algorithm is used to search for feasible energy combination spaces, iteratively updating the positions of individuals in the population set to optimize the objective function value and generate a preliminary energy combination. Based on the preliminary energy combination, life cycle cost analysis technology is introduced to evaluate the total cost over the usage period. Combining energy conversion efficiency and environmental impact, a combination evaluation index is generated. The combination evaluation index is quantified, and the overall performance of the preliminary energy combination is comprehensively compared and analyzed to generate the optimal energy combination.

[0101] Suppose we are in a field medical mobile unit, and we need to optimize energy configuration to improve energy efficiency and reduce costs. We extract external environmental parameters, such as temperature, humidity, light intensity, and wind speed, from operational status reports. We also extract internal power consumption data, such as total power and power consumption of individual equipment. We define an optimization objective function that comprehensively considers multiple objectives, such as energy efficiency, cost, and environmental impact, to balance the importance of each objective. We initialize a population set for a differential evolution algorithm, with each individual representing a possible energy combination. Using the differential evolution algorithm, we iteratively search and optimize to update the positions of individuals in the population set, thereby optimizing the objective function value and generating preliminary energy combinations. Based on these preliminary energy combinations, we introduce lifecycle cost analysis to assess the total cost over the usage period, including initial investment, operation and maintenance costs, and disposal costs. We combine energy conversion efficiency and environmental impact to generate combination evaluation indicators. We quantify these evaluation indicators and comprehensively compare and analyze the overall performance of the preliminary energy combinations. Finally, we select the energy combination with the best performance as the final optimal energy combination.

[0102] Through the above steps, the mobile hospital management personnel can generate the optimal energy combination scheme, improve energy utilization efficiency, reduce operating costs, reduce environmental impact, and ensure the efficient and stable operation of the power supply system.

[0103] Optionally, the step of using a differential evolution algorithm to search for feasible energy combination space and iteratively update the positions of individuals in the population set to optimize the function value and generate preliminary energy combinations based on the optimization objective function includes: setting differential evolution algorithm parameter configuration based on the optimization objective function, randomly sampling energy combinations to generate an initial population set; evaluating the fitness of each individual based on the initial population set, calculating the corresponding objective function value, comprehensively considering multiple dimensions to measure individual performance, and generating a fitness evaluation result; using the differential evolution algorithm based on the fitness evaluation result to iteratively update the positions of individuals in the initial population set, adjusting the proportion of each energy combination, optimizing the objective function value, and generating an optimized population set; and performing iterative processing with a set number of iterations based on the optimized population set to ensure high fitness and optimal objective function value, thereby generating preliminary energy combinations.

[0104] In this embodiment, the parameters of the differential evolution algorithm are configured according to the optimization objective function, such as population size, number of iterations, mutation factor, and crossover probability. Energy combinations are randomly sampled to generate an initial population set, with each individual representing a possible energy combination. Fitness is evaluated for each individual in the initial population set, and the corresponding objective function value is calculated. Multiple dimensions are considered to measure individual performance, generating a fitness evaluation result. Based on the fitness evaluation result, the differential evolution algorithm is used to iteratively update the positions of individuals in the initial population set, adjust the proportions of each energy combination, optimize the objective function value, and generate an optimized population set. The number of iterations is set, and multiple iterations are performed to ensure high fitness and optimal objective function value, generating preliminary energy combinations.

[0105] Suppose that in an emergency rescue command center, energy configuration needs to be optimized to improve energy efficiency and reduce costs. The differential evolution algorithm parameters are configured, and an optimization objective function is defined, comprehensively considering objectives such as energy efficiency, cost, and environmental impact. Energy combinations are randomly sampled to generate 50 initial individuals, each representing a possible energy combination, including solar energy, diesel generators, and batteries. The fitness of each individual in the initial population is evaluated, and the corresponding objective function value is calculated, comprehensively considering dimensions such as energy efficiency, cost, and environmental impact to generate a fitness evaluation result. Based on the fitness evaluation result, the differential evolution algorithm is used to iteratively update the positions of individuals in the initial population, adjust the proportions of each energy combination, optimize the objective function value, and generate an optimized population. In each iteration, individuals with higher fitness are selected for mutation and crossover operations to generate new individuals, replacing those with lower fitness. The number of iterations is set to 100, and multiple iterations are performed to ensure high fitness and optimal objective function values. Finally, a preliminary energy combination is generated, including the optimal energy combination proportions and configuration scheme.

[0106] Through the above steps, the emergency rescue command module can generate a preliminary optimal energy combination plan, improve energy utilization efficiency, control system operating costs and environmental impact, and ensure the efficient and stable operation of the power supply system.

[0107] This application takes into account that the formula is mainly used to find the optimal solution through iterative search and optimization based on swarm intelligence. It is suitable for multi-objective optimization problems and can handle complex nonlinear optimization tasks. By introducing a variety of nonlinear terms and additional optimization terms, the robustness and convergence speed of the algorithm are enhanced, ensuring that the solution space can be effectively explored and the global optimal solution can be found during the optimization process.

[0108] Optionally, based on the fitness evaluation results, a differential evolution algorithm is used to iteratively update the positions of individuals in the initial population set, adjust the proportions of each energy combination, optimize the objective function value, and generate an optimized population set, including:

[0109] Based on the fitness assessment results, three individuals are randomly selected from the initial population set as reference individuals to determine the direction of movement;

[0110] Introducing global information from the initial population set and setting relevant control parameters ensures the effectiveness of the optimization process, thereby generating individual locations;

[0111] The individual's location can be calculated using the following formula:

[0112]

[0113] Where V i,G+1 Let X be the position of the i-th individual in the G+1 generation; i,G X represents the position of the i-th individual in generation G; F is the scaling factor, typically ranging from [0,2]; r1,G ,X r2,G ,X r3,G X represents the positions of three distinct individuals randomly selected from the current population; α is a parameter controlling the strength of the nonlinear term; β is the frequency parameter of the nonlinear term; η is a parameter controlling the strength of the extra optimization term; λ is a parameter controlling the decay rate of the extra optimization term with the number of iterations; avg,G κ is the average position of all individuals in the population of generation G; G0 is the parameter controlling the rate of change of the fractional term; G0 is the number of central iterations of the fractional term; G max G represents the maximum number of iterations; G represents the current number of iterations.

