Multi-source complementary intelligent power supply management method and system in mobile square cabin

Abstract

The invention provides a multi-source complementary intelligent power supply management method and system in a mobile square cabin. The method comprises the following steps: collecting real-time state data of multi-type energy supply equipment, and generating a comprehensive energy management data set; based on the comprehensive energy management data set, dynamically evaluating real-time efficiency by adopting an interval optimization algorithm based on an evolutionary strategy, and generating an energy combination strategy report by applying a Markov decision process; based on the energy combination strategy report, adopting a gradient descent optimization algorithm to adjust working parameters of the multi-type energy supply equipment, and applying a real-time energy balance technology to generate an optimized energy configuration scheme; and based on the optimized energy configuration scheme, monitoring the operation condition of a power supply system, optimizing working parameters, and generating an intelligent power supply management parameter set. According to the technical scheme, the intelligent power supply management effect can be effectively improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of cabin power supply management, and in particular to a multi-source complementary intelligent power supply management method and system in a mobile cabin. Background Art

[0002] With the widespread use of mobile shelters in various emergency and remote operation scenarios, the collaborative work of multiple types of energy supply equipment (such as solar energy, wind energy, energy storage batteries, etc.) has become increasingly important. In order to achieve efficient and stable energy supply and management, it is necessary to collect real-time status data from multiple types of energy supply equipment in real time, combine it with the real-time power consumption data inside the mobile shelter, and generate a comprehensive energy management data set.

[0003] The existing intelligent power supply management system mainly relies on a single energy source and lacks the ability to comprehensively manage multiple types of energy. In addition, the existing optimization algorithms mostly use static models, which cannot dynamically evaluate real-time performance and predict future changes in energy demand, resulting in inflexible and inefficient energy configuration.

[0004] The existing solutions have obvious deficiencies in multi-energy complementarity and cannot effectively deal with the volatility and uncertainty of energy supply. In addition, the existing optimization algorithms lack real-time and dynamic adjustment capabilities, making it difficult to ensure the balance of energy supply and demand, resulting in unstable system operation and low energy utilization efficiency. These problems have seriously affected the reliability and economy of mobile shelters in complex environments. Summary of the invention

[0005] The embodiments of the present application provide a method and system for intelligent power supply management with multi-source complementarity in a mobile cabin, so as to solve the problem of poor intelligent power supply management effect in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a multi-source complementary intelligent power supply management method in a mobile cabin, comprising:

[0007] Collect real-time status data from multiple types of energy supply equipment and combine it with real-time power consumption data inside the mobile cabin to generate a comprehensive energy management data set;

[0008] Based on the comprehensive energy management data set, an interval optimization algorithm based on evolutionary strategy is used to dynamically evaluate real-time performance, and a Markov decision process is used to predict future energy demand changes and generate an energy portfolio strategy report;

[0009] Based on the energy combination strategy report, a gradient descent optimization algorithm is used to adjust the working parameters of the multi-type energy supply equipment, and real-time energy balance technology is applied to ensure the balance of energy supply and demand during the regulation process, thereby generating an optimized energy configuration plan;

[0010] Based on the optimized energy configuration scheme, the operation status of the power supply system is monitored, the execution performance is evaluated multiple times, the working parameters of the multiple types of energy supply equipment are optimized, and an intelligent power supply management parameter set is generated.

[0011] Optionally, based on the comprehensive energy management data set, an interval optimization algorithm based on evolutionary strategy is used to dynamically evaluate real-time performance, and a Markov decision process is used to predict future energy demand changes and generate an energy combination strategy report, including:

[0012] Based on the comprehensive energy management data set, normalizing the data scale range to generate a standardized data set;

[0013] Based on the standardized data set, an interval optimization algorithm based on evolutionary strategy is used to perform interval analysis, evaluate the real-time performance of the multi-type energy supply equipment, and generate performance evaluation results;

[0014] Based on the performance evaluation results, a number of external environmental parameters are integrated, and the Markov decision process is used to analyze current and historical data, predict future energy demand trends, and generate energy demand forecast results;

[0015] Based on the energy demand forecast results, a number of criteria factors are comprehensively considered and an energy combination strategy is generated through multi-criteria decision analysis;

[0016] Based on the energy combination strategy, the future optimal working state and output power of the multi-type energy supply equipment are analyzed in detail to generate an energy combination strategy report.

[0017] Optionally, based on the standardized data set, interval analysis is performed using an interval optimization algorithm based on evolutionary strategies to evaluate the real-time performance of the multi-type energy supply equipment and generate performance evaluation results, including:

[0018] Based on the standardized data set, outliers and noise are removed, and input into the interval optimization algorithm based on evolution strategy to generate an input data set;

[0019] Based on the input data set, an interval optimization algorithm based on evolutionary strategy is used to perform interval analysis, process the uncertainty and volatility of the input data set, and generate equipment performance intervals;

[0020] Based on the equipment performance range, evaluate the real-time performance of the multiple types of energy supply equipment, analyze potential performance bottlenecks, and generate a performance analysis report;

[0021] Based on the performance analysis report and in combination with historical real-time performance data, the optimization potential of the multi-type energy supply equipment is evaluated, various real-time performance indicators are output, and performance evaluation results are generated.

[0022] Optionally, based on the input data set, an interval optimization algorithm based on evolutionary strategy is used to perform interval analysis, process uncertainty and volatility of the input data set, and generate equipment performance intervals, including:

[0023] Based on the input data set, standardize the data to make it comparable;

[0024] Selecting features that significantly affect the performance of the multi-type energy supply equipment, constructing an initial population of evolutionary strategies, and generating an optimization function based on the evolutionary strategy;

[0025] The optimization function based on evolution strategy is calculated by the following formula:

[0026]

[0027] Among them, ES(x i ) is the optimization function based on evolution strategy; x i is the input data set of the i-th energy supply device; k is the index of the population individual in the evolution strategy, from 1 to N; N is the population size in the evolution strategy; w k is the weight of the kth individual, reflecting its importance in optimization; μ k is the mean of the kth individual, reflecting its central position in the performance interval; σ k is the standard deviation of the kth individual, reflecting its distribution range in the efficiency interval; β1, β2 are optimization parameters obtained by fitting historical data; ω1 is the time frequency factor, reflecting the influence of periodic changes; δ is the smoothing factor, preventing the logarithmic function from diverging near zero;

[0028] Based on the optimization function based on evolution strategy, an analyzable function interval is constructed by optimizing the maximum value of the function value, an interval analysis function is introduced, nonlinear transformation is performed, and uncertainty and volatility of the function are processed to generate an optimization efficiency factor;

[0029] The optimization efficiency factor is calculated by the following formula:

[0030]

[0031] Among them, F i is the optimization efficiency factor of the i-th energy supply equipment; ES(x i ) is the optimization function based on evolution strategy; β1, β2 are optimization parameters obtained by fitting historical data; ω1, ω2, ω3, ω4 are time frequency factors, reflecting the influence of periodic changes; t is the current time; δ is the smoothing factor to prevent the logarithmic function from diverging near zero; IA(ES(x i )) is the interval analysis function, IA(ES(x i))=β3·(max(ES(x i )+α·σ(x i ))-min(ES(x i )-α·σ(x i ))), α is the confidence level coefficient, σ(x i ) is the standard deviation of the input data set of the ith energy supply device, β3 is the optimization parameter; sinh is the hyperbolic sine function;

[0032] Based on the optimized performance factor, the optimized performance factor is adjusted through the current time information, the influence of time attenuation and periodic changes is comprehensively considered, and reasonable upper and lower limit thresholds are set in combination with the statistical characteristics of historical data to generate a device performance range.

[0033] Optionally, based on the performance evaluation result, a plurality of external environmental parameters are integrated, and a Markov decision process is used to analyze current and historical data, predict future energy demand change trends, and generate energy demand prediction results, including:

[0034] Based on the performance evaluation results, multiple external environmental parameters are collected and integrated to generate a comprehensive environmental parameter set;

[0035] Based on the comprehensive environmental parameter set, using the Markov decision process, analyzing the energy demand variation pattern in each time period, and generating demand analysis results;

[0036] Based on the demand analysis results, determine the demand in each time period through time series analysis, predict the trend of future energy demand changes, and generate preliminary energy demand forecast results;

[0037] The preliminary energy demand forecast results are used to divide the time periods into high-resolution time periods, analyze the specific energy demand changes in each time period, and generate energy demand forecast results.

[0038] Optionally, based on the energy combination strategy report, a gradient descent optimization algorithm is used to adjust the working parameters of the multi-type energy supply equipment, and real-time energy balance technology is applied to ensure the balance of energy supply and demand in the regulation process, and generate an optimized energy configuration plan, including:

[0039] Based on the energy combination strategy report, extract the optimal working state and output power information of the multi-type energy supply equipment to generate an initial energy configuration plan;

[0040] Based on the initial energy configuration scheme, a gradient descent optimization algorithm is used to adjust the operating parameters of the multi-type energy supply equipment, minimize the energy consumption cost, and generate the optimal parameter settings;

[0041] Based on the optimal parameter settings, real-time energy balance technology is applied to monitor and adjust the output power to ensure the balance of energy supply and demand during the regulation process and generate real-time energy balance results;

[0042] Based on the real-time energy balance result, the actual energy demand matching situation is comprehensively evaluated to generate an optimized energy configuration plan.

