Continuous power supply method and system for mobile square cabin

Abstract

The invention provides a continuous power supply method and system for a mobile shelter, and the method comprises the steps: comprehensively detecting an energy available type and an environment change trend through an environment sensing module, and generating environment energy data through combining with meteorological parameters such as temperature and humidity; a machine learning algorithm is adopted to deeply analyze the data and real-time power demands, and an optimal energy combination mode is predicted. Based on the mode, the intelligent load distribution mechanism adjusts the working state of the electric equipment to obtain an optimized equipment operation state. The self-adaptive control technology is used for dynamically adjusting the output proportion of each energy source, a battery management system is combined for monitoring the charging and discharging states of a battery, optimized energy source output configuration is formed, an auxiliary power generation unit (such as a flexible solar panel, a micro wind driven generator and a kinetic energy recovery system) intelligently supplements the surplus power demand, and finally an efficient power supply scheme is formed. According to the scheme, continuous, efficient and stable power supply of the movable square cabin is achieved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of continuous power supply for mobile cabins, and in particular to a continuous power supply method for mobile cabins. Background Art

[0002] As a flexible and efficient temporary facility, mobile shelters are widely used in military, medical, emergency rescue, field operations and other scenarios. These scenarios are usually located in remote areas or extreme environments, and traditional fixed power supply methods are difficult to meet their continuous and stable power needs.

[0003] At present, the power supply system of mobile cabins mainly adopts the following solutions: single energy power supply, hybrid energy power supply and manual load adjustment. The single energy power supply solution is simple and easy to implement, but it is easy to cause power outages when the single energy is unavailable or unstable; the hybrid energy power supply solution improves the reliability of power supply through the complementarity of multiple energy sources, but there are still problems such as insufficient intelligence in energy management and low energy utilization efficiency; the manual load adjustment solution is cumbersome to operate, prone to errors, and unable to respond to environmental changes in real time. Although these existing solutions have met some needs to a certain extent, they still have obvious defects, such as high dependence on a single energy source, insufficient intelligence in energy management, frequent manual intervention, and lack of comprehensive environmental monitoring and prediction capabilities. These problems have led to the poor performance of existing solutions in terms of multi-energy adaptability, real-time monitoring and prediction, and intelligent load management. There is an urgent need for an intelligent power supply method that can comprehensively solve these problems. Summary of the invention

[0004] The embodiment of the present invention provides a method and system for continuous power supply of a mobile cabin, which is used to solve the problems in the prior art of high dependence on a single energy source, insufficient intelligence in energy management, frequent manual intervention, and lack of comprehensive environmental monitoring and prediction capabilities.

[0005] In a first aspect, an embodiment of the present invention provides a method for continuously supplying power to a mobile cabin, comprising:

[0006] The environment sensing module is used to comprehensively detect the energy available types and change trends of the environment in which the mobile cabin is located, and the environmental energy data is generated in combination with meteorological parameters, wherein the meteorological parameters include: temperature and humidity;

[0007] Use machine learning algorithms to conduct in-depth analysis of the environmental energy data and the real-time power demand of the mobile cabin, calculate weather forecast information within a preset time, and predict the optimal energy combination mode;

[0008] By using the intelligent load distribution mechanism, based on the optimal energy combination mode, the working state of the electrical equipment in the mobile cabin is finely adjusted to obtain the adjusted equipment operating state;

[0009] Adaptive control technology is used to dynamically adjust the output ratio of each energy source according to the adjusted equipment operation status, and the battery management system is combined to monitor the charge and discharge status of the battery pack in real time to obtain an optimized energy output configuration;

[0010] Auxiliary power generation units, including flexible solar panels integrated on the surface of the cabin, micro wind turbines and a power generation system based on mobile kinetic energy recovery of the cabin, are used to intelligently supplement the remaining power demand under the optimized energy output configuration, and a power supply plan is formed in combination with the optimized energy output configuration.

[0011] Optionally, an intelligent load distribution mechanism is used to finely adjust the working state of the electrical equipment in the mobile cabin based on the optimal energy combination mode to obtain the adjusted equipment operating state, including:

[0012] Use the intelligent load distribution mechanism to comprehensively evaluate the current working status of all electrical equipment in the mobile cabin and generate an initial equipment operating status report;

[0013] Analyze the initial equipment operation status report using a priority scheduling algorithm to determine the importance, energy consumption characteristics and adjustable range of each device, and generate a list of devices sorted by priority;

[0014] Dynamically adjust the power output of the power-consuming equipment according to the equipment list sorted by priority using dynamic power adjustment technology to obtain the adjusted equipment operation status;

[0015] The adjusted equipment operation state is optimized by using an energy-saving optimization algorithm, and the optimized equipment operation state is obtained by simulating the working modes of different equipment combinations;

[0016] A real-time feedback control system is used to monitor the optimized equipment operation status in real time, and an adaptive learning algorithm is used to automatically adjust the equipment's operating parameters according to actual operation data and external environmental changes to form a target equipment operation status.

[0017] Optionally, a dynamic power adjustment technology is used to dynamically adjust the power output of the power-consuming device according to the device list sorted by priority to obtain an adjusted device operation state, including:

[0018] Using a priority scheduling algorithm to evaluate the importance of each power-consuming device in the initial device operation status report, calculate the functional importance of the device, the impact on business continuity, and the emergency response requirements, and obtain the importance score of the device;

[0019] Based on the importance score of the device, combined with the energy consumption characteristics of each device, including average power consumption, peak power consumption and power consumption fluctuation range, the energy efficiency of the device is evaluated to obtain an energy efficiency score;

[0020] Analyze the adjustable range of each device, including the minimum operating power, maximum operating power and power adjustment step, evaluate the flexibility of the device and obtain a flexibility score;

[0021] Comprehensively evaluate all electrical equipment using a multi-factor comprehensive evaluation model by combining the importance score, energy efficiency score and flexibility score to generate a comprehensive score;

[0022] All electrical devices are sorted according to the comprehensive scores to generate a priority-sorted device list.

[0023] Optionally, the importance score, energy efficiency score and flexibility score are comprehensively evaluated by using a multi-factor comprehensive evaluation model to generate a comprehensive score, including:

[0024] Using a multi-factor comprehensive evaluation model, combined with a preset weight coefficient, the importance score, energy efficiency score and flexibility score are weighted to calculate a basic comprehensive score for each electrical device;

[0025] Based on the basic comprehensive score, all electrical devices are classified using a cluster analysis algorithm to form a plurality of device categories, wherein each device category represents a group of devices with similar characteristics;

[0026] For each equipment category, the analytic hierarchy process is used to refine the evaluation criteria, calculate the differences between equipment within the equipment category, and generate category-specific scoring criteria, which include: equipment usage frequency, maintenance cost, and environmental adaptability;

[0027] Combined with the category-specific scoring criteria, a decision tree algorithm is used to conduct a secondary evaluation of each electrical device to generate a refined comprehensive score;

[0028] The refined comprehensive score is optimized by using a genetic algorithm, and the best score combination is found by simulating the process of natural selection and genetic variation to obtain a target comprehensive score.

[0029] Optionally, for each equipment category, the analytic hierarchy process is used to refine the evaluation criteria, calculate the differences between the equipment within the equipment category, and generate category-specific scoring criteria, which include: equipment usage frequency, maintenance cost, and environmental adaptability, including:

[0030] For each equipment category, an evaluation index system is constructed using the hierarchical analysis method, which includes: equipment usage frequency, maintenance cost, environmental adaptability and equipment reliability;

[0031] Quantify each indicator in the evaluation indicator system by using the hierarchical analysis method, calculate the relative weight of each indicator, and generate an indicator weight table;

[0032] Based on the indicator weight table, the equipment within each equipment category is evaluated, the differences in the frequency of use, maintenance cost, environmental adaptability and reliability of the equipment are calculated, and an equipment difference report is generated;

[0033] In combination with the device difference report, a multi-criteria decision analysis method is used to conduct a comprehensive evaluation of each device to generate category-specific scoring criteria, which are used to reflect the relative advantages and disadvantages of the device in a specific category.

