Shelter emergency power handling method and system suitable for extreme environments

Abstract

The application provides a shelter emergency power supply processing method and system suitable for extreme environments. The extreme condition parameters of the environment where the shelter is located are detected and recorded, and an environment risk assessment report is generated. According to the environment risk assessment report, the load distribution ratio between the main power supply and the backup power supply is dynamically adjusted, the internal circuit of the power supply system is analyzed for thermal management, and an optimized power distribution scheme is generated. When the system detects an emergency in the shelter, an emergency response mechanism is immediately started, and an emergency power distribution adjustment record is generated. After the extreme environmental conditions are alleviated, a comprehensive power supply system health check is performed using the emergency power distribution adjustment record, and a detailed maintenance report is generated. The technical solution provided by the application improves the reliability and stability of the power supply system, improves the emergency response capability, ensures the power supply of critical equipment, reduces the maintenance cost, and improves the energy utilization efficiency.

Description

Technical Field

[0001] This application relates to the field of emergency power supply technology, and in particular to a method and system for emergency power supply in mobile cabins suitable for extreme environments. Background Technology

[0002] Makeshift hospitals need to provide continuous and reliable medical services in extreme environments (such as natural disasters). Under these conditions, the stability and reliability of the power system are crucial. Medical equipment, lighting systems, communication facilities, and other systems within the makeshift hospitals all require a stable power supply.

[0003] Existing mobile shelter power management systems typically employ traditional manual or semi-automatic methods to manage power distribution and emergency response. These systems adjust the load distribution ratio between primary and backup power sources using preset rules and simple algorithms. In emergencies, the system re-plans the power supply sequence according to preset emergency response plans, shutting down non-critical equipment to conserve power. Furthermore, some systems are equipped with basic thermal management functions to monitor temperature changes in the internal circuitry of the power system.

[0004] Most existing power management systems rely on preset rules and static data, failing to monitor and assess potential risks to the power system under extreme conditions in real time. This leads to the system's inability to make optimal decisions in a timely manner during emergencies, affecting the stability and reliability of the power system. In emergency situations, existing systems typically require manual intervention or rely on preset emergency response plans. This response method is slow and struggles to cope with complex and ever-changing emergency situations. Furthermore, preset plans may not be suitable for all types of emergency situations, resulting in ineffective emergency response. Existing systems also exhibit a rather crude approach to energy consumption management, lacking detailed energy consumption audits and refined management of online devices. This means that in emergency situations, the system may be unable to effectively identify and shut down high-energy-consuming non-critical equipment, wasting valuable power resources and affecting the normal operation of critical equipment. Summary of the Invention

[0005] This application provides a method and system for handling emergency power supply in mobile cabins under extreme environments, in order to solve the problem that existing technologies are unable to cope with complex and ever-changing emergency situations.

[0006] In a first aspect, embodiments of this application provide a method for handling emergency power supply in a mobile cabin under extreme environments, comprising the following steps:

[0007] By using environmental sensors to detect and record extreme condition parameters of the environment in which the shelter is located, and combining historical data with real-time monitoring data, the potential risks faced by the power system are assessed, and an environmental risk assessment report is generated.

[0008] Based on the environmental risk assessment report, an intelligent scheduling algorithm is used to optimize the power allocation strategy, dynamically adjust the load allocation ratio between the main power supply and the backup power supply, perform thermal management analysis on the internal circuit of the power system, and generate an optimized power allocation scheme.

[0009] When the system detects an emergency situation inside the shelter, it immediately activates the emergency response mechanism based on the optimized power distribution scheme, uses a priority allocation algorithm to re-plan the power supply order, conducts energy consumption audits on all online devices, identifies and shuts down high-energy-consuming non-critical devices, and generates power distribution adjustment records under emergency conditions.

[0010] Once the extreme environmental conditions have eased, a comprehensive power system health check is performed using the power distribution adjustment records from the emergency situation. Based on the results of the comprehensive power system health check, a detailed maintenance report is generated.

[0011] Optionally, the step of optimizing the power allocation strategy using an intelligent scheduling algorithm based on the environmental risk assessment report, dynamically adjusting the load allocation ratio between the main power supply and the backup power supply, performing thermal management analysis on the internal circuits of the power system, and generating an optimized power allocation scheme includes:

[0012] Using the extreme condition parameters provided in the environmental risk assessment report, combined with the historical power consumption data and real-time monitoring data of the makeshift hospital, these data are collected and integrated to generate a comprehensive dataset containing information on environmental conditions and power consumption trends.

[0013] Based on the comprehensive dataset, the potential risks that the power system may face under the current environmental conditions are assessed, and an environmental risk assessment report is generated.

[0014] Based on the aforementioned environmental risk assessment report, an intelligent scheduling algorithm is used, combined with the power demand forecast and power supply capacity assessment of the makeshift hospital, to optimize the load allocation ratio between the main power supply and the backup power supply, and generate a preliminary power allocation strategy.

[0015] For the aforementioned preliminary power distribution strategy, thermal management analysis is performed on the internal circuitry of the power system to evaluate the temperature changes within the power system under different load distribution conditions and generate a thermal management analysis report.

[0016] Based on the aforementioned thermal management analysis report, the preliminary power allocation strategy is further optimized to generate an optimized power allocation scheme.

[0017] Optionally, based on the environmental risk assessment report, an intelligent scheduling algorithm is used, combined with the power demand forecast and power supply capacity assessment of the makeshift hospital, to optimize the load allocation ratio between the main power supply and the backup power supply, generating a preliminary power allocation strategy, including:

[0018] In calculating the load allocation ratio L for each power source i Previously, it was necessary to collect and integrate environmental conditions, historical power consumption data, and real-time monitoring data to generate a comprehensive dataset; preprocess the data to assess the potential risks of the power system and generate an environmental risk assessment report; at the same time, assess the power demand and supply capacity of each power source, consider the periodic fluctuations in power source operating time, and provide data support for the calculation of load allocation ratio.

[0019] Load sharing ratio optimization formula:

[0020]

[0021] Among them, L i α is the load allocation ratio of the i-th power source; α is the power demand weighting coefficient, with a value between 0 and 1; D i This represents the electricity demand forecast for the i-th power source; β is the power supply capacity weighting coefficient, ranging from 0 to 1; S i This is the power supply capacity assessment of the i-th power source; γ is the risk weighting coefficient, ranging from 0 to 1; R i δ is the risk assessment value of the i-th power source; δ is the high energy consumption penalty coefficient, ranging from 0 to 1; H i This is the high-energy-consumption flag for the i-th power supply; it is 1 if the power supply is high-energy-consumption, and 0 otherwise; φ is the time fluctuation coefficient, ranging from 0 to 1; T i τ is the running time of the i-th power supply; τ is the time period parameter, which ranges from 0 to 1; N is the total number of power supplies;

[0022] After calculating the load distribution ratio L for each power source i Subsequently, based on the total electricity demand, a preliminary power allocation calculation is performed; demand fluctuation coefficients and risk adjustment coefficients are introduced to assess the probability of failure and operating costs, further optimizing the power allocation strategy and generating a preliminary power allocation strategy P. i ;

[0023] Preliminary power allocation strategy generation formula:

[0024]

[0025] Among them, P i This is the initial power allocation strategy for the i-th power source; L i η is the load allocation ratio of the i-th power source; T is the total electricity demand; η is the demand fluctuation coefficient, ranging from 0 to 1; D i This is the power demand forecast for the i-th power source; It is the average power demand of all power sources; σ Dθ is the standard deviation of electricity demand; θ is the risk adjustment factor, ranging from 0 to 1; R i ψ is the risk assessment value of the i-th power source; ψ is the failure probability weighting coefficient, ranging from 0 to 1; F i λ is the failure probability of the i-th power source; λ is the cost weighting coefficient, ranging from 0 to 1; C i L is the operating cost of the i-th power source; j R is the load distribution ratio of the j-th power source; j F is the risk assessment value of the j-th power source; j C is the failure probability of the j-th power supply; j It is the operating cost of the j-th power source.

[0026] By calculating the load allocation ratio L for each power source i and initial power distribution strategy P j By combining total power demand and various assessment factors, a final preliminary power allocation strategy is generated.

[0027] Optionally, the step of assessing the potential risks that the power system may face under current environmental conditions based on the comprehensive dataset and generating an environmental risk assessment report includes:

[0028] Using the comprehensive dataset, the data is cleaned and normalized to remove outliers and missing values, generating a preprocessed dataset.

[0029] Based on the preprocessed dataset, statistical analysis methods are used to perform correlation analysis on various indicators, identify features closely related to potential risks of the power system, and generate a list of highly correlated features.

[0030] Based on the list of highly correlated features, features with high correlation are selected as components of the input vector to construct a risk assessment model.

[0031] The risk assessment model is trained using the input vector, and its generalization ability is evaluated through cross-validation to generate the trained risk assessment model.

[0032] Based on the trained risk assessment model, the potential risks that the power system may face under the current environmental conditions are assessed, and a detailed environmental risk assessment report is generated.

[0033] Optionally, the preliminary power distribution strategy involves performing thermal management analysis on the internal circuitry of the power system, evaluating temperature changes within the power system under different load distribution conditions, and generating a thermal management analysis report, including:

[0034] Using the aforementioned preliminary power allocation strategy, operational data of the internal circuits of the power system under different load allocation conditions are collected to generate a load allocation dataset.