[0043] Optionally, based on the initial energy configuration scheme, a gradient descent optimization algorithm is used to adjust the operating parameters of the multi-type energy supply equipment, minimize the energy consumption cost, and generate the optimal parameter settings, including:

[0044] Based on the initial energy configuration scheme, extracting preliminary working parameters of the multi-type energy supply equipment to generate an initial parameter set;

[0045] Based on the initial parameter set, an energy consumption cost function is constructed, an energy consumption cost calculation formula is defined, and an energy consumption cost model is generated;

[0046] Based on the energy consumption cost model, a gradient descent optimization algorithm is used to iteratively adjust the preliminary working parameters, gradually reduce the energy consumption cost function value, and generate intermediate optimization parameters;

[0047] Based on the intermediate optimization parameters, repeated verification and optimization processing are performed to ensure the stability and effectiveness of parameter settings and generate optimal parameter settings.

[0048] Optionally, based on the energy consumption cost model, a gradient descent optimization algorithm is used to iteratively adjust the preliminary working parameters, gradually reduce the energy consumption cost function value, and generate intermediate optimization parameters, including:

[0049] Based on the energy cost model, analyzing the sensitivity of each parameter to the energy cost and identifying key parameters;

[0050] Combining historical data with actual operating conditions, further calibrating the energy consumption cost model to ensure the accuracy of the key parameters, so as to generate an initial energy consumption cost function value;

[0051] The initial energy consumption cost function value is calculated using the following formula:

[0052]

[0053] Among them, C0(p) is the initial energy consumption cost function value; p is the initial parameter set; M is the number of multi-type energy supply equipment; α j ,β j ,γ j To optimize the parameters, we fit the historical data; P j (p) is the energy consumption of the jth device under parameter set p; P j,ref is the reference energy consumption of the jth device; Pj,max is the maximum energy consumption of the jth device; j is the index of the multi-type energy supply device, from 1 to M;

[0054] Based on the initial energy consumption cost function value, determining the adjustment direction and amplitude of the initial parameter set through gradient calculation, setting the learning rate and optimization parameters to generate an iterative parameter set;

[0055] The iteration parameter set is calculated using the following formula:

[0056]

[0057] Among them, p k+1 is the k+1th iteration parameter set; p k is the k-th iteration parameter set; η is the learning rate, which controls the step size of each iteration; is the energy cost function gradient of the kth iteration; ω is the time frequency factor, reflecting the impact of periodic changes; t is the current time; δ is the smoothing factor, preventing the exponential function from diverging near zero; C k (p k ) is the energy cost function value of the kth iteration; C min The lowest energy consumption cost in history; C max is the highest energy consumption cost in history; β1, β2 are optimization parameters obtained by fitting historical data; ω1 is the time frequency factor, reflecting the influence of periodic changes; C0(p) is the initial energy consumption cost function value;

[0058] Based on the iterative parameter set, verify rationality and feasibility, evaluate the impact of the iterative parameter set on the energy consumption cost model, repeatedly calculate the energy consumption cost function value for multiple iterations, ensure the convergence of the energy consumption cost function value, and generate intermediate optimization parameters.

[0059] Optionally, the energy configuration scheme based on the optimization, monitoring the operation status of the power supply system, evaluating the execution performance multiple times, optimizing the working parameters of the multi-type energy supply equipment, and generating a smart power supply management parameter set include:

[0060] Based on the optimized energy configuration scheme, real-time monitoring of the power supply system operation status is carried out, actual working data of the power supply system is collected, and an operation status report is generated;

[0061] Based on the operation status report, repeatedly evaluate the execution performance of the power supply system and generate a performance evaluation report;

[0062] Based on the performance evaluation report, analyze the operating parameters in depth, identify the limitations and optimization space of the power supply system, and generate power supply optimization suggestions;

[0063] Based on the power supply optimization suggestions, the operating parameters and control logic are finely adjusted, the power supply management strategy is optimized, and an intelligent power supply management parameter set is generated.

[0064] In a second aspect, an embodiment of the present application provides a multi-source complementary intelligent power supply management system in a mobile cabin, comprising:

[0065] The collection module is used to collect real-time status data from multiple types of energy supply equipment and combine it with the real-time power consumption data inside the mobile cabin to generate a comprehensive energy management data set;

[0066] An evaluation module, for dynamically evaluating real-time performance based on the comprehensive energy management data set, using an interval optimization algorithm based on evolutionary strategies, applying a Markov decision process, predicting future energy demand changes, and generating an energy combination strategy report;

[0067] An adjustment module is used to adjust the working parameters of the multi-type energy supply equipment based on the energy combination strategy report by using a gradient descent optimization algorithm, apply real-time energy balance technology, ensure the balance of energy supply and demand during the adjustment process, and generate an optimized energy configuration plan;

[0068] The monitoring module is used to monitor the operation status of the power supply system based on the optimized energy configuration plan, evaluate the execution performance multiple times, optimize the working parameters of the multiple types of energy supply equipment, and generate an intelligent power supply management parameter set.

[0069] 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 multi-source complementary intelligent power supply management method in a mobile cabin as described in the first aspect.

[0070] 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, it implements a multi-source complementary intelligent power supply management method in a mobile cabin as described in the first aspect.

[0071] In an embodiment of the present application, real-time status data from multiple types of energy supply equipment is collected and combined with the real-time power consumption data inside the mobile cabin to generate a comprehensive energy management data set; based on the comprehensive energy management data set, an interval optimization algorithm based on evolutionary strategies is adopted to dynamically evaluate real-time performance, and a Markov decision process is used to predict future energy demand changes and generate an energy combination strategy report; based on the energy combination strategy report, a gradient descent optimization algorithm is adopted to adjust the working parameters of the multiple types of energy supply equipment, and real-time energy balance technology is applied to ensure the balance of energy supply and demand during the adjustment process, and generate an optimized energy configuration plan; based on the optimized energy configuration plan, the operating status of the power supply system is monitored, the execution performance is evaluated multiple times, the working parameters of the multiple types of energy supply equipment are optimized, and an intelligent power supply management parameter set is generated.

[0072] The technical solution of this application has the following beneficial effects:

[0073] By collecting the real-time status data of various types of energy supply equipment and the real-time power consumption data inside the mobile cabin, a comprehensive energy management data set is generated, which realizes the comprehensive monitoring and management of the energy system and improves the accuracy and real-time nature of the data; based on the comprehensive energy management data set, an interval optimization algorithm based on evolutionary strategy is adopted to dynamically evaluate the real-time performance, and the Markov decision process is used to predict future energy demand changes, and an energy combination strategy report is generated, which improves the intelligence level and prediction accuracy of energy management; based on the energy combination strategy report, a gradient descent optimization algorithm is adopted to adjust the working parameters of various types of energy supply equipment, and real-time energy balance technology is applied to ensure the balance of energy supply and demand during the adjustment process, generate an optimized energy configuration plan, and improve energy utilization efficiency and system stability; based on the optimized energy configuration plan, the operation status of the power supply system is monitored, the execution performance is evaluated multiple times, the working parameters of various types of energy supply equipment are optimized, and an intelligent power supply management parameter set is generated, which realizes continuous optimization and intelligent management of the system and improves the reliability and economy of the overall energy management.

[0074] Furthermore, based on the comprehensive energy management data set, the interval optimization algorithm based on evolutionary strategy is used to dynamically evaluate the real-time performance, and the Markov decision process is used to predict future energy demand changes and generate an energy combination strategy report. The specific steps include: normalizing the data to generate a standardized data set; performing interval analysis based on the standardized data set to evaluate the real-time performance of multiple types of energy supply equipment; integrating external environmental parameters to predict future energy demand trends; generating energy combination strategies through multi-criteria decision analysis; and analyzing the optimal future working state and output power of the equipment in detail to generate an energy combination strategy report. By normalizing the data and performing interval optimization analysis, the real-time performance of multiple types of energy supply equipment is dynamically evaluated, which improves the accuracy and reliability of the evaluation. The Markov decision process is used to predict future changes in energy demand and generate detailed energy demand forecast results, which improves the accuracy and foresight of demand forecasts. Based on the performance evaluation results and energy demand forecasts, a number of criteria are comprehensively considered, and the optimal energy combination strategy is generated through multi-criteria decision analysis to ensure the scientificity and rationality of energy configuration. The future optimal working state and output power of various types of energy supply equipment are analyzed in detail to generate a comprehensive energy combination strategy report, which provides a scientific basis for the optimized operation and management of the system. Through dynamic evaluation and prediction, the energy configuration is optimized to ensure the stable operation of the system under different working conditions, reduce energy waste, and improve the economy and reliability of the overall system. The generated energy combination strategy report provides decision makers with detailed data support, which helps to formulate more effective energy management strategies and improve the overall management level of the system.