[0034] Optionally, adaptive control technology is used to dynamically adjust the output ratio of each energy source according to the adjusted device operation status, and the battery management system is combined to monitor the charge and discharge status of the battery pack in real time to obtain an optimized energy output configuration, including:

[0035] Analyzing the adjusted equipment operation status by using adaptive control technology, and generating a preliminary energy output plan in combination with the charging and discharging status of the battery management system;

[0036] Optimizing the preliminary energy output plan using a deep reinforcement learning algorithm, calculating energy conversion efficiency, battery health status, and energy change trend, and obtaining an optimized energy output plan;

[0037] evaluating the optimized energy output plan using a predictive maintenance algorithm to generate maintenance recommendations for the battery pack;

[0038] Using a multi-objective optimization algorithm to comprehensively adjust the optimized energy output plan and the maintenance suggestion to obtain an adjusted energy output strategy;

[0039] The adjusted energy output strategy is adjusted using a fuzzy logic control system to form a target energy output configuration according to real-time environmental changes and changes in the equipment operating status.

[0040] Optionally, auxiliary power generation units, including flexible solar panels integrated on the surface of the shelter, micro wind turbines, and power generation systems based on mobile kinetic energy recovery of the shelter, are used to intelligently supplement the remaining power demand under the optimized energy output configuration. Combined with the optimized energy output configuration, a power supply solution is formed, including:

[0041] Using auxiliary power generation units to conduct a preliminary assessment of the remaining power demand under the optimized energy output configuration, determine the power generation potential of each auxiliary power generation unit, and generate a power generation potential report of the auxiliary power generation unit;

[0042] Based on the power generation potential report of the auxiliary power generation unit, an intelligent scheduling algorithm is used to schedule the flexible solar panels, micro wind turbines, and the power generation system based on mobile kinetic energy recovery of the cabin to generate a scheduling plan for the auxiliary power generation unit;

[0043] In combination with the scheduling plan of the auxiliary power generation unit, the output power of each auxiliary power generation unit is dynamically adjusted by using the energy management system to generate the output configuration of the auxiliary power generation unit;

[0044] The output configuration of the auxiliary power generation unit is optimized by using a predictive control algorithm, the weather changes and the movement path of the shelter within a preset time are calculated, and an optimized output plan of the auxiliary power generation unit is generated;

[0045] According to the optimized auxiliary power generation unit output plan, combined with the optimized energy output configuration, all energy sources are uniformly managed and dispatched using an integrated energy management system to form a power supply plan.

[0046] In a second aspect, an embodiment of the present application provides a mobile cabin continuous power supply system, comprising:

[0047] The detection module uses the environmental perception module to comprehensively detect the energy available types and change trends of the environment in which the mobile cabin is located, and generates environmental energy data in combination with meteorological parameters, wherein the meteorological parameters include: temperature and humidity;

[0048] The analysis module uses a machine learning algorithm to conduct an in-depth analysis of the environmental energy data and the real-time power demand of the mobile cabin, calculate the weather forecast information within a preset time, and predict the optimal energy combination mode;

[0049] The distribution module uses the intelligent load distribution mechanism to finely distribute the working status of the electrical equipment in the mobile cabin based on the optimal energy combination mode to obtain the fine equipment operation status;

[0050] The adjustment module uses adaptive control technology to dynamically adjust the output ratio of each energy source according to the operating status of the refined equipment, and combines the battery management system to monitor the charging and discharging status of the battery pack in real time to obtain an optimized energy output configuration;

[0051] The supplementary module utilizes auxiliary power generation units, including flexible solar panels integrated on the surface of the cabin, micro wind turbines, and a power generation system based on mobile kinetic energy recovery of the cabin, to intelligently supplement the remaining power demand under the optimized energy output configuration, and form a power supply plan in combination with the optimized energy output configuration.

[0052] In a third aspect, an embodiment of the present invention provides a computing device, comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a method for continuous power supply to a mobile cabin as described in any one of the first aspects.

[0053] In a fourth aspect, an embodiment of the present invention provides a computer storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement a method for continuous power supply to a mobile cabin as described in any one of the first aspects.

[0054] In an embodiment of the present invention, an environmental perception module is used to comprehensively detect the available energy types and changing trends of the environment in which the mobile cabin is located, and environmental energy data is generated in combination with meteorological parameters; a machine learning algorithm is used to deeply analyze the environmental energy data and the real-time power demand of the mobile cabin, calculate the weather forecast information within a preset time, and predict the optimal energy combination mode; an intelligent load distribution mechanism is used, and based on the optimal energy combination mode, the working state of the electrical equipment in the mobile cabin is finely adjusted to obtain the adjusted equipment operation state; adaptive control technology is used to dynamically adjust the output ratio of each energy according to the adjusted equipment operation state, and at the same time, the battery management system is combined to monitor the charge and discharge state of the battery pack in real time to obtain an optimized energy output configuration; an auxiliary power generation unit is used, including a flexible solar panel integrated on the surface of the cabin, a micro wind turbine, and a power generation system based on the mobile kinetic energy recovery of the cabin, to intelligently supplement the remaining power demand under the optimized energy output configuration, and a power supply plan is formed in combination with the optimized energy output configuration. The technical solution provided by the present invention realizes efficient, reliable and flexible power supply through multi-energy adaptability, intelligent prediction, refined load management, dynamic energy output adjustment and intelligent supplementation of auxiliary power generation units, and improves the continuous power supply capacity and overall performance of the mobile cabin in complex environments;

[0055] Furthermore, the intelligent load distribution mechanism is used to comprehensively evaluate the current working status of all electrical equipment in the mobile cabin and generate an initial equipment operation status report. Users can dynamically adjust the evaluation rules and parameters, and the system can respond to changes in equipment status and the latest needs of users in real time. Through intelligent evaluation strategy adjustment, it ensures that the generated equipment operation status report meets current needs and maintains high quality.

[0056] The priority scheduling algorithm is used to analyze the initial equipment operation status report, determine the importance, energy consumption characteristics and adjustable range of each device, and generate a list of devices sorted by priority. Users can dynamically adjust the priority scheduling rules and parameters. The system can respond to changes in equipment characteristics and the latest needs of users in real time. Through intelligent priority scheduling strategy adjustments, it ensures that the sorting of the equipment list meets current needs and maintains high accuracy.

[0057] Dynamic power adjustment technology is used to dynamically adjust the power output of power-consuming equipment according to the equipment list sorted by priority, and the adjusted equipment operating status is obtained. Users can dynamically adjust the power adjustment rules and parameters. The system can respond to changes in equipment power requirements and users' latest needs in real time. Through intelligent power adjustment strategy adjustments, it ensures that the power output of the equipment meets current needs and maintains high efficiency and energy saving;

[0058] The energy-saving optimization algorithm is used to optimize the adjusted equipment operation status, and the optimized equipment operation status is obtained by simulating the working modes of different equipment combinations. Users can dynamically adjust the energy-saving optimization rules and parameters. The system can respond to changes in equipment combinations and the latest needs of users in real time, and ensure that the equipment operation status achieves the best effect in energy saving through intelligent energy-saving optimization strategy adjustment;

[0059] The real-time feedback control system is used to monitor the optimized equipment operation status in real time. According to the actual operation data and changes in the external environment, the adaptive learning algorithm is used to automatically adjust the equipment's operating parameters to form the target equipment operation status. Users can dynamically adjust the monitoring and adaptive adjustment rules and parameters. The system can respond to changes in the equipment's operating status and external environment in real time. Through intelligent adaptive learning strategy adjustments, it ensures that the equipment's operating parameters always meet current needs and remain stable and efficient.

[0060] These and other aspects of the present invention will become more apparent from the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0062] Figure 1 A flow chart of a method for continuous power supply to a mobile cabin provided by an embodiment of the present invention;

[0063] Figure 2A structural schematic diagram of a mobile cabin continuous power supply system provided by an embodiment of the present invention;

[0064] Figure 3 A schematic diagram of the structure of a computing device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0065] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0066] In some of the processes described in the specification and claims of the present invention 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 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.