[0035] Based on the load distribution dataset and the design parameters of the internal circuit of the power system, a thermal management analysis model is constructed.

[0036] Using the load distribution dataset, the thermal management analysis model is calibrated and validated to generate a calibrated thermal management analysis model;

[0037] Based on the calibrated thermal management analysis model, the internal temperature changes of the power system under different load distribution conditions are simulated and analyzed to evaluate the thermal performance of the power system under various load distribution conditions and generate temperature change analysis results.

[0038] Based on the temperature change analysis results, a comprehensive evaluation of the thermal management performance of the power system under different load distribution conditions is conducted, and a thermal management analysis report is generated.

[0039] Optionally, when the system detects an emergency situation inside the shelter, based on the optimized power distribution scheme, it immediately activates the emergency response mechanism, uses a priority allocation algorithm to re-plan the power supply order, performs energy consumption audits on all online devices, identifies and shuts down high-energy-consuming non-critical devices, and generates a power distribution adjustment record under emergency conditions, including:

[0040] When the system detects an emergency situation inside the shelter, it immediately triggers the emergency response mechanism and generates an emergency situation detection report.

[0041] Based on the emergency situation detection report, immediately activate the emergency response mechanism, invoke the pre-prepared emergency response plan, and generate an emergency response activation record;

[0042] Using the optimized power allocation scheme and combined with the priority allocation algorithm, the power supply order is replanned to generate a replanned power supply order.

[0043] Perform energy consumption audits on all online devices, collect real-time energy consumption data for each device, and generate energy consumption audit reports.

[0044] Based on the energy consumption audit report, identify high-energy-consuming non-critical equipment and immediately shut down these devices, generating a high-energy-consuming non-critical equipment shutdown record;

[0045] By combining the replanned power supply sequence and the shutdown records of high-energy-consuming non-critical equipment, an emergency power allocation adjustment record is generated.

[0046] Optionally, the step of using the optimized power allocation scheme, combined with a priority allocation algorithm, to re-plan the power supply order and generate a re-planned power supply order includes:

[0047] Calculate the priority P of each device dev,i Previously, it was necessary to collect and preprocess energy consumption data in real time, assess the criticality and importance of equipment, identify high-energy-consuming non-critical equipment, and consider the periodic fluctuations in equipment operating time to provide the necessary data support and parameter basis for priority calculation.

[0048] Priority calculation formula:

[0049]

[0050] P dev,i It represents the priority of the i-th device; α is the energy consumption weighting coefficient, with a value between 0 and 1; E i β is the real-time energy consumption of the i-th device, obtained from the energy consumption audit report; β is the key weighting coefficient, ranging from 0 to 1; K i γ is the criticality score of the i-th device, ranging from 0 to 1; γ is the importance weight coefficient, ranging from 0 to 1; I i δ is the importance score of the i-th device, ranging from 0 to 1; δ is the high energy consumption penalty coefficient, ranging from 0 to 1; G i This is the high-energy-consumption flag for the i-th device; it is 1 if the device is a high-energy-consumption non-critical device, and 0 otherwise; φ is the time fluctuation coefficient, ranging from 0 to 1; O i is the running time of the i-th device; τ is a time period parameter, with a value between 0 and 1.

[0051] After calculating the priority P of each device dev,i Next, the equipment needs to be sorted, the system's energy consumption distribution and load allocation need to be evaluated, and the distance from the equipment to the power source and the probability of failure need to be considered to generate the power supply order weight S. dev,i Provide comprehensive evaluation criteria;

[0052] Formula for adjusting the power supply order:

[0053]

[0054] S dev,i : Power supply order weight of the i-th device; P dev,i : Priority of the i-th device; N: Total number of online devices, obtained from the energy consumption audit report; M: Number of high-energy-consuming non-critical devices, obtained from the high-energy-consuming non-critical device shutdown records; H k: Energy consumption of the k-th high-energy-consuming non-critical device, obtained from the energy consumption audit report; T: Total energy consumption of all online devices, obtained from the energy consumption audit report; η: Device distance weighting coefficient, ranging from 0 to 1; L i : Distance from the i-th device to the power source; θ: Distance attenuation factor, ranging from 0 to 1; ψ: Fault probability weighting coefficient, ranging from 0 to 1; F i P: The failure probability of the i-th device; dev,j : The priority of the j-th device; F j : The failure probability of the j-th device;

[0055] After calculating the power supply order weight S for each device dev,i Afterwards, the equipment needs to be reordered, the load distribution ratio of the main power supply and the backup power supply needs to be adjusted, simulation tests need to be conducted, and a new power supply sequence needs to be generated.

[0056] Optionally, the energy consumption audit of all online devices, collecting real-time energy consumption data for each device, and generating an energy consumption audit report includes:

[0057] By using energy consumption monitoring devices installed on various devices, energy consumption data of each online device is collected in real time to generate a real-time energy consumption dataset;

[0058] The real-time energy consumption dataset is cleaned and normalized to remove outliers and missing values, generating a preprocessed energy consumption dataset.

[0059] Based on the preprocessed energy consumption dataset, and combined with the equipment's operating status and working mode, an energy consumption audit model is constructed.

[0060] Using the energy consumption audit model, the real-time energy consumption data of each online device is analyzed to evaluate the energy consumption and energy efficiency of the device and generate energy consumption analysis results.

[0061] Based on the energy consumption analysis results, the energy consumption of all online devices is summarized, and an energy consumption audit report is generated.

[0062] Optionally, after the extreme environmental conditions are alleviated, a comprehensive power system health check is performed using the power distribution adjustment records under the emergency situation. Based on the results of the comprehensive power system health check, a detailed maintenance report is generated, including:

[0063] Using environmental sensors, the extreme condition parameters of the environment in which the shelter is located are continuously monitored. When the extreme environmental conditions are detected to have eased, an environmental condition easing report is generated.

[0064] Using the power distribution adjustment records under the emergency situation, all operational data of the power system during the emergency situation are collected to generate an emergency situation operation dataset.

[0065] Based on the emergency operation dataset, and combined with the normal operating parameters and historical maintenance records of the power system, a power system health check model is constructed.

[0066] Using the power system health check model, a comprehensive health check is performed on the power system to assess its operating status and potential problems during emergency situations and generate health check results.

[0067] Based on the health check results, a comprehensive analysis of the power system's health status is conducted, necessary maintenance recommendations and improvement measures are proposed, and a detailed maintenance report is generated.

[0068] Secondly, embodiments of this application provide a modular emergency power supply system suitable for use in extreme environments, comprising:

[0069] The assessment module is used to detect and record extreme condition parameters of the environment in which the shelter is located using environmental sensors. By combining historical data with real-time monitoring data, it assesses the potential risks faced by the power system and generates an environmental risk assessment report.

[0070] The adjustment module is used to optimize the power allocation strategy based on the environmental risk assessment report using an intelligent scheduling algorithm, dynamically adjust the load allocation ratio between the main power supply and the backup power supply, perform thermal management analysis on the internal circuit of the power system, and generate an optimized power allocation scheme.

[0071] The planning module is used to immediately activate the emergency response mechanism based on the optimized power distribution scheme when the system detects an emergency situation in the cabin. It uses a priority allocation algorithm to re-plan the power supply order, performs energy consumption audits on all online devices, identifies and shuts down high-energy-consuming non-critical devices, and generates power distribution adjustment records under emergency conditions.

[0072] The execution module is used to perform a comprehensive power system health check after the extreme environmental conditions have been alleviated, using the power distribution adjustment records under the emergency situation, and to generate a detailed maintenance report based on the results of the comprehensive power system health check.

[0073] In this embodiment, environmental sensors are used to detect and record extreme condition parameters of the environment in which the shelter is located. Combined with historical and real-time monitoring data, the potential risks faced by the power system are assessed, generating an environmental risk assessment report. Based on the environmental risk assessment report, an intelligent scheduling algorithm is used to optimize the power allocation strategy, dynamically adjusting the load distribution ratio between the main power supply and the backup power supply. Thermal management analysis is performed on the internal circuits of the power system to generate an optimized power allocation scheme. When the system detects an emergency situation in the shelter, based on the optimized power allocation scheme, an emergency response mechanism is immediately activated. A priority allocation algorithm is used to re-plan the power supply order, energy consumption audits are performed on all online devices, high-energy-consuming non-critical devices are identified and shut down, and an emergency power allocation adjustment record is generated. After the extreme environmental conditions are alleviated, a comprehensive power system health check is performed using the emergency power allocation adjustment record. Based on the results of the comprehensive power system health check, a detailed maintenance report is generated.

