Multifunctional shelter power supply method and system suitable for field operation

Abstract

The invention provides a multifunctional shelter power supply method and system suitable for field operation. Wherein an operation condition report is generated by deploying an environment sensor, collecting external environment parameters and combining real-time power consumption data in the shelter; based on the operation state report, a differential evolution algorithm is adopted, multi-objective optimization analysis is carried out, a life cycle cost analysis technology is introduced, the resource utilization rate is maximized, and an optimal energy combination is generated; dynamically adjusting working parameters by adopting a sliding mode variable structure control algorithm based on the optimal energy combination, and generating an energy conversion distribution log through a power demand side management technology; and based on the energy conversion distribution log, continuously monitoring an operation state, carrying out normalized fault diagnosis, recording operation data, and generating an optimized power supply scheme. According to the technical scheme, the energy utilization efficiency and the system stability can be improved, and long-term reliable operation of the power supply system is ensured.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of cabin power supply technology, and more particularly to a multifunctional cabin power supply method and system suitable for field operations. Background Art

[0002] As the field operation environment becomes increasingly complex and diverse, the demand for multifunctional shelters in various application scenarios continues to increase. In order to ensure the stable operation of the shelters in extreme environments, an efficient power supply system becomes the key.

[0003] At present, the power supply system of multifunctional shelters mainly relies on a single energy supply method, such as diesel generators or solar panels. Although these systems can meet basic power needs to a certain extent, they have obvious deficiencies in multi-energy collaborative management, resource optimization allocation and fault diagnosis; in addition, the existing power supply system often lacks comprehensive monitoring of environmental parameters and real-time data analysis capabilities, resulting in low energy utilization efficiency and high operating costs.

[0004] Existing solutions mostly rely on a single energy supply and cannot achieve coordinated management of multiple energy sources, resulting in unstable power supply in certain environments; the lack of multi-objective optimization analysis and life cycle cost analysis leads to inefficient resource utilization and high operating costs; the lack of real-time monitoring and normalized fault diagnosis mechanisms makes it difficult to detect and resolve system failures in a timely manner, affecting the long-term stable operation of the system. Summary of the invention

[0005] The embodiments of the present application provide a multifunctional cabin power supply method and system suitable for field operations, so as to solve the problem of low energy utilization efficiency in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a multifunctional shelter 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 operation status report is generated;

[0008] Based on the operation status report, a differential evolution algorithm is used 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;

[0009] Based on the optimal energy combination, a sliding mode variable structure control algorithm is used to dynamically adjust the working parameters of each energy conversion device, and through power demand side management technology, the power distribution strategy is accurately implemented to generate an energy conversion distribution log;

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

[0011] Optionally, based on the operation status report, a differential evolution algorithm is used 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, including:

[0012] Based on the operation status report, extract key operation data, initialize input parameters of the differential evolution algorithm, and generate an optimization objective function;

[0013] Based on the optimization objective function, a differential evolution algorithm is used to search the feasible energy combination space, and the positions of individuals in the population set are iteratively updated to optimize the function value, thereby generating a preliminary energy combination;

[0014] Based on the preliminary energy combination, life cycle cost analysis technology is introduced to evaluate the total cost during the use cycle, and a combination evaluation index is generated by combining energy conversion efficiency and environmental impact;

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

[0016] Optionally, based on the optimization objective function, using a differential evolution algorithm, searching a 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, comprises:

[0017] Based on the optimization objective function, setting the differential evolution algorithm parameter configuration, randomly sampling the energy combination, and generating 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 the individual performance is measured by comprehensively considering multiple dimensions to generate a fitness evaluation result;

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

[0020] Based on the optimized population set, the number of times is set to perform iterative processing to ensure high fitness and optimal objective function value, and generate a preliminary energy combination.

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

[0022] Based on the fitness evaluation result, three individuals in the initial population set are randomly selected as reference individuals to determine the moving direction;

[0023] Introducing the global information of the initial population set, setting relevant control parameters, and ensuring the effectiveness of the optimization process to generate individual positions;

[0024] The individual position is calculated using the following formula:

[0025]

[0026] Where V i,G+1 is the position of the ith individual in the G+1th generation; X i,G is the position of the ith individual in the Gth generation; F is the scaling factor, which usually ranges from [0,2]; X r1,G ,X r2,G ,X r3,G are the individual positions of three different individuals randomly selected from the current population; α is the parameter that controls the strength of the nonlinear term; β is the frequency parameter of the nonlinear term; η is the parameter that controls the strength of the additional optimization term; λ is the parameter that controls the decay speed of the additional optimization term with the number of iterations; X avg,G is the average value of all individual positions in the G-th generation population; κ is the parameter that controls the rate of change of the fractional term; G0 is the number of central iterations of the fractional term; G max is the maximum number of iterations; G is the current number of iterations;

[0027] Based on the individual position, identify the distribution characteristics, extract the key potential properties, combine with the relative positions of other individuals, and comprehensively consider the degree of influence on the objective function to generate the individual fitness value;

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

[0029]

[0030] Among them, f(X i,G+1 ) is the individual fitness value of the ith individual in the G+1 generation; f(X i,G ) is the individual fitness value of the i-th individual in the G-th generation; CR is the crossover probability, and its value range is usually [0,1]; f(V i,G+1 ) is the individual fitness value of the i-th individual in the new position of the G+1 generation; γ is the parameter that controls the intensity of the first nonlinear optimization item; ω is the frequency parameter that controls the first nonlinear optimization item; f(X best,G) is the individual fitness value of the best individual in the G-th generation population; μ is the parameter controlling the strength of the second nonlinear optimization item; v is the frequency parameter controlling the second nonlinear optimization item; ρ is the parameter controlling the rate of change of the fractional item; G1 is the number of central iterations of the fractional item; θ is the parameter controlling the strength of the third nonlinear optimization item; σ is the frequency parameter controlling the third nonlinear optimization item; τ is the parameter controlling the rate of change of the third fractional item; G2 is the number of central iterations of the third fractional item; G is the current number of iterations;

[0031] Based on the individual positions and the individual fitness values, the initial population set is updated, multiple iterations are performed to reach a maximum number of iterations, convergence is repeatedly evaluated, and an optimized population set is generated.

[0032] Optionally, based on the preliminary energy combination, life cycle cost analysis technology is introduced to evaluate the total cost within the use cycle, and a combination evaluation index is generated by combining energy conversion efficiency and environmental impact, including:

[0033] Based on the preliminary energy combination and in combination with the complete life cycle, the cost data of each energy type is collated and collected to generate a life cycle cost database;

[0034] Based on the life cycle cost database, combined with the conversion efficiency data of each energy type, the total cost of the initial energy combination within the use period is comprehensively evaluated through life cycle cost analysis technology to generate a cost evaluation report;

[0035] Based on the cost assessment report, deeply analyze the environmental impact of the preliminary energy mix, adopt a multi-criteria decision analysis method, organically integrate the cost assessment report and the environmental impact, and generate a comprehensive performance assessment report;

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

[0037] Optionally, based on the optimal energy combination, a sliding mode variable structure control algorithm is adopted to dynamically adjust the working parameters of each energy conversion device, and the power demand side management technology is used to accurately implement the power distribution strategy and generate an energy conversion distribution log, including:

[0038] Based on the optimal energy combination, determine the specific contribution ratio of each energy type and generate an energy contribution ratio table;

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

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

[0041] Based on the efficient electric energy 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 distribution log.

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

[0043] Based on the energy contribution ratio table, determine the specific role of each energy type, guide the control strategy of each energy conversion device, and generate a basic energy control plan;

[0044] Based on the basic energy control scheme, a sliding mode variable structure control algorithm is used to dynamically adjust the working parameters of each energy conversion device in combination with real-time environmental conditions to generate preliminary adjustment parameters;

[0045] Based on the preliminary adjustment parameters, the operation effect of each energy conversion device is monitored in real time, and the working 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, they are applied in actual operation to evaluate the operation effect of each energy conversion device again, to ensure that each energy conversion device operates efficiently under different environmental conditions and to generate optimized working parameters.