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0068] Since there are still obvious deficiencies in the prior art, such as high dependence on a single energy source, insufficient intelligence in energy management, frequent manual intervention, lack of comprehensive environmental monitoring and prediction capabilities, etc., based on this, the present invention provides a method for continuous power supply of a mobile cabin, such as Figure 1 ,include:

[0069] Step 101: Use the environment perception module to comprehensively detect the energy available types and change trends of the mobile cabin's environment, and generate environmental energy data in combination with meteorological parameters, wherein the meteorological parameters include: temperature and humidity

[0070] In this step, an environmental sensing module is installed on the mobile cabin and equipped with a variety of sensors, such as light sensors, wind speed sensors, temperature sensors, and humidity sensors, which collect environmental data in real time;

[0071] Meteorological parameters include temperature and humidity, which are measured by corresponding sensors in the environmental perception module;

[0072] Environmental energy data refers to the data collected by the above sensors, which reflects the types of energy available in the current environment (such as solar energy, wind energy) and their changing trends;

[0073] Generating environmental energy data means processing and integrating the collected environmental data to form structured environmental energy data, providing a basis for subsequent analysis.

[0074] Step 102: using a machine learning algorithm to deeply analyze the environmental energy data and the real-time power demand of the mobile cabin, calculate the weather forecast information within a preset time, and predict the optimal energy combination mode;

[0075] In this step, the machine learning algorithm learns the relationship between environmental changes and energy demand by training on historical environmental energy data and electricity demand data;

[0076] Deep analysis refers to using trained machine learning models to analyze current environmental energy data and real-time power demand to identify potential energy supply and demand patterns;

[0077] Weather forecast information: Based on historical data and current environmental data, forecast weather changes in the future;

[0078] The optimal energy combination model refers to calculating the most reasonable energy combination plan based on predicted weather conditions and current electricity demand to meet future electricity demand.

[0079] Step 103: referencing the intelligent load distribution mechanism, and based on the optimal energy combination mode, finely adjusting the working state of the electrical equipment in the mobile cabin to obtain the adjusted equipment operating state;

[0080] In this step, the intelligent load distribution mechanism refers to the use of priority scheduling algorithms and dynamic power adjustment technology to evaluate and sort all electrical equipment in the mobile cabin;

[0081] The optimal energy combination mode is used as input to guide the intelligent load distribution mechanism on how to adjust the working status of the equipment;

[0082] Fine-tuning means making detailed adjustments to the working status of the equipment according to its importance, energy consumption characteristics and adjustable range, to ensure that the equipment operates efficiently under the optimal energy combination mode;

[0083] The adjusted equipment operation status refers to a new equipment operation status report generated after adjustment by the intelligent load distribution mechanism, reflecting the new working parameters of the equipment.

[0084] Step 104: using adaptive control technology to dynamically adjust the output ratio of each energy source according to the adjusted equipment operation status, and combining with the battery management system to monitor the charge and discharge status of the battery pack in real time to obtain an optimized energy output configuration;

[0085] In this step, adaptive control technology is used to adjust the output ratio of each energy source in real time according to the adjusted equipment operating status to ensure that the energy supply matches the equipment demand;

[0086] The battery management system is used to monitor the charge and discharge status of the battery pack in real time to ensure the safe and efficient use of the battery;

[0087] Dynamic adjustment refers to dynamically adjusting the output ratio of solar energy, wind energy and other energy sources according to the actual operating status of the equipment and the status of the battery pack;

[0088] Optimized energy output configuration refers to the generation of optimized energy output configuration through the collaborative work of adaptive control technology and battery management system to ensure the stability and efficiency of energy supply.

[0089] Step 105: Utilize auxiliary power generation units, including flexible solar panels integrated on the surface of the shelter, micro wind turbines, and a power generation system based on mobile kinetic energy recovery of the shelter, to intelligently supplement the remaining power demand under the optimized energy output configuration, and form a power supply plan in combination with the optimized energy output configuration;

[0090] In this step, the auxiliary power generation unit includes a flexible solar panel, a micro wind turbine and a kinetic energy recovery system to supplement the shortage of the main energy supply.

[0091] Smart supplementation means that auxiliary power generation units intelligently supplement the remaining power demand based on the optimized energy output configuration;

[0092] The power supply plan refers to the combination of the main energy supply and the supplement of auxiliary power generation units to form a comprehensive power supply plan to ensure continuous power supply to the mobile cabin under various environmental conditions.

[0093] Suppose a mobile cabin is located in a remote mountainous area for medical rescue missions. The weather in the area is changeable and the power grid is unstable. An intelligent power supply system is needed to ensure continuous and stable power supply. At 8 o'clock in the morning, the environmental sensing module is installed on the mobile cabin, including light sensors, wind speed sensors, temperature sensors and humidity sensors. These sensors detect the following data: light intensity is 300 lux, wind speed is 2 meters per second, temperature is 15 degrees Celsius, and humidity is 60%. These data are processed and integrated into environmental energy data, showing that solar energy is sufficient and wind energy is weak in the current environment.

[0094] Subsequently, the machine learning algorithm was trained on historical data and current environmental data to predict weather changes in the next 24 hours. The forecast results showed that there would be plenty of sunshine from 9 a.m. to 5 p.m., which was suitable for the use of solar energy. The wind speed would increase from 5 p.m. to 8 a.m. the next day, which was suitable for the use of wind energy. The total electricity demand for the day was 100 kWh. Based on the forecast results, the optimal energy combination mode was calculated: solar panels were mainly used to generate electricity from 9 a.m. to 5 p.m., and micro wind turbines were mainly used to generate electricity from 5 p.m. to 8 a.m. the next day. The battery energy storage system stored excess electricity during the day and released it at night.

[0095] Next, the intelligent load distribution mechanism evaluates and sorts all electrical equipment in the mobile cabin through priority scheduling algorithms and dynamic power adjustment technology. Important equipment such as medical equipment and communication equipment are given priority in power supply, and non-critical equipment such as lighting and air conditioning are adjusted according to actual conditions to generate a new equipment operation status report: medical equipment maintains full power operation (20 kilowatts), communication equipment maintains full power operation (5 kilowatts), lighting equipment power is reduced to 70% (3 kilowatts), and air conditioning equipment power is reduced to 50% (5 kilowatts).

[0096] Adaptive control technology adjusts the output ratio of solar panels and wind turbines in real time according to the adjusted equipment operating status. The battery management system monitors the charge and discharge status of the battery pack in real time to ensure the safe and efficient use of the battery and generate an optimized energy output configuration: from 9 a.m. to 5 p.m., the solar panel output power is 25 kilowatts, and the remaining electrical energy is stored in the battery; from 5 p.m. to 8 a.m. the next day, the micro wind turbine output power is 15 kilowatts, and the battery releases the stored energy to make up for the shortfall.

[0097] Finally, the auxiliary power generation unit includes flexible solar panels, micro wind turbines and kinetic energy recovery systems. When solar energy and wind energy are not enough to meet the electricity demand, the auxiliary power generation unit intelligently supplements the remaining electricity demand. Combining the main energy supply and the supplement of the auxiliary power generation unit, a comprehensive power supply plan is formed: from 9 am to 5 pm, solar panels are the main power source, and flexible solar panels are used as a supplement. From 5 pm to 8 am the next day, micro wind turbines are the main power source, and the kinetic energy recovery system is used as a supplement. The battery energy storage system stores excess electricity during the day and releases it at night to ensure a stable power supply.

[0098] The embodiments of the present invention achieve efficient, reliable and flexible power supply by improving the flexibility, automation level and data quality of environmental perception, prediction, load management, energy output adjustment, auxiliary power generation units and the overall power supply solution. Specifically, the environmental perception module can collect environmental data in real time and accurately, the machine learning algorithm can intelligently predict future energy demand, the intelligent load distribution mechanism can finely adjust the working state of the equipment, the adaptive control technology can dynamically adjust the energy output ratio, and the auxiliary power generation unit can intelligently supplement the remaining power demand. These measures together ensure the continuous power supply capability and efficient operation of the mobile cabin in various complex environments, and improve the overall energy efficiency, reliability and adaptability of the system.