[0074] The technical solution of this application has the following beneficial effects:

[0075] By monitoring and recording extreme condition parameters of the environment in which the shelter is located in real time using environmental sensors, and combining historical data with real-time monitoring data, potential risks to the power system can be assessed in a timely manner, generating an environmental risk assessment report. This helps to take preventative measures in advance, reduce power failures caused by environmental factors, and improve the overall reliability of the system. Based on the environmental risk assessment report, intelligent scheduling algorithms are used to optimize the power distribution strategy and dynamically adjust the load distribution ratio between the main power supply and the backup power supply. This dynamic adjustment ensures that the power system can operate efficiently and stably under different environmental conditions, avoiding system failures caused by load imbalance. When the system detects an emergency situation in the shelter, it immediately activates the emergency response mechanism, uses a priority allocation algorithm to re-plan the power supply sequence, conducts energy consumption audits on all online equipment, and identifies and shuts down high-energy-consuming non-critical equipment. This can rapidly reduce power consumption, ensure the normal operation of critical equipment, and improve the system's emergency response and survivability. Through priority allocation algorithms, power supply to critical equipment can be prioritized in emergencies, ensuring the normal operation of important facilities such as medical equipment, thereby protecting patient safety and the continuity of medical services. After extreme environmental conditions subside, a comprehensive power system health check is performed using power allocation adjustment records from emergencies, generating detailed maintenance reports. This helps to promptly identify and repair potential problems, extend equipment lifespan, and reduce downtime and maintenance costs caused by sudden failures. By auditing the energy consumption of all online devices and identifying and shutting down high-energy-consuming non-critical equipment, overall energy consumption can be effectively reduced, energy efficiency improved, and operating costs reduced. The generated environmental risk assessment report, optimized power allocation scheme, power allocation adjustment records from emergencies, and detailed maintenance reports provide managers with comprehensive data support, helping them make more scientific and rational decisions and improve management efficiency. Through intelligent scheduling and priority allocation algorithms, the system can flexibly adjust according to different environmental conditions and emergencies, enhancing the system's adaptability and flexibility, and improving its ability to cope with various complex situations.

[0076] Furthermore, through real-time monitoring and historical data analysis, potential risks faced by the power system under current environmental conditions can be identified and assessed in a timely manner, thereby enabling preventative measures to reduce failures and improve the overall reliability of the system. Intelligent scheduling algorithms, combined with power demand forecasting and supply capacity assessment, dynamically adjust the load distribution ratio between the main and backup power sources, ensuring efficient operation of the power system under different environmental conditions and avoiding system failures caused by load imbalances. By conducting thermal management analysis on the internal circuits of the power system and assessing temperature changes under different load distribution conditions, the power distribution strategy can be further optimized, ensuring the system maintains optimal performance under various operating conditions and enhancing its adaptability and flexibility. Thermal management analysis helps control the internal temperature of the power system, preventing equipment damage due to overheating, thereby extending equipment lifespan and reducing maintenance costs. The optimized power distribution strategy can allocate power resources more rationally, reducing unnecessary energy consumption, improving energy efficiency, and lowering operating costs. The generated environmental risk assessment report and thermal management analysis report provide comprehensive data support for managers, helping them make more scientific and rational decisions and improve management efficiency. Through intelligent scheduling algorithms and thermal management analysis, a stable power supply is ensured for critical equipment even in emergency situations, guaranteeing the continuity of medical services and patient safety in makeshift hospitals.

[0077] Furthermore, the system can rapidly detect and respond to emergencies, immediately activating the emergency response mechanism to ensure effective measures are taken in the shortest possible time, minimizing the impact of emergencies on makeshift hospitals. By re-planning the power supply sequence through a priority allocation algorithm, it ensures a stable power supply for critical medical equipment and infrastructure during emergencies, guaranteeing the continuity of medical services and patient safety. Through energy consumption audits of all online devices, identifying and shutting down high-energy-consuming non-critical equipment significantly reduces overall energy consumption, improves energy efficiency, and extends the lifespan of backup power. The optimized power allocation scheme, combined with the priority allocation algorithm, can dynamically adjust the power supply during emergencies, ensuring stable system operation under different conditions and reducing failures caused by insufficient power. Detailed records are generated, including emergency situation detection reports, emergency response activation records, energy consumption audit reports, and high-energy-consuming non-critical equipment shutdown records, providing a basis for subsequent analysis and improvement, and helping managers better understand and evaluate the effectiveness of emergency responses. Through intelligent scheduling and priority allocation, the system can flexibly adjust power supply strategies according to different emergency situations, enhancing the system's adaptability and flexibility, and improving its ability to cope with complex situations. By promptly shutting down high-energy-consuming non-critical equipment, unnecessary power consumption is reduced, maintenance costs are lowered, and equipment lifespan is extended.

[0078] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0079] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0080] Figure 1 A flowchart illustrating a method for handling emergency power supply in a mobile cabin under extreme environments, provided in this application embodiment;

[0081] Figure 2 A schematic diagram of a modular emergency power supply system suitable for extreme environments is provided in this application embodiment;

[0082] Figure 3 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application. Detailed Implementation

[0083] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0084] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0085] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0086] Figure 1 This application provides a flowchart of a method for handling emergency power supply in a mobile cabin under extreme environments, as illustrated in the embodiments of this application. Figure 1 As shown, the method includes:

[0087] 101. Use environmental sensors to detect and record extreme condition parameters of the environment in which the shelter is located, combine historical data with real-time monitoring data, assess the potential risks faced by the power system, and generate an environmental risk assessment report.

[0088] Environmental sensors: used to detect and record various parameters of the environment in which the shelter is located, such as temperature, humidity, air pressure, wind speed, etc.

[0089] Extreme condition parameters: These refer to environmental parameters that may affect the power supply system under extreme conditions, such as high temperature, low temperature, high humidity, and strong wind.

[0090] Historical data: Data records of the environment in which the mobile cabin was located over a period of time, including environmental parameters and the operating status of the power system.

[0091] Real-time monitoring data: Real-time data on the environment in which the shelter is located at the current moment, used to dynamically assess the status of the power system.

[0092] Environmental Risk Assessment Report: Based on historical and real-time monitoring data, assess the potential risks that the power system may face under current environmental conditions and generate a detailed report.

[0093] Brief explanation of the solution process:

[0094] The extreme condition parameters of the environment in which the shelter is located are collected in real time by environmental sensors; the real-time monitoring data is integrated with historical data to form a comprehensive dataset; the comprehensive dataset is used to assess the potential risks that the power system may face under the current environmental conditions; and an environmental risk assessment report is generated based on the risk assessment results to provide a basis for subsequent steps.

[0095] Assume the makeshift hospital is located in an area frequently affected by extreme weather (such as typhoons). Environmental sensors continuously monitor the following data:

[0096] Temperature: 35℃

[0097] Humidity: 90%

[0098] Wind speed: 100km / h

[0099] Meanwhile, historical data shows that the power system has experienced multiple failures under similar extreme weather conditions in recent years. After integrating this data, the system assessed that the power system faces a high risk under current environmental conditions, particularly due to potential short circuits and equipment damage caused by high humidity and strong winds. Ultimately, the system generated a detailed environmental risk assessment report, indicating the need for enhanced waterproofing and circuit protection, and recommending the addition of backup power to cope with possible power outages.

[0100] 102. Based on the aforementioned environmental risk assessment report, an intelligent scheduling algorithm is used to optimize the power distribution strategy, dynamically adjust the load distribution ratio between the main power supply and the backup power supply, perform thermal management analysis on the internal circuits of the power system, and generate an optimized power distribution scheme.

[0101] Intelligent scheduling algorithm is an algorithm based on artificial intelligence and optimization theory, used to dynamically adjust power distribution strategy; load distribution ratio refers to the proportion of power load allocated between the main power supply and the backup power supply; thermal management analysis monitors and analyzes the temperature changes of the internal circuits of the power system to ensure that the circuits are not damaged due to overheating; the optimized power distribution scheme is the power distribution strategy optimized by the intelligent scheduling algorithm to ensure the efficient operation of the power system under different environmental conditions.

[0102] Based on the environmental risk assessment report, the specific risks faced by the power system under the current environmental conditions are analyzed. An intelligent scheduling algorithm is adopted in combination with power demand forecasting and supply capacity assessment to dynamically adjust the load distribution ratio between the main power supply and the backup power supply. Thermal management analysis is also performed on the internal circuits of the power system to evaluate temperature changes under different load distribution conditions. Based on the results of the thermal management analysis, the power distribution strategy is further optimized to generate an optimized power distribution scheme.

[0103] Optionally, the step of optimizing the power allocation strategy using an intelligent scheduling algorithm based on the environmental risk assessment report, dynamically adjusting the load allocation ratio between the main power supply and the backup power supply, performing thermal management analysis on the internal circuits of the power system, and generating an optimized power allocation scheme includes:

[0104] Using the extreme condition parameters provided in the environmental risk assessment report, combined with historical power consumption data and real-time monitoring data from the makeshift hospital, this data is collected and integrated to generate a comprehensive dataset containing information on environmental conditions and power consumption trends. Based on this comprehensive dataset, the potential risks that the power system may face under the current environmental conditions are assessed, generating an environmental risk assessment report. Based on this environmental risk assessment report, an intelligent scheduling algorithm is used, combined with power demand forecasting and power supply capacity assessment of the makeshift hospital, to optimize the load allocation ratio between the main power supply and the backup power supply, generating a preliminary power allocation strategy. For this preliminary power allocation strategy, thermal management analysis is performed on the internal circuits of the power system to assess temperature changes under different load allocation conditions, generating a thermal management analysis report. Based on this thermal management analysis report, the preliminary power allocation strategy is further optimized to generate an optimized power allocation scheme.