[0075] Furthermore, based on the energy combination strategy report, the gradient descent optimization algorithm is used to adjust the working parameters of various types of energy supply equipment, and the real-time energy balance technology is applied to ensure the balance of energy supply and demand during the adjustment process, and to generate an optimized energy configuration plan. The specific steps include: extracting the optimal working state and output power information of various types of energy supply equipment to generate an initial energy configuration plan; using the gradient descent optimization algorithm to adjust the equipment working parameters, minimize the energy consumption cost, and generate the optimal parameter settings; applying the real-time energy balance technology to monitor and adjust the output power, ensure the balance of energy supply and demand during the adjustment process, and generate real-time energy balance results; comprehensively evaluate the actual energy demand matching situation, and generate an optimized energy configuration plan. By extracting the optimal working state and output power information from the energy combination strategy report, the initial energy configuration plan is generated to ensure that the starting point of parameter adjustment is reasonable; the gradient descent optimization algorithm is used to adjust the equipment working parameters to minimize energy consumption costs, improve energy utilization efficiency, and reduce operating costs; real-time energy balance technology is applied to monitor and adjust the output power to ensure the balance of energy supply and demand during the adjustment process, avoiding energy waste and insufficient supply problems; through real-time energy balance technology and optimized parameter settings, the stable operation of the system under different working conditions is ensured, and the reliability and safety of the system are improved; a comprehensive assessment of the actual energy demand matching situation is made to generate an optimized energy configuration plan, which provides a scientific basis for the long-term operation of the system and enhances the overall management level of the system; the generated optimized energy configuration plan provides decision makers with detailed data support, which helps to formulate more effective energy management strategies and improve the operating efficiency and economic benefits of the system.

[0076] These and other aspects of the present application will become more apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] 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.

[0078] Figure 1 A flowchart of a multi-source complementary intelligent power supply management method in a mobile cabin provided in an embodiment of the present application;

[0079] Figure 2 A schematic diagram of the structure of a multi-source complementary intelligent power supply management system in a mobile cabin provided in an embodiment of the present application;

[0080] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0081] 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.

[0082] 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.

[0083] 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.

[0084] Figure 1 A flowchart of a multi-source complementary intelligent power supply management method in a mobile cabin is provided for the embodiment of the present application, such as Figure 1 As shown, the method includes:

[0085] 101. Collect real-time status data from multiple types of energy supply equipment, combine it with real-time power consumption data inside the mobile cabin, and generate a comprehensive energy management data set;

[0086] In this step, various types of energy supply equipment include but are not limited to solar panels, wind turbines, diesel generators and other different types of energy generation devices. These devices can provide the necessary power support for the mobile cabin. Real-time status data refers to the instant working status information obtained from the above energy supply equipment, such as power generation, equipment temperature, fault alarm, etc., as well as environmental factors such as light intensity, wind speed, etc. These data are used to evaluate the current working status and performance of each device.

[0087] The real-time power consumption data inside the mobile shelter includes the real-time power consumption data of each power-consuming device in the shelter, such as the power consumption of air conditioning, lighting, communication equipment, etc. These data are used to understand the power demand and consumption inside the shelter.

[0088] The comprehensive energy management dataset integrates the real-time status data of multiple types of energy supply equipment and the real-time power consumption data inside the mobile cabin to form a dataset containing comprehensive information for subsequent energy management and optimization.

[0089] In an embodiment of the present application, status data of multiple types of energy supply equipment and power consumption data inside the mobile cabin are collected in real time through sensors and monitoring equipment; the collected data are transmitted to a central data processing system through wired or wireless communications; in the central data processing system, data from different sources are integrated to generate a comprehensive energy management data set; the generated comprehensive energy management data set is stored in a database for subsequent analysis and use.

[0090] Suppose there is a mobile cabin equipped with three energy supply devices: solar panels, wind turbines and energy storage batteries. At the same time, there are electrical equipment such as air conditioning, lighting and communication equipment in the cabin.

[0091] Through sensors and monitoring equipment, the status data of various types of energy supply equipment and the power consumption data inside the mobile cabin are collected in real time; the data is transmitted to the central data processing system using a wireless sensor network; in the central data processing system, the data from solar panels, wind turbines, energy storage batteries and electrical equipment inside the cabin are integrated to generate a comprehensive energy management data set; the generated comprehensive energy management data set is stored in a database, such as a relational database.

[0092] Through this process, the energy supply and consumption of the mobile cabin can be understood in real time, providing accurate data support for subsequent energy management and optimization.

[0093] 102. Based on the comprehensive energy management data set, an interval optimization algorithm based on evolutionary strategy is used to dynamically evaluate real-time performance, and a Markov decision process is used to predict future energy demand changes and generate an energy combination strategy report;

[0094] In this step, the interval optimization algorithm of evolutionary strategies is an optimization algorithm that simulates natural selection and genetic mechanisms to find the optimal solution or approximate optimal solution. In this scenario, it can help determine the best energy supply combination to meet current and future energy needs.

[0095] Dynamic evaluation of real-time performance refers to continuously updating and evaluating the efficiency and performance of the current energy system based on data collected in real time to ensure maximum energy utilization.

[0096] Markov decision process is a mathematical framework used to describe a decision process with some randomness, where the next state can be predicted given the current state. In this scenario, it is used to predict future energy demand trends.

[0097] The Energy Portfolio Strategy Report is a report generated based on the above analysis results, which proposes a recommended energy supply portfolio to improve energy efficiency and reduce costs.

[0098] In the embodiment of the present application, the comprehensive energy management data set is normalized to generate a standardized data set; an interval analysis is performed using an interval optimization algorithm based on an evolutionary strategy to evaluate the real-time performance of the equipment; a Markov decision process is used to analyze current and historical data to predict future trends in energy demand; and a detailed energy combination strategy report is generated through multi-criteria decision analysis to provide a basis for subsequent optimization.

[0099] Optionally, the method in step 102, based on the comprehensive energy management data set, adopts an interval optimization algorithm based on evolutionary strategies to dynamically evaluate real-time performance, uses a Markov decision process to predict future energy demand changes, and generates an energy combination strategy report, including: based on the comprehensive energy management data set, normalizing the data scale range to generate a standardized data set; based on the standardized data set, using an interval optimization algorithm based on evolutionary strategies to perform interval analysis, evaluate the real-time performance of the multiple types of energy supply equipment, and generate performance evaluation results; based on the performance evaluation results, comprehensively integrating multiple external environmental parameters, using a Markov decision process to analyze current and historical data, predict future energy demand change trends, and generate energy demand prediction results; based on the energy demand prediction results, comprehensively considering multiple criteria factors, and generating an energy combination strategy through multi-criteria decision analysis; based on the energy combination strategy, analyzing in detail the future optimal working state and output power of the multiple types of energy supply equipment to generate an energy combination strategy report.

[0100] In this step, the real-time status data of various types of energy supply equipment and the real-time power consumption data inside the mobile cabin are collected for comprehensive monitoring and management of the energy system. Data of different scales are converted into a unified scale range to generate a standardized data set to eliminate dimensional differences and improve the accuracy of data analysis. By simulating natural selection and genetic mechanisms, the data is analyzed in intervals to evaluate the real-time performance of various types of energy supply equipment. The evaluation results generated based on the interval optimization algorithm reflect the current working status and performance of the equipment. By analyzing current and historical data, the trend of future energy demand changes is predicted and energy demand forecast results are generated. Taking into account multiple criteria factors, the optimal energy combination strategy is generated; the future optimal working status and output power of various types of energy supply equipment are analyzed in detail to generate a comprehensive energy combination strategy report.

[0101] In the embodiment of the present application, it is assumed that there is a mobile medical cabin equipped with three energy supply devices: a diesel generator, a fuel cell, and a solar panel. At the same time, there are medical equipment, air conditioning, lighting systems and other electrical equipment in the cabin;

[0102] First, real-time status data from diesel generators, fuel cells and solar panels, as well as power consumption data inside the cabin, are collected; normalization is performed to convert them into a unified scale range to generate a standardized data set to eliminate dimensional differences between different data.

[0103] Secondly, based on the generated standardized data set, an interval optimization algorithm based on evolutionary strategies is used to evaluate the real-time performance of diesel generators, fuel cells and solar panels, and generate detailed performance evaluation results; combined with external environmental parameters (such as weather forecasts, medical activity plans, etc.), the Markov decision process is used to analyze current and historical data, predict the trend of energy demand changes in the future, generate energy demand forecast results, and provide a basis for subsequent energy management.

[0104] Furthermore, based on the energy demand forecast results, comprehensive consideration of multiple factors such as economy, environmental protection, reliability and safety is carried out, and the optimal energy combination strategy is generated through multi-criteria decision analysis to ensure the scientificity and rationality of energy allocation.

[0105] Finally, the future optimal working status and output power of diesel generators, fuel cells and solar panels are analyzed in detail to generate a comprehensive energy combination strategy report, providing a scientific basis for the optimized operation and management of the mobile medical cabin and ensuring the efficient and stable operation of the system.