[0099] Based on this, the present invention provides a specific embodiment, wherein the step 102 refers to the intelligent load distribution mechanism, and based on the optimal energy combination mode, finely adjusts the working state of the electrical equipment in the mobile cabin to obtain the adjusted equipment operating state, which specifically includes the following steps:

[0100] Step 201: using the intelligent load distribution mechanism to comprehensively evaluate the current working status of all electrical equipment in the mobile cabin and generate an initial equipment operating status report;

[0101] In this step, the intelligent load distribution mechanism will conduct a comprehensive assessment of all electrical equipment in the mobile cabin, including factors such as the current working status, power consumption, and importance of the equipment;

[0102] During the comprehensive evaluation process, the system collects real-time data of each device, such as current, voltage, power, etc., and analyzes it in combination with the historical data of the device and the preset performance indicators;

[0103] Generating an initial equipment operation status report means that the evaluation results will be organized into a detailed report, which contains information such as the current working status, power consumption, and importance of each device;

[0104] Step 202: Analyze the initial equipment operation status report using a priority scheduling algorithm to determine the importance, energy consumption characteristics and adjustable range of each device, and generate a list of devices sorted by priority;

[0105] In this step, the priority scheduling algorithm analyzes the importance, energy consumption characteristics, and adjustable range of each device based on the initial device operation status report;

[0106] The importance refers to the importance of the equipment determined according to its functions and mission requirements in the mobile cabin;

[0107] Energy consumption characteristics refer to analyzing the power consumption characteristics of the equipment, including average power consumption, peak power consumption, etc.

[0108] The adjustable range refers to the power adjustment range of the device, that is, the maximum and minimum power that the device can accept;

[0109] Generating a device list sorted by priority means sorting the devices in descending order of importance according to the above analysis results to generate a priority list.

[0110] Step 203: using a dynamic power adjustment technology to dynamically adjust the power output of the power-consuming device according to the device list sorted by priority, and obtaining an adjusted device operating state;

[0111] In this step, dynamic power adjustment technology adjusts the power output of each device in real time based on the prioritized device list;

[0112] Dynamic adjustment means adjusting the power output of each device according to the current energy supply and device needs to ensure that key devices receive sufficient power supply while optimizing overall energy efficiency;

[0113] The adjusted device operation state is obtained, which means that the adjusted device operation state records the new power output value of each device to ensure that the device operates stably under the new power setting.

[0114] Step 204: optimizing the adjusted equipment operation state by using an energy-saving optimization algorithm, and obtaining an optimized equipment operation state by simulating working modes of different equipment combinations;

[0115] In this step, the energy-saving optimization algorithm will further optimize the adjusted equipment operating status;

[0116] Simulating the working modes of different equipment combinations means finding the most energy-efficient equipment operation state by simulating the working modes of different equipment combinations;

[0117] The optimized equipment operation state means that after optimization, the equipment operation state will be more energy-efficient while meeting the performance requirements of the equipment.

[0118] Step 205: using a real-time feedback control system to monitor the optimized equipment operation status in real time, and using an adaptive learning algorithm to automatically adjust the equipment's operating parameters according to actual operation data and external environment changes to form a target equipment operation status;

[0119] In this step, the real-time feedback control system continuously monitors the optimized equipment operating status;

[0120] Real-time monitoring means that the system will collect the actual operating data of the equipment in real time, such as current, voltage, temperature, etc.;

[0121] External environment changes mean that the system will also monitor changes in the external environment, such as temperature, humidity, etc.;

[0122] Adaptive learning algorithm means that according to actual operating data and changes in the external environment, the adaptive learning algorithm will automatically adjust the operating parameters of the equipment to maintain the best operating state;

[0123] Forming the target equipment operating state means that after adaptive adjustment, the equipment will reach the optimal working state to ensure efficient and stable operation.

[0124] The embodiments of the present invention achieve refined management and optimized operation of electrical equipment by improving the flexibility, automation level and data quality of equipment evaluation, priority sorting, power adjustment, energy-saving optimization, real-time monitoring and adaptive adjustment.

[0125] Based on this, the present invention provides a specific embodiment, wherein the step 203 uses a dynamic power adjustment technology to dynamically adjust the power output of the power-consuming device according to the device list sorted by priority to obtain the adjusted device operation state, which specifically includes the following steps:

[0126] Step 301: using a priority scheduling algorithm to evaluate the importance of each electrical device in the initial device operation status report, calculating the functional importance of the device, the impact on business continuity, and the emergency response requirements, and obtaining an importance score for the device;

[0127] In this step, the priority scheduling algorithm evaluates the importance of each power-consuming device in the initial device operation status report;

[0128] Functional importance refers to the core function and role of the equipment in the mobile shelter. For example, medical equipment is crucial to rescue missions and has a high functional importance;

[0129] Impact on business continuity refers to the assessment of the impact of equipment failure or downtime on overall business continuity. For example, the interruption of communication equipment may cause information transmission to be blocked, which has a significant impact;

[0130] Emergency response requirements refer to the response requirements of the equipment in emergency situations, for example, the emergency lighting system needs to be activated quickly at night or in emergencies;

[0131] Importance score refers to the importance score of each device calculated based on the evaluation results of the above three aspects. The higher the score, the more important the device.

[0132] Step 302: Based on the importance score of the device and in combination with the energy consumption characteristics of each device, including average power consumption, peak power consumption and power consumption fluctuation range, the energy consumption efficiency of the device is evaluated to obtain an energy consumption efficiency score;

[0133] In this step, energy consumption characteristics refer to evaluating the energy consumption characteristics of each device, including:

[0134] Average power consumption: the average power consumption of the device during normal operation;

[0135] Peak power consumption: the power consumption of the device at maximum load;

[0136] The power consumption fluctuation range refers to the power consumption variation range of the device under different working conditions;

[0137] Energy efficiency evaluation refers to the evaluation of the energy efficiency of equipment based on the above energy consumption characteristics. For example, if a device can maintain low power consumption under high load, its energy efficiency is high.

[0138] The energy efficiency score refers to the energy efficiency score of each device calculated based on the energy efficiency evaluation results. The higher the score, the better the energy efficiency of the device.

[0139] Step 303: Analyze the adjustable range of each device, including the minimum operating power, the maximum operating power and the power adjustment step, evaluate the flexibility of the device, and obtain a flexibility score;

[0140] In this step, the adjustable range refers to evaluating the adjustable range of each device, including:

[0141] Minimum operating power: the minimum power at which the device can work normally;

[0142] Maximum operating power: the maximum power at which the device can work normally;

[0143] Power adjustment step: the smallest adjustment unit when the device adjusts the power;

[0144] Flexibility assessment refers to the assessment of the flexibility of the device based on the above adjustable range. For example, if a device can be flexibly adjusted within a wider power range and the adjustment step is smaller, its flexibility is higher;

[0145] The flexibility score refers to the flexibility score of each device calculated based on the flexibility assessment results. The higher the score, the better the flexibility of the device.

[0146] Step 304: Comprehensively evaluate all electrical equipment by combining the importance score, energy efficiency score and flexibility score using a multi-factor comprehensive evaluation model to generate a comprehensive score;

[0147] In this step, the multi-factor comprehensive evaluation model refers to a comprehensive evaluation of all electrical equipment by combining importance scores, energy efficiency scores, and flexibility scores;

[0148] Comprehensive evaluation refers to inputting the importance score, energy efficiency score and flexibility score of each device into a multi-factor comprehensive evaluation model for weighted calculation;

[0149] Generating a comprehensive score means generating a comprehensive score for each device based on the comprehensive evaluation results. The comprehensive score reflects the comprehensive performance of the device in terms of importance, energy efficiency and flexibility.