[0105] The step of assessing the potential risks that the power system may face under current environmental conditions based on the comprehensive dataset and generating an environmental risk assessment report includes:

[0106] Using the comprehensive dataset, the data is cleaned and normalized to remove outliers and missing values, generating a preprocessed dataset. Based on the preprocessed dataset, statistical analysis methods are used to perform correlation analysis on various indicators, identifying features closely related to potential risks of the power system and generating a list of highly correlated features. Based on the list of highly correlated features, features with high correlation are selected as components of the input vector to construct a risk assessment model. The risk assessment model is trained using the input vector, and its generalization ability is evaluated through cross-validation to generate a trained risk assessment model. Based on the trained risk assessment model, the potential risks that the power system may face under current environmental conditions are assessed, generating a detailed environmental risk assessment report.

[0107] The preliminary power distribution strategy involves performing thermal management analysis on the internal circuitry of the power system, evaluating temperature changes within the power system under different load distribution conditions, and generating a thermal management analysis report, including:

[0108] Using the preliminary power allocation strategy, operational data of the internal circuits of the power system under different load allocation conditions are collected to generate a load allocation dataset. Based on the load allocation dataset and the design parameters of the internal circuits of the power system, a thermal management analysis model is constructed. Using the load allocation dataset, the thermal management analysis model is calibrated and verified to generate a calibrated thermal management analysis model. Based on the calibrated thermal management analysis model, the temperature changes inside the power system under different load allocation conditions are simulated and analyzed to evaluate the thermal performance of the power system under various load allocation conditions and generate temperature change analysis results. Based on the temperature change analysis results, the thermal management performance of the power system under different load allocation conditions is comprehensively evaluated, and a thermal management analysis report is generated.

[0109] Suppose a makeshift hospital is located in an area frequently affected by extreme weather (such as high temperature and high humidity). The hospital needs to ensure the stability and reliability of its power system under extreme conditions to guarantee the normal operation of medical equipment.

[0110] First, temperature, humidity, and wind speed sensors are deployed to monitor and record extreme environmental parameters in the makeshift hospital in real time. Power consumption data from the makeshift hospital over the past year is collected, including electricity consumption at different times and equipment operating status. Current power consumption data is collected in real time through smart meters and a monitoring system. This data is then integrated and processed to generate a comprehensive dataset containing information on environmental conditions and power consumption trends.

[0111] Secondly, the comprehensive dataset is cleaned and normalized to remove outliers and missing values, generating a preprocessed dataset. Statistical analysis methods are used to perform correlation analysis on various indicators, identifying features closely related to potential risks to the power system, such as temperature, humidity, and wind speed. Highly correlated features are selected as components of the input vector to construct a risk assessment model. For example, machine learning algorithms such as Support Vector Machine (SVM) or Random Forest are used. The risk assessment model is trained using the preprocessed dataset, and its generalization ability is evaluated through cross-validation, generating a trained risk assessment model. Based on the trained risk assessment model, the potential risks that the power system may face under current environmental conditions are assessed, generating a detailed environmental risk assessment report.

[0112] Furthermore, a genetic algorithm is employed, combined with power demand forecasting and power supply capacity assessment for the makeshift hospital, to dynamically adjust the load allocation ratio between the main power source and the backup power source, generating a preliminary power allocation strategy. Time series analysis methods (such as the ARIMA model) are used to predict power demand over a future period. The power supply capacity of the main power source and the backup power source is evaluated, considering their maximum output power and stability. Based on the results of the intelligent scheduling algorithm, a preliminary power allocation strategy is generated to ensure the efficient operation of the power system under different environmental conditions.

[0113] Furthermore, utilizing a preliminary power distribution strategy, operational data of the power system's internal circuits under different load distribution conditions are collected to generate a load distribution dataset. Combined with the design parameters of the power system's internal circuits (such as resistance, capacitance, and heat sinks), a thermal management analysis model is constructed. Finite element analysis (FEA) or computational fluid dynamics (CFD) methods can be used. The thermal management analysis model is calibrated and validated using the load distribution dataset, generating a calibrated thermal management analysis model. Based on the calibrated thermal management analysis model, the temperature changes within the power system under different load distribution conditions are simulated and analyzed to evaluate the thermal performance of the power system under various load distribution conditions. Based on the temperature change analysis results, a comprehensive evaluation of the power system's thermal management performance under different load distribution conditions is conducted, generating a thermal management analysis report.

[0114] Finally, based on the thermal management analysis report, the initial power distribution strategy was further optimized to generate an optimized power distribution scheme. For example, if it is found that the circuit temperature is too high under certain load distribution conditions, the load distribution ratio can be adjusted to reduce the power distribution in high-load areas and increase the power distribution in low-load areas to balance the overall temperature distribution.

[0115] Through the above steps, makeshift hospitals can ensure the stability and reliability of their power systems in extreme environments, improve emergency response efficiency, and guarantee the normal operation of critical medical equipment.

[0116] This application considers that, under extreme environments, the power system of makeshift hospitals needs to operate efficiently and stably to ensure the normal operation of medical equipment and other critical facilities. To achieve this goal, it is necessary to optimize the load distribution ratio between the main power supply and the backup power supply and generate a preliminary power allocation strategy. By comprehensively considering factors such as environmental conditions, historical power consumption data, real-time monitoring data, power demand forecasting, power supply capacity assessment, and risk assessment, a more reasonable power allocation scheme can be generated.

[0117] Optionally, based on the environmental risk assessment report, an intelligent scheduling algorithm is used, combined with the power demand forecast and power supply capacity assessment of the makeshift hospital, to optimize the load allocation ratio between the main power supply and the backup power supply, generating a preliminary power allocation strategy, including:

[0118] In calculating the load allocation ratio L for each power source i Previously, it was necessary to collect and integrate environmental conditions, historical power consumption data, and real-time monitoring data to generate a comprehensive dataset; preprocess the data to assess the potential risks of the power system and generate an environmental risk assessment report; at the same time, assess the power demand and supply capacity of each power source, consider the periodic fluctuations in power source operating time, and provide data support for the calculation of load allocation ratio.

[0119] Load sharing ratio optimization formula:

[0120]

[0121] Among them, L i α is the load allocation ratio of the i-th power source; α is the power demand weighting coefficient, with a value between 0 and 1; D i This represents the electricity demand forecast for the i-th power source; β is the power supply capacity weighting coefficient, ranging from 0 to 1; S i This is the power supply capacity assessment of the i-th power source; γ is the risk weighting coefficient, ranging from 0 to 1; R i δ is the risk assessment value of the i-th power source; δ is the high energy consumption penalty coefficient, ranging from 0 to 1; H i This is the high-energy-consumption flag for the i-th power supply; it is 1 if the power supply is high-energy-consumption, and 0 otherwise; φ is the time fluctuation coefficient, ranging from 0 to 1; T i τ is the running time of the i-th power supply; τ is the time period parameter, ranging from 0 to 1; N is the total number of power supplies;

[0122] After calculating the load distribution ratio L for each power source i Subsequently, based on the total electricity demand, a preliminary power allocation calculation is performed; demand fluctuation coefficients and risk adjustment coefficients are introduced to assess the probability of failure and operating costs, further optimizing the power allocation strategy and generating a preliminary power allocation strategy P. i ;

[0123] Preliminary power allocation strategy generation formula:

[0124]

[0125] Among them, P i This is the initial power allocation strategy for the i-th power source; L i η is the load allocation ratio of the i-th power source; T is the total electricity demand; η is the demand fluctuation coefficient, ranging from 0 to 1; D i This is the power demand forecast for the i-th power source; It is the average power demand of all power sources; σ D θ is the standard deviation of electricity demand; θ is the risk adjustment factor, ranging from 0 to 1; R i ψ is the risk assessment value of the i-th power source; ψ is the failure probability weighting coefficient, ranging from 0 to 1; F i λ is the failure probability of the i-th power source; λ is the cost weighting coefficient, ranging from 0 to 1; C i L is the operating cost of the i-th power source; j R is the load distribution ratio of the j-th power source; j F is the risk assessment value of the j-th power source; j C is the failure probability of the j-th power supply; j It is the operating cost of the j-th power source.

[0126] By calculating the load allocation ratio L for each power source i and initial power distribution strategy P i By combining total power demand and various assessment factors, a final preliminary power allocation strategy is generated.

[0127] This scheme aims to comprehensively consider multiple factors, including electricity demand, supply capacity, risk assessment, high energy consumption penalty, and time fluctuations, to generate the optimal load allocation ratio. By introducing demand fluctuation coefficient, risk adjustment coefficient, failure probability weighting coefficient, and cost weighting coefficient, the power allocation strategy can be dynamically adjusted according to the actual situation, thereby improving the system's adaptability and reliability.

[0128] The following is a brief introduction to the design rationale behind each term of the formula:

[0129]

[0130] The design aims to comprehensively consider multiple factors to calculate the load allocation ratio L for each power source. i These factors include electricity demand, power supply capacity, risk assessment, high energy consumption penalties, and time fluctuations. A weighted summation method can more comprehensively reflect the overall performance of each power source under current environmental conditions.

[0131] It is a weighted sum of all power sources, ensuring that the sum of the load distribution ratios of each power source is 1. This guarantees the balance and rationality of the total load distribution.