[0114] Based on the individual's location, distribution characteristics are identified, key potential properties are extracted, and combined with the relative locations of other individuals, the degree of influence on the objective function is comprehensively considered to generate an individual fitness value.

[0115] The individual fitness value is calculated using the following formula:

[0116]

[0117] Where, f(X) i,G+1 f(X) represents the fitness value of the i-th individual in the (G+1)-th generation; i,G f(V) represents the fitness value of the i-th individual in the G-th generation; CR is the crossover probability, which typically ranges from [0,1]; f(V) represents the crossover probability. i,G+1 f(X) represents the fitness value of the i-th individual at its new position in generation G+1; γ is a parameter controlling the strength of the first nonlinear optimization term; ω is a frequency parameter controlling the first nonlinear optimization term; ... best,GLet be the fitness value of the individual with the best fitness in the G-th generation population; μ be the parameter controlling the strength of the second nonlinear optimization term; ν be the frequency parameter controlling the second nonlinear optimization term; ρ be the parameter controlling the rate of change of the fractional term; G1 be the number of central iterations of the fractional term; θ be the parameter controlling the strength of the third nonlinear optimization term; σ be the frequency parameter controlling the third nonlinear optimization term; τ be the parameter controlling the rate of change of the third fractional term; G2 be the number of central iterations of the third fractional term; and G be the current iteration number.

[0118] Based on the individual's location and fitness value, the initial population set is updated, and multiple iterations are performed to reach the maximum number of iterations. Convergence is repeatedly evaluated to generate an optimized population set.

[0119] This method aims to enhance the robustness and convergence speed of the algorithm by introducing multiple nonlinear terms and additional optimization terms, ensuring that the solution space can be effectively explored and the global optimum can be found during the optimization process. By dynamically adjusting the individual position and fitness value, and combining global and local information, the search efficiency and optimization effect are improved. The introduction of nonlinear terms and additional optimization terms can better adapt to complex and changing environmental conditions, ensuring the stability and effectiveness of the algorithm in different scenarios.

[0120] In an individual location, the current location X i,G : Serves as the basis for iterative updates; difference vector Used to determine the direction of movement, introducing nonlinear changes to enhance search capabilities; nonlinear disturbance term α.sin(β·(X) r3,G -X i,G ): Used to enhance local search capabilities; global optimization term. Incorporate global information to enhance global search capabilities;

[0121] In the individual fitness values, retain the current fitness value (1-CR)·f(X). i,G ): Ensure the stability of the algorithm and avoid over-reliance on new solutions; the fitness value CR·f(V) at the new position. i,G+1 ): Introducing new solutions increases the diversity of the search; nonlinear optimization term γ·sin(ω·(f(X) best,G )-f(X i,G ): By introducing nonlinear perturbations, the local search capability is enhanced, helping the algorithm escape local optima; nonlinear optimization terms By introducing nonlinear variations, the local search capability is further enhanced, and the robustness of the algorithm is improved; nonlinear optimization terms By introducing logarithmic and exponential functions, the local search capability is further enhanced, ensuring that the algorithm can still be effectively optimized in later iterations;

[0122] Among them, the scaling factor F is usually determined experimentally, with a value range of [0,2]; the crossover probability CR is also determined experimentally, with a value range of [0,1]; the nonlinear term intensity parameters α, γ, μ, θ are determined experimentally, with a value range of [0,1]; the frequency parameters β, ω, v, σ are determined experimentally, with a value range of [0,1]; the rate of change parameters k, λ, ρ, τ are determined experimentally, with a value range of [0,1]; the number of central iterations G0, G1, G2 are determined experimentally, with a value range of [0,G_{max}]; and the maximum number of iterations G... max Experiments have shown that the value range is positive integers.

[0123] Suppose that in a smart factory energy management module, it is necessary to optimize the energy mix, improve energy efficiency, and reduce operating costs; initialize the population by randomly generating 50 initial individuals, each representing an energy mix including the proportions of solar, wind, diesel generators, and batteries; randomly select three individuals X. r1,G ,X r2,G ,X r3,G As a reference individual, global information is introduced and relevant control parameters are set;

[0124] Individual location:

[0125]

[0126] Individual fitness value:

[0127] The population is updated by adding the new individual positions and fitness values ​​to the initial population set, and this process is repeated multiple times until the maximum number of iterations G is reached. max Repeatedly evaluate convergence and generate an optimized population set;

[0128] Based on the calculation results, an individual fitness value of 0.89 indicates that the optimized energy combination significantly improved the fitness value in the first iteration, meaning that energy utilization efficiency and cost-effectiveness were significantly improved. Assuming a threshold of 0.85 was set, the result of 0.89, which is greater than this threshold, indicates that the current optimization scheme is effective and can significantly improve the system's performance and reliability. Through the above steps, the managers of the smart factory energy management module can optimize the energy combination, improve energy utilization efficiency, reduce operating costs, and ensure the system's stability and efficiency.

[0129] Optionally, based on the preliminary energy mix, life cycle cost analysis technology is introduced to assess the total cost over the usage period. Combining energy conversion efficiency and environmental impact, a combination evaluation index is generated. This includes: based on the preliminary energy mix and considering the complete life cycle, collecting cost data for each energy type to generate a life cycle cost database; based on the life cycle cost database and combining conversion efficiency data for each energy type, using life cycle cost analysis technology, comprehensively assessing the total cost over the usage period of the preliminary energy mix, and generating a cost assessment report; based on the cost assessment report, deeply analyzing the environmental impact of the preliminary energy mix, employing a multi-criteria decision analysis method, organically integrating the cost assessment report and environmental impact, and generating a comprehensive performance assessment report; based on the comprehensive performance assessment report, ensuring long-term sustainability, comprehensively assessing the economic and environmental benefits of the preliminary energy mix, and generating combination evaluation indexes.