[0150] Step 305: sorting all electrical devices according to the comprehensive scores, and generating a device list sorted by priority;

[0151] In this step, the comprehensive score means that in the previous steps, each device has been fully evaluated through the importance score, energy efficiency score and flexibility score, and a comprehensive score has been generated. This comprehensive score reflects the comprehensive performance of the device in multiple aspects;

[0152] Sorting algorithm refers to a sorting algorithm (such as bubble sort, quick sort or merge sort, etc.) that sorts all devices according to their comprehensive scores. The sorting is based on the comprehensive score, with devices with higher scores being ranked first and devices with lower scores being ranked last;

[0153] Generating a priority list means generating a device list sorted by priority after the sorting is completed. This list arranges the devices from high to low according to their comprehensive scores, thus determining the priority order of the devices;

[0154] Application priority list refers to the generated priority list that will be used for subsequent load management and energy allocation. For example, when the power supply is tight, the system can give priority to ensuring the power supply of high-priority devices; when adjusting power, it can also be determined based on the priority list which devices need to adjust power first.

[0155] The embodiment of the present invention ensures that key equipment is given priority by sorting the equipment according to the comprehensive score, thereby improving the orderliness and scientificity of equipment management; allocating resources according to the priority list ensures that limited power resources are reasonably utilized, improves energy utilization efficiency, and optimizes resource allocation; high-priority equipment can be better protected, ensuring the smooth progress of key tasks, while improving the system's adaptability under different environmental conditions, and enhancing the system's reliability and adaptability; the priority list provides clear guidance for operators, simplifies complex decision-making processes, improves work efficiency, and simplifies the decision-making process.

[0156] Based on this, the present invention provides a specific embodiment, wherein the step 304 comprehensively evaluates all electrical devices by combining the importance score, energy efficiency score and flexibility score, and generates a comprehensive score using a multi-factor comprehensive evaluation model, specifically including the following steps:

[0157] Step 401: using a multi-factor comprehensive evaluation model, combined with a preset weight coefficient, weighting the importance score, energy efficiency score and flexibility score to calculate a basic comprehensive score for each electrical device;

[0158] In this step, the multi-factor comprehensive evaluation model combines the importance score, energy efficiency score and flexibility score, and weights these scores through the preset weight coefficient; the weight coefficient refers to setting different weights for each scoring item according to actual needs and expert opinions, for example, the importance score may account for 50% of the total score, the energy efficiency score accounts for 30%, and the flexibility score accounts for 20%. ; Weighted processing refers to multiplying the scores of each device by the corresponding weight coefficient, and then summing them to obtain the basic comprehensive score of each device; the basic comprehensive score refers to the score after weighted processing, which reflects the comprehensive performance of the device in multiple aspects;

[0159]

[0160] Among them, S k is the basic comprehensive score of the kth electrical equipment; I k is the importance score of the kth electrical equipment, ranging from 1 to 9, 1 represents the least important and 9 represents the most important; E k is the energy efficiency score of the kth electrical equipment, ranging from 1 to 9, 1 represents the highest energy consumption and 9 represents the lowest energy consumption; F k is the flexibility score of the kth power-consuming device, ranging from 1 to 9, with 1 being the least flexible and 9 being the most flexible; 1 is the weight coefficient of importance score; w 2 is the weight coefficient of energy efficiency score; w 3 is the weight coefficient of flexibility score; V k is the frequency score of the kth electrical equipment, ranging from 1 to 9, 1 represents the lowest frequency and 9 represents the highest frequency; T k is the maintenance cost score of the kth electrical equipment, ranging from 1 to 9, 1 means the lowest maintenance cost and 9 means the highest maintenance cost; U k is the environmental adaptability score of the k-th electrical equipment, with a score range of 1 to 9, 1 represents the worst environmental adaptability, and 9 represents the best environmental adaptability; k is the number of the electrical equipment, indicating the k-th electrical equipment; m is the total number of electrical equipment;

[0161] Among them, I j is the importance score of the jth electrical equipment, ranging from 1 to 9, 1 represents the least important and 9 represents the most important; E jis the energy efficiency score of the jth electrical equipment, ranging from 1 to 9, 1 represents the highest energy consumption and 9 represents the lowest energy consumption; F j is the flexibility score of the jth electrical equipment, ranging from 1 to 9, 1 being the least flexible and 9 being the most flexible; V j is the usage frequency score of the jth electrical equipment, ranging from 1 to 9, 1 represents the lowest usage frequency and 9 represents the highest usage frequency; T j is the maintenance cost score of the jth electrical equipment, ranging from 1 to 9, 1 means the lowest maintenance cost and 9 means the highest maintenance cost; U j is the environmental adaptability score of the j-th electrical equipment, the score range is 1 to 9, 1 represents the worst environmental adaptability, 9 represents the best environmental adaptability; j is the number of the electrical equipment, indicating the j-th electrical equipment, where j ranges from 1 to m.

[0162] Step 402: Based on the basic comprehensive score, all electrical devices are classified using a cluster analysis algorithm to form a plurality of device categories, wherein each device category represents a group of devices with similar characteristics;

[0163] In this step, cluster analysis algorithms (such as K-means, hierarchical clustering, etc.) are used to group devices according to their basic comprehensive scores and other characteristics (such as power consumption, working mode, etc.); device categories refer to multiple device categories formed as a result of cluster analysis, and the devices in each category have similar characteristics. For example, one category may be high-importance, low-energy consumption devices, and another category may be medium-importance, high-energy consumption devices.

[0164] Step 403: for each equipment category, the evaluation criteria are refined by using the hierarchical analysis method, the differences between the equipment within the equipment category are calculated, and the category-specific scoring criteria are generated. The scoring criteria include: the frequency of use of the equipment, the maintenance cost, and the environmental adaptability;

[0165] In this step, the AHP is used to further refine the evaluation criteria for each equipment category. The AHP breaks down complex problems into multiple levels by establishing a hierarchical structure and evaluates each level one by one. Scoring criteria refer to the generation of specific scoring criteria for each equipment category based on the results of the AHP. These criteria include the frequency of use, maintenance cost, and environmental adaptability of the equipment. Differences refer to the ability to more accurately evaluate the differences between equipment within a category through the AHP, ensuring that the scoring criteria can reflect the subtle differences between equipment.

[0166] Step 404: Combine the category-specific scoring criteria and use a decision tree algorithm to perform a secondary evaluation on each electrical device to generate a refined comprehensive score;

[0167] In this step, a decision tree algorithm is used to conduct a second evaluation of each device. The decision tree algorithm performs predictions and evaluations by constructing decision trees, and can handle complex nonlinear relationships. Combined with category-specific scoring criteria, the decision tree algorithm will re-evaluate each device and generate a refined comprehensive score that more accurately reflects the performance of the device in multiple aspects.

[0168] Step 405: optimizing the refined comprehensive score by using a genetic algorithm, finding the best score combination by simulating the process of natural selection and genetic variation, and obtaining a target comprehensive score;

[0169] In this step, a genetic algorithm is used to optimize the refined comprehensive score. The genetic algorithm simulates the process of natural selection and genetic mutation, and gradually optimizes the score combination through iterative selection, crossover and mutation operations. The genetic algorithm selects the optimal score combination in each generation, and generates new score combinations through crossover and mutation operations, and iterates continuously until the best score combination is found. The target comprehensive score refers to the target comprehensive score obtained after optimization by the genetic algorithm, which is the optimal score combination, reflecting the best performance of the equipment in multiple aspects.

[0170] The embodiment of the present invention improves the comprehensiveness and accuracy of the evaluation by using a multi-factor comprehensive evaluation model and hierarchical analysis method, combined with preset weight coefficients and specific scoring criteria; uses a cluster analysis algorithm to classify equipment, ensures the scientificity and rationality of equipment categories, helps subsequent refined management, and enhances the scientificity and rationality of equipment classification; uses a decision tree algorithm and a genetic algorithm to perform secondary evaluation and optimization on the equipment, and the generated refined comprehensive scores and target comprehensive scores are more detailed and accurate, which improves the detail and accuracy of the scores; the final target comprehensive score provides a scientific basis for resource allocation and management decisions, ensures that key equipment is given priority, improves the overall operating efficiency and reliability of the system, and optimizes resource allocation and management decisions.