[0047] Optionally, based on the basic energy control scheme, a sliding mode variable structure control algorithm is used to dynamically adjust the working parameters of each energy conversion device in combination with real-time environmental conditions to generate preliminary adjustment parameters, including:

[0048] Based on the energy control basic scheme, calculating the error value of each energy conversion device;

[0049] Collect key parameter values ​​of each energy conversion device, analyze current environmental conditions, and formulate a control strategy in combination with 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 is the new control parameter of the i-th energy conversion device in the G+1 generation; P i,Gis the current parameter of the i-th energy conversion device in the G-th generation; K p is the proportional gain; K i is the integral gain; K d is the differential gain; E i,G is the error of the i-th energy conversion device in the G-th generation; E i,G-1 is the error of the i-th energy conversion device in the G-1 generation; P avg,G is the average value of the parameters of all energy conversion devices of the Gth generation; α is the parameter controlling the intensity of the nonlinear term; β is the frequency parameter of the nonlinear term; is the cumulative error from generation 0 to generation G; is the rate of change of the G-th generation error; G is the number of iterations;

[0053] Based on the new control parameters, the new control parameters are applied to each energy conversion device, the operating performance of each energy conversion device is monitored in real time, and preliminary feedback adjustments are made according to the monitoring data to generate an environmental adaptability assessment value;

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

[0055]

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

[0057] Based on the environmental fitness evaluation value, the environmental fitness evaluation 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, applied to actual operation and the effect is re-evaluated to generate preliminary adjustment parameters.

[0058] Optionally, based on the energy conversion and allocation log, continuously monitoring the operating status of the energy supply system, performing normalized fault diagnosis, recording the operating data of the energy supply system, and generating an optimized power supply plan include:

[0059] Based on the energy conversion and allocation log, continuously monitor the operating status of the energy supply system, ensure that the energy supply system operates stably and efficiently, and generate system operation monitoring data;

[0060] Based on the system operation monitoring data, the energy supply system is routinely diagnosed through fault diagnosis technology, potential problems are identified and warned, and a fault diagnosis report is generated;

[0061] Based on the fault diagnosis report, systematically analyze the energy supply system operation data, output fault handling measures, and generate 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 plan, and optimization is performed in combination with improvement points to generate an optimized power supply plan.

[0063] In a second aspect, an embodiment of the present application provides a multifunctional shelter power supply system suitable for field operations, comprising:

[0064] The collection module is used to collect external environmental parameters by deploying environmental sensors and generate an operation status report based on the real-time power consumption data inside the shelter;

[0065] An analysis module, for using a differential evolution algorithm based on the operation status report to conduct multi-objective optimization analysis by comprehensively considering multiple dimensions, introducing life cycle cost analysis technology, maximizing resource utilization, and generating an optimal energy combination;

[0066] An adjustment module is used to dynamically adjust the working parameters of each energy conversion device based on the optimal energy combination by using a sliding mode variable structure control algorithm, accurately implement the power distribution strategy through power demand side management technology, and generate an energy conversion distribution log;

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

[0068] In a third aspect, an embodiment of the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a multifunctional cabin power supply method suitable for field operations as described in the first aspect.

[0069] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, a multifunctional cabin power supply method suitable for field operations as described in the first aspect is implemented.

[0070] In an embodiment of the present application, by deploying environmental sensors to collect external environmental parameters, an operation status report is generated in combination with the real-time power consumption data inside the cabin; based on the operation 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 an optimal energy combination; based on the optimal energy combination, a sliding mode variable structure control algorithm is used to dynamically adjust the working parameters of each energy conversion device, and through the power demand side management technology, the power distribution strategy is accurately implemented to generate an energy conversion distribution log; based on the energy conversion distribution log, the operating status of the energy supply system is continuously monitored, normalized fault diagnosis is performed, the operating data of the energy supply system is recorded, and an optimized power supply plan is generated.

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

[0072] This application deploys environmental sensors to collect external environmental parameters, combines the real-time power consumption data inside the cabin, and generates an operation status report, thereby achieving comprehensive monitoring of the external environment and internal power consumption and improving the accuracy and real-time nature of the data; based on the operation status report, a differential evolution algorithm is used to comprehensively consider multiple dimensions for multi-objective optimization analysis, introduce life cycle cost analysis technology, maximize resource utilization, generate the optimal energy combination, and ensure the scientific and economic nature of energy configuration; based on the optimal energy combination, a sliding mode variable structure control algorithm is used to dynamically adjust the working parameters of each energy conversion device, and through power demand side management technology, the power distribution strategy is accurately implemented, and an energy conversion distribution log is generated, which improves the response speed and distribution accuracy of the system; based on the energy conversion distribution log, the operating status of the energy supply system is continuously monitored, normalized fault diagnosis is performed, the operating data of the energy supply system is recorded, and an optimized power supply plan is generated to ensure the long-term stable operation and efficient maintenance of the system.

[0073] Furthermore, key operating data are extracted, the input parameters of the differential evolution algorithm are initialized, and the optimization objective function is generated; the differential evolution algorithm is used to search the feasible energy combination space, and the positions of individuals in the population set are iteratively updated to optimize the function value and generate a preliminary energy combination; life cycle cost analysis technology is introduced to evaluate the total cost during the use cycle, and the energy conversion efficiency and environmental impact are combined to generate combination evaluation indicators; the key indicators of the energy combination are quantified, the overall performance is comprehensively compared and analyzed, and the optimal energy combination is generated. Through the differential evolution algorithm, multi-objective optimization analysis is carried out by comprehensively considering multiple dimensions, ensuring the scientific nature and flexibility of energy configuration and improving resource utilization; the life cycle cost analysis technology is introduced to evaluate the total cost within the use cycle, and the energy conversion efficiency and environmental impact are combined to generate a combination evaluation index, which reduces the overall operating cost and improves economic benefits; the key indicators of the energy combination are quantified, the overall performance is comprehensively compared and analyzed, and the optimal energy combination is generated to ensure the efficient operation and environmental friendliness of the system; by iteratively updating the position of individuals in the population set and dynamically adjusting the energy combination, the adaptability and response speed of the system are improved, 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, normalized fault diagnosis is performed, operating data is recorded, and optimized power supply plans are generated to ensure the long-term stability and efficient management of the system.

[0074] Furthermore, the specific contribution ratio of each energy type is determined, and an energy contribution ratio table is generated; the sliding mode variable structure control algorithm is used to dynamically adjust the working parameters of each energy conversion device and generate optimized working parameters; through the power demand side management technology, the power demand changes inside the cabin are monitored in real time, the power distribution strategy is accurately implemented, and an efficient power distribution plan is generated; in the control and scheduling process, the operating data of each energy conversion device is recorded in real time, and an energy conversion distribution log is generated; the sliding mode variable structure control algorithm is used to dynamically adjust the working parameters of each energy conversion device to ensure efficient operation under different environmental conditions, thereby improving the response speed and adaptability of the system; through the power demand side management technology, the power demand changes inside the cabin are monitored in real time, the power distribution strategy is accurately implemented, the overall power utilization efficiency is optimized, and energy waste is reduced; an energy contribution ratio table is generated to clarify the contribution ratio of each energy type, ensure the reasonable allocation and utilization of resources, and improve the overall performance of the system; in the control and scheduling process, the operating data of each energy conversion device is recorded in real time, and an energy conversion distribution log is generated to facilitate subsequent fault diagnosis and performance optimization, and ensure the long-term stable operation of the system; through optimized working parameters and efficient power distribution plans, the overall operating efficiency and reliability of the system are improved, maintenance costs are reduced, and the service life of the equipment is extended.