[0106] Among them, based on the standardized data set, an interval optimization algorithm based on evolutionary strategies is used to perform interval analysis, evaluate the real-time performance of the multiple types of energy supply equipment, and generate performance evaluation results, including: based on the standardized data set, outliers and noise are removed, and input into the interval optimization algorithm based on evolutionary strategies to generate an input data set; based on the input data set, an interval optimization algorithm based on evolutionary strategies is used to perform interval analysis, process the uncertainty and volatility of the input data set, and generate equipment performance intervals; based on the equipment performance intervals, the real-time performance of the multiple types of energy supply equipment is evaluated, potential performance bottlenecks are analyzed, and a performance analysis report is generated; based on the performance analysis report, combined with historical real-time performance data, the optimization potential of the multiple types of energy supply equipment is evaluated, various real-time performance indicators are output, and performance evaluation results are generated.

[0107] In this step, the original data is normalized to generate a data set with a unified scale range, which is used to eliminate dimensional differences and improve the accuracy of data analysis. By simulating natural selection and genetic mechanisms, the data is analyzed in intervals to evaluate the real-time performance of multiple types of energy supply equipment and handle the uncertainty and volatility of the data; after removing outliers and noise from the standardized data set, the clean data set generated is used to input into the interval optimization algorithm. The equipment performance range generated by the interval optimization algorithm reflects the performance of the equipment under different conditions. Based on the equipment performance range, the real-time performance of multiple types of energy supply equipment is evaluated, potential performance bottlenecks are analyzed, and a detailed report is generated. Combined with historical real-time performance data, the optimization potential of multiple types of energy supply equipment is evaluated, and various real-time performance indicators are output to generate the final evaluation results.

[0108] In the embodiment of the present application, it is assumed that there is a mobile rescue command center equipped with three energy supply devices: wind turbines, photovoltaic panels, and batteries. At the same time, there are communication equipment, monitoring systems, emergency lighting and other electrical equipment in the command center;

[0109] First, the energy consumption data of wind turbines, photovoltaic panels and batteries are collected and combined with the electricity consumption data within the command center to perform standardized data processing, remove outliers and noise from the standardized data set, and generate a clean input data set.

[0110] Secondly, the input data set is fed into an interval optimization algorithm based on evolutionary strategies to handle the uncertainty and volatility of the data and generate equipment efficiency intervals that reflect the performance of wind turbines, photovoltaic panels, and batteries under different conditions.

[0111] Furthermore, based on the generated equipment efficiency intervals, the real-time efficiency of wind turbines, photovoltaic panels and batteries is evaluated, potential performance bottlenecks are analyzed, and efficiency analysis reports are generated. For example, it is found that wind turbines are less efficient under low wind speed conditions, and photovoltaic panels cannot generate electricity at night.

[0112] Finally, the optimization potential of wind turbines, photovoltaic panels and batteries is evaluated by combining historical real-time performance data, and various real-time performance indicators are output to generate the final performance evaluation results. For example, it is recommended to give priority to the use of photovoltaic panels during low wind speed periods, while increasing the capacity of batteries to cope with nighttime and low wind speed conditions.

[0113] Through this process, the real-time performance of various types of energy supply equipment in the mobile rescue command center can be comprehensively evaluated, performance bottlenecks can be identified, and optimization suggestions can be made to ensure the efficient and stable operation of the system.

[0114] Among them, based on the performance evaluation results, a plurality of external environmental parameters are integrated, and the Markov decision process is used to analyze current and historical data, predict future energy demand change trends, and generate energy demand prediction results, including: based on the performance evaluation results, a plurality of external environmental parameters are collected and integrated to generate a comprehensive environmental parameter set; based on the comprehensive environmental parameter set, the Markov decision process is used to analyze the energy demand change law in each time period to generate a demand analysis result; based on the demand analysis results, the demand in each time period is determined through time series analysis, the future energy demand change trend is predicted, and a preliminary energy demand prediction result is generated; using the preliminary energy demand prediction results, high-resolution time period division is adopted to analyze the specific energy demand changes in each time period to generate an energy demand prediction result.

[0115] In this step, the evaluation results generated based on the equipment performance range reflect the real-time performance and potential performance bottlenecks of various types of energy supply equipment; collect external environmental parameters, including weather forecasts, user activity plans, seasonal changes and other external factors that affect energy demand; integrate the performance evaluation results with external environmental parameters to generate a data set containing comprehensive information; by analyzing current and historical data, a mathematical model is developed to predict future energy demand change trends; based on a comprehensive environmental parameter set, the analysis results of the energy demand change law in each time period are generated through a Markov decision process; the demand for each time period is determined through time series analysis to generate preliminary energy demand forecast results; and high-resolution time period division is used to analyze the specific energy demand changes in each time period to generate the final energy demand forecast results.

[0116] In the embodiment of the present application, it is assumed that there is a mobile operating room equipped with three energy supply devices: solar panels, wind turbines, and energy storage batteries. At the same time, there are electrical equipment such as communication equipment, cooling systems, and backup power supplies in the operating room;

[0117] First, the power generation and charging status data of the three energy supply devices are collected, and the power consumption data of each electrical equipment in the operating room are collected. Combined with external environmental parameters such as weather forecasts, user activity plans, seasonal changes, etc., a comprehensive environmental parameter set is generated.

[0118] Secondly, based on the comprehensive environmental parameter set, the Markov decision process is used to analyze the energy demand changes in each time period and generate demand analysis results. For example, it is found that the power consumption of communication equipment and cooling systems increases significantly during the high temperature period in summer.

[0119] Furthermore, based on the demand analysis results, the demand for each time period is determined through time series analysis to generate preliminary energy demand forecast results. For example, it is predicted that the total electricity consumption during the high temperature period in summer will be 20% higher than that in other periods.

[0120] Finally, using the preliminary energy demand forecast results, high-resolution time periods (such as every hour or every half hour) are used to analyze the specific energy demand changes in each time period to generate the final energy demand forecast results. For example, it is predicted that the electricity consumption will be highest between 12:00 and 14:00 every day. It is recommended to increase the power generation of solar panels and reduce the discharge of energy storage batteries during this period.

[0121] Through this process, the future changes in energy demand for mobile operating rooms can be comprehensively predicted, providing a scientific basis for energy management and optimization, and ensuring the efficient and stable operation of the system.

[0122] This application considers that this formula is mainly used in the performance evaluation of various types of energy supply equipment, which needs to deal with the uncertainty and volatility of data. The interval optimization algorithm based on evolutionary strategy can effectively deal with these uncertainties and generate equipment performance intervals by simulating natural selection and genetic mechanisms. This method ensures the accuracy and reliability of the evaluation results by constructing complex optimization functions and performance factors.

[0123] Optionally, based on the input data set, an interval optimization algorithm based on evolutionary strategy is used to perform interval analysis, process uncertainty and volatility of the input data set, and generate equipment performance intervals, including:

[0124] Based on the input data set, standardize the data to make it comparable;

[0125] Selecting features that significantly affect the performance of the multi-type energy supply equipment, constructing an initial population of evolutionary strategies, and generating an optimization function based on the evolutionary strategy;

[0126] The optimization function based on evolution strategy is calculated by the following formula:

[0127]

[0128] Among them, ES(x i ) is the optimization function based on evolution strategy; x i is the input data set of the i-th energy supply device; k is the index of the population individual in the evolution strategy, from 1 to N; N is the population size in the evolution strategy; W k is the weight of the kth individual, reflecting its importance in optimization; μ k is the mean of the kth individual, reflecting its central position in the performance interval; σ k is the standard deviation of the kth individual, reflecting its distribution range in the efficiency interval; β1, β2 are optimization parameters obtained by fitting historical data; ω1 is the time frequency factor, reflecting the influence of periodic changes; δ is the smoothing factor, preventing the logarithmic function from diverging near zero;

[0129] Based on the optimization function based on evolution strategy, an analyzable function interval is constructed by optimizing the maximum value of the function value, an interval analysis function is introduced, nonlinear transformation is performed, and uncertainty and volatility of the function are processed to generate an optimization efficiency factor;

[0130] The optimization efficiency factor is calculated by the following formula:

[0131]

[0132] Among them, F i is the optimization efficiency factor of the i-th energy supply equipment; ES(x i ) is the optimization function based on evolution strategy; β1, β2 are optimization parameters obtained by fitting historical data; ω1, ω2, ω3, ω4 are time frequency factors, reflecting the influence of periodic changes; t is the current time; δ is the smoothing factor to prevent the logarithmic function from diverging near zero; IA(ES(x i )) is the interval analysis function, IA(ES(x i ))=β3·(max(ES(x i )+α·σ(x i ))-min(ES(x i )-α·σ(x i ))), α is the confidence level coefficient, σ(x i ) is the standard deviation of the input data set of the ith energy supply device, β3 is the optimization parameter; sinh is the hyperbolic sine function;

[0133] Based on the optimized performance factor, the optimized performance factor is adjusted through the current time information, the influence of time attenuation and periodic changes is comprehensively considered, and reasonable upper and lower limit thresholds are set in combination with the statistical characteristics of historical data to generate a device performance range.