[0130] In this embodiment, based on a preliminary energy mix, and considering the entire life cycle, cost data for each energy type is collected and organized, including initial investment, operation and maintenance costs, and disposal costs, forming a detailed life cycle cost database. Combining conversion efficiency data for each energy type, life cycle cost analysis techniques are used to comprehensively assess the total cost of the preliminary energy mix throughout its entire life cycle, generating a detailed cost assessment report. The environmental impact of the preliminary energy mix, such as carbon emissions and pollutant emissions, is analyzed in depth. A multi-criteria decision analysis method is employed to organically combine the cost assessment report with environmental impact data, generating a comprehensive performance assessment report. Finally, the economic and environmental benefits of the preliminary energy mix are comprehensively evaluated, generating a set of comprehensive evaluation indicators covering total cost, energy conversion efficiency, and environmental impact.

[0131] Imagine a smart agricultural greenhouse in the field, where energy configuration needs to be optimized to improve greenhouse efficiency. Based on a preliminary energy mix, cost data for each energy type—solar, wind, biomass, etc.—is collected throughout its entire lifecycle, including initial investment, operation and maintenance costs, and disposal costs. This data is then compiled to generate a lifecycle cost database. Based on this database, and combined with conversion efficiency data for each energy type, lifecycle cost analysis techniques are used to comprehensively assess the total cost of the preliminary energy mix over its lifespan. A cost assessment report is generated, detailing the initial investment, operation and maintenance costs, and disposal costs for each energy type. Based on this report, the environmental impacts of the preliminary energy mix, such as carbon emissions and pollutant emissions, are analyzed in depth. A multi-criteria decision analysis method is employed to organically integrate the cost assessment report and environmental impact data, generating a comprehensive performance assessment report. This report includes assessments of economic costs, environmental impacts, and energy conversion efficiency. Based on this report, long-term sustainability is ensured by comprehensively evaluating the economic and environmental benefits of the preliminary energy mix. Combined evaluation indicators, including total cost, energy conversion efficiency, and environmental impact, are generated to ensure optimal economic and environmental performance of the preliminary energy mix.

[0132] Through the above steps, the outdoor intelligent agricultural greenhouse modular unit can optimize its own energy configuration and improve the operating efficiency of the agricultural greenhouse.

[0133] 103. Based on the optimal energy combination, a sliding mode variable structure control algorithm is adopted to dynamically adjust the operating parameters of each energy conversion device. Through power demand-side management technology, the power allocation strategy is accurately implemented, and an energy conversion and allocation log is generated.

[0134] In this step, the sliding mode variable structure control algorithm is a method in control theory that can maintain the stability and robustness of a system under conditions of changing system parameters or external disturbances. In this scenario, it is used to dynamically adjust the operating parameters of the energy conversion device to adapt to constantly changing demands.

[0135] Demand-side management (DSM) technology refers to the use of various means and technologies to guide users to consume electricity rationally, thereby improving the efficiency and reliability of the power system. In this scenario, it is used to ensure the efficient distribution of electrical energy.

[0136] The energy conversion and allocation log records the process and results of energy conversion and allocation, which is very helpful for subsequent analysis and optimization.

[0137] In this embodiment, based on the optimal energy combination, the specific contribution ratio of each energy type is determined, and an energy contribution ratio table is generated; a sliding mode variable structure control algorithm is adopted to dynamically adjust the operating parameters of each energy conversion device to ensure efficient operation under different environmental conditions and generate optimized operating parameters; through power demand-side management technology, changes in power demand inside the container are monitored in real time, power allocation strategies are accurately implemented, overall power utilization efficiency is optimized, and an efficient power allocation plan is generated; during the control and scheduling process, the operating data of each energy conversion device is recorded in real time to generate an energy conversion and allocation log.

[0138] Optionally, step 103, which involves using a sliding mode variable structure control algorithm to dynamically adjust the operating parameters of each energy conversion device based on the optimal energy combination, and precisely implementing an energy allocation strategy through power demand-side management technology to generate an energy conversion and allocation log, includes: determining the specific contribution ratio of each energy type based on the optimal energy combination and generating an energy contribution ratio table; using a sliding mode variable structure control algorithm based on the energy contribution ratio table to dynamically adjust the operating parameters of each energy conversion device to ensure efficient operation of each energy conversion device under different environmental conditions and generating optimized operating parameters; using power demand-side management technology based on the optimized operating parameters to monitor changes in power demand inside the container in real time, precisely implement an energy allocation strategy, optimize overall energy utilization efficiency, and generate an efficient energy allocation plan; and recording the operating data of each energy conversion device in real time during the control and scheduling process based on the efficient energy allocation plan and generating an energy conversion and allocation log.

[0139] In this embodiment, based on the optimal energy combination, the specific contribution ratio of each energy type is determined, and a detailed energy contribution ratio table is generated. The sliding mode variable structure control algorithm maintains the system's stability and performance in uncertain environments. Power demand-side management technology refers to optimizing power allocation and improving overall power utilization efficiency by monitoring and adjusting power demand in real time. The specific contribution ratio of each energy type is determined based on the optimal energy combination to guide the adjustment of operating parameters for energy conversion devices. Optimized operating parameters are generated using the sliding mode variable structure control algorithm to ensure efficient operation of each energy conversion device under different environmental conditions. An efficient power allocation plan is formulated using power demand-side management technology, real-time monitoring of changes in power demand inside the container, precise implementation of power allocation strategies, and optimization of overall power utilization efficiency. The operating parameters and power allocation status of each energy conversion device are recorded in the energy conversion and allocation log for subsequent fault diagnosis and performance optimization.

[0140] Assuming a field meteorological observation cabin requires ensuring the stability and reliability of energy supply while minimizing environmental impact, the following steps are taken: Based on the optimal energy combination, the specific contribution ratios of each energy type (solar, wind, and battery) are determined, generating an energy contribution ratio table. Using this table, a sliding mode variable structure control algorithm is employed to dynamically adjust the operating parameters of the solar panels, wind turbines, and batteries. Based on weather forecasts and real-time environmental data, the angle of the solar panels and the speed of the wind turbines are adjusted to ensure efficient operation under different environmental conditions, generating optimized operating parameters. Based on these optimized parameters, electricity demand-side management technology is used to monitor changes in internal electricity demand in real time. For example, based on real-time power consumption data, the power allocation strategy is adjusted, prioritizing the use of solar and wind power, with any shortfall supplemented by batteries, generating an efficient power allocation plan. According to this efficient power allocation plan, during the control and scheduling process, the operating data of each energy conversion device is recorded in real time, including operating parameters, power allocation status, solar panel power generation, wind turbine power generation, and battery charging and discharging status, generating a detailed energy conversion and allocation log.