[0075] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0077] Figure 1 A flowchart of a multifunctional cabin power supply method suitable for field operations provided in an embodiment of the present application;

[0078] Figure 2 A schematic diagram of the structure of a multifunctional shelter power supply system suitable for field operations provided in an embodiment of the present application;

[0079] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0080] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0081] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0082] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0083] Figure 1 A flowchart of a multifunctional shelter power supply method suitable for field operations is provided for the embodiment of the present application, such as Figure 1 As shown, the method includes:

[0084] 101. By deploying environmental sensors to collect external environmental parameters and combining them with the real-time power consumption data inside the shelter, an operation status report is generated;

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

[0086] External environmental parameters include temperature, humidity, light intensity, wind speed, air pressure, etc. These parameters have a direct impact on the performance and efficiency of energy conversion devices. For example, too high or too low temperature will affect the charging and discharging efficiency of the battery, and light intensity will affect the power generation efficiency of the solar panel.

[0087] The real-time power consumption data inside the shelter refers to the real-time power consumption of each electrical equipment in the shelter, including air conditioning, lighting, communication equipment, etc., which is used to evaluate the power demand inside the shelter and ensure the reasonable distribution of the power supply system.

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

[0089] In the embodiment of the present application, a variety of environmental sensors are deployed at key locations 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 by wireless or wired means; the central control system processes and analyzes the received data and generates an operation status report. The report content includes current environmental conditions, power consumption, equipment operation status, etc.; based on the results of processing and analysis, a detailed operation status report is generated to provide a basis for subsequent optimization and management.

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

[0091] Temperature sensors, humidity sensors, light intensity sensors and wind speed sensors are deployed outside the shelter to monitor external environmental conditions in real time; current sensors and voltage sensors are deployed at key locations inside the shelter (such as air conditioning, lighting, communication equipment, etc.) to monitor power consumption data in real time; the temperature sensor collects data every 5 minutes, the humidity sensor collects data every 10 minutes, and the light intensity sensor and wind speed sensor collect data every 15 minutes; the internal power consumption data is collected every 1 minute and transmitted to the central control system via wired means; the central control system processes the received external environmental parameters and internal power consumption data in real time, calculates the impact of the current environmental conditions on the equipment in the shelter, analyzes the power consumption inside the shelter, identifies high-energy-consuming equipment and time periods, and generates a preliminary operating status report; based on the results of processing and analysis, a detailed operating status report is generated by comprehensively considering the current external environmental conditions and the power consumption inside the shelter, combining the equipment operating status and energy utilization efficiency evaluation.

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

[0093] 102. Based on the operation 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 an optimal energy combination;

[0094] In this step, the differential evolution algorithm is an efficient global search algorithm that is particularly suitable for solving complex multi-objective optimization problems. In this scenario, it is used to find the best energy combination to adapt to the changing external environment and internal power demand.

[0095] Multi-objective optimization analysis considers multiple conflicting objectives, such as minimum cost, maximum efficiency, and minimum environmental impact, with the goal of finding a balance.

[0096] Life cycle cost analysis technology is a method to evaluate the total cost of a product or system throughout its life cycle, including purchase cost, operating cost, maintenance cost, etc., which helps to make more economically reasonable decisions.

[0097] The optimal energy mix refers to the best energy supply plan determined while meeting all goals.

[0098] In an embodiment of the present application, key external environmental parameters and internal power consumption data are extracted from the operating 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 a feasible energy combination space, and the positions of individuals in the population set are iteratively updated to optimize the function value, thereby generating a preliminary energy combination; life cycle cost analysis technology is introduced to evaluate the total cost within the use cycle, and a combination evaluation index is generated by combining energy conversion efficiency and environmental impact; based on the combination evaluation index, various 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, the method in step 102 is based on the operating status report, adopts a differential evolution algorithm, comprehensively considers multiple dimensions to perform multi-objective optimization analysis, introduces life cycle cost analysis technology, maximizes resource utilization, and generates an optimal energy combination, including: based on the operating status report, extracts key operating data, initializes the input parameters of the differential evolution algorithm, and generates an optimization objective function; based on the optimization objective function, uses the differential evolution algorithm to search the feasible energy combination space, iteratively updates the position of individuals in the population set to optimize the function value, and generates a preliminary energy combination; based on the preliminary energy combination, introduces life cycle cost analysis technology, evaluates the total cost within the use cycle, combines energy conversion efficiency and environmental impact, and generates a combination evaluation index; based on the combination evaluation index, quantifies the key indicators of the preliminary energy combination, comprehensively compares and analyzes the overall performance, and generates the optimal energy combination.

[0100] In an embodiment of the present application, key external environmental parameters and internal power consumption data are extracted from the operating status report and used 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, environmental impact, etc.; the differential evolution algorithm is used to search for a feasible energy combination space, iteratively update the position of individuals in the population set to optimize the objective function value, and generate a preliminary energy combination; based on the preliminary energy combination, the life cycle cost analysis technology is introduced to evaluate the total cost within the use cycle, and the energy conversion efficiency and environmental impact are combined to generate a combination evaluation index; 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] Assume that in a field medical cabin, it is necessary to optimize the energy configuration to improve energy utilization efficiency and reduce costs; extract external environmental parameters such as temperature, humidity, light intensity, wind speed, etc. from the operation status report; extract the power consumption data inside the cabin, such as total power, power of each device, etc.; define the optimization objective function, comprehensively consider multiple goals, such as energy utilization efficiency, cost, environmental impact, etc., to balance the importance of each goal; initialize the population set of the differential evolution algorithm, and each individual represents a possible energy combination; use the differential evolution algorithm to update the position of the individuals in the population set through iterative search and optimization to optimize the objective function value and generate a preliminary energy combination; based on the preliminary energy combination, introduce life cycle cost analysis technology to evaluate the total cost within the use cycle, including initial investment, operation and maintenance costs, scrapping costs, etc.; combine energy conversion efficiency and environmental impact to generate combination evaluation indicators; quantify the combination evaluation indicators, and comprehensively compare and analyze the overall performance of the preliminary energy combination; select the energy combination with the best performance as the final optimal energy combination.

[0102] Through the above steps, cabin managers can generate the optimal energy combination plan, improve energy utilization efficiency, reduce operating costs, reduce environmental impact, and ensure efficient and stable operation of the power supply system.

[0103] Optionally, based on the optimization objective function, a differential evolution algorithm is used to search the feasible energy combination space, and the positions of individuals in the population set are iteratively updated to optimize the function value, and a preliminary energy combination is generated, including: based on the optimization objective function, a differential evolution algorithm parameter configuration is set, and energy combinations are randomly sampled to generate an initial population set; based on the initial population set, a fitness evaluation is performed on each individual, and the corresponding objective function value is calculated, and multiple dimensions are comprehensively considered to measure individual performance to generate a fitness evaluation result; based on the fitness evaluation result, 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; based on the optimized population set, a number of times is set for iterative processing to ensure high fitness and optimal objective function value, and generate a preliminary energy combination.

[0104] In an embodiment of the present application, according to the optimization objective function, the parameter configuration of the differential evolution algorithm is set, such as population size, number of iterations, mutation factor, crossover probability, etc.; the energy combinations are randomly sampled to generate an initial population set, and each individual represents a possible energy combination; the fitness of each individual in the initial population set is evaluated, the corresponding objective function value is calculated, and the individual performance is measured by comprehensively considering multiple dimensions to generate a fitness evaluation result; based on the fitness evaluation result, the differential evolution algorithm is used to iteratively update the position of the individuals in the initial population set, adjust the proportion of each energy combination, optimize the objective function value, and generate an optimized population set; the number of iterations is set, and multiple iterative processes are performed to ensure high fitness and optimal objective function value, and generate a preliminary energy combination.

[0105] Assume that in an emergency rescue command cabin, it is necessary to optimize the energy configuration to improve energy utilization efficiency and reduce costs; set the differential evolution algorithm parameter configuration, define the optimization objective function, and comprehensively consider the objectives such as energy utilization efficiency, cost, and environmental impact; randomly sample the energy combination to generate 50 initial individuals, each of which represents a possible energy combination, including solar energy, diesel generators, batteries, etc.; evaluate the fitness of each individual in the initial population set, calculate the corresponding objective function value, and comprehensively consider the dimensions such as energy utilization efficiency, cost, and environmental impact to generate the fitness evaluation result; based on the fitness evaluation result, use the differential evolution algorithm to iteratively update the position of the individuals in the initial population set, adjust the proportion of each energy combination, optimize the objective function value, and generate the optimized population set; in each iteration, select individuals with higher fitness for mutation and crossover operations to generate new individuals and replace individuals with lower fitness; set the number of iterations to 100, and perform multiple iterations to ensure high fitness and optimal objective function value; finally generate a preliminary energy combination, including the optimal energy combination ratio and configuration scheme.