[0134] The method aims to deal with the uncertainty and volatility of data from multi-type energy supply equipment through evolutionary strategies and interval analysis, and generate optimized performance intervals. This helps to dynamically evaluate the real-time performance of equipment and provide a scientific basis for energy management and optimization.

[0135] In the optimization function ES(x i ), the Gaussian kernel function The distribution characteristics and uncertainty of the data are processed; the sine term β1·sin(ω1·x i ): Introducing the influence of periodic changes; logarithmic terms Deal with the nonlinear characteristics of the data and prevent the logarithmic function from diverging around zero.

[0136] In the optimization efficiency factor, the sine term sin(ω1·ES(x i)): Introducing the influence of periodic changes; logarithmic term log(δ+ES(x i ) 2 ): Process the nonlinear characteristics of the data to prevent the logarithmic function from diverging near zero; the exponential term exp(-β1·ES(x i ))cos(ω2·t): Introduces time decay effect; square root term Introduce the influence of time periodic changes; interval analysis item IA(ES(x i )): Process the interval characteristics of data and enhance robustness; the denominator 1+tan(ω4·ES(x i ))+sinh(β2·ES(x i )):Introduce nonlinear transformation to enhance the adaptability of the model;

[0137] Among them, x i is the input data set of the i-th energy supply device, which is collected in real time through sensors and monitoring equipment; k is the index of the population individual in the evolution strategy, ranging from 1 to N, determined according to the population size; N is the population size in the evolution strategy, which is set according to actual needs; w k is the weight of the kth individual, reflecting its importance in optimization, obtained by fitting historical data; μ k is the mean of the kth individual, reflecting its central position in the performance interval, calculated through historical data; σ k is the standard deviation of the kth individual, reflecting its distribution range in the efficiency interval, which is calculated through historical data; β1, β2 are optimization parameters, which are obtained by fitting historical data; ω1, ω2, ω3, ω4 are time frequency factors, which reflect the influence of periodic changes and are set according to actual conditions; δ is a smoothing factor, which prevents the logarithmic function from diverging near zero and is set according to actual conditions; y is the current time, which is obtained through the system clock; α is the confidence level coefficient, which is set according to actual needs; β3 is an optimization parameter, which is obtained by fitting historical data; IA(ES(x i )) is the interval analysis function, which is obtained by interval analysis calculation;

[0138] Suppose there is a mobile medical treatment center equipped with three energy supply devices: solar panels, wind turbines and energy storage batteries. At the same time, there are medical equipment, air conditioning systems, communication equipment and other electrical equipment in the station;

[0139] Assume that the input data set is x1=120 (solar panel power generation), x2=80 (wind turbine power generation), x3=50 (remaining power of energy storage battery);

[0140] Assume that the population size is N = 50; weight w k =[0.1,0.2,0.3,0.2,0.2]; mean μk =[100,90,80,70,60]; standard deviation σ k =[10,15,20,15,10]; optimization parameters β1=0.5,β2=0.3; time frequency factor ω1=0.1; smoothing factor δ=0.01; 3;

[0141] Optimization function based on evolution strategy:

[0142]

[0143] Optimizing performance factors:

[0144]

[0145] Among them, F1 = 2.444 reflects that under the current conditions, the solar panels have high efficiency, good stability and reliability. Assuming that the set threshold is 2.0, since the result 2.444 is greater than the set threshold, it shows that the current solar panels have excellent performance and stability and can effectively meet the system requirements. Through the above steps, a scientific basis is provided for the energy management and optimization of the mobile medical treatment center to ensure the efficient and stable operation of the system.

[0146] 103. Based on the energy combination strategy report, a gradient descent optimization algorithm is used to adjust the working parameters of the multi-type energy supply equipment, and real-time energy balance technology is applied to ensure the balance of energy supply and demand during the adjustment process, so as to generate an optimized energy configuration plan;

[0147] In this step, the gradient descent optimization algorithm is a commonly used optimization algorithm that gradually approaches the minimum point of the objective function through iteration. Here, it is used to adjust the working parameters of the energy supply equipment to achieve a higher energy efficiency ratio.

[0148] Real-time energy balance technology refers to technical measures to ensure that energy supply and demand are balanced at any time. This requires the system to respond quickly to load changes and adjust energy output in a timely manner.

[0149] The optimized energy configuration plan refers to the final energy supply plan formed after the above adjustments, which aims to maximize energy utilization efficiency while ensuring the stable operation of the system.

[0150] In the embodiment of the present application, the optimal operating state and output power information of the equipment are extracted from the energy combination strategy report to generate an initial energy configuration plan; a gradient descent optimization algorithm is used to adjust the equipment operating parameters to minimize energy consumption costs; real-time energy balance technology is applied to monitor and adjust the output power to ensure the balance of energy supply and demand during the adjustment process; and an optimized energy configuration plan is generated to provide guidance for actual operation.

[0151] Optionally, in step 103, based on the energy combination strategy report, a gradient descent optimization algorithm is used to adjust the working parameters of the multi-type energy supply equipment, and real-time energy balance technology is applied to ensure the balance of energy supply and demand in the regulation process, and an optimized energy configuration plan is generated, including: based on the energy combination strategy report, the optimal working state and output power information of the multi-type energy supply equipment are extracted to generate an initial energy configuration plan; based on the initial energy configuration plan, a gradient descent optimization algorithm is used to adjust the working parameters of the multi-type energy supply equipment, minimize energy consumption costs, and generate optimal parameter settings; based on the optimal parameter settings, real-time energy balance technology is applied to monitor and adjust the output power to ensure the balance of energy supply and demand in the regulation process and generate real-time energy balance results; based on the real-time energy balance results, the actual energy demand matching situation is comprehensively evaluated to generate an optimized energy configuration plan.

[0152] In this step, based on the report generated by the comprehensive energy management data set, the future optimal working state and output power of various types of energy supply equipment are analyzed in detail; the initial configuration plan is generated by extracting the optimal working state and output power information of the equipment from the energy combination strategy report; the optimization algorithm that minimizes the energy consumption cost is used by iteratively adjusting parameters; the equipment working parameters generated by the gradient descent optimization algorithm ensure that the energy consumption cost is minimized; the technology of ensuring the balance of energy supply and demand during the regulation process is used by monitoring and adjusting the output power; the balance results generated after applying the real-time energy balance technology reflect the current energy supply and demand situation; the actual energy demand matching situation is comprehensively evaluated to generate the final optimized configuration plan.

[0153] In an embodiment of the present application, it is assumed that there is a mobile treatment room equipped with a variety of energy supply equipment, and there are electrical equipment such as meteorological observation equipment, data transmission equipment and backup power supply in the treatment room; based on the energy combination strategy report, the optimal working state and output power information of each energy supply equipment are extracted; an initial energy configuration plan is generated, including the optimal working state and output power of each device; based on the initial energy configuration plan, a gradient descent optimization algorithm is used to adjust the working parameters of each energy supply equipment, and the optimal parameter settings are generated by minimizing the energy consumption cost to ensure that each device operates in the optimal state and reduce the overall energy consumption cost; based on the optimal parameter setting, real-time energy balance technology is applied to monitor and adjust the output power of each device to ensure the balance of energy supply and demand during the adjustment process, generate real-time energy balance results, reflect the current energy supply and demand situation, and ensure the stable operation of the system; based on the real-time energy balance results, the actual energy demand matching situation is comprehensively evaluated to ensure the normal operation of each electrical equipment in the treatment room; an optimized energy configuration plan is generated to provide a scientific basis for the long-term operation of the system and ensure the efficient and stable operation of the system.

[0154] Through this process, the optimal balance between energy supply and consumption of the mobile clinic can be ensured, improving the overall operating efficiency and reliability of the system.

[0155] Among them, based on the initial energy configuration plan, a gradient descent optimization algorithm is used to adjust the working parameters of the multi-type energy supply equipment, minimize the energy consumption cost, and generate the optimal parameter settings, including: based on the initial energy configuration plan, extracting the preliminary working parameters of the multi-type energy supply equipment to generate an initial parameter set; based on the initial parameter set, constructing an energy consumption cost function, defining an energy consumption cost calculation formula, and generating an energy consumption cost model; based on the energy consumption cost model, a gradient descent optimization algorithm is used to iteratively adjust the preliminary working parameters, gradually reduce the energy consumption cost function value, and generate intermediate optimization parameters; based on the intermediate optimization parameters, repeated verification and optimization processing are performed to ensure the stability and effectiveness of parameter settings and generate optimal parameter settings.

[0156] In this step, based on the report generated by the comprehensive energy management data set, the future optimal working state and output power of various types of energy supply equipment are analyzed in detail; the initial configuration plan is generated by extracting the optimal working state and output power information of the equipment from the energy combination strategy report; the optimization algorithm that minimizes the energy consumption cost is used by iteratively adjusting parameters; the equipment working parameters generated by the gradient descent optimization algorithm ensure that the energy consumption cost is minimized; the technology of ensuring the balance of energy supply and demand during the regulation process is used by monitoring and adjusting the output power; the balance results generated after applying the real-time energy balance technology reflect the current energy supply and demand situation; the actual energy demand matching situation is comprehensively evaluated to generate the final optimized configuration plan.