[0171] Based on this, the present invention provides a specific embodiment. In step 403, for each device category, the hierarchical analysis method is used to refine the evaluation criteria, calculate the differences between devices within the device category, and generate category-specific scoring criteria. The scoring criteria include: the frequency of use, maintenance cost, and environmental adaptability of the device. Specifically, the following steps are included:

[0172] Step 501: for each equipment category, construct an evaluation index system using the hierarchical analysis method, wherein the evaluation index system includes: equipment usage frequency, maintenance cost, environmental adaptability, and equipment reliability;

[0173] In this step, for each equipment category, the analytic hierarchy process is used to construct an evaluation index system. The analytic hierarchy process breaks down complex problems into multiple levels by establishing a hierarchical structure and evaluates each level. The evaluation index system includes the following four main indicators:

[0174] Frequency of use: how often the device is used in daily operations;

[0175] Maintenance cost: the cost of equipment maintenance and upkeep;

[0176] Environmental adaptability: the ability of the equipment to adapt to different environmental conditions;

[0177] Reliability: the stability and failure rate of equipment during long-term operation;

[0178] Constructing a hierarchy means organizing these indicators into a hierarchy and clarifying the relationship and importance of each indicator.

[0179] Step 502: quantify each indicator in the evaluation indicator system using the hierarchical analysis method, calculate the relative weight of each indicator, and generate an indicator weight table;

[0180] In this step, quantifying the evaluation indicators means comparing each evaluation indicator pairwise through the analytic hierarchy process to determine their relative importance;

[0181] Calculating relative weights means calculating the relative weight of each indicator based on the results of pairwise comparisons, which is usually accomplished by solving eigenvectors and consistency tests;

[0182] Generating an indicator weight table means recording the relative weight of each indicator in the indicator weight table, for example, the weight of frequency of use is 0.3, the weight of maintenance cost is 0.2, the weight of environmental adaptability is 0.25, and the weight of reliability is 0.25.

[0183] Step 503: Based on the indicator weight table, evaluate the devices within each device category, calculate the differences in use frequency, maintenance cost, environmental adaptability and reliability of the devices, and generate a device difference report;

[0184] In this step, evaluating the equipment means evaluating the equipment within each equipment category according to the indicator weight table. Specifically, the score of each equipment in terms of frequency of use, maintenance cost, environmental adaptability and reliability is calculated;

[0185] Calculating differences means comparing the scores of different devices on different indicators and calculating the differences between them. For example, a device may score higher in frequency of use but lower in maintenance cost.

[0186] Generating a device diversity report means recording the scores of each device on different indicators and their differences in the device diversity report, which reflects the performance differences of each device in a specific category.

[0187] Step 504: Combine the device difference report and use a multi-criteria decision analysis method to comprehensively evaluate each device and generate category-specific scoring criteria, wherein the scoring criteria are used to reflect the relative advantages and disadvantages of the device in a specific category;

[0188] In this step, a multi-criteria decision analysis method (such as TOPS IS, AHP-MCDM, etc.) is used to comprehensively evaluate each device; the comprehensive evaluation refers to combining the data in the device difference report to comprehensively evaluate the performance of each device on different indicators, which usually involves multiplying the score of each indicator by its weight and then adding them up to get the total score;

[0189] Generating scoring criteria refers to generating category-specific scoring criteria based on the comprehensive evaluation results. These scoring criteria describe in detail the relative strengths and weaknesses of each device in a specific category. For example, a device may perform well in terms of usage frequency and reliability, but perform poorly in terms of maintenance cost.

[0190] The embodiment of the present invention constructs an evaluation index system through a hierarchical analysis method and calculates the relative weight of each index, thereby improving the comprehensiveness and scientificity of the evaluation; generates an equipment difference report by quantifying each index and calculating the difference between devices, thereby enhancing the meticulousness and accuracy of the equipment evaluation; comprehensively evaluates the equipment through a multi-criteria decision analysis method, generates category-specific scoring criteria, and optimizes resource allocation and management decisions; detailed scoring criteria and difference reports provide transparent information support for equipment management, making it easier for operators to understand and implement management decisions.

[0191] Based on this, the present invention provides a specific embodiment, wherein the step 104 uses adaptive control technology to dynamically adjust the output ratio of each energy source according to the adjusted device operation state, and combines the battery management system to monitor the charge and discharge state of the battery pack in real time to obtain an optimized energy output configuration, which specifically includes the following steps:

[0192] Step 601: Analyze the adjusted equipment operation status by using adaptive control technology, and generate a preliminary energy output plan in combination with the charge and discharge status of the battery management system;

[0193] In this step, adaptive control technology is used to monitor the operating status of the equipment in real time and dynamically adjust the control strategy based on this data;

[0194] The battery management system (BMS) provides information on the charge and discharge status of the battery pack, including remaining power, charge rate, discharge rate, etc.

[0195] The preliminary energy output plan refers to combining the equipment operation status and the battery charge and discharge status to generate a preliminary energy output plan, which takes into account the current energy demand and the available capacity of the battery.

[0196] Step 602: Optimize the preliminary energy output plan using a deep reinforcement learning algorithm, calculate energy conversion efficiency, battery health status, and energy change trend, and obtain an optimized energy output plan;

[0197] In this step, the deep reinforcement learning algorithm learns how to make optimal decisions in different situations to maximize long-term benefits (such as energy efficiency) through interaction with the environment;

[0198] Energy conversion efficiency refers to evaluating the efficiency of different energy conversion options and selecting the most efficient one;

[0199] Battery health refers to the current health of the battery, avoiding overcharging and discharging to extend battery life;

[0200] Energy trending refers to predicting future energy demand and supply to ensure the sustainability of plans;

[0201] The optimized energy output plan refers to a more optimized energy output plan generated through a deep reinforcement learning algorithm, which takes into account efficiency, health status and trends.

[0202] Step 603: Evaluate the optimized energy output plan using a predictive maintenance algorithm to generate maintenance recommendations for the battery pack;

[0203] In this step, the predictive maintenance algorithm analyzes historical data and current status, predicts possible battery problems, and makes maintenance recommendations.

[0204] Maintenance recommendations refer to generating specific maintenance recommendations based on the prediction results, such as replacing certain battery cells, adjusting charging strategies, etc.

[0205] Evaluation of the optimized energy output plan is used to ensure that the plan does not lead to premature battery failure or performance degradation.

[0206] Step 604: using a multi-objective optimization algorithm to comprehensively adjust the optimized energy output plan and the maintenance suggestion to obtain an adjusted energy output strategy;

[0207] In this step, a multi-objective optimization algorithm is used to simultaneously consider multiple objectives (such as energy efficiency, battery life, cost, etc.) to find the optimal balance point;

[0208] Comprehensive adjustment refers to combining the optimized energy output plan and maintenance recommendations to make comprehensive adjustments to ensure coordination among various goals;

[0209] The adjusted energy output strategy refers to generating an energy output strategy that is both efficient and reliable, taking into account both energy utilization and equipment maintenance.

[0210] Step 605: using a fuzzy logic control system to adjust the adjusted energy output strategy, and forming a target energy output configuration according to real-time environmental changes and changes in the equipment operating status;

[0211] In this step, uncertainty and fuzzy information are processed through fuzzy logic, so that the system can better cope with complex and dynamic environments;

[0212] Real-time environmental changes mean that the system will continuously monitor environmental changes, such as light intensity, wind speed, etc.

[0213] Changes in the operating status of the device means that the system will also monitor the real-time operating status of the device, such as power demand, temperature, etc.;

[0214] Target energy output configuration refers to dynamically adjusting the energy output strategy according to changes in the real-time environment and equipment status to form the final target energy output configuration.

[0215] Through adaptive control technology and deep reinforcement learning algorithms, the embodiments of the present invention enable the system to utilize energy more efficiently, reduce waste, and improve energy utilization efficiency; through predictive maintenance algorithms and multi-objective optimization algorithms, the system can better manage and maintain batteries and extend battery life; through fuzzy logic control systems, the system can better respond to real-time environmental changes and changes in equipment operating status, thereby enhancing the reliability and stability of the system.