[0141] Through the above steps, the managers of the field meteorological observation cabin can generate the optimal energy combination scheme, improve energy utilization efficiency, reduce operating costs, and ensure the efficient and stable operation of the power supply system.

[0142] Optionally, the step of dynamically adjusting the operating parameters of each energy conversion device based on the energy contribution ratio table using a sliding mode variable structure control algorithm to ensure efficient operation of each energy conversion device under different environmental conditions and generate optimized operating parameters includes: determining the specific role of each energy type based on the energy contribution ratio table to guide the control strategy of each energy conversion device and generate a basic energy control scheme; based on the basic energy control scheme, using a sliding mode variable structure control algorithm combined with real-time environmental conditions to dynamically adjust the operating parameters of each energy conversion device and generate preliminary adjustment parameters; based on the preliminary adjustment parameters, monitoring the operating effect of each energy conversion device in real time, optimizing the operating parameters of each energy conversion device through feedback to ensure overall system stability and generate intermediate optimized parameters; and based on the intermediate optimized parameters, applying them to actual operation to re-evaluate the operating effect of each energy conversion device to ensure efficient operation of each energy conversion device under different environmental conditions and generate optimized operating parameters.

[0143] In this embodiment, based on the energy contribution ratio table, the specific roles and functions of each energy type are clarified, and a basic energy control scheme is generated. Based on this scheme, a sliding mode variable structure control algorithm is used, combined with real-time environmental conditions, to dynamically adjust the operating parameters of each energy conversion device, generating preliminary adjustment parameters. Based on these preliminary adjustment parameters, the actual operating performance of each energy conversion device is continuously monitored, and the device parameters are iterated using a feedback mechanism to optimize the operating parameters of each energy conversion device, generating intermediate optimized parameters. Based on these intermediate optimized parameters, the operation is applied to actual operation, and the operating effect of each energy conversion device is re-evaluated to ensure efficient operation under different environmental conditions, generating optimized operating parameters.

[0144] Assuming a field communication container, it is necessary to ensure a stable and efficient energy supply while minimizing energy waste. Based on an energy contribution ratio table, the specific roles of each energy type, such as diesel generators, solar photovoltaic panels, and batteries, are clearly defined. For example, diesel generators are mainly used for power supply at night and on cloudy days, solar photovoltaic panels are mainly used for power supply during the day, and batteries are used to smooth supply and demand fluctuations. Based on the basic energy control scheme, a sliding mode variable structure control algorithm is used, combined with real-time environmental conditions (such as weather and load changes), to dynamically adjust the output power of the diesel generator, the angle of the solar photovoltaic panels, and the charging and discharging rate of the batteries, generating preliminary adjustment parameters. Based on weather forecasts and real-time load data, the diesel generator output power, solar photovoltaic panel angle, and battery charging and discharging rate are adjusted. The generator's output power ensures sufficient power supply at night and on cloudy days. Based on initial parameter adjustments, the operating performance of each energy conversion device is monitored in real time. Feedback is used to optimize the operating parameters of each energy conversion device, generating intermediate optimized parameters. The output power and efficiency of the diesel generator are monitored in real time. If low efficiency is detected, its operating parameters are adjusted to improve efficiency. Based on the intermediate optimized parameters, they are applied to actual operation to re-evaluate the operating performance of each energy conversion device, ensuring efficient operation under different environmental conditions and generating optimized operating parameters. The optimized operating parameters are applied to actual operation, and the operating performance of the diesel generator, solar photovoltaic panels, and batteries is continuously monitored to ensure the overall system is stable and efficient.

[0145] By following the steps above, the field communication container can ensure a stable and efficient energy supply inside the container while reducing energy waste.

[0146] Optionally, based on the aforementioned energy control scheme, a sliding mode variable structure control algorithm is adopted, combined with real-time environmental conditions, to dynamically adjust the operating parameters of each energy conversion device and generate preliminary adjustment parameters, including:

[0147] Based on the aforementioned energy control scheme, the error values ​​of each energy conversion device are calculated.

[0148] Collect key parameter values ​​of each energy conversion device, analyze current environmental conditions, and formulate control strategies based on the energy contribution ratio table to generate new control parameters;

[0149] The new control parameters are calculated using the following formula:

[0150]

[0151] Among them, C i,G+1 P represents the new control parameters for the i-th energy conversion device in generation G+1; i,G K represents the current parameters of the i-th energy conversion device in generation G; p For proportional gain; K i K is the integral gain; d E is the differential gain; i,G E represents the error of the i-th energy conversion device in the G-th generation; i,G-1 P represents the error of the i-th energy conversion device in the (G-1)th generation; avg,G α is the average value of all energy conversion device parameters in the Gth generation; α is the parameter controlling the strength of the nonlinear term; β is the frequency parameter of the nonlinear term. This represents the cumulative error from generation 0 to generation G; Let G be the rate of change of error in the Gth generation; G is the number of iterations.

[0152] Based on the new control parameters, they are applied to each energy conversion device to monitor the operating performance of each energy conversion device in real time. Preliminary feedback adjustments are made based on the monitoring data to generate an environmental adaptability assessment value.

[0153] The environmental adaptability assessment value is calculated using the following formula:

[0154]

[0155] Among them, A i,G+1 C represents the environmental adaptability assessment value of the i-th energy conversion device in generation G+1; i,G+1 For the new control parameters of the i-th energy conversion device in generation G+1; C best,G γ represents the optimal value of the new control parameters for all energy conversion devices in Generation G; γ is the parameter representing the strength of the control fitness evaluation term; δ is the parameter representing the strength of the control cosine term; η is the frequency parameter of the control cosine term; E i,g E represents the error of the i-th energy conversion device in the G-th generation; avg,G Let be the average error of all energy conversion devices in the Gth generation; θ is the parameter controlling the strength of the logarithmic term; σ is the frequency parameter controlling the logarithmic term; μ is the parameter controlling the rate of change of the fractional term; G0 is the central iteration number of the fractional term; φ is the parameter controlling the strength of the tangent term; ψ is the frequency parameter controlling the tangent term; E min,GThe minimum error of all energy conversion devices in the Gth generation; G is the iteration number;

[0156] Based on the environmental adaptability assessment values, the environmental adaptability assessment values ​​of each energy conversion device are sorted, the new control parameters are adjusted and optimized, the stability of the new control parameters is checked, the effect is evaluated again in actual operation, and preliminary adjustment parameters are generated.