[0157] Assume that there is a mobile medical emergency station equipped with a variety of energy supply equipment. At the same time, there are medical equipment, air conditioning systems, communication equipment and other electrical equipment in the emergency station. Based on the initial energy configuration plan, the preliminary working parameters of each energy supply equipment are extracted to generate an initial parameter set, such as extracting the tilt angle of the solar panel, the speed of the wind turbine, the charge and discharge rate of the energy storage battery and other parameters. Based on the initial parameter set, an energy consumption cost function is constructed, the energy consumption cost calculation formula is defined, and an energy consumption cost model is generated. For example, the energy consumption cost function is defined as the weighted sum of the energy consumption of each device, and the weight is determined according to the importance and frequency of use of the equipment, including the power generation cost of the solar panel, the wind power generation cost, and the energy consumption cost of the wind turbine. The maintenance cost of the wind turbine and the charging and discharging loss of the energy storage battery are taken into account; based on the energy consumption cost model, the gradient descent optimization algorithm is used to iteratively adjust the preliminary working parameters, gradually reduce the energy consumption cost function value, and generate intermediate optimization parameters; through multiple iterations, the inclination angle of the solar panel, the speed of the wind turbine and the charging and discharging strategy of the energy storage battery are gradually adjusted to reduce the overall energy consumption cost; based on the intermediate optimization parameters, repeated verification and optimization processing are carried out to ensure the stability and effectiveness of the parameter settings and generate the optimal parameter settings; through multiple experiments, it is verified whether the optimized parameter settings can maintain the efficient operation of the system under different weather conditions, and finally the optimal parameter settings are generated.

[0158] Through this process, the energy supply and consumption of the mobile medical emergency station can be optimally balanced, the overall operating efficiency and reliability of the system can be improved, and the stable operation of medical equipment and communication systems can be ensured.

[0159] This application considers that this formula is mainly used for the energy cost optimization problem of various types of energy supply equipment in actual operation, especially how to gradually adjust the working parameters of the equipment through the gradient descent optimization algorithm in an environment of uncertainty and volatility to achieve the goal of minimizing energy cost. By constructing an energy cost model and optimization function, it ensures that the equipment operates in an efficient and stable state.

[0160] Optionally, based on the energy consumption cost model, a gradient descent optimization algorithm is used to iteratively adjust the preliminary working parameters, gradually reduce the energy consumption cost function value, and generate intermediate optimization parameters, including:

[0161] Based on the energy cost model, analyzing the sensitivity of each parameter to the energy cost and identifying key parameters;

[0162] Combining historical data with actual operating conditions, further calibrating the energy consumption cost model to ensure the accuracy of the key parameters, so as to generate an initial energy consumption cost function value;

[0163] The initial energy consumption cost function value is calculated using the following formula:

[0164]

[0165] Among them, C0(p) is the initial energy consumption cost function value; p is the initial parameter set; M is the number of multi-type energy supply equipment; α j ,β j ,γ j To optimize the parameters, we fit the historical data; P j (p) is the energy consumption of the jth device under parameter set p; Pj ,ref is the reference energy consumption of the jth device; P j,max is the maximum energy consumption of the jth device; j is the index of the multi-type energy supply device, from 1 to M;

[0166] Based on the initial energy consumption cost function value, determining the adjustment direction and amplitude of the initial parameter set through gradient calculation, setting the learning rate and optimization parameters to generate an iterative parameter set;

[0167] The iteration parameter set is calculated using the following formula:

[0168]

[0169] Among them, p k+1 is the k+1th iteration parameter set; p k is the k-th iteration parameter set; η is the learning rate, which controls the step size of each iteration; is the energy cost function gradient of the kth iteration; ω is the time frequency factor, reflecting the impact of periodic changes; t is the current time; δ is the smoothing factor, preventing the exponential function from diverging near zero; C k (p k ) is the energy cost function value of the kth iteration; C min The lowest energy consumption cost in history; C max is the highest energy consumption cost in history; β1, β2 are optimization parameters obtained by fitting historical data; ω1 is the time frequency factor, reflecting the influence of periodic changes; C0(p) is the initial energy consumption cost function value;

[0170] Based on the iterative parameter set, verify rationality and feasibility, evaluate the impact of the iterative parameter set on the energy consumption cost model, repeatedly calculate the energy consumption cost function value for multiple iterations, ensure the convergence of the energy consumption cost function value, and generate intermediate optimization parameters.

[0171] This method aims to iteratively adjust the working parameters of the equipment through the gradient descent optimization algorithm, gradually reduce the energy cost function value, and generate intermediate optimization parameters. This helps to minimize the energy cost in actual operation and improve the overall operating efficiency and economy of the system.

[0172] In the initial energy cost function value, the square term Measures the deviation between the equipment energy consumption and the reference energy consumption, emphasizing the relative change of energy consumption; index item Exponential decay is introduced to deal with the nonlinear characteristics of energy consumption and prevent excessive deviation from affecting the results;

[0173] In the iterative parameter set, the gradient term Adjust the parameters according to the gradient direction and amplitude to ensure that the energy cost function value gradually decreases; the periodic term Introducing the impact of periodic changes and considering the impact of time factors on energy consumption; exponential smoothing term A smoothing factor is introduced to prevent the gradient from diverging in extreme cases; the sine term β1·sin(ω1·C0(p)): introduces the influence of periodic changes to enhance the adaptability of the model; the logarithmic term β2·log(δ+C0(p) 2 ): Process the nonlinear characteristics of the data and prevent the logarithmic function from diverging near zero;

[0174] Among them, p is the initial parameter set; it is initialized by the actual operation data and historical data of the equipment; M is the number of multi-type energy supply equipment, which is determined according to the actual number of equipment; α j ,β j ,γ j To optimize the parameters, we fit the historical data; P j (p) is the energy consumption of the jth device under parameter set p, which is calculated based on the actual operation data of the device; P j,ref is the reference energy consumption of the jth device, determined according to the rated value or historical average value of the device; P j,max is the maximum energy consumption of the jth device, which is determined according to the rated value of the device; j is the index of the multi-type energy supply device, ranging from 1 to M, which is determined according to the number of devices; η is the learning rate, which is set according to actual needs and is usually determined through experiments; is the energy cost function gradient of the kth iteration, which is obtained through gradient calculation; ω is the time frequency factor, which is set according to the actual situation; t is the current time, which is obtained through the system clock; δ is the smoothing factor, which is set according to the actual situation; C k (p k ) is the energy cost function value of the kth iteration, which is calculated by the energy cost function; C min is the lowest energy consumption cost in history, calculated through historical data; C max is the historical highest energy consumption cost, which is calculated through historical data; β1, β2 are optimization parameters, which are obtained by fitting historical data; ω1 is the time frequency factor, which is set according to actual conditions; C0(p) is the initial energy consumption cost function value, which is calculated through the initial energy consumption cost function.

[0175] Assume that there is a mobile diagnosis and treatment information exchange center equipped with three types of energy supply equipment, and there are medical equipment, air conditioning system, communication equipment and other electrical equipment in the center;

[0176] Assume that the number of devices M = 3; the initial parameter set p = [p1, p2, p3] = [0.8, 0.7, 0.6]; the optimization parameter α j =[0.5,0.4,0.3],β j =[0.3,0.2,0.1],γ j =[0.1,0.2,0.3]; Equipment energy consumption P1(p)=120, P2(p)=80, P3(p)=50; Reference energy consumption P 1,ref =100,P 2,ref =70,P 3,ref =40; Maximum energy consumption P 1,max =150,P 2,max =100,P 3,max =60;

[0177] Calculate the initial energy cost function value:

[0178]

[0179] Substitute the values

[0180] The calculation result is C0(p)=0.604913.

[0181] Calculate the iterative parameter set learning rate η = 0.1; current time t = 10; smoothing factor δ = 0.01; historical minimum energy consumption cost C min =0.5; the highest energy consumption cost in history C max =1.0; optimization parameters β1=0.5, β2=0.3; time frequency factor ω=0.1, ω1=0.2; initial energy cost function value C0(p)=0.60491; energy cost function value C of the kth iteration k (p k )=0.60491; Gradient of energy cost function of the kth iteration

[0182] Substituting the values:

[0183] Calculation result p k+1 =[1.0303825,0.928555,0.8267275];

[0184] Among them, p k+1=[1.0303825,0.928555,0.8267275] indicates that under the current conditions, the working parameters of the equipment have been reasonably adjusted through the gradient descent optimization algorithm, and the energy cost function value has gradually decreased, approaching the optimal solution; assuming that the set threshold vector is [1.1,1.0,0.9], since all values ​​in the result are less than the set threshold vector, it shows that the current optimization process is effective, the working parameters of the equipment are being gradually optimized, and the energy cost is effectively controlled. Through the above steps, the working parameters of the equipment in the mobile diagnosis and treatment information interaction center have been reasonably adjusted, the energy cost function value has gradually decreased, and the optimization effect is significant, ensuring the efficient and stable operation of the system.