[0216] Based on this, the present invention provides a specific embodiment, wherein the step 105 utilizes an auxiliary power generation unit, including a flexible solar panel integrated on the surface of the shelter, a micro wind turbine, and a power generation system based on mobile kinetic energy recovery of the shelter, to intelligently supplement the remaining power demand under the optimized energy output configuration, and forms a power supply plan in combination with the optimized energy output configuration, which specifically includes the following steps:

[0217] Step 701: using auxiliary power generation units to preliminarily evaluate the remaining power demand under the optimized energy output configuration, determine the power generation potential of each auxiliary power generation unit, and generate a power generation potential report of the auxiliary power generation unit;

[0218] In this step, the auxiliary power generation units include flexible solar panels, micro wind turbines and power generation systems based on mobile kinetic energy recovery in the cabin; the preliminary assessment refers to the assessment of the remaining power demand based on the optimized energy output configuration; the power generation potential refers to the assessment of the maximum power generation capacity of each auxiliary power generation unit under current conditions; the power generation potential report is used to record the power generation potential of each auxiliary power generation unit to provide a basis for subsequent scheduling.

[0219] Step 702: Based on the power generation potential report of the auxiliary power generation unit, an intelligent scheduling algorithm is used to schedule the flexible solar panels, micro wind turbines, and the power generation system based on mobile kinetic energy recovery of the cabin to generate a scheduling plan for the auxiliary power generation unit;

[0220] In this step, the intelligent scheduling algorithm is used to optimize the working arrangements of each auxiliary power generation unit based on the power generation potential report and the current energy demand; the scheduling plan refers to generating a detailed scheduling plan that specifies the working status and output power of each auxiliary power generation unit in different time periods.

[0221] Step 703: combining the scheduling plan of the auxiliary power generation unit, using the energy management system to dynamically adjust the output power of each auxiliary power generation unit, and generating an output configuration of the auxiliary power generation unit;

[0222] In this step, the energy management system (EMS) is used to monitor and manage the output power of each auxiliary power generation unit in real time; dynamic adjustment refers to dynamically adjusting the output power of each auxiliary power generation unit according to the scheduling plan and actual operation conditions; output configuration refers to generating a specific output configuration to ensure that each auxiliary power generation unit operates as planned.

[0223] Step 704: Optimizing the output configuration of the auxiliary power generation unit using a predictive control algorithm, calculating weather changes and cabin movement paths within a preset time, and generating an optimized output plan for the auxiliary power generation unit;

[0224] In this step, the predictive control algorithm is used to optimize the current output configuration by predicting future weather changes and the movement path of the cabin. Weather changes refer to meteorological factors such as light intensity and wind speed in the future. The movement path of the cabin refers to the movement path of the cabin, predicting possible kinetic energy recovery opportunities. The optimized output plan refers to generating a better output plan to ensure that the power demand is met within the preset time.

[0225] Step 705: Based on the optimized auxiliary power generation unit output plan and the optimized energy output configuration, all energy sources are uniformly managed and dispatched using an integrated energy management system to form a power supply plan;

[0226] In this step, the integrated energy management system (IEMS) is used to uniformly manage and dispatch all energy equipment, including the main energy supply system and auxiliary power generation units; unified management and dispatch refers to combining the optimized auxiliary power generation unit output plan with the main energy output configuration for overall optimization; the power supply plan refers to generating the final power supply plan to ensure the stable operation and efficient utilization of the system.

[0227] Through dynamic adjustment and predictive control, the system of the embodiment of the present invention can better cope with environmental changes and emergencies. Through reasonable scheduling and maintenance, the excessive use of equipment is reduced, the service life of the equipment is extended, and comprehensive optimization and efficient utilization of energy are achieved.

[0228] Figure 2 A structural schematic diagram of a mobile cabin continuous power supply system is provided for an embodiment of the present application, such as Figure 2 As shown, the system includes:

[0229] The detection module 21 is used to use the environment perception module to comprehensively detect the energy available type and the change trend of the environment in which the mobile cabin is located, and generate environmental energy data in combination with meteorological parameters, wherein the meteorological parameters include: temperature and humidity;

[0230] The analysis module 22 is used to use a machine learning algorithm to perform in-depth analysis on the environmental energy data and the real-time power demand of the mobile cabin, calculate the weather forecast information within a preset time, and predict the optimal energy combination mode;

[0231] The distribution module 23 is used to refer to the intelligent load distribution mechanism, and based on the optimal energy combination mode, finely distribute the working status of the electrical equipment in the mobile cabin to obtain the fine equipment operation status;

[0232] The adjustment module 24 is used to dynamically adjust the output ratio of each energy source according to the refined equipment operation status by using adaptive control technology, and monitor the charging and discharging status of the battery pack in real time in combination with the battery management system to obtain an optimized energy output configuration;

[0233] The supplementary module 25 is used to utilize auxiliary power generation units, including flexible solar panels integrated on the surface of the cabin, micro wind turbines, and a power generation system based on mobile kinetic energy recovery of the cabin, to intelligently supplement the remaining power demand under the optimized energy output configuration, and form a power supply plan in combination with the optimized energy output configuration.

[0234] Figure 2 The mobile cabin continuous power supply system can be implemented Figure 1The implementation principle and technical effect of the xx method described in the embodiment shown will not be repeated. The specific way in which each module and unit performs operations in the mobile cabin continuous power supply system in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0235] Figure 2 A mobile cabin continuous power supply system 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;

[0236] 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 .

[0237] The processing component 32 is used to use the environmental perception module to comprehensively detect the energy available types and changing trends of the environment in which the mobile cabin is located, and generate environmental energy data in combination with meteorological parameters; use machine learning algorithms to deeply analyze the environmental energy data and the real-time power demand of the mobile cabin, calculate the weather forecast information within a preset time, and predict the optimal energy combination mode; use an intelligent load distribution mechanism to finely adjust the working status of the electrical equipment in the mobile cabin based on the optimal energy combination mode to obtain the adjusted equipment operation status; use adaptive control technology to dynamically adjust the output ratio of each energy according to the adjusted equipment operation status, and at the same time combine with the battery management system to monitor the charging and discharging status of the battery pack in real time to obtain an optimized energy output configuration; use auxiliary power generation units, including flexible solar panels integrated on the surface of the cabin, micro wind turbines, and a power generation system based on the mobile kinetic energy recovery of the cabin, to intelligently supplement the remaining power demand under the optimized energy output configuration, and form a power supply plan in combination with the optimized energy output configuration.

[0238] The processing component 32 includes 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 can also be implemented by one or more application-specific integrated circuits (AICs), digital signal processors (DPs), digital signal processing devices (DPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0239] 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 (RAM), 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.

[0240] Computing devices also include other components, such as input / output interfaces, display components, and communication components.

[0241] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above-mentioned peripheral interface module can be an output device or an input device.

[0242] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0243] 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.

[0244] The embodiment of the present invention further provides a computer storage medium storing a computer program, which can achieve the above-mentioned Figure 1 A method and system for continuous power supply to a mobile cabin in the illustrated embodiment.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the 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 invention.

Claims

1. A method for continuous power supply of a mobile shelter, characterized in that: include: The environment sensing module is used to comprehensively detect the energy available types and change trends of the environment in which the mobile cabin is located, and the environmental energy data is generated in combination with meteorological parameters, wherein the meteorological parameters include: temperature and humidity; Use machine learning algorithms to conduct in-depth analysis of the environmental energy data and the real-time power demand of the mobile cabin, calculate weather forecast information within a preset time, and predict the optimal energy combination mode; By using the intelligent load distribution mechanism, based on the optimal energy combination mode, the working state of the electrical equipment in the mobile cabin is finely adjusted to obtain the adjusted equipment operating state; Adaptive control technology is used to dynamically adjust the output ratio of each energy source according to the adjusted equipment operation status, and the battery management system is combined to monitor the charge and discharge status of the battery pack in real time to obtain an optimized energy output configuration; Auxiliary power generation units, including flexible solar panels integrated on the surface of the cabin, micro wind turbines and a power generation system based on mobile kinetic energy recovery of the cabin, are used to intelligently supplement the remaining power demand under the optimized energy output configuration, and a power supply plan is formed in combination with the optimized energy output configuration.