[0157] This method aims to dynamically adjust the operating parameters of each energy conversion device by combining real-time environmental conditions, generating preliminary adjustment parameters, and optimizing system performance. Specifically, by introducing error values, key parameter values, and nonlinear terms, the algorithm's adaptability and optimization effect are enhanced, ensuring that the system can operate efficiently under different environmental conditions.

[0158] In the new control parameters, the proportional term K p ·(E i,G -E i,G-1 ): Used for rapid response to error changes, improving the transient performance of the system; integral term Used to eliminate steady-state errors and improve the steady-state performance of the system; differential term Used to suppress the rate of error change and improve the system's anti-interference capability; nonlinear term Used to introduce nonlinear disturbances to enhance the robustness and adaptability of the system;

[0159] In the environmental adaptability assessment value, the proportion item Used to evaluate the difference between new control parameters and optimal control parameters, improving the accuracy of the evaluation; cosine term δ·cos(η·(E) i,G -E avg,G ): Used to assess the difference between the error and the average error, enhancing the stability of the assessment; for logarithmic terms Used to assess the difference between new control parameters and average control parameters, enhancing the sensitivity of the assessment; tangent term φ·tan(ψ·(E) i,G -E min,G ): Used to assess the difference between the error and the minimum error, thereby enhancing the accuracy of the assessment;

[0160] Wherein, the proportional gain K p The integral gain K is usually determined experimentally and its value range is positive real numbers. i The value is usually determined experimentally and is within the range of positive real numbers; the differential gain K... dThe following parameters are typically determined experimentally, with values ​​ranging from positive real numbers: The strength parameter α of the nonlinear term is determined experimentally, with a value range of [0,1]; the frequency parameter β of the nonlinear term is determined experimentally, with a value range of [0,1]; the strength parameter γ of the fitness evaluation term is determined experimentally, with a value range of positive real numbers; the strength parameter δ of the cosine term is determined experimentally, with a value range of [0,1]; the frequency parameter η of the cosine term is determined experimentally, with a value range of [0,1]; the strength parameter θ of the logarithmic term is determined experimentally, with a value range of [0,1]; the frequency parameter σ of the logarithmic term is determined experimentally, with a value range of [0,1]; the rate of change parameter μ of the fractional term is determined experimentally, with a value range of positive real numbers; the number of central iterations G0 of the fractional term is determined experimentally, with a value range of positive integers; the strength parameter φ of the tangent term is determined experimentally, with a value range of [0,1]; and the frequency parameter ψ of the tangent term is determined experimentally, with a value range of [0,1].

[0161] Suppose that in a field smart grid management container, the goal is to optimize the operating parameters of each energy conversion device, improve energy utilization efficiency, and reduce operating costs;

[0162] Set initial parameter P i,0 The values ​​[1.0, 1.1, 1.2] correspond to the initial parameters of the three energy conversion devices; the proportional gain K is set. p =0.5, Integral gain K i =0.1, differential gain K d =0.2; Set the nonlinear term strength parameter α =0.3, the nonlinear term frequency parameter β =0.1; Set the fitness evaluation term strength parameter γ =1.0, the cosine term strength parameter δ =0.2, the cosine term frequency parameter η =0.1; Set the logarithmic term strength parameter θ =0.1, the logarithmic term frequency parameter σ =0.05, the fractional term change rate parameter μ =0.01, the fractional term central iteration number G0 =10; Set the tangent term strength parameter φ =0.1, the tangent term frequency parameter ψ =0.05; Set the initial iteration number G =0;

[0163] Assume the error value of generation 0 is E i,0 The error value E for the first generation is [0.1, 0.2, 0.3]. i,1 The result is [0.05, 0.1, 0.15].

[0164]

[0165] Assuming the cumulative error of the first generation The error change rate is [0.05, 0.1, 0.15]. The average value P of all energy conversion device parameters is [-0.05, -0.1, -0.15]. avg,1The average error E of all energy conversion devices is 1.066. avg,1 The minimum error E of all energy conversion devices is 0.1. min,1 The optimal value C for the new control parameters of all energy conversion devices is 0.05. best,1 It is 1.149;

[0166]

[0167] According to the calculation results, [A 1,1 A 2,1 A 3,1 The result [0.932, 0.938, 0.706] indicates that after the first generation of optimization, the environmental adaptability of each energy conversion device has been significantly improved, and the system performance has been effectively enhanced. Assuming the set threshold vector is [0.9, 0.9, 0.7], since all values ​​in the result are greater than this set threshold vector, it indicates that the current optimization process is effective, the operating parameters of each energy conversion device are being gradually optimized, and the system performance is being effectively improved. Through the above steps, the field smart grid management container successfully optimized the operating parameters of each energy conversion device, improved energy utilization efficiency, reduced operating costs, and ensured the stability and high performance of the system.

[0168] 104. Based on the energy conversion and allocation log, continuously monitor the operating status of the energy supply system, perform routine fault diagnosis, record the operating data of the energy supply system, and generate an optimized power supply scheme.

[0169] In this step, routine fault diagnosis refers to performing regular or real-time health checks on the system to promptly identify and resolve problems and prevent faults from occurring.

[0170] Energy supply system operation data includes all relevant data on system operation, such as the working status of each device and power consumption. This data is crucial for system optimization.

[0171] Optimizing the power supply scheme is based on the analysis of operational data, proposing improvement measures to further enhance the system's efficiency and stability.

[0172] Based on energy conversion and distribution logs, the system continuously monitors the operational status of the energy supply system, including the operating parameters and power distribution of each energy conversion device; it periodically performs fault detection and diagnosis to promptly identify and resolve potential problems, ensuring the long-term stable operation of the system; during the control and scheduling process, it records the operational data of each energy conversion device in real time, including operating parameters and power distribution; based on the recorded operational data and fault diagnosis results, it generates optimized power supply schemes, proposes improvement measures and optimization suggestions, and enhances the overall performance and reliability of the system.