[0185] 104. Based on the optimized energy configuration scheme, monitor the operation status of the power supply system, evaluate the execution performance multiple times, optimize the working parameters of the multi-type energy supply equipment, and generate an intelligent power supply management parameter set.

[0186] In this step, monitoring the operating status of the power supply system involves continuously tracking the actual operating data of the power supply system, including but not limited to indicators such as voltage, current, and frequency, to ensure the safety and reliability of the system.

[0187] Repeated evaluation of execution effectiveness means evaluating the actual performance of the energy supply system regularly or irregularly to check whether the expected goals are achieved and make corresponding adjustments accordingly.

[0188] The intelligent power supply management parameter set is a set of parameter settings formed after multiple optimizations. It covers the best practices in all aspects from energy collection, conversion to distribution, and helps to improve the intelligence level of the entire power supply system.

[0189] In the embodiment of the present application, the operating status of the power supply system is monitored in real time and actual operating data is collected; the execution performance of the optimization plan is evaluated multiple times to ensure its effectiveness and stability in practical applications; the working parameters of the equipment are further optimized based on the evaluation results; and an intelligent power supply management parameter set is generated to provide a scientific basis for the long-term operation and management of the system.

[0190] Optionally, the energy configuration scheme based on the optimization in step 104 monitors the operating status of the power supply system, evaluates the execution performance multiple times, optimizes the working parameters of the multiple types of energy supply equipment, and generates an intelligent power supply management parameter set, including: based on the optimized energy configuration scheme, real-time monitoring of the operating status of the power supply system, collecting actual working data of the power supply system, and generating an operating status report; based on the operating status report, repeatedly evaluating the execution performance of the power supply system and generating a performance evaluation report; based on the performance evaluation report, in-depth analysis of operating parameters, identifying limitations and optimization space of the power supply system, and generating power supply optimization suggestions; based on the power supply optimization suggestions, fine-tuning operating parameters and control logic, optimizing power supply management strategies, and generating an intelligent power supply management parameter set.

[0191] In this step, based on the energy configuration plan generated by the previous optimization, ensure that various types of energy supply equipment operate in the optimal state; monitor the operating status of the power supply system in real time, collect the actual working data of the power supply system, and generate a detailed report; based on the operating status report, evaluate the execution performance of the power supply system and generate an evaluation result; based on the performance evaluation report, deeply analyze the operating parameters, identify the limitations and optimization space of the power supply system, and generate optimization suggestions; according to the power supply optimization suggestions, fine-tune the operating parameters and control logic, optimize the power supply management strategy, and generate the final parameter set.

[0192] Suppose there is a mobile medical rescue station equipped with a variety of energy supply equipment, and it is necessary to improve the overall operating efficiency and reliability of the system; based on the optimized energy configuration plan, the operating status of the power supply system is monitored in real time, and the actual working data of the power supply system is collected, including the power generation of each energy supply equipment, the system operating temperature, etc., and an operating status report is generated to record the real-time operating status and data of the system; based on the operating status report, the execution performance of the power supply system is repeatedly evaluated, and the stability and efficiency of the system are analyzed; an efficiency evaluation report is generated to reflect the performance of the system in different time periods; based on the efficiency evaluation report, the operating parameters are deeply analyzed to identify the limitations and optimization space of the power supply system, for example, it is found that the power generation efficiency of the solar panels is low in certain time periods, and the charging and discharging strategy of the energy storage battery is not optimized enough; power supply optimization suggestions are generated, and specific improvement measures are proposed, such as adjusting the angle of the solar panels, optimizing the charging and discharging strategy of the energy storage battery, etc.; based on the power supply optimization suggestions, the operating parameters and control logic are finely adjusted, the power supply management strategy is optimized, the tilt angle of the solar panels is adjusted to improve the power generation efficiency, and the charging and discharging strategy of the energy storage battery is optimized to extend the battery life; an intelligent power supply management parameter set is generated to ensure the efficient and stable operation of the system.

[0193] Through this process, the optimal balance between energy supply and consumption of the mobile medical rescue station can be ensured, the overall operating efficiency and reliability of the system can be improved, and the stable operation of medical equipment and communication systems can be ensured.

[0194] Figure 2 The present application provides a schematic diagram of a multi-source complementary intelligent power supply management system in a mobile cabin, as shown in FIG. Figure 2 As shown, the device comprises:

[0195] The collection module 21 is used to collect real-time status data from various types of energy supply equipment and generate a comprehensive energy management data set in combination with the real-time power consumption data inside the mobile cabin;

[0196] An evaluation module 22 is used to dynamically evaluate real-time performance based on the comprehensive energy management data set, use an interval optimization algorithm based on evolutionary strategies, apply a Markov decision process, predict future energy demand changes, and generate an energy combination strategy report;

[0197] An adjustment module 23 is used to adjust the working parameters of the multi-type energy supply equipment based on the energy combination strategy report by using a gradient descent optimization algorithm, apply real-time energy balance technology, ensure the balance of energy supply and demand during the adjustment process, and generate an optimized energy configuration plan;

[0198] The monitoring module 24 is used to monitor the operation status of the power supply system based on the optimized energy configuration scheme, evaluate the execution performance multiple times, optimize the working parameters of the multi-type energy supply equipment, and generate an intelligent power supply management parameter set.

[0199] Figure 2 The multi-source complementary intelligent power supply management system in the mobile cabin can execute Figure 1 The implementation principle and technical effects of the multi-source complementary intelligent power supply management method in a mobile cabin described in the embodiment shown will not be repeated. The specific way in which each module and unit performs operations in the multi-source complementary intelligent power supply management system in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0200] In one possible design, Figure 2 The multi-source complementary intelligent power supply management system in a mobile cabin of 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;

[0201] 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 .

[0202] The processing component 32 is used to: collect real-time status data from multiple types of energy supply equipment, combine it with the real-time power consumption data inside the mobile cabin, and generate a comprehensive energy management data set; based on the comprehensive energy management data set, use an interval optimization algorithm based on evolutionary strategies to dynamically evaluate real-time performance, use the Markov decision process to predict future energy demand changes, and generate an energy combination strategy report; based on the energy combination strategy report, use a gradient descent optimization algorithm to adjust the working parameters of the multiple types of energy supply equipment, apply real-time energy balance technology to ensure the balance of energy supply and demand during the adjustment process, and generate an optimized energy configuration plan; based on the optimized energy configuration plan, monitor the operating status of the power supply system, evaluate the execution performance multiple times, optimize the working parameters of the multiple types of energy supply equipment, and generate an intelligent power supply management parameter set.

[0203] 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.

[0204] 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.

[0205] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0206] 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.

[0207] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0208] 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.

[0209] 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 method for managing multi-source complementary intelligent power supply in a mobile cabin.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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 multi-source complementary intelligent power supply management method in a mobile shelter, characterized in that: include: Collect real-time status data from multiple types of energy supply equipment and combine it with real-time power consumption data inside the mobile cabin to generate a comprehensive energy management data set; Based on the comprehensive energy management data set, an interval optimization algorithm based on evolutionary strategy is used to dynamically evaluate real-time performance, and a Markov decision process is used to predict future energy demand changes and generate an energy portfolio strategy report; Based on the energy combination strategy report, a gradient descent optimization algorithm is used to adjust the working parameters of the multi-type energy supply equipment, and real-time energy balance technology is applied to ensure the balance of energy supply and demand during the regulation process, thereby generating an optimized energy configuration plan; Based on the optimized energy configuration scheme, the operation status of the power supply system is monitored, the execution performance is evaluated multiple times, the working parameters of the multiple types of energy supply equipment are optimized, and an intelligent power supply management parameter set is generated.

2. The method according to claim 1, characterized in that Based on the comprehensive energy management data set, the interval optimization algorithm based on evolutionary strategy is adopted to dynamically evaluate the real-time performance, use the Markov decision process to predict future energy demand changes, and generate an energy combination strategy report, including: Based on the comprehensive energy management data set, normalizing the data scale range to generate a standardized data set; Based on the standardized data set, an interval optimization algorithm based on evolutionary strategy is used to perform interval analysis, evaluate the real-time performance of the multi-type energy supply equipment, and generate performance evaluation results; Based on the performance evaluation results, a number of external environmental parameters are integrated, and the Markov decision process is used to analyze current and historical data, predict future energy demand trends, and generate energy demand forecast results; Based on the energy demand forecast results, a number of criteria factors are comprehensively considered and an energy combination strategy is generated through multi-criteria decision analysis; Based on the energy combination strategy, the future optimal working state and output power of the multi-type energy supply equipment are analyzed in detail to generate an energy combination strategy report.

3. The method according to claim 2, characterized in that Based on the standardized data set, an interval optimization algorithm based on evolution strategy is used to perform interval analysis, evaluate the real-time performance of the multi-type energy supply equipment, and generate performance evaluation results, including: Based on the standardized data set, outliers and noise are removed, and input into the interval optimization algorithm based on evolution strategy to generate an input data set; Based on the input data set, an interval optimization algorithm based on evolutionary strategy is used to perform interval analysis, process the uncertainty and volatility of the input data set, and generate equipment performance intervals; Based on the equipment performance range, evaluate the real-time performance of the multiple types of energy supply equipment, analyze potential performance bottlenecks, and generate a performance analysis report; Based on the performance analysis report and in combination with historical real-time performance data, the optimization potential of the multi-type energy supply equipment is evaluated, various real-time performance indicators are output, and performance evaluation results are generated.