[0106] Through the above steps, the emergency rescue command cabin can generate a preliminary optimal energy combination plan, improve energy utilization efficiency, control system operating costs and environmental impact, and ensure 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, is suitable for multi-objective optimization problems, and can handle complex nonlinear optimization tasks. By introducing multiple 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 result, a differential evolution algorithm is used to iteratively update individual positions in the initial population set, adjust the ratio of each energy combination, optimize the objective function value, and generate an optimized population set, including:

[0109] Based on the fitness evaluation result, three individuals in the initial population set are randomly selected as reference individuals to determine the moving direction;

[0110] Introducing the global information of the initial population set, setting relevant control parameters, and ensuring the effectiveness of the optimization process to generate individual positions;

[0111] The individual position is calculated using the following formula:

[0112]

[0113] Where V i,G+1 is the position of the ith individual in the G+1th generation; X i,G is the position of the ith individual in the Gth generation; F is the scaling factor, which usually ranges from [0,2]; X r1,G ,X r2,G ,X r3,G are the individual positions of three different individuals randomly selected from the current population; α is the parameter that controls the strength of the nonlinear term; β is the frequency parameter of the nonlinear term; η is the parameter that controls the strength of the additional optimization term; λ is the parameter that controls the decay speed of the additional optimization term with the number of iterations; X avg,G is the average value of all individual positions in the G-th generation population; κ is the parameter that controls the rate of change of the fractional term; G0 is the number of central iterations of the fractional term; G max is the maximum number of iterations; G is the current number of iterations;

[0114] Based on the individual position, identify the distribution characteristics, extract the key potential properties, combine with the relative positions of other individuals, and comprehensively consider the degree of influence on the objective function to generate the individual fitness value;

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

[0116]

[0117] Among them, f(X i,G+1 ) is the individual fitness value of the ith individual in the G+1 generation; f(X i,G ) is the individual fitness value of the i-th individual in the G-th generation; CR is the crossover probability, and its value range is usually [0,1]; f(V i,G+1 ) is the individual fitness value of the i-th individual in the new position of the G+1 generation; γ is the parameter that controls the intensity of the first nonlinear optimization item; ω is the frequency parameter that controls the first nonlinear optimization item; f(X best,G) is the individual fitness value of the best individual in the G-th generation population; μ is the parameter controlling the strength of the second nonlinear optimization item; ν is the frequency parameter controlling the second nonlinear optimization item; ρ is the parameter controlling the rate of change of the fractional item; G1 is the number of central iterations of the fractional item; θ is the parameter controlling the strength of the third nonlinear optimization item; σ is the frequency parameter controlling the third nonlinear optimization item; τ is the parameter controlling the rate of change of the third fractional item; G2 is the number of central iterations of the third fractional item; G is the current number of iterations;

[0118] Based on the individual positions and the individual fitness values, the initial population set is updated, multiple iterations are performed to reach a maximum number of iterations, convergence is repeatedly evaluated, and an optimized population set is generated.

[0119] This method aims to enhance the robustness and convergence speed of the algorithm by introducing a variety of nonlinear terms and additional optimization terms, ensuring that the solution space can be effectively explored and the global optimal solution can be found during the optimization process. By dynamically adjusting individual positions and fitness values ​​and combining global and local information, the search efficiency and optimization effect can be improved. Nonlinear terms and additional optimization terms are introduced to better adapt to complex and changeable environmental conditions, ensuring the stability and effectiveness of the algorithm in different scenarios.

[0120] In the individual position, the current position X i,G : As the basis for iterative updates; difference vector Used to determine the moving direction, introduce nonlinear changes, and enhance the search capability; the nonlinear perturbation term α.sin(β·(X r3,G -X i,G )): used to enhance local search capabilities; global optimization items Introduce global information to enhance global search capabilities;

[0121] In the individual fitness value, keep the current fitness value (1-CR)·f(X i,G ): Ensure the stability of the algorithm and avoid over-reliance on the new solution; the fitness value of the new position CR·f(V i,G+1 ): Introducing new solutions to increase the diversity of 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 to help the algorithm escape from the local optimum; nonlinear optimization items By introducing nonlinear changes, the local search capability is further enhanced and the robustness of the algorithm is improved; nonlinear optimization items By introducing logarithmic and exponential functions, the local search capability is further enhanced to ensure that the algorithm can still be effectively optimized in later iterations;

[0122] Among them, the scaling factor F is usually determined by experiments, and the value range is [0,2]; the crossover probability CR is also determined by experiments, and the value range is [0,1]; the nonlinear term strength parameters α, γ, μ, θ are determined by experiments, and the value range is [0,1]; the frequency parameters β, ω, v, σ are determined by experiments, and the value range is [0,1]; the change rate parameters k, λ, ρ, τ are determined by experiments, and the value range is [0,1]; the center iteration number G0, G1, G2 is determined by experiments, and the value range is [0,G_{max}]; the maximum iteration number G max Determined through experiments, the value range is a positive integer;

[0123] Assume that in a smart factory energy management cabin, it is necessary to optimize the energy combination, improve energy utilization efficiency, and reduce operating costs; initialize the population and randomly generate 50 initial individuals, each of which represents an energy combination, including the proportion of solar energy, wind energy, 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] Update the population, update the new individual position and fitness value to the initial population set, and perform multiple iterations until the maximum number of iterations G is reached max ,Repeatedly evaluate convergence and generate the optimized population set;

[0128] According to the calculation results, the individual fitness value is 0.89, which indicates that the optimized energy combination significantly improves the fitness value in the first iteration, that is, the energy utilization efficiency and cost-effectiveness have been significantly improved; assuming that the set threshold is 0.85, since the result 0.89 is greater than the set threshold, it shows that the current optimization scheme is effective and can significantly improve the performance and reliability of the system. Through the above steps, managers of the smart factory energy management cabin can optimize the energy combination, improve energy utilization efficiency, reduce operating costs, and ensure the stability and efficiency of the system.

[0129] Optionally, based on the preliminary energy combination, life cycle cost analysis technology is introduced to evaluate the total cost within the use cycle, and combined with energy conversion efficiency and environmental impact, a combination evaluation index is generated, including: based on the preliminary energy combination, combined with the complete life cycle, the cost data of each energy type is sorted and collected to generate a life cycle cost database; based on the life cycle cost database, combined with the conversion efficiency data of each energy type, the total cost of the preliminary energy combination within the use cycle is comprehensively evaluated through life cycle cost analysis technology to generate a cost evaluation report; based on the cost evaluation report, the environmental impact of the preliminary energy combination is deeply analyzed, and a multi-criteria decision analysis method is adopted to organically integrate the cost evaluation report and the environmental impact to generate a comprehensive performance evaluation report; based on the comprehensive performance evaluation report, long-term sustainability is ensured, the economic and environmental benefits of the preliminary energy combination are comprehensively evaluated, and a combination evaluation index is generated.

[0130] In the embodiments of the present application, based on the preliminary energy combination, the complete life cycle is comprehensively considered, and the cost data of each energy type is collected and sorted, including the initial investment, operation and maintenance costs, and scrapping costs, to form a detailed life cycle cost database; combined with the conversion efficiency data of each energy type, the life cycle cost analysis technology is used to comprehensively evaluate the total cost of the preliminary energy combination during the entire use cycle, and a detailed cost evaluation report is generated; the impact of the preliminary energy combination on the environment, such as carbon emissions and pollutant emissions, is deeply analyzed. The multi-criteria decision analysis method is used to organically combine the cost evaluation report with the environmental impact data to generate a comprehensive comprehensive performance evaluation report; the economic and environmental benefits of the preliminary energy combination are comprehensively evaluated to generate a set of comprehensive combination evaluation indicators, covering total cost, energy conversion efficiency, and environmental impact.