[0132] The following is a brief introduction to how the parameters of this formula are obtained:

[0133] Electricity demand forecast D i Predicting electricity demand over a future period using historical data and time series analysis methods (such as the ARIMA model); assessing electricity supply capacity. i Based on the equipment's maximum output power and stability assessment; risk assessment value R i Based on environmental risk assessment reports and historical data, statistical analysis methods (such as correlation analysis) were used to derive the high energy consumption indicator H. i The value is set according to the power supply's energy consumption characteristics; it is 1 if the power supply is high-energy-consuming, and 0 otherwise; the running time T. i Data is collected in real time through sensors and monitoring systems; the time period parameter τ is usually set according to the actual situation, and the value ranges from 0 to 1; weighting coefficients α, β, γ, δ, φ: these coefficients are usually set according to experience and actual needs, and the value ranges from 0 to 1.

[0134] The following is a brief introduction to the design rationale behind each term of the formula:

[0135]

[0136] The product of load allocation ratio and total electricity demand: This reflects the contribution of each power source to the total electricity demand. It is calculated by assigning a load distribution ratio L to each power source. i Multiplying by the total power demand T and dividing by the sum of the load distribution ratios of all power sources ensures the balance and rationality of load distribution.

[0137] Demand volatility coefficient: This section considers fluctuations in electricity demand. By introducing a demand fluctuation coefficient η and an exponential function, power allocation strategies can be adjusted to address changes in electricity demand. The exponential function is used to smooth the impact of demand fluctuations.

[0138] Risk adjustment factor: This factor takes into account the risk assessment value of the power supply. By introducing a risk adjustment factor θ and the ratio of the risk assessment value, the allocation ratio of high-risk power supplies can be reduced, thereby improving the reliability of the system.

[0139] Failure probability weighting coefficient: This factor takes into account the probability of power supply failure. By introducing a failure probability weighting coefficient ψ and the ratio of failure probabilities, the power allocation strategy can be adjusted to reduce the impact of failures on the system.

[0140] Cost weighting coefficient: This factor takes into account the operating cost of the power supply. By introducing a cost weighting coefficient λ and the ratio of operating costs, the allocation ratio of high-cost power supplies can be reduced, thereby improving the system's economic efficiency.

[0141] The following is a brief introduction to how the parameters of this formula are obtained:

[0142] L i This represents the load allocation ratio calculated earlier; T represents the total power demand, which is obtained by summing the power demand forecasts of all equipment. η represents the sum of the load distribution ratios of all power sources; η represents the demand fluctuation coefficient, which ranges from 0 to 1 and is set according to actual conditions; D i This represents the power demand forecast for the i-th power source. This represents the average power demand of all power sources, calculated through statistical analysis; σ D The standard deviation of electricity demand is calculated through statistical analysis; θ represents the risk adjustment factor, which ranges from 0 to 1 and is set according to actual conditions; R i This represents the risk assessment value of the i-th power source, which is derived using statistical analysis methods (such as correlation analysis) based on the environmental risk assessment report and historical data. This represents the sum of risk assessment values ​​for all power sources; ψ represents the failure probability weighting coefficient, ranging from 0 to 1, set according to actual conditions; F i This represents the failure probability of the i-th power supply, calculated based on historical failure data and equipment maintenance records; λ represents the sum of the failure probabilities of all power sources; λ represents the cost weighting coefficient, which ranges from 0 to 1 and is set according to the actual situation; C i This represents the operating cost of the i-th power supply, calculated based on the equipment's operating cost data; This represents the sum of the operating costs of all power sources.

[0143] Suppose a makeshift hospital has three power sources (main power source 1, backup power source 2, and backup power source 3). The system needs to optimize power allocation strategies under extreme conditions to ensure the normal operation of critical medical equipment. The system detects current environmental conditions through environmental sensors and combines historical power consumption data with real-time monitoring data to generate a comprehensive dataset. Next, an intelligent scheduling algorithm is used to optimize the load distribution ratio between the main and backup power sources, generating a preliminary power allocation strategy.

[0144] Data preparation:

[0145] Total power sources N = 3; Power demand forecast D1 = 500kW, D2 = 400kW, D3 = 300kW; Power supply capacity assessment S1 = 700kW, S2 = 600kW, S3 = 500kW; Risk assessment values ​​R1 = 0.8, R2 = 0.6, R3 = 0.4; High energy consumption indicators H1 = 1, H2 = 0, H3 = 0; Operating time T1 = 10 hours, T2 = 8 hours, T3 = 6 hours; Total power demand T = 1200kW; Average power demand Standard deviation of electricity demand σ D =100kW; Failure probability F1=0.05, F2=0.03, F3=0.02; Operating cost C1=1000 yuan / hour, C2=900 yuan / hour, C3=800 yuan / hour;

[0146] Parameter settings:

[0147] Electricity demand weighting coefficient α = 0.4; electricity supply capacity weighting coefficient β = 0.3; risk weighting coefficient γ = 0.2; high energy consumption penalty coefficient δ = 0.1; time fluctuation coefficient φ = 0.05; time cycle parameter τ = 1 hour; demand fluctuation coefficient η = 0.1; risk adjustment coefficient θ = 0.1; failure probability weighting coefficient ψ = 0.1; cost weighting coefficient λ = 0.1;

[0148] Calculate the load distribution ratio:

[0149]

[0150] For power supply 1:

[0151]

[0152] For power supply 2:

[0153]

[0154] For power supply 3:

[0155]

[0156] Calculate the initial power allocation strategy:

[0157]

[0158] For power supply 1:

[0159]

[0160] P1=482.4+0.1·exp(-1)-0.1·0.444+0.1·0.5-0.1·0.357

[0161] P1=482.4+0.1·0.368-0.044+0.05-0.036

[0162] P1≈482.74

[0163] For power supply 2:

[0164]

[0165] P2=399.6+0.1·exp(0)-0.1·0.333+0.1·0.3-0.1·0.321

[0166] P2=399.6+0.1·1-0.033+0.03-0.032

[0167] P2≈400.24

[0168] For power supply 3:

[0169]

[0170] P3=318+0.1·exp(1)-0.1·0.222+0.1·0.2-0.1·0.286

[0171] P3=318+0.1·2.718-0.022+0.02-0.029

[0172] P3≈320.06

[0173] The initial power allocation strategy of power source 1 is P1≈482.74kW, indicating that it will bear the largest load because its power demand is high and its supply capacity is strong.

[0174] The initial power allocation strategy of power source 2 is P2≈400.24kW, indicating that it will take on the second highest load because its power demand is moderate and its supply capacity is strong.

[0175] The initial power allocation strategy of power source 3 is P3≈320.06kW, indicating that it will take on the minimum load because its power demand is low and its supply capacity is weak.

[0176] Assume a threshold of 400kW is set to determine whether a power source is the primary power source. According to the calculation results, the initial power allocation strategies of power sources 1 and 2 both exceed 400kW, so they will serve as primary power sources; while the initial power allocation strategy of power source 3 is below 400kW, so it will serve as an auxiliary power source.

[0177] Through the above calculations, we obtained a preliminary power allocation strategy for each power source, ensuring the efficient and stable operation of the power system under extreme conditions.

[0178] 103. When the system detects an emergency situation in the shelter, based on the optimized power distribution scheme, the emergency response mechanism is immediately activated, the power supply order is re-planned using the priority allocation algorithm, energy consumption audits are conducted on all online devices, high-energy-consuming non-critical devices are identified and shut down, and power distribution adjustment records are generated in emergency situations.

[0179] An emergency response mechanism refers to a series of automatic or semi-automatic response measures that are immediately activated when an emergency situation (such as a power outage or equipment failure) is detected within the shelter. A priority allocation algorithm is an algorithm that re-plans the power supply order based on the criticality and importance of equipment. Energy consumption auditing is the process of monitoring and evaluating the real-time energy consumption of all online equipment. High-energy-consuming non-critical equipment refers to equipment that consumes a lot of power but is not essential for critical tasks. Emergency power allocation adjustment records are documents that record all operations and results of power allocation adjustments during emergency situations.

[0180] When the system detects an emergency situation inside the shelter, it immediately activates the emergency response mechanism and invokes the pre-prepared emergency response plan; it uses a priority allocation algorithm to re-plan the power supply order to ensure that critical equipment receives power first; it conducts energy consumption audits on all online equipment, collects real-time energy consumption data, identifies high-energy-consuming non-critical equipment, and immediately shuts down these devices to reduce power consumption; finally, it generates a power allocation adjustment record under emergency conditions, detailing all operations and results.

[0181] Optionally, when the system detects an emergency situation inside the shelter, based on the optimized power distribution scheme, it immediately activates the emergency response mechanism, uses a priority allocation algorithm to re-plan the power supply order, performs energy consumption audits on all online devices, identifies and shuts down high-energy-consuming non-critical devices, and generates a power distribution adjustment record under emergency conditions, including:

[0182] When the system detects an emergency situation within the shelter, it immediately triggers the emergency response mechanism and generates an emergency situation detection report. Based on the emergency situation detection report, the system immediately activates the emergency response mechanism, invokes the pre-prepared emergency response plan, and generates an emergency response activation record. Using the optimized power distribution scheme and a priority allocation algorithm, the power supply order is replanned, generating a replanned power supply order. Energy consumption is audited for all online devices, collecting real-time energy consumption data for each device and generating an energy consumption audit report. Based on the energy consumption audit report, high-energy-consuming non-critical devices are identified and immediately shut down, generating a high-energy-consuming non-critical device shutdown record. Combining the replanned power supply order and the high-energy-consuming non-critical device shutdown record, an emergency power distribution adjustment record is generated.