4. The method according to claim 3, characterized in that: Based on the input data set, an interval optimization algorithm based on evolutionary strategy is used to perform interval analysis, process the uncertainty and volatility of the input data set, and generate equipment performance intervals, including: Based on the input data set, standardize the data to make it comparable; Selecting features that significantly affect the performance of the multi-type energy supply equipment, constructing an initial population of evolutionary strategies, and generating an optimization function based on the evolutionary strategy; The optimization function based on evolution strategy is calculated by the following formula: Among them, ES(x i ) is the optimization function based on evolution strategy; x i is the input data set of the i-th energy supply device; k is the index of the population individual in the evolution strategy, from 1 to N; N is the population size in the evolution strategy; w k is the weight of the kth individual, reflecting its importance in optimization; μ k is the mean of the kth individual, reflecting its central position in the performance interval; σ k is the standard deviation of the kth individual, reflecting its distribution range in the efficiency interval; β1, β2 are optimization parameters obtained by fitting historical data; ω1 is the time frequency factor, reflecting the influence of periodic changes; δ is the smoothing factor, preventing the logarithmic function from diverging near zero; Based on the optimization function based on evolution strategy, an analyzable function interval is constructed by optimizing the maximum value of the function value, an interval analysis function is introduced, nonlinear transformation is performed, and uncertainty and volatility of the function are processed to generate an optimization efficiency factor; The optimization efficiency factor is calculated by the following formula: Among them, F i is the optimization efficiency factor of the i-th energy supply equipment; ES(x i ) is the optimization function based on evolution strategy; β1, β2 are optimization parameters obtained by fitting historical data; ω1, ω2, ω3, ω4 are time frequency factors, reflecting the influence of periodic changes; t is the current time; δ is the smoothing factor to prevent the logarithmic function from diverging near zero; IA(ES(x i )) is the interval analysis function, IA(ES(x i ))=β3·(max(ES(x i )+α·σ(x i ))-min(ES(x i )-α·σ(x i ))), α is the confidence level coefficient, σ(x i ) is the standard deviation of the input data set of the ith energy supply device, β3 is the optimization parameter; sinh is the hyperbolic sine function; Based on the optimized performance factor, the optimized performance factor is adjusted through the current time information, the influence of time attenuation and periodic changes is comprehensively considered, and reasonable upper and lower limit thresholds are set in combination with the statistical characteristics of historical data to generate a device performance range.

5. The method according to claim 2, characterized in that: Based on the performance evaluation results, a number of external environmental parameters are integrated, and the Markov decision process is used to analyze current and historical data, predict future energy demand change trends, and generate energy demand forecast results, including: Based on the performance evaluation results, multiple external environmental parameters are collected and integrated to generate a comprehensive environmental parameter set; Based on the comprehensive environmental parameter set, using the Markov decision process, analyzing the energy demand variation pattern in each time period, and generating demand analysis results; Based on the demand analysis results, determine the demand in each time period through time series analysis, predict the trend of future energy demand changes, and generate preliminary energy demand forecast results; The preliminary energy demand forecast results are used to divide the time periods into high-resolution time periods, analyze the specific energy demand changes in each time period, and generate energy demand forecast results.

6. The method according to claim 1, characterized in that Based on the energy combination strategy report, a gradient descent optimization algorithm is used to adjust the working parameters of the multi-type energy supply equipment, and real-time energy balance technology is applied to ensure the balance of energy supply and demand during the adjustment process, and generate an optimized energy configuration plan, including: Based on the energy combination strategy report, extract the optimal working state and output power information of the multi-type energy supply equipment to generate an initial energy configuration plan; Based on the initial energy configuration scheme, a gradient descent optimization algorithm is used to adjust the operating parameters of the multi-type energy supply equipment, minimize the energy consumption cost, and generate the optimal parameter settings; Based on the optimal parameter settings, real-time energy balance technology is applied to monitor and adjust the output power to ensure the balance of energy supply and demand during the regulation process and generate real-time energy balance results; Based on the real-time energy balance result, the actual energy demand matching situation is comprehensively evaluated to generate an optimized energy configuration plan.

7. The method according to claim 6, characterized in that The method of adjusting the operating parameters of the multi-type energy supply equipment based on the initial energy configuration scheme, using a gradient descent optimization algorithm, minimizing energy consumption costs, and generating optimal parameter settings includes: Based on the initial energy configuration scheme, extracting preliminary working parameters of the multi-type energy supply equipment to generate an initial parameter set; Based on the initial parameter set, an energy consumption cost function is constructed, an energy consumption cost calculation formula is defined, and an energy consumption cost model is generated; Based on the energy consumption cost model, a gradient descent optimization algorithm is used to iteratively adjust the preliminary working parameters, gradually reduce the energy consumption cost function value, and generate intermediate optimization parameters; Based on the intermediate optimization parameters, repeated verification and optimization processing are performed to ensure the stability and effectiveness of parameter settings and generate optimal parameter settings.

8. The method according to claim 7, characterized in that Based on the energy cost model, a gradient descent optimization algorithm is used to iteratively adjust the preliminary working parameters, gradually reduce the energy cost function value, and generate intermediate optimization parameters, including: Based on the energy cost model, analyzing the sensitivity of each parameter to the energy cost and identifying key parameters; Combining historical data with actual operating conditions, further calibrating the energy consumption cost model to ensure the accuracy of the key parameters, so as to generate an initial energy consumption cost function value; The initial energy consumption cost function value is calculated using the following formula: Among them, C0(p) is the initial energy consumption cost function value; p is the initial parameter set; M is the number of multi-type energy supply equipment; α j ,β j ,γ j To optimize the parameters, we fit the historical data; P j (p) is the energy consumption of the jth device under parameter set p; P j,ref is the reference energy consumption of the jth device; P j,max is the maximum energy consumption of the jth device; J is the index of multiple types of energy supply devices, from 1 to M; Based on the initial energy consumption cost function value, determining the adjustment direction and amplitude of the initial parameter set through gradient calculation, setting the learning rate and optimization parameters to generate an iterative parameter set; The iteration parameter set is calculated using the following formula: Among them, p k+1 is the k+1th iteration parameter set; p k is the k-th iteration parameter set; η is the learning rate, which controls the step size of each iteration; is the energy cost function gradient of the kth iteration; ω is the time frequency factor, reflecting the impact of periodic changes; t is the current time; δ is the smoothing factor, preventing the exponential function from diverging near zero; C k (p k ) is the energy cost function value of the kth iteration; C min The lowest energy consumption cost in history; C max is the highest energy consumption cost in history; β1, β2 are optimization parameters obtained by fitting historical data; ω1 is the time frequency factor, reflecting the influence of periodic changes; C0(p) is the initial energy consumption cost function value; Based on the iterative parameter set, verify rationality and feasibility, evaluate the impact of the iterative parameter set on the energy consumption cost model, repeatedly calculate the energy consumption cost function value for multiple iterations, ensure the convergence of the energy consumption cost function value, and generate intermediate optimization parameters.

9. The method according to claim 1, characterized in that: The energy configuration scheme based on the optimization, monitoring the operation status of the power supply system, evaluating the execution performance multiple times, optimizing the working parameters of the multi-type energy supply equipment, and generating a smart power supply management parameter set include: Based on the optimized energy configuration scheme, real-time monitoring of the power supply system operation status is carried out, actual working data of the power supply system is collected, and an operation status report is generated; Based on the operation status report, repeatedly evaluate the execution performance of the power supply system and generate a performance evaluation report; Based on the performance evaluation report, analyze the operating parameters in depth, identify the limitations and optimization space of the power supply system, and generate power supply optimization suggestions; Based on the power supply optimization suggestions, the operating parameters and control logic are finely adjusted, the power supply management strategy is optimized, and an intelligent power supply management parameter set is generated.

10. An intelligent power supply management system with multi-source complementarity in a mobile shelter, characterized in that: include: The collection module is used to collect real-time status data from multiple types of energy supply equipment and combine it with the real-time power consumption data inside the mobile cabin to generate a comprehensive energy management data set; An evaluation module, for dynamically evaluating real-time performance based on the comprehensive energy management data set, using an interval optimization algorithm based on evolutionary strategies, applying a Markov decision process, predicting future energy demand changes, and generating an energy combination strategy report; An adjustment module is used to adjust the working parameters of the multi-type energy supply equipment based on the energy combination strategy report by using a gradient descent optimization algorithm, apply real-time energy balance technology, ensure the balance of energy supply and demand during the adjustment process, and generate an optimized energy configuration plan; The monitoring module is used to monitor the operation status of the power supply system based on the optimized energy configuration plan, evaluate the execution performance multiple times, optimize the working parameters of the multiple types of energy supply equipment, and generate an intelligent power supply management parameter set.

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