[0131] Assume that in a field smart agricultural greenhouse cabin, it is necessary to optimize the energy configuration to improve the efficiency of the agricultural greenhouse; based on the preliminary energy combination, collect the cost data of various energy types such as solar energy, wind energy, biomass energy, etc. throughout the life cycle, including initial investment, operation and maintenance costs, scrapping costs, etc., organize these data, and generate a life cycle cost database; based on the life cycle cost database, combined with the conversion efficiency data of each energy type, through the life cycle cost analysis technology, comprehensively evaluate the total cost of the preliminary energy combination during the use cycle; generate a cost evaluation report, and record in detail the initial investment, operation and maintenance costs, scrapping costs, etc. of each energy type; based on the cost evaluation report, deeply analyze the environmental impact of the preliminary energy combination, such as carbon emissions, pollutant emissions, etc., adopt a multi-criteria decision analysis method, organically integrate the cost evaluation report and environmental impact, and generate a comprehensive performance evaluation report, which includes evaluation results of economic costs, environmental impacts, energy conversion efficiency, etc.; based on the comprehensive performance evaluation report, ensure long-term sustainability and comprehensively evaluate the economic and environmental benefits of the preliminary energy combination; generate combination evaluation indicators, including total cost, energy conversion efficiency, environmental impact, etc., to ensure the optimal performance of the preliminary energy combination in economic and environmental aspects.

[0132] Through the above steps, the field smart agricultural greenhouse cabin 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 used to dynamically adjust the working parameters of each energy conversion device, and through power demand side management technology, the power distribution strategy is accurately implemented to generate an energy conversion distribution log;

[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 the system under the condition of system parameter changes or external disturbances. In this scenario, it is used to dynamically adjust the operating parameters of the energy conversion device to adapt to the changing needs.

[0135] Power demand side management technology refers to guiding users to use electricity rationally and improving the efficiency and reliability of the power system through various means and technologies. In this scenario, it is used to ensure the efficient distribution of electric 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 the embodiment of the present application, 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 working parameters of each energy conversion device to ensure efficient operation under different environmental conditions and generate optimized working parameters; through power demand side management technology, real-time monitoring of changes in power demand inside the cabin, accurate implementation of power distribution strategies, optimization of overall power utilization efficiency, and generation of an efficient power distribution plan; during the control and scheduling process, the operating data of each energy conversion device is recorded in real time to generate an energy conversion distribution log.

[0138] Optionally, in step 103, based on the optimal energy combination, a sliding mode variable structure control algorithm is used to dynamically adjust the working parameters of each energy conversion device, and through the power demand side management technology, the power distribution strategy is accurately implemented to generate an energy conversion distribution log, including: based on the optimal energy combination, determining the specific contribution ratio of each energy type, and generating an energy contribution ratio table; based on the energy contribution ratio table, a sliding mode variable structure control algorithm is used to dynamically adjust the working parameters of each energy conversion device to ensure that each energy conversion device operates efficiently under different environmental conditions and generates optimized working parameters; based on the optimized working parameters, through the power demand side management technology, real-time monitoring of changes in power demand inside the cabin, accurate implementation of the power distribution strategy, optimization of the overall power utilization efficiency, and generation of an efficient power distribution plan; based on the efficient power distribution plan, real-time recording of the operating data of each energy conversion device during the control and scheduling process, and generating an energy conversion distribution log.

[0139] In the embodiments of the present application, based on the optimal energy combination, the specific contribution ratio of each energy type is clarified, and a detailed energy contribution ratio table is generated; the stability and performance of the system are maintained in an uncertain environment through a sliding mode variable structure control algorithm; the use of power demand side management technology refers to optimizing power distribution and improving overall power utilization efficiency by real-time monitoring and adjustment of power demand; the specific contribution ratio of each energy type is determined based on the optimal energy combination to guide the adjustment of the working parameters of the energy conversion device; the optimized working parameters are generated through a sliding mode variable structure control algorithm to ensure that each energy conversion device operates efficiently under different environmental conditions; an efficient power distribution plan is formulated through power demand side management technology, the changes in power demand inside the cabin are monitored in real time, the power distribution strategy is accurately implemented, and the overall power utilization efficiency is optimized; the working parameters and power distribution of each energy conversion device are recorded through the energy conversion distribution log for subsequent fault diagnosis and performance optimization.

[0140] Assume that in a field meteorological observation cabin, it is necessary to ensure the stability and reliability of energy supply while reducing the impact on the environment; based on the optimal energy combination, determine the specific contribution ratio of each energy type such as solar energy, wind energy and batteries, and generate an energy contribution ratio table; based on the energy contribution ratio table, use the sliding mode variable structure control algorithm to dynamically adjust the working parameters of solar panels, wind turbines and batteries, and adjust the angle of solar panels and the speed of wind turbines according to weather forecasts and real-time environmental data to ensure efficient operation under different environmental conditions and generate optimized working parameters; based on the optimized working parameters, use the power demand side management technology to monitor the changes in power demand inside the meteorological observation station in real time, for example, according to the real-time power consumption data of the equipment, adjust the power distribution strategy, give priority to the use of solar energy and wind energy, and supplement the insufficient part with batteries to generate an efficient power distribution plan; according to the efficient power distribution plan, record the operating data of each energy conversion device in real time during the control and scheduling process, including working parameters, power distribution status, etc., the power generation of solar panels, the power generation of wind turbines, and the charging and discharging status of batteries, and generate a detailed energy conversion distribution log.

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

[0142] Optionally, based on the energy contribution ratio table, a sliding mode variable structure control algorithm is adopted to dynamically adjust the working parameters of each energy conversion device to ensure that each energy conversion device operates efficiently under different environmental conditions and generate optimized working parameters, including: based on the energy contribution ratio table, determining the specific role of each energy type, guiding the control strategy of each energy conversion device, and generating a basic energy control plan; based on the basic energy control plan, a sliding mode variable structure control algorithm is adopted, combined with real-time environmental conditions, to dynamically adjust the working parameters of each energy conversion device and generate preliminary adjustment parameters; based on the preliminary adjustment parameters, the operating effect of each energy conversion device is monitored in real time, and the working parameters of each energy conversion device are optimized through feedback to ensure the overall stability of the system and generate intermediate optimization parameters; based on the intermediate optimization parameters, they are applied to actual operation, and the operating effect of each energy conversion device is evaluated again to ensure that each energy conversion device operates efficiently under different environmental conditions and generate optimized working parameters.

[0143] In the embodiments of the present application, according to the energy contribution ratio table, the specific role and function of each energy type are clarified, and a basic energy control plan is generated; according to the basic energy control plan, a sliding mode variable structure control algorithm is used, combined with real-time environmental conditions, to dynamically adjust the working parameters of each energy conversion device to generate preliminary adjustment parameters; according to the preliminary adjustment parameters, the actual operating results of each energy conversion equipment are continuously monitored, and the equipment parameters are iterated with the help of a feedback mechanism to optimize the working parameters of each energy conversion device and generate intermediate optimization parameters; based on the intermediate optimization parameters, they are applied to actual operation, and the operating effect of each energy conversion device is evaluated again to ensure efficient operation under different environmental conditions and generate optimized working parameters.