[0173] Optionally, step 104, which involves continuously monitoring the operating status of the energy supply system based on the energy conversion and allocation log, performing routine fault diagnosis, recording energy supply system operating data, and generating an optimized power supply scheme, includes: continuously monitoring the operating status of the energy supply system based on the energy conversion and allocation log to ensure stable and efficient operation of the energy supply system and generating system operation monitoring data; performing routine fault diagnosis on the energy supply system using fault diagnosis technology based on the system operation monitoring data, identifying and warning of potential problems, and generating a fault diagnosis report; systematically analyzing the energy supply system operating data based on the fault diagnosis report, outputting fault handling measures, and generating a system operation record; and using statistical analysis to evaluate the efficiency and reliability of the current power supply scheme based on the system operation record, optimizing it in conjunction with improvement points, and generating an optimized power supply scheme.

[0174] In this embodiment, the operating status of the energy supply system is continuously monitored based on energy conversion and distribution log information to maintain stable system operation and improve efficiency, while recording system operation monitoring data. Using this monitoring data and fault diagnosis technology, periodic fault prediction analysis is performed on the energy supply system to identify and warn of potential problems in advance, generating fault diagnosis reports. The operating data of the energy supply system is analyzed in depth to develop targeted fault solutions, and the system operation log is updated to record the implementation status of these solutions. By analyzing the system operation records and using statistical and evaluation methods, the efficiency and reliability of the current power supply strategy are comprehensively reviewed, and optimization designs are implemented for identified areas for improvement, generating optimized power supply schemes.

[0175] Imagine a mobile communication shelter in the field. To ensure the stability and efficiency of the energy supply system, and to promptly detect and address potential faults, the system continuously monitors the operational status of the mobile communication base station's energy supply system based on energy conversion and distribution logs. This involves recording the operating parameters and power distribution of each energy conversion device, generating system operation monitoring data, such as real-time monitoring of diesel generator output power, solar photovoltaic panel power generation, and battery charging / discharging status. Based on this monitoring data, fault diagnosis technology is used to perform routine fault diagnosis of the energy supply system, identify and warn of potential problems, and generate fault diagnosis reports. Data analysis is then used to identify potential issues with the diesel generator. Significant fluctuations in output power may indicate a risk of malfunction, prompting the generation of a corresponding fault diagnosis report. Based on this report, the system systematically analyzes the energy supply system's operational data, outputs fault handling measures, and generates a system operation record. Regarding the issue of fluctuating diesel generator output power, it is recommended to regularly inspect and maintain the generator, recording the fault handling process and results. Based on the system operation record, statistical analysis is used to evaluate the efficiency and reliability of the current power supply scheme, and optimization is implemented based on improvement points to generate an optimized power supply scheme. Statistical analysis reveals that the power generation efficiency of solar photovoltaic panels is low at certain times, suggesting increasing energy storage capacity or adjusting the angle of the photovoltaic panels to generate an optimized power supply scheme.

[0176] By following the steps above, the managers of the mobile communication cabin can ensure the stability and efficiency of the energy supply system, promptly detect and handle potential faults, and improve the overall performance of the system.

[0177] Figure 2 This application provides a schematic diagram of the structure of a multifunctional modular power supply system suitable for field operations, as shown in the embodiments of this application. Figure 2 As shown, the device includes:

[0178] The data acquisition module 21 is used to collect external environmental parameters by deploying environmental sensors, and combine them with real-time power consumption data inside the container to generate an operation status report.

[0179] Analysis module 22 is used to perform multi-objective optimization analysis based on the operation status report, using differential evolution algorithm, comprehensively considering multiple dimensions, introducing life cycle cost analysis technology, maximizing resource utilization, and generating optimal energy combination;

[0180] The adjustment module 23 is used to dynamically adjust the operating parameters of each energy conversion device based on the optimal energy combination and using a sliding mode variable structure control algorithm. Through power demand-side management technology, it accurately implements the power allocation strategy and generates an energy conversion and allocation log.

[0181] The monitoring module 24 is used to continuously monitor the operating status of the energy supply system based on the energy conversion and distribution log, perform routine fault diagnosis, record the operating data of the energy supply system, and generate an optimized power supply scheme.

[0182] Figure 2 The aforementioned multi-functional modular power supply system suitable for field operations can perform... Figure 1 The implementation principle and technical effects of the multifunctional modular power supply method for field operations described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the multifunctional modular power supply system for field operations described in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0183] In one possible design, Figure 2 The illustrated embodiment of a multi-functional modular power supply system suitable for field operations can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0184] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0185] The processing component 32 is used to: collect external environmental parameters by deploying environmental sensors, combine them with real-time power consumption data inside the container, and generate an operation status report; based on the operation status report, use a differential evolution algorithm to comprehensively consider multiple dimensions for multi-objective optimization analysis, introduce life cycle cost analysis technology, maximize resource utilization, and generate an optimal energy combination; based on the optimal energy combination, use a sliding mode variable structure control algorithm to dynamically adjust the operating parameters of each energy conversion device, and implement a precise power allocation strategy through power demand-side management technology to generate an energy conversion and allocation log; based on the energy conversion and allocation log, continuously monitor the operating status of the energy supply system, perform routine fault diagnosis, record the operating data of the energy supply system, and generate an optimized power supply scheme.

[0186] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0187] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0188] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0189] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0190] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0191] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0192] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown illustrates a power supply method for a multi-functional modular container suitable for field operations.

[0193] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0194] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0195] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0196] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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 this application.