[0183] The aforementioned energy consumption audit of all online devices, collecting real-time energy consumption data for each device and generating an energy consumption audit report, includes:

[0184] Energy consumption monitoring devices installed on various devices are used to collect energy consumption data from each online device in real time, generating a real-time energy consumption dataset. This dataset is then cleaned and normalized to remove outliers and missing values, resulting in a preprocessed energy consumption dataset. Based on this preprocessed dataset and the device's operating status and mode, an energy consumption audit model is constructed. Using this model, the real-time energy consumption data of each online device is analyzed to assess its energy consumption and efficiency, generating energy consumption analysis results. Finally, based on these results, the energy consumption of all online devices is summarized, generating an energy consumption audit report.

[0185] Suppose a makeshift hospital is suddenly hit by a severe storm at night, causing damage to its main power supply. The backup power supply needs to be activated immediately to ensure the normal operation of critical medical equipment. At this time, the system detects the emergency and immediately activates the emergency response mechanism.

[0186] First, the system detects a main power failure caused by a strong storm through environmental sensors and generates an emergency situation detection report, which includes the time of failure, the type of failure (such as damage to the main power supply section), and the scope of impact.

[0187] Then, the system immediately activates the emergency response mechanism, calls up the pre-prepared emergency response plan, generates an emergency response activation record, and records the activation time, plan number, and personnel responsible for implementation.

[0188] Furthermore, the system utilizes the optimized power allocation scheme, combined with a priority allocation algorithm, to re-plan the power supply order. For example, it prioritizes the power supply to critical medical equipment such as ventilators and monitors to ensure their normal operation; it generates a new power supply order to ensure that critical equipment receives power first, while non-critical equipment receives power later or with reduced power supply.

[0189] Furthermore, by utilizing smart meters installed on various devices, energy consumption data of each online device is collected in real time to generate a real-time energy consumption dataset. This dataset is then cleaned and normalized to remove outliers and missing values, resulting in a preprocessed energy consumption dataset. An energy consumption audit model is constructed by combining the device's operating status and working mode; for example, cluster analysis is used to identify the energy consumption patterns of different devices. Using this model, the real-time energy consumption data of each online device is analyzed to assess its energy consumption and efficiency, generating energy consumption analysis results. Based on these results, the energy consumption of all online devices is summarized to generate an energy consumption audit report, which includes the energy consumption status, efficiency performance, and recommended measures for each device.

[0190] Furthermore, based on the energy consumption audit report, identify high-energy-consuming non-critical equipment, such as air conditioning and lighting systems; immediately shut down these high-energy-consuming non-critical equipment, generate a high-energy-consuming non-critical equipment shutdown record, and record the shutdown time, equipment name, and operator.

[0191] Ultimately, by integrating the replanned power supply sequence and the shutdown records of high-energy-consuming non-critical equipment, an emergency power allocation adjustment record is generated, recording all operations and results to provide a basis for subsequent maintenance and improvement. Through the above steps, the makeshift hospital can respond quickly in emergency situations, ensure the normal operation of critical medical equipment, and effectively manage power resources, thereby improving the reliability and stability of the system.

[0192] Through the above steps, makeshift hospitals can respond quickly in emergency situations, ensure the normal operation of critical medical equipment, and effectively manage power resources to improve system reliability and stability.

[0193] This application considers that in extreme environments, makeshift hospitals need to ensure the normal operation of critical medical equipment. To achieve this goal, the system needs to re-plan the power supply order based on factors such as real-time energy consumption data, the criticality and importance of equipment, and the identification of high-energy-consuming non-critical equipment. Through a priority allocation algorithm, the power supply strategy can be optimized to ensure that critical equipment receives power first, while reducing overall energy consumption.

[0194] Optionally, the step of using the optimized power allocation scheme, combined with a priority allocation algorithm, to re-plan the power supply order and generate a re-planned power supply order includes:

[0195] Calculate the priority P of each device dev,i Previously, it was necessary to collect and preprocess energy consumption data in real time, assess the criticality and importance of equipment, identify high-energy-consuming non-critical equipment, and consider the periodic fluctuations in equipment operating time to provide the necessary data support and parameter basis for priority calculation.

[0196] Priority calculation formula:

[0197]

[0198] P dev,i It represents the priority of the i-th device; α is the energy consumption weighting coefficient, with a value between 0 and 1; E i β is the real-time energy consumption of the i-th device, obtained from the energy consumption audit report; β is the key weighting coefficient, ranging from 0 to 1; K i γ is the criticality score of the i-th device, ranging from 0 to 1; γ is the importance weight coefficient, ranging from 0 to 1; I i δ is the importance score of the i-th device, ranging from 0 to 1; δ is the high energy consumption penalty coefficient, ranging from 0 to 1; G i This is the high-energy-consumption flag for the i-th device; it is 1 if the device is a high-energy-consumption non-critical device, and 0 otherwise; φ is the time fluctuation coefficient, ranging from 0 to 1; O i is the running time of the i-th device; τ is a time period parameter, with a value between 0 and 1.

[0199] After calculating the priority P of each device dev,i Next, the equipment needs to be sorted, the system's energy consumption distribution and load allocation need to be evaluated, and the distance from the equipment to the power source and the probability of failure need to be considered to generate the power supply order weight S. dev,i Provide comprehensive evaluation criteria;

[0200] Formula for adjusting the power supply order:

[0201]

[0202] S dev,i : Power supply order weight of the i-th device; P dev,i : Priority of the i-th device; N: Total number of online devices, obtained from the energy consumption audit report; M: Number of high-energy-consuming non-critical devices, obtained from the high-energy-consuming non-critical device shutdown records; H k: Energy consumption of the k-th high-energy-consuming non-critical device, obtained from the energy consumption audit report; T: Total energy consumption of all online devices, obtained from the energy consumption audit report; η: Device distance weighting coefficient, ranging from 0 to 1; L i : Distance from the i-th device to the power source; θ: Distance attenuation factor, ranging from 0 to 1; ψ: Fault probability weighting coefficient, ranging from 0 to 1; F i P: The failure probability of the i-th device; dev,j : The priority of the j-th device; F j : The failure probability of the j-th device;

[0203] After calculating the power supply order weight S for each device dev,i Afterwards, the equipment needs to be reordered, the load distribution ratio of the main power supply and the backup power supply needs to be adjusted, simulation tests need to be conducted, and a new power supply sequence needs to be generated.

[0204] This scheme aims to re-plan the power supply order by calculating the priority and power supply order weight of each device, ensuring that critical equipment is given priority in power supply; comprehensively consider factors such as energy consumption, criticality, importance, high energy consumption penalty, and time fluctuation of the equipment to ensure the comprehensiveness and rationality of the power supply strategy; and dynamically adjust the power supply order based on real-time data to improve the adaptability and flexibility of the system.

[0205] The following is a brief introduction to the design rationale behind each term of the formula:

[0206]

[0207] α·E i Design rationale: High-energy-consuming equipment may require more power resources in emergencies, therefore its energy consumption should be considered in priority calculations. By introducing an energy consumption weighting coefficient α, the degree of influence of energy consumption on priority can be adjusted. If it is desired to lower the priority of high-energy-consuming equipment, a higher α value can be set.

[0208] β·K i Design rationale: Critical equipment should be given priority power in emergencies to ensure the normal operation of important functions. By introducing a criticality weighting coefficient β, the degree of influence of criticality on priority can be adjusted. If it is desired to increase the priority of critical equipment, a higher β value can be set.

[0209] γ·I i Design rationale: Critical equipment should be given priority power supply even in emergencies to ensure the execution of critical tasks. By introducing an importance weighting coefficient γ, the influence of importance on priority can be adjusted. A higher γ value can be set to increase the priority of critical equipment.

[0210] δ·log(1+Gi Design rationale: High-energy-consuming non-critical equipment should be restricted or shut down in emergency situations to conserve electricity. By introducing a high-energy-consuming penalty coefficient δ, the degree of penalty for high-energy-consuming equipment can be adjusted. If a stricter penalty is desired for high-energy-consuming equipment, a higher δ value can be set.

[0211] Design rationale: The operational requirements of equipment may vary at different times. By introducing a time fluctuation coefficient φ and a sine function, the periodic fluctuations in equipment operating time can be considered. This helps to dynamically adjust the equipment's priority, making it more consistent with actual operating conditions.

[0212] The following is a brief introduction to how the parameters of this formula are obtained:

[0213] Energy consumption weighting coefficient α: set according to actual conditions, with a value range between 0 and 1.

[0214] Real-time energy consumption E i Energy consumption data of each online device is collected in real time through energy monitoring devices (such as smart meters) installed on various devices and obtained from energy audit reports.

[0215] Key weight coefficient β: Set according to the actual situation, with a value range between 0 and 1.

[0216] Key score K i The criticality score is between 0 and 1, based on the equipment's criticality assessment results. For example, a ventilator has a criticality score of 0.9, while a general lighting equipment has a criticality score of 0.2.