[0144] Assume that in a field communication cabin, it is necessary to ensure a stable and efficient supply of energy inside the cabin while reducing energy waste; based on the energy contribution ratio table, clarify the specific role of each energy type such as diesel generators, solar photovoltaic panels and batteries. 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 solution, use the sliding mode variable structure control algorithm, combined with real-time environmental conditions (such as weather, load changes, etc.), dynamically adjust the output power of the diesel generator, the angle of the solar photovoltaic panel and the charge and discharge rate of the battery, generate preliminary adjustment parameters, and adjust the diesel generator according to weather forecasts and real-time load data. The output power of the generator ensures that sufficient electricity is provided at night and on cloudy days; based on the preliminary adjustment parameters, the operating effect of each energy conversion device is monitored in real time, the working parameters of each energy conversion device are optimized through feedback, and intermediate optimization parameters are generated. The output power and efficiency of the diesel generator are monitored in real time. If the efficiency is found to be low, its working parameters are adjusted to improve the efficiency; based on the intermediate optimization parameters, applied to actual operation, the operating effect of each energy conversion device is evaluated again to ensure efficient operation under different environmental conditions and generate optimized working parameters; the optimized working parameters are applied to actual operation, and the operating effects of the diesel generator, solar photovoltaic panels and batteries are continuously monitored to ensure the overall stability and efficiency of the system.

[0145] Through the above steps, the field communication cabin can ensure a stable and efficient supply of energy inside the cabin while reducing energy waste.

[0146] Optionally, based on the basic energy control scheme, a sliding mode variable structure control algorithm is used to dynamically adjust the working parameters of each energy conversion device in combination with real-time environmental conditions to generate preliminary adjustment parameters, including:

[0147] Based on the energy control basic scheme, calculating the error value of each energy conversion device;

[0148] Collect key parameter values ​​of each energy conversion device, analyze current environmental conditions, and formulate a control strategy in combination with 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 is the new control parameter of the i-th energy conversion device in the G+1 generation; P i,G is the current parameter of the i-th energy conversion device in the G-th generation; K p is the proportional gain; K i is the integral gain; K d is the differential gain; E i,G is the error of the i-th energy conversion device in the G-th generation; E i,G-1 is the error of the i-th energy conversion device in the G-1 generation; P avg,G is the average value of the parameters of all energy conversion devices of the Gth generation; α is the parameter controlling the intensity of the nonlinear term; β is the frequency parameter of the nonlinear term; is the cumulative error from generation 0 to generation G; is the rate of change of the G-th generation error; G is the number of iterations;

[0152] Based on the new control parameters, the new control parameters are applied to each energy conversion device, the operating performance of each energy conversion device is monitored in real time, and preliminary feedback adjustments are made according to the monitoring data to generate an environmental adaptability assessment value;

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

[0154]

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

[0156] Based on the environmental adaptability evaluation value, the environmental adaptability evaluation 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, applied to actual operation and the effect is re-evaluated to generate preliminary adjustment parameters.

[0157] This method aims to dynamically adjust the working parameters of each energy conversion device by combining real-time environmental conditions, generate preliminary adjustment parameters, and optimize system performance. Specifically, by introducing error values, key parameter values, and nonlinear terms, the adaptability and optimization effect of the algorithm are enhanced to ensure 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 to quickly respond to error changes and improve 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 error change rate and improve the system's anti-interference ability; nonlinear term Used to introduce nonlinear disturbances to enhance the robustness and adaptability of the system;

[0159] In the environmental fitness evaluation value, the proportional term It is used to evaluate the gap between the new control parameters and the optimal control parameters and improve the accuracy of the evaluation; the cosine term δ·cos(η·(E i,G -E avg,G )): Used to evaluate the difference between the error and the average error and enhance the stability of the evaluation; logarithmic term It is used to evaluate the gap between the new control parameters and the average control parameters and enhance the sensitivity of the evaluation; the tangent term φ·tan(ψ·(E i,G -E min,G )): Used to evaluate the difference between the error and the minimum error and enhance the accuracy of the evaluation;

[0160] Among them, the proportional gain K p Usually determined by experiment, the value range is positive real number; integral gain K i Usually determined by experiment, the value range is positive real number; differential gain K dUsually determined by experiments, the value range is a positive real number; the nonlinear term strength parameter α is determined by experiments, and the value range is [0,1]; the nonlinear term frequency parameter β is determined by experiments, and the value range is [0,1]; the fitness evaluation term strength parameter γ is determined by experiments, and the value range is a positive real number; the cosine term strength parameter δ is determined by experiments, and the value range is [0,1]; the cosine term frequency parameter η is determined by experiments, and the value range is [0,1]; the logarithmic term strength parameter θ is determined by experiments, and the value range is [0,1]; the logarithmic term frequency parameter σ is determined by experiments, and the value range is [0,1]; the fractional term change rate parameter μ is determined by experiments, and the value range is a positive real number; the fractional term center iteration number G0 is determined by experiments, and the value range is a positive integer; the tangent term strength parameter φ is determined by experiments, and the value range is [0,1]; the tangent term frequency parameter ψ is determined by experiments, and the value range is [0,1];

[0161] Assume that in a field smart grid management cabin, it is necessary to optimize the working parameters of each energy conversion device, improve energy utilization efficiency, and reduce operating costs;

[0162] Set the initial parameter P i,0 is [1.0, 1.1, 1.2], corresponding to the initial parameters of the three energy conversion devices; set the proportional gain K 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 center 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 that the error value E of the 0th generation i,0 is [0.1, 0.2, 0.3], the error value E of the first generation i,1 is [0.05, 0.1, 0.15];

[0164]

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

[0166]

[0167] According to the calculation results, [A 1,1 ,A 2,1 ,A 3,1 ]=[0.932,0.938,0.706] indicates that after the first generation of optimization, the environmental adaptability of each energy conversion device is significantly improved, and the system performance is effectively improved; assuming that the threshold vector is set to [0.9,0.9,0.7], since all values ​​in the results are greater than the set threshold vector, it shows that the current optimization process is effective, the working parameters of each energy conversion device are being gradually optimized, and the system performance is effectively improved. Through the above steps, the field smart grid management cabin successfully optimized the working 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 regular fault diagnosis, record the operating data of the energy supply system, and generate an optimized power supply plan.

[0169] In this step, normalized fault diagnosis refers to regular or real-time health checks on the system to promptly detect and resolve problems and avoid failures.

[0170] The energy supply system operation data includes all relevant data of the system operation, such as the working status of each device, power consumption, etc. These data are crucial for the optimization of the system.

[0171] The optimized power supply plan is based on the analysis of operating data and proposes improvement measures to further improve the efficiency and stability of the system.

[0172] Based on the energy conversion and distribution log, the operating status of the energy supply system is continuously monitored, including the operating parameters and power distribution of each energy conversion device; regular fault detection and diagnosis are performed on the system to promptly discover and resolve potential problems and ensure the long-term stable operation of the system; during the control and scheduling process, the operating data of each energy conversion device is recorded in real time, including operating parameters, power distribution, etc.; based on the recorded operating data and fault diagnosis results, an optimized power supply plan is generated, and improvement measures and optimization suggestions are proposed to improve the overall performance and reliability of the system.

[0173] Optionally, the steps in step 104, based on the energy conversion and allocation log, continuously monitor the operating status of the energy supply system, perform normalized fault diagnosis, record the operating data of the energy supply system, and generate an optimized power supply plan, include: based on the energy conversion and allocation log, continuously monitor the operating status of the energy supply system, ensure that the energy supply system operates stably and efficiently, and generate system operation monitoring data; based on the system operation monitoring data, perform normalized fault diagnosis on the energy supply system through fault diagnosis technology, identify and warn of potential problems, and generate a fault diagnosis report; based on the fault diagnosis report, systematically analyze the operating data of the energy supply system, output fault handling measures, and generate a system operation record; based on the system operation record, use statistical analysis to evaluate the efficiency and reliability of the current power supply plan, optimize it based on improvement points, and generate an optimized power supply plan.

[0174] In an embodiment of the present application, based on the energy conversion and distribution log information, the operating status of the energy supply system is continuously monitored to maintain the stable operation of the system and improve efficiency, and the system operation monitoring data is recorded at the same time; the system operation monitoring data is used in combination with fault diagnosis technology to conduct regular fault prediction and analysis of the energy supply system, discover and warn of potential problems in advance, and form a fault diagnosis report; the operating data of the energy supply system is deeply analyzed, and targeted fault solutions are formulated, and the system operation log is updated to record the implementation of the solution; 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 the optimization design is performed for the improvement space found, and an optimized power supply plan is generated.