Claims

1. A method for powering a multi-functional modular container suitable for field operations, characterized in that, include: By deploying environmental sensors to collect external environmental parameters and combining them with real-time power consumption data inside the shelter, an operational status report is generated. Based on the aforementioned operational status report, a differential evolution algorithm is employed to conduct multi-objective optimization analysis, taking into account multiple dimensions. Life cycle cost analysis technology is introduced to maximize resource utilization and generate the optimal energy combination. Based on the optimal energy combination, a sliding mode variable structure control algorithm is used to dynamically adjust the operating parameters of each energy conversion device. Through power demand-side management technology, a precise power allocation strategy is implemented, generating an energy conversion and allocation log, including: Based on the optimal energy combination, the specific contribution ratio of each energy type is determined, and an energy contribution ratio table is generated. Based on the energy contribution ratio table, a sliding mode variable structure control algorithm is used to dynamically adjust the operating parameters of each energy conversion device to ensure efficient operation of each energy conversion device under different environmental conditions, generating optimized operating parameters, including: Based on the energy contribution ratio table, the specific role of each energy type is determined, the control strategy of each energy conversion device is guided, and a basic energy control scheme is generated. Based on the aforementioned energy control scheme, a sliding mode variable structure control algorithm is adopted, and combined with real-time environmental conditions, the operating parameters of each energy conversion device are dynamically adjusted to generate preliminary adjustment parameters. Based on the initial adjustment parameters, the operating effect of each energy conversion device is monitored in real time. The operating parameters of each energy conversion device are optimized through feedback to ensure the overall stability of the system and generate intermediate optimization parameters. These intermediate optimization parameters are used to improve the adaptability of each energy conversion device under different environmental conditions. Based on the intermediate optimization parameters, they are applied to actual operation to re-evaluate the operating effect of each energy conversion device, ensuring that each energy conversion device operates efficiently under different environmental conditions and generating optimized operating parameters. Based on the optimized working parameters, the changes in power demand inside the container are monitored in real time through power demand-side management technology, the power allocation strategy is implemented accurately, the overall power utilization efficiency is optimized, and an efficient power allocation plan is generated. Based on the aforementioned efficient power distribution plan, the operating data of each energy conversion device is recorded in real time during the control and scheduling process to generate an energy conversion and distribution log; Based on the energy conversion and allocation log, the operating status of the energy supply system is continuously monitored, routine fault diagnosis is performed, energy supply system operating data is recorded, and optimized power supply schemes are generated.

2. The method according to claim 1, characterized in that, Based on the operational status report, a differential evolution algorithm is used to comprehensively consider multiple dimensions for multi-objective optimization analysis, and life cycle cost analysis technology is introduced to maximize resource utilization and generate the optimal energy combination, including: Based on the operational status report, key operational data are extracted, the input parameters of the differential evolution algorithm are initialized, and an optimization objective function is generated. Based on the aforementioned objective function, the differential evolution algorithm is used to search the space of feasible energy combinations, iteratively update the position of individuals in the population set to optimize the function value, and generate preliminary energy combinations. Based on the aforementioned preliminary energy mix, life cycle cost analysis technology is introduced to assess the total cost over the usage period, and combined with energy conversion efficiency and environmental impact, a mix evaluation index is generated. Based on the aforementioned combined evaluation indicators, the key indicators of the energy combination are quantified, and the overall performance is comprehensively compared and analyzed to generate the optimal energy combination.

3. The method according to claim 2, characterized in that, Based on the optimized objective function, the differential evolution algorithm is used to search the feasible energy combination space, iteratively update the position of individuals in the population set to optimize the function value, and generate preliminary energy combinations, including: Based on the optimization objective function, the differential evolution algorithm parameters are configured, and energy combinations are randomly sampled to generate an initial population set. Based on the initial population set, the fitness of each individual is evaluated, the corresponding objective function value is calculated, and multiple dimensions are comprehensively considered to measure individual performance, thereby generating fitness evaluation results. Based on the fitness evaluation results, the differential evolution algorithm is used to iteratively update the individual positions in the initial population set, adjust the proportion of each energy combination, optimize the objective function value, and generate an optimized population set. Based on the optimized population set, an iterative process is performed with a set number of iterations to ensure high fitness and optimal objective function value, thereby generating an initial energy combination.

4. The method according to claim 3, characterized in that, Based on the fitness evaluation results, a differential evolution algorithm is used to iteratively update the positions of individuals in the initial population set, adjust the proportions of each energy combination, optimize the objective function value, and generate an optimized population set, including: Based on the fitness assessment results, three individuals are randomly selected from the initial population set as reference individuals to determine the direction of movement; Introducing global information from the initial population set and setting relevant control parameters ensures the effectiveness of the optimization process, thereby generating individual locations; The individual's location can be calculated using the following formula: ； in For the first The individual in the first The individual position of a generation; For the first The individual in the first The individual position of a generation; This is the scaling factor, and its value range is typically [range missing]. These are the individual positions of three randomly selected distinct individuals from the current population. Parameters used to control the strength of nonlinear terms; The frequency parameter of the nonlinear term; Parameters used to control the strength of additional optimization terms; A parameter used to control the rate at which additional optimization terms decay with each iteration; For the first The average position of all individuals in the population; Parameters used to control the rate of change of fractional terms; The central iteration number of the fractional term; This represents the maximum number of iterations. This represents the current iteration number; Based on the individual's location, distribution characteristics are identified, key potential properties are extracted, and combined with the relative locations of other individuals, the degree of influence on the objective function is comprehensively considered to generate an individual fitness value. The individual fitness value is calculated using the following formula: ； in, For the first The individual in the first The individual fitness value of the generation; For the first The individual in the first The individual fitness value of the generation; The crossover probability typically ranges from 1 to 10. For the first The individual in the first The fitness value of an individual at a new location; The parameter used to control the strength of the first nonlinear optimization term; To control the frequency parameter of the first nonlinear optimization term; For the first The individual fitness value of the best-fitting individual in the population; The parameter used to control the strength of the second nonlinear optimization term; To control the frequency parameter of the second nonlinear optimization term; Parameters used to control the rate of change of fractional terms; The central iteration number of the fractional term; The parameter used to control the strength of the third nonlinear optimization term; To control the frequency parameter of the third nonlinear optimization term; The parameter used to control the rate of change of the third fractional term; The central iteration number of the third fraction term; This represents the current iteration number; Based on the individual's location and fitness value, the initial population set is updated, and multiple iterations are performed to reach the maximum number of iterations. Convergence is repeatedly evaluated to generate an optimized population set.

5. The method according to claim 2, characterized in that, The process of introducing life cycle cost analysis technology based on the preliminary energy mix, assessing the total cost over the usage period, and generating a mix evaluation index by combining energy conversion efficiency and environmental impact includes: Based on the aforementioned preliminary energy mix, and combined with the complete life cycle, cost data for each energy type are collected and organized to generate a life cycle cost database. Based on the life cycle cost database and combined with the conversion efficiency data of each energy type, the total cost of the initial energy combination during its usage cycle is comprehensively evaluated using life cycle cost analysis technology, and a cost assessment report is generated. Based on the cost assessment report, the environmental impact of the preliminary energy combination is analyzed in depth. A multi-criteria decision analysis method is adopted to organically integrate the cost assessment report and the environmental impact to generate a comprehensive performance assessment report. Based on the aforementioned comprehensive performance assessment report, to ensure long-term sustainability, the economic and environmental benefits of the preliminary energy combination are comprehensively evaluated, and combination evaluation indicators are generated.