2. The method according to claim 1, characterized in that: By using the intelligent load distribution mechanism, based on the optimal energy combination mode, the working state of the electrical equipment in the mobile cabin is finely adjusted to obtain the adjusted equipment operating state, including: Use the intelligent load distribution mechanism to comprehensively evaluate the current working status of all electrical equipment in the mobile cabin and generate an initial equipment operating status report; Analyze the initial equipment operation status report using a priority scheduling algorithm to determine the importance, energy consumption characteristics and adjustable range of each device, and generate a list of devices sorted by priority; Dynamically adjust the power output of the power-consuming equipment according to the equipment list sorted by priority using dynamic power adjustment technology to obtain the adjusted equipment operation status; The adjusted equipment operation state is optimized by using an energy-saving optimization algorithm, and the optimized equipment operation state is obtained by simulating the working modes of different equipment combinations; A real-time feedback control system is used to monitor the optimized equipment operation status in real time, and an adaptive learning algorithm is used to automatically adjust the equipment's operating parameters according to actual operation data and external environmental changes to form a target equipment operation status.

3. The method according to claim 2, characterized in that The dynamic power adjustment technology is used to dynamically adjust the power output of the power-consuming equipment according to the equipment list sorted by priority, and the adjusted equipment operation status is obtained, including: Using a priority scheduling algorithm to evaluate the importance of each power-consuming device in the initial device operation status report, calculate the functional importance of the device, the impact on business continuity, and the emergency response requirements, and obtain the importance score of the device; Based on the importance score of the device, combined with the energy consumption characteristics of each device, including average power consumption, peak power consumption and power consumption fluctuation range, the energy efficiency of the device is evaluated to obtain an energy efficiency score; Analyze the adjustable range of each device, including the minimum operating power, maximum operating power and power adjustment step, evaluate the flexibility of the device and obtain a flexibility score; Comprehensively evaluate all electrical equipment using a multi-factor comprehensive evaluation model by combining the importance score, energy efficiency score and flexibility score to generate a comprehensive score; All electrical devices are sorted according to the comprehensive scores to generate a priority-sorted device list.

4. The method according to claim 3, characterized in that: The importance score, energy efficiency score and flexibility score are combined to comprehensively evaluate all electrical equipment using a multi-factor comprehensive evaluation model to generate a comprehensive score, including: Using a multi-factor comprehensive evaluation model, combined with a preset weight coefficient, the importance score, energy efficiency score and flexibility score are weighted to calculate a basic comprehensive score for each electrical device; Based on the basic comprehensive score, all electrical devices are classified using a cluster analysis algorithm to form a plurality of device categories, wherein each device category represents a group of devices with similar characteristics; For each equipment category, the analytic hierarchy process is used to refine the evaluation criteria, calculate the differences between equipment within the equipment category, and generate category-specific scoring criteria, which include: equipment usage frequency, maintenance cost, and environmental adaptability; Combined with the category-specific scoring criteria, a decision tree algorithm is used to conduct a secondary evaluation of each electrical device to generate a refined comprehensive score; The refined comprehensive score is optimized by using a genetic algorithm, and the best score combination is found by simulating the process of natural selection and genetic variation to obtain a target comprehensive score.

5. The method according to claim 4, characterized in that For each equipment category, the analytic hierarchy process is used to refine the evaluation criteria, calculate the differences between equipment within the equipment category, and generate category-specific scoring criteria, which include: equipment usage frequency, maintenance cost, and environmental adaptability, including: For each equipment category, an evaluation index system is constructed using the hierarchical analysis method, which includes: equipment usage frequency, maintenance cost, environmental adaptability and equipment reliability; Quantify each indicator in the evaluation indicator system by using the hierarchical analysis method, calculate the relative weight of each indicator, and generate an indicator weight table; Based on the indicator weight table, the equipment within each equipment category is evaluated, the differences in the frequency of use, maintenance cost, environmental adaptability and reliability of the equipment are calculated, and an equipment difference report is generated; In combination with the device difference report, a multi-criteria decision analysis method is used to conduct a comprehensive evaluation of each device to generate category-specific scoring criteria, which are used to reflect the relative advantages and disadvantages of the device in a specific category.

6. The method according to claim 1, characterized in that Adaptive control technology is used to dynamically adjust the output ratio of each energy source according to the adjusted equipment operation status. At the same time, the battery management system is combined to monitor the charge and discharge status of the battery pack in real time to obtain an optimized energy output configuration, including: Analyzing the adjusted equipment operation status by using adaptive control technology, and generating a preliminary energy output plan in combination with the charging and discharging status of the battery management system; Optimizing the preliminary energy output plan using a deep reinforcement learning algorithm, calculating energy conversion efficiency, battery health status, and energy change trend, and obtaining an optimized energy output plan; evaluating the optimized energy output plan using a predictive maintenance algorithm to generate maintenance recommendations for the battery pack; Using a multi-objective optimization algorithm to comprehensively adjust the optimized energy output plan and the maintenance suggestion to obtain an adjusted energy output strategy; The adjusted energy output strategy is adjusted using a fuzzy logic control system to form a target energy output configuration according to real-time environmental changes and changes in the equipment operating status.

7. The method according to claim 1, characterized in that The auxiliary power generation unit, including the flexible solar panels integrated on the surface of the shelter, the micro wind turbine and the power generation system based on the mobile kinetic energy recovery of the shelter, is used to intelligently supplement the remaining power demand under the optimized energy output configuration. Combined with the optimized energy output configuration, a power supply solution is formed, including: Using auxiliary power generation units to conduct a preliminary assessment of the remaining power demand under the optimized energy output configuration, determine the power generation potential of each auxiliary power generation unit, and generate a power generation potential report of the auxiliary power generation unit; Based on the power generation potential report of the auxiliary power generation unit, an intelligent scheduling algorithm is used to schedule the flexible solar panels, micro wind turbines, and the power generation system based on mobile kinetic energy recovery of the cabin to generate a scheduling plan for the auxiliary power generation unit; In combination with the scheduling plan of the auxiliary power generation unit, the output power of each auxiliary power generation unit is dynamically adjusted by using the energy management system to generate the output configuration of the auxiliary power generation unit; The output configuration of the auxiliary power generation unit is optimized by using a predictive control algorithm, the weather changes and the movement path of the shelter within a preset time are calculated, and an optimized output plan of the auxiliary power generation unit is generated; According to the optimized auxiliary power generation unit output plan, combined with the optimized energy output configuration, all energy sources are uniformly managed and dispatched using an integrated energy management system to form a power supply plan.

8. A mobile shelter continuous power supply system, characterized in that: include: The detection module uses the environmental perception module to comprehensively detect the energy available types and change trends of the environment in which the mobile cabin is located, and generates environmental energy data in combination with meteorological parameters, wherein the meteorological parameters include: temperature and humidity; The analysis module uses a machine learning algorithm to conduct an in-depth analysis of the environmental energy data and the real-time power demand of the mobile cabin, calculate the weather forecast information within a preset time, and predict the optimal energy combination mode; The distribution module uses the intelligent load distribution mechanism to finely distribute the working status of the electrical equipment in the mobile cabin based on the optimal energy combination mode to obtain the fine equipment operation status; The adjustment module uses adaptive control technology to dynamically adjust the output ratio of each energy source according to the operating status of the refined equipment, and combines the battery management system to monitor the charging and discharging status of the battery pack in real time to obtain an optimized energy output configuration; The supplementary module utilizes auxiliary power generation units, including flexible solar panels integrated on the surface of the cabin, micro wind turbines, and a power generation system based on mobile kinetic energy recovery of the cabin, to intelligently supplement the remaining power demand under the optimized energy output configuration, and form a power supply plan in combination with the optimized energy output configuration.

9. A computing device, characterized in that It comprises 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 continuous power supply method for a mobile cabin as claimed in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a method for continuous power supply to a mobile cabin as claimed in any one of claims 1 to 7 is implemented.

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