[0175] Suppose that in a field mobile communication cabin, it is necessary to ensure the stability and efficiency of the energy supply system, and timely discover and deal with potential faults; based on the energy conversion and distribution log, continuously monitor the operating status of the energy supply system of the mobile communication base station, record the working parameters and power distribution of each energy conversion device, and generate system operation monitoring data, such as real-time monitoring of the output power of the diesel generator, the power generation of the solar photovoltaic panel, the charge and discharge status of the battery, etc.; based on the system operation monitoring data, through fault diagnosis technology, perform normalized fault diagnosis on the energy supply system, identify and warn potential problems, and generate fault diagnosis reports; through data analysis, find out that the diesel generator The output power fluctuates greatly, and there may be a risk of failure, so a corresponding fault diagnosis report is generated. Based on the fault diagnosis report, the energy supply system operation data is systematically analyzed, fault handling measures are output, and system operation records are generated. In response to the problem of diesel generator output power fluctuations, it is recommended to regularly inspect and maintain the generator, and record the fault handling process and results. Based on the system operation records, statistical analysis is used to evaluate the efficiency and reliability of the current power supply plan, and optimization is performed based on improvement points to generate an optimized power supply plan. Through statistical analysis, it is found that the power generation efficiency of solar photovoltaic panels is low in certain time periods. It is recommended to increase the energy storage capacity or adjust the angle of the photovoltaic panels to generate an optimized power supply plan.

[0176] Through the above steps, managers of business mobile communication shelters can ensure the stability and efficiency of the energy supply system, detect and handle potential faults in a timely manner, and improve the overall performance of the system.

[0177] Figure 2 A structural schematic diagram of a multifunctional shelter power supply system suitable for field operations is provided for the embodiment of the present application, such as Figure 2 As shown, the device comprises:

[0178] The collection module 21 is used to collect external environmental parameters by deploying environmental sensors, and generate an operation status report in combination with the real-time power consumption data inside the shelter;

[0179] An analysis module 22 is used to use a differential evolution algorithm based on the operation status report to conduct multi-objective optimization analysis by comprehensively considering multiple dimensions, introduce life cycle cost analysis technology, maximize resource utilization, and generate an optimal energy combination;

[0180] The adjustment module 23 is used to dynamically adjust the working parameters of each energy conversion device based on the optimal energy combination by using a sliding mode variable structure control algorithm, accurately implement the power distribution strategy through power demand side management technology, and generate an energy conversion distribution 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 allocation log, perform normalized fault diagnosis, record the operating data of the energy supply system, and generate an optimized power supply plan.

[0182] Figure 2 The multifunctional shelter power supply system suitable for field operations can be implemented Figure 1 The implementation principle and technical effect of the multifunctional shelter power supply method suitable for field operations described in the embodiment shown are not repeated here. The specific way in which each module and unit performs operations in the multifunctional shelter power supply system suitable for field operations in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0183] In one possible design, Figure 2 The multifunctional shelter power supply system suitable for field operations in the embodiment shown 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 called and executed by the processing component 32 .

[0185] The processing component 32 is used to: collect external environmental parameters by deploying environmental sensors, and generate an operation status report in combination with the real-time power consumption data inside the cabin; based on the operation status report, adopt a differential evolution algorithm, comprehensively consider multiple dimensions to perform 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, adopt a sliding mode variable structure control algorithm to dynamically adjust the working parameters of each energy conversion device, and through the power demand side management technology, accurately implement the power distribution strategy and generate an energy conversion distribution log; based on the energy conversion distribution log, continuously monitor the operation status of the energy supply system, perform normalized fault diagnosis, record the operation data of the energy supply system, and generate an optimized power supply plan.

[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 method. Of course, the processing component may also be implemented by 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 method.

[0187] The 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 memory, flash memory, magnetic disk or optical disk.

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

[0189] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0190] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

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

[0192] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment is a multifunctional cabin power supply method suitable for field operations.

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

[0194] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0195] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment 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 the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multifunctional shelter power supply method 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 operation status report is generated; Based on the operation status report, a differential evolution algorithm is used 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; Based on the optimal energy combination, a sliding mode variable structure control algorithm is used to dynamically adjust the working parameters of each energy conversion device, and through power demand side management technology, the power distribution strategy is accurately implemented to generate an energy conversion distribution log; Based on the energy conversion and allocation log, the operating status of the energy supply system is continuously monitored, normalized fault diagnosis is performed, the operating data of the energy supply system is recorded, and an optimized power supply plan is generated.

2. The method according to claim 1, characterized in that Based on the operation status report, the differential evolution algorithm is used to comprehensively consider multiple dimensions for multi-objective optimization analysis, introduce life cycle cost analysis technology, maximize resource utilization, and generate the optimal energy combination, including: Based on the operation status report, extract key operation data, initialize input parameters of the differential evolution algorithm, and generate an optimization objective function; Based on the optimization objective function, a differential evolution algorithm is used to search the feasible energy combination space, and the positions of individuals in the population set are iteratively updated to optimize the function value, thereby generating a preliminary energy combination; Based on the preliminary energy combination, life cycle cost analysis technology is introduced to evaluate the total cost during the use cycle, and a combination evaluation index is generated by combining energy conversion efficiency and environmental impact; Based on the combination evaluation index, the key indicators of the preliminary energy combination are quantified, the overall performance is comprehensively compared and analyzed, and the optimal energy combination is generated.

3. The method according to claim 2, characterized in that Based on the optimization 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 a preliminary energy combination, including: Based on the optimization objective function, setting the differential evolution algorithm parameter configuration, randomly sampling the energy combination, and generating an initial population set; Based on the initial population set, each individual is evaluated for fitness, the corresponding objective function value is calculated, and the individual performance is measured by comprehensively considering multiple dimensions to generate a fitness evaluation result; Based on the fitness evaluation result, a differential evolution algorithm is used to iteratively update the individual positions in the initial population set, adjust the ratio of each energy combination, optimize the objective function value, and generate an optimized population set; Based on the optimized population set, the number of times is set to perform iterative processing to ensure high fitness and optimal objective function value, and generate a preliminary energy combination.

4. The method according to claim 3, characterized in that Based on the fitness evaluation result, a differential evolution algorithm is used to iteratively update the individual positions in the initial population set, adjust the ratio of each energy combination, optimize the objective function value, and generate an optimized population set, including: Based on the fitness evaluation result, three individuals in the initial population set are randomly selected as reference individuals to determine the moving direction; Introducing the global information of the initial population set, setting relevant control parameters, and ensuring the effectiveness of the optimization process to generate individual positions; The individual position is calculated using the following formula: Where V i,G+1 is the position of the ith individual in the G+1th generation; X i,G is the position of the ith individual in the Gth generation; F is the scaling factor, which usually ranges from [0,2]; X r1,G ,X r2,G ,X r3,G are the individual positions of three different individuals randomly selected from the current population; α is the parameter that controls the strength of the nonlinear term; β is the frequency parameter of the nonlinear term; η is the parameter that controls the strength of the additional optimization term; λ is the parameter that controls the decay speed of the additional optimization term with the number of iterations; X avg,G is the average value of all individual positions in the G-th generation population; k is the parameter that controls the rate of change of the fractional term; G0 is the number of central iterations of the fractional term; G max is the maximum number of iterations; G is the current number of iterations; Based on the individual position, the distribution characteristics are identified, key potential properties are extracted, and the degree of influence on the objective function is comprehensively considered in combination with the relative positions of other individuals to generate the individual fitness value; The individual fitness value is calculated using the following formula: Among them, f(X i,G+1 ) is the individual fitness value of the ith individual in the G+1 generation; f(X i,G ) is the individual fitness value of the i-th individual in the G-th generation; CR is the crossover probability, and its value range is usually [0,1]; f(V i,G+1 ) is the individual fitness value of the i-th individual in the new position of the G+1 generation; γ is the parameter that controls the intensity of the first nonlinear optimization item; ω is the frequency parameter that controls the first nonlinear optimization item; f(X best,G ) is the individual fitness value of the best individual in the G-th generation population; μ is the parameter controlling the strength of the second nonlinear optimization item; v is the frequency parameter controlling the second nonlinear optimization item; ρ is the parameter controlling the rate of change of the fractional item; G1 is the number of central iterations of the fractional item; θ is the parameter controlling the strength of the third nonlinear optimization item; σ is the frequency parameter controlling the third nonlinear optimization item; τ is the parameter controlling the rate of change of the third fractional item; G2 is the number of central iterations of the third fractional item; G is the current number of iterations; Based on the individual positions and the individual fitness values, the initial population set is updated, multiple iterations are performed to reach a maximum number of iterations, convergence is repeatedly evaluated, and an optimized population set is generated.