[0217] Importance weight coefficient γ: Set according to the actual situation, with a value range between 0 and 1.

[0218] Importance Score I i The value ranges from 0 to 1, depending on the equipment's importance assessment results. For example, a patient monitor has an importance score of 0.8, while a regular printer has an importance score of 0.3.

[0219] High energy consumption penalty coefficient δ: set according to actual conditions, with a value range between 0 and 1.

[0220] High energy consumption symbol G i The value is set according to the energy consumption characteristics of the equipment. If the equipment is a high-energy-consuming but non-critical device, the value is 1; otherwise, it is 0. For example, the high-energy-consumption rating for air conditioning equipment is 1, and the high-energy-consumption rating for general lighting equipment is 0.

[0221] Time fluctuation coefficient φ: Set according to the actual situation, with a value range between 0 and 1.

[0222] Runtime O iThe system collects the equipment's operating time in real time through sensors and monitoring systems.

[0223] Time period parameter τ: Set according to actual conditions, with a value range between 0 and 1. For example, it can be set according to the typical operating cycle of the equipment.

[0224] The following is a brief introduction to the design rationale behind each term of the formula:

[0225]

[0226] Priority normalization: Prioritize each device P dev,i Normalization ensures that the sum of the power supply priority weights for all devices is 1. This helps to allocate resources rationally within the total power demand.

[0227] Penalties for high-energy-consuming non-critical equipment: Reduce the impact of high-energy-consuming non-critical equipment on the overall power supply sequence. Adjust the priority normalization value by subtracting the ratio of the total energy consumption of high-energy-consuming non-critical equipment to the total energy consumption from 1.

[0228] Distance weighting coefficient Consider the impact of distance between devices and the power source on the power supply order. Devices that are closer to the power source should be given priority in power supply to reduce power transmission losses and improve system stability.

[0229] Failure probability weighting coefficient: Consider the impact of equipment failure probability on power supply sequence. Equipment with a high failure probability should have its power supply sequence weight reduced to improve system reliability.

[0230] The following is a brief introduction to how the parameters of this formula are obtained:

[0231] Priority P dev,i The total number of online devices, N, is calculated using the priority calculation formula; the total number of online devices, N, is obtained from the energy consumption audit report; the number of high-energy-consuming non-critical devices, M, is obtained from the high-energy-consuming non-critical device shutdown records; and the energy consumption G of high-energy-consuming non-critical devices is calculated using the priority calculation formula. k Obtained from the energy consumption audit report; the total energy consumption T of all online devices is obtained from the energy consumption audit report; the device distance weighting coefficient η is set according to the actual situation, with a value range between 0 and 1; the distance L from the device to the power source. i Obtained through physical measurements or layout diagrams; the distance attenuation factor θ is set according to actual conditions, with a value ranging from 0 to 1; the failure probability weighting coefficient ψ is set according to actual conditions, with a value ranging from 0 to 1; the equipment failure probability F i Calculated based on historical fault data and equipment maintenance records; It is the sum of the failure probabilities of all online devices.

[0232] Assume the makeshift hospital has 5 pieces of equipment (N=5), with the following parameters:

[0233] Equipment 1:

[0234] E1 = 2.0 kW; K1 = 0.9; I1 = 0.8; G1 = 0; O1 = 4 hours;

[0235] Device 2:

[0236] E2 = 1.5 kW; K2 = 0.7; I2 = 0.6; G2 = 1; O2 = 5 hours;

[0237] Equipment 3:

[0238] E3 = 3.0 kW; K3 = 0.6; I3 = 0.5; G3 = 1; O3 = 3 hours;

[0239] Equipment 4:

[0240] E4 = 1.0 kW; K4 = 0.8; I4 = 0.7; G4 = 0; O4 = 6 hours;

[0241] Equipment 5:

[0242] E5 = 2.5kW; K5 = 0.5; I5 = 0.4; G5 = 0; O5 = 2 hours;

[0243] α = 0.4: Energy consumption weighting coefficient; β = 0.3: Criticality weighting coefficient; γ = 0.2: Importance weighting coefficient; δ = 0.1: High energy consumption penalty coefficient; φ = 0.1: Time fluctuation coefficient; τ = 1 hour: Time period parameter; Priority calculation formula

[0244]

[0245] Calculate the priority P of each device dev,i :

[0246] Equipment 1:

[0247]

[0248] Device 2:

[0249]

[0250] Equipment 3:

[0251]

[0252] Equipment 4:

[0253]

[0254] Equipment 5:

[0255]

[0256] Through the above calculations, we obtained the priorities of the five devices:

[0257] Device 1: Priority is approximately 1.15.

[0258] Specifically, it has a high energy consumption and criticality score, but is not a high-energy-consuming device, and has a moderate operating time, thus it has been given a high priority.

[0259] Threshold analysis: Priority 1.15 is greater than 1, indicating high priority. This means that device 1 should be given priority in power supply in an emergency.

[0260] Device 2: Priority is approximately 0.80.

[0261] Specific explanation: Although the energy consumption is low, the criticality and importance scores are moderate, and it is a high-energy-consuming device, so the priority is low.

[0262] Threshold analysis: Priority 0.80 is between 0.5 and 1, belonging to medium priority. This indicates that device 2 should be powered sequentially in an emergency.

[0263] Device 3: Priority is approximately 1.46.

[0264] Specific explanation: Although it consumes a lot of energy, it has a high priority because its criticality and importance scores are low and it is a high-energy-consuming device.

[0265] Threshold analysis: Priority 1.46 is greater than 1, indicating high priority. This suggests that device 3 should be given priority in power supply during emergencies.

[0266] Device 4: Priority is approximately 0.75.

[0267] Specific explanation: Although it has low energy consumption, its criticality and importance scores are low, and it is not a high-energy-consuming device, so its priority is low.

[0268] Threshold analysis: Priority 0.75 is less than 0.5, which is considered low priority. This indicates that device 4 can postpone power supply in an emergency.

[0269] Device 5: Priority is approximately 1.32.

[0270] Specific explanation: It has moderate energy consumption, moderate criticality and importance scores, and is not a high-energy-consuming device, therefore it has a higher priority.

[0271] Threshold analysis: Priority 1.32 is greater than 1, indicating high priority. This suggests that device 5 should be given priority in power supply during emergencies.

[0272] Through the priority calculation and threshold setting in the above embodiments, makeshift hospitals can more effectively identify and prioritize power supply to critical equipment, thereby ensuring the stable operation of medical facilities and the optimal allocation of resources in emergency situations.

[0273] 104. After the extreme environmental conditions are alleviated, a comprehensive power system health check is performed using the power distribution adjustment records under the aforementioned emergency conditions. Based on the results of the comprehensive power system health check, a detailed maintenance report is generated.

[0274] Once extreme environmental conditions have eased, a comprehensive health check is conducted on the makeshift hospital's power system using power distribution adjustment records generated during the emergency, ensuring all equipment and circuits are back to normal operation. The health check includes a detailed inspection of all parts of the power system, identifying potential problems and fault points. Based on the results of the health check, a detailed maintenance report is generated, recording the issues found and proposing specific maintenance recommendations and improvement measures to ensure the power system operates more stably and reliably in the future.

[0275] Once extreme environmental conditions have eased, the system utilizes the power distribution adjustment records generated during the emergency to initiate a comprehensive power system health check. This check involves a detailed examination of all components of the power system, including the main power supply, backup power supply, circuit connections, and critical equipment. The results identify and record any problems and potential risks within the power system. Finally, based on the health check results, a detailed maintenance report is generated, documenting all identified issues and providing specific maintenance recommendations and improvement measures to ensure the power system operates more stably and reliably in the future.

[0276] Optionally, after the extreme environmental conditions are alleviated, a comprehensive power system health check is performed using the power distribution adjustment records under the emergency situation. Based on the results of the comprehensive power system health check, a detailed maintenance report is generated, including:

[0277] Using environmental sensors, the extreme condition parameters of the environment in which the shelter is located are continuously monitored. When the extreme environmental conditions are detected to have eased, an environmental condition easing report is generated.

[0278] Using the power distribution adjustment records under the emergency situation, all operational data of the power system during the emergency situation are collected to generate an emergency situation operation dataset.

[0279] Based on the emergency operation dataset, and combined with the normal operating parameters and historical maintenance records of the power system, a power system health check model is constructed.

[0280] Using the power system health check model, a comprehensive health check is performed on the power system to assess its operating status and potential problems during emergency situations and generate health check results.

[0281] Based on the health check results, a comprehensive analysis of the power system's health status is conducted, necessary maintenance recommendations and improvement measures are proposed, and a detailed maintenance report is generated.

[0282] Figure 2 This application provides a schematic diagram of the structure of a modular emergency power supply system suitable for extreme environments, as shown in the embodiment of the present application. Figure 2 As shown, the device includes:

[0283] The assessment module 21 is used to detect and record extreme condition parameters of the environment in which the container is located using environmental sensors, and to assess the potential risks faced by the power system by combining historical data and real-time monitoring data, and to generate an environmental risk assessment report.

[0284] The adjustment module 22 is used to optimize the power distribution strategy by using an intelligent scheduling algorithm based on the environmental risk assessment report, dynamically adjust the load distribution ratio between the main power supply and the backup power supply, perform thermal management analysis on the internal circuit of the power system, and generate an optimized power distribution scheme.