6. The method according to claim 1, characterized in that, Based on the aforementioned energy control scheme, a sliding mode variable structure control algorithm is adopted, combined with real-time environmental conditions, to dynamically adjust the operating parameters of each energy conversion device, generating preliminary adjustment parameters, including: Based on the aforementioned energy control scheme, the error values ​​of each energy conversion device are calculated. Collect key parameter values ​​of each energy conversion device, analyze current environmental conditions, and formulate control strategies based on the energy contribution ratio table to generate new control parameters; The new control parameters are calculated using the following formula: ； in, For the first The energy conversion device is in the first New control parameters for the generation; For the first The energy conversion device is in the first The current parameter of the generation; For proportional gain; This is the integral gain; This is the differential gain; For the first The energy conversion device is in the first Errors in generation; For the first The energy conversion device is in the first Errors in generation; For the first The average value of all energy conversion device parameters; Parameters used to control the strength of nonlinear terms; The frequency parameter of the nonlinear term; From generation 0 to generation 1 Cumulative error of the generation; For the first Rate of change of substitution error; This represents the number of iterations. Based on the new control parameters, they are applied to each energy conversion device to monitor the operating performance of each energy conversion device in real time. Preliminary feedback adjustments are made based on the monitoring data to generate an environmental adaptability assessment value. The environmental adaptability assessment value is calculated using the following formula: ； in, For the first The energy conversion device is in the first Environmental adaptability assessment value of the generation; The first one calculated according to Formula 1 The energy conversion device is in the first New control parameters for the generation; For the first The optimal values ​​for new control parameters in all energy conversion devices; Parameters for controlling the strength of fitness evaluation items; Parameters used to control the strength of the cosine term; To control the frequency parameters of the cosine term; For the first The energy conversion device is in the first Errors in generation; For the first The average value of the errors of all energy conversion devices; Parameters for controlling the strength of the logarithmic term; To control the frequency parameter of the logarithmic term; A parameter used to control the rate of change of fractional terms; The central iteration number of the fractional term; Parameters for controlling the intensity of the tangent term; To control the frequency parameter of the tangent term; For the first The minimum value of the error of all energy conversion devices; This represents the number of iterations. Based on the environmental adaptability assessment values, the environmental adaptability assessment values ​​of each energy conversion device are sorted, the new control parameters are adjusted and optimized, the stability of the new control parameters is checked, the effect is evaluated again in actual operation, and preliminary adjustment parameters are generated.

7. The method according to claim 1, characterized in that, The process of continuously monitoring the operating status of the energy supply system based on the energy conversion and allocation log, performing routine fault diagnosis, recording energy supply system operating data, and generating optimized power supply schemes includes: Based on the energy conversion and allocation log, the operating status of the energy supply system is continuously monitored to ensure stable and efficient operation of the energy supply system and generate system operation monitoring data. Based on the system operation monitoring data, fault diagnosis technology is used to perform routine fault diagnosis on the energy supply system, identify and warn of potential problems, and generate fault diagnosis reports. Based on the fault diagnosis report, the system systematically analyzes the energy supply system operation data, outputs fault handling measures, and generates system operation records. Based on the system operation records, statistical analysis is used to evaluate the efficiency and reliability of the current power supply scheme, and optimization is carried out in combination with improvement points to generate an optimized power supply scheme.

8. A multi-functional modular power supply system suitable for field operations, characterized in that, include: The data acquisition module is used to collect external environmental parameters by deploying environmental sensors, and combine them with real-time power consumption data inside the container to generate an operational status report. The analysis module is used to perform multi-objective optimization analysis based on the operation status report, using differential evolution algorithm, comprehensively considering multiple dimensions, introducing life cycle cost analysis technology, maximizing resource utilization, and generating the optimal energy combination. The adjustment module is used to dynamically adjust the operating parameters of each energy conversion device based on the optimal energy combination, using a sliding mode variable structure control algorithm. Through power demand-side management technology, it precisely implements the power allocation strategy and generates an energy conversion and allocation log, including: Based on the optimal energy combination, the specific contribution ratio of each energy type is determined, and an energy contribution ratio table is generated. Based on the energy contribution ratio table, a sliding mode variable structure control algorithm is used to dynamically adjust the operating parameters of each energy conversion device to ensure efficient operation of each energy conversion device under different environmental conditions, generating optimized operating parameters, including: Based on the energy contribution ratio table, the specific role of each energy type is determined, the control strategy of each energy conversion device is guided, and a basic energy control scheme is generated. Based on the aforementioned energy control scheme, a sliding mode variable structure control algorithm is adopted, and combined with real-time environmental conditions, the operating parameters of each energy conversion device are dynamically adjusted to generate preliminary adjustment parameters. Based on the initial adjustment parameters, the operating effect of each energy conversion device is monitored in real time. The operating parameters of each energy conversion device are optimized through feedback to ensure the overall stability of the system and generate intermediate optimization parameters. These intermediate optimization parameters are used to improve the adaptability of each energy conversion device under different environmental conditions. Based on the intermediate optimization parameters, they are applied to actual operation to re-evaluate the operating effect of each energy conversion device, ensuring that each energy conversion device operates efficiently under different environmental conditions and generating optimized operating parameters. Based on the optimized working parameters, the changes in power demand inside the container are monitored in real time through power demand-side management technology, the power allocation strategy is implemented accurately, the overall power utilization efficiency is optimized, and an efficient power allocation plan is generated. Based on the aforementioned efficient power distribution plan, the operating data of each energy conversion device is recorded in real time during the control and scheduling process to generate an energy conversion and distribution log; The monitoring module is used to continuously monitor the operating status of the energy supply system based on the energy conversion and distribution log, perform routine fault diagnosis, record the operating data of the energy supply system, and generate optimized power supply schemes.

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