5. The method according to claim 2, characterized in that: Based on the preliminary energy combination, the life cycle cost analysis technology is introduced to evaluate the total cost during the use cycle, and the energy conversion efficiency and environmental impact are combined to generate a combination evaluation index, including: Based on the preliminary energy combination and in combination with the complete life cycle, the cost data of each energy type is collated and collected to generate a life cycle cost database; Based on the life cycle cost database, combined with the conversion efficiency data of each energy type, the total cost of the initial energy combination within the use period is comprehensively evaluated through life cycle cost analysis technology to generate a cost evaluation report; Based on the cost assessment report, deeply analyze the environmental impact of the preliminary energy mix, adopt a multi-criteria decision analysis method, organically integrate the cost assessment report and the environmental impact, and generate a comprehensive performance assessment report; Based on the comprehensive performance evaluation report, to ensure long-term sustainability, the economic and environmental benefits of the preliminary energy portfolio are comprehensively evaluated to generate portfolio evaluation indicators.

6. The method according to claim 1, characterized in that Based on the optimal energy combination, a sliding mode variable structure control algorithm is adopted to dynamically adjust the working parameters of each energy conversion device, and the power demand side management technology is used to accurately implement the power distribution strategy and generate an energy conversion distribution log, including: Based on the optimal energy combination, determine the specific contribution ratio of each energy type and generate an energy contribution ratio table; Based on the energy contribution ratio table, a sliding mode variable structure control algorithm is used to dynamically adjust the working parameters of each energy conversion device to ensure that each energy conversion device operates efficiently under different environmental conditions and generates optimized working parameters; Based on the optimized working parameters, the power demand changes inside the shelter are monitored in real time through power demand side management technology, the power distribution strategy is accurately implemented, the overall power utilization efficiency is optimized, and an efficient power distribution plan is generated; Based on the efficient electric energy 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 distribution log.

7. The method according to claim 6, characterized in that Based on the energy contribution ratio table, a sliding mode variable structure control algorithm is used to dynamically adjust the working parameters of each energy conversion device to ensure that each energy conversion device operates efficiently under different environmental conditions and generates optimized working parameters, including: Based on the energy contribution ratio table, determine the specific role of each energy type, guide the control strategy of each energy conversion device, and generate a basic energy control plan; Based on the basic energy control scheme, a sliding mode variable structure control algorithm is used to dynamically adjust the working parameters of each energy conversion device in combination with real-time environmental conditions to generate preliminary adjustment parameters; Based on the preliminary adjustment parameters, the operation effect of each energy conversion device is monitored in real time, and the working parameters of each energy conversion device are optimized through feedback to ensure the overall stability of the system and generate intermediate optimization parameters; Based on the intermediate optimization parameters, they are applied in actual operation to evaluate the operation effect of each energy conversion device again, to ensure that each energy conversion device operates efficiently under different environmental conditions and to generate optimized working parameters.

8. The method according to claim 7, characterized in that Based on the basic energy control scheme, a sliding mode variable structure control algorithm is used to dynamically adjust the working parameters of each energy conversion device in combination with real-time environmental conditions to generate preliminary adjustment parameters, including: Based on the energy control basic scheme, calculating the error value of each energy conversion device; Collect key parameter values ​​of each energy conversion device, analyze current environmental conditions, and formulate a control strategy in combination with the energy contribution ratio table to generate new control parameters; The new control parameters are calculated using the following formula: Among them, C i,G+1 is the new control parameter of the i-th energy conversion device in the G+1 generation; P i,G is the current parameter of the i-th energy conversion device in the G-th generation; K p is the proportional gain; K i is the integral gain; K d is the differential gain; E i,G is the error of the i-th energy conversion device in the G-th generation; E i,G-1 is the error of the i-th energy conversion device in the G-1 generation; P avg,G is the average value of the parameters of all energy conversion devices of the Gth generation; α is the parameter controlling the intensity of the nonlinear term; β is the frequency parameter of the nonlinear term; is the cumulative error from generation 0 to generation G; is the rate of change of the G-th generation error; G is the number of iterations; Based on the new control parameters, the new control parameters are applied to each energy conversion device, the operating performance of each energy conversion device is monitored in real time, and preliminary feedback adjustments are made according to the monitoring data to generate an environmental adaptability assessment value; The environmental adaptability evaluation value is calculated using the following formula: Among them, A i,G+1 is the environmental adaptability evaluation value of the i-th energy conversion device in the G+1 generation; C i,G+1 is the new control parameter of the i-th energy conversion device in the G+1 generation; C best,G is the optimal value of the new control parameter in all energy conversion devices of the Gth generation; γ is the parameter controlling the strength of the fitness evaluation term; δ is the parameter controlling the strength of the cosine term; η is the frequency parameter controlling the cosine term; E i,G is the error of the i-th energy conversion device in the G-th generation; E avg,G is the average value of the error of all energy conversion devices of the Gth generation; θ is the parameter for controlling the strength of the logarithmic term; σ is the frequency parameter for controlling the logarithmic term; μ is the parameter for controlling the rate of change of the fractional term; G0 is the number of central iterations of the fractional term; φ is the parameter for controlling the strength of the tangent term; ψ is the frequency parameter for controlling the tangent term; E min,G is the minimum error of all energy conversion devices of the Gth generation; G is the number of iterations; Based on the environmental fitness evaluation value, the environmental fitness evaluation 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, applied to actual operation and the effect is re-evaluated to generate preliminary adjustment parameters.

9. The method according to claim 1, characterized in that: Based on the energy conversion and allocation log, the operation status of the energy supply system is continuously monitored, normalized fault diagnosis is performed, the operation data of the energy supply system is recorded, and an optimized power supply plan is generated, including: Based on the energy conversion and allocation log, continuously monitor the operating status of the energy supply system, ensure that the energy supply system operates stably and efficiently, and generate system operation monitoring data; Based on the system operation monitoring data, the energy supply system is routinely diagnosed through fault diagnosis technology, potential problems are identified and warned, and a fault diagnosis report is generated; Based on the fault diagnosis report, systematically analyze the energy supply system operation data, output fault handling measures, and generate system operation records; Based on the system operation records, statistical analysis is used to evaluate the efficiency and reliability of the current power supply plan, and optimization is performed in combination with improvement points to generate an optimized power supply plan.

10. A multifunctional shelter power supply system suitable for field operations, characterized in that: include: The collection module is used to collect external environmental parameters by deploying environmental sensors and generate an operation status report based on the real-time power consumption data inside the shelter; An analysis module, for using a differential evolution algorithm based on the operation status report to conduct multi-objective optimization analysis by comprehensively considering multiple dimensions, introducing life cycle cost analysis technology, maximizing resource utilization, and generating an optimal energy combination; An adjustment module is used to dynamically adjust the working parameters of each energy conversion device based on the optimal energy combination by using a sliding mode variable structure control algorithm, accurately implement the power distribution strategy through power demand side management technology, and generate an energy conversion distribution log; The monitoring module is used to continuously monitor the operating status of the energy supply system based on the energy conversion and allocation log, perform normalized fault diagnosis, record the operating data of the energy supply system, and generate an optimized power supply plan.

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