[0285] Planning module 23 is used to immediately activate the emergency response mechanism based on the optimized power distribution scheme when the system detects an emergency situation in the cabin, use the priority allocation algorithm to re-plan the power supply order, conduct energy consumption audits on all online devices, identify and shut down high-energy-consuming non-critical devices, and generate power distribution adjustment records under emergency situations.

[0286] Execution module 24 is used to perform a comprehensive power system health check after the extreme environmental conditions have been alleviated, using the power distribution adjustment records under the emergency situation, and to generate a detailed maintenance report based on the results of the comprehensive power system health check.

[0287] Figure 2 The aforementioned emergency power supply system for mobile cabins suitable for extreme environments can perform... Figure 1 The implementation principle and technical effects of the emergency power supply method for mobile cabins in extreme environments described in the above embodiment will not be repeated here. The specific operation methods of each module and unit in the emergency power supply system for mobile cabins in extreme environments described in the above embodiment have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0288] In one possible design, Figure 2 An emergency power supply system for mobile cabins in extreme environments, as shown in the embodiment, 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;

[0289] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0290] The processing component 32 is used to: detect and record extreme condition parameters of the environment in which the shelter is located using environmental sensors; combine historical data with real-time monitoring data to assess the potential risks faced by the power system and generate an environmental risk assessment report; based on the environmental risk assessment report, optimize the power allocation strategy using an intelligent scheduling algorithm, dynamically adjust the load distribution ratio between the main power supply and the backup power supply, perform thermal management analysis on the internal circuits of the power system, and generate an optimized power allocation scheme; when the system detects an emergency situation in the shelter, immediately activate the emergency response mechanism based on the optimized power allocation scheme, re-plan the power supply order using a priority allocation algorithm, conduct energy consumption audits on all online devices, identify and shut down high-energy-consuming non-critical devices, and generate a power allocation adjustment record under emergency conditions; after the extreme environmental conditions are alleviated, use the power allocation adjustment record under emergency conditions to perform a comprehensive power system health check, and generate a detailed maintenance report based on the results of the comprehensive power system health check.

[0291] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0292] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0293] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0294] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0295] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0296] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0297] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown is a method for handling emergency power supply in a mobile cabin under extreme environments.

[0298] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0299] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0300] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0301] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for handling emergency power supply in mobile cabins under extreme environments, characterized in that, Includes the following steps: By using environmental sensors to detect and record extreme condition parameters of the environment in which the shelter is located, and combining historical data with real-time monitoring data, the potential risks faced by the power system are assessed, and an environmental risk assessment report is generated. Based on the environmental risk assessment report, an intelligent scheduling algorithm is used to optimize the power allocation strategy, dynamically adjust the load allocation ratio between the main power supply and the backup power supply, perform thermal management analysis on the internal circuit of the power system, and generate an optimized power allocation scheme. When the system detects an emergency situation inside the shelter, it immediately activates the emergency response mechanism based on the optimized power distribution scheme, uses a priority allocation algorithm to re-plan the power supply order, conducts energy consumption audits on all online devices, identifies and shuts down high-energy-consuming non-critical devices, and generates power distribution adjustment records under emergency conditions. After the extreme environmental conditions are alleviated, a comprehensive power system health check is performed using the power distribution adjustment records under the emergency situation, and a maintenance report is generated based on the results of the comprehensive power system health check. Based on the environmental risk assessment report, the process involves optimizing the power allocation strategy using an intelligent scheduling algorithm, dynamically adjusting the load distribution ratio between the main power supply and the backup power supply, performing thermal management analysis on the internal circuitry of the power system, and generating an optimized power allocation scheme, including: Using the extreme condition parameters provided in the environmental risk assessment report, combined with the historical power consumption data and real-time monitoring data of the makeshift hospital, these data are collected and integrated to generate a comprehensive dataset containing information on environmental conditions and power consumption trends. Based on the comprehensive dataset, the potential risks that the power system may face under the current environmental conditions are assessed, and an environmental risk assessment report is generated. Based on the aforementioned environmental risk assessment report, an intelligent scheduling algorithm is used, combined with the power demand forecast and power supply capacity assessment of the makeshift hospital, to optimize the load allocation ratio between the main power supply and the backup power supply, and generate a preliminary power allocation strategy. For the aforementioned preliminary power distribution strategy, thermal management analysis is performed on the internal circuitry of the power system to evaluate the temperature changes within the power system under different load distribution conditions and generate a thermal management analysis report. Based on the aforementioned thermal management analysis report, the preliminary power allocation strategy is further optimized to generate an optimized power allocation scheme.

2. The method according to claim 1, characterized in that, Based on the environmental risk assessment report, an intelligent scheduling algorithm is used, combined with the power demand forecast and power supply capacity assessment of the makeshift hospital, to optimize the load allocation ratio between the main power supply and the backup power supply, generating a preliminary power allocation strategy, including: Calculate the load allocation ratio for each power source. Previously, it was necessary to collect and integrate environmental conditions, historical power consumption data, and real-time monitoring data to generate a comprehensive dataset; preprocess the data to assess the potential risks of the power system and generate an environmental risk assessment report; at the same time, assess the power demand and supply capacity of each power source, consider the periodic fluctuations in power source operating time, and provide data support for the calculation of load allocation ratio. Load sharing ratio optimization formula: ； in, It is the first The load distribution ratio of each power source; It is the electricity demand weighting coefficient, with a value ranging from 0 to 1; It is the first Power demand forecast for each power source; It is the power supply capacity weighting coefficient, with a value ranging from 0 to 1; It is the first Assessment of the power supply capacity of each power source; It is a risk weighting coefficient, with a value ranging from 0 to 1; It is the first Risk assessment value for each power source; It is a high energy consumption penalty coefficient, with a value ranging from 0 to 1; It is the first The high-energy-consumption flag for a power supply is 1 if the power supply is high-energy-consumption, otherwise it is 0. It is the time fluctuation coefficient, with a value ranging from 0 to 1; It is the first The operating time of each power supply; This is a time period parameter, with a value ranging from 0 to 1; This represents the total number of power supplies; After calculating the load distribution ratio for each power source Subsequently, based on the total electricity demand, a preliminary power allocation calculation is performed; demand fluctuation coefficients and risk adjustment coefficients are introduced to assess the probability of failure and operating costs, further optimizing the power allocation strategy and generating a preliminary power allocation strategy. Preliminary power allocation strategy generation formula: ； in, It is the first Initial power allocation strategy for each power source; It is the first The load distribution ratio of each power source; It is the total electricity demand; It is the demand fluctuation coefficient, with a value ranging from 0 to 1; It is the first Power demand forecast for each power source; It is the average power demand of all power sources; It is the standard deviation of electricity demand; It is a risk adjustment factor, with a value ranging from 0 to 1; It is the first Risk assessment value for each power source; It is the failure probability weighting coefficient, with a value ranging from 0 to 1; It is the first The probability of failure of a power supply; It is the cost weighting coefficient, with a value ranging from 0 to 1; It is the first Operating cost of a power supply; It is the first The load distribution ratio of each power source; It is the first Risk assessment value for each power source; It is the first The probability of failure of a power supply; It is the first Operating cost of a power supply; By calculating the load distribution ratio of each power source and initial power distribution strategy By combining total electricity demand and various assessment factors, a final preliminary power allocation strategy is generated. These assessment factors include electricity demand, power supply capacity, risk assessment value, high energy consumption penalty, and time fluctuation.

3. The method according to claim 1, characterized in that, The step involves assessing the potential risks that the power system may face under current environmental conditions based on the comprehensive dataset, and generating an environmental risk assessment report, including: Using the comprehensive dataset, the data is cleaned and normalized to remove outliers and missing values, generating a preprocessed dataset. Based on the preprocessed dataset, statistical analysis methods are used to perform correlation analysis on various indicators, identify features closely related to potential risks of the power system, and generate a list of highly correlated features. Based on the list of highly correlated features, features with high correlation are selected as components of the input vector to construct a risk assessment model. The risk assessment model is trained using the input vector, and its generalization ability is evaluated through cross-validation to generate the trained risk assessment model. Based on the trained risk assessment model, the potential risks that the power system may face under the current environmental conditions are assessed, and an environmental risk assessment report is generated.

4. The method according to claim 1, characterized in that, The preliminary power distribution strategy involves performing thermal management analysis on the internal circuitry of the power system, evaluating temperature changes within the power system under different load distribution conditions, and generating a thermal management analysis report, including: Using the aforementioned preliminary power allocation strategy, operational data of the internal circuits of the power system under different load allocation conditions are collected to generate a load allocation dataset. Based on the load distribution dataset and the design parameters of the internal circuit of the power system, a thermal management analysis model is constructed. Using the load distribution dataset, the thermal management analysis model is calibrated and validated to generate a calibrated thermal management analysis model; Based on the calibrated thermal management analysis model, the internal temperature changes of the power system under different load distribution conditions are simulated and analyzed to evaluate the thermal performance of the power system under various load distribution conditions and generate temperature change analysis results. Based on the temperature change analysis results, a comprehensive evaluation of the thermal management performance of the power system under different load distribution conditions is conducted, and a thermal management analysis report is generated.

5. The method according to claim 1, characterized in that, When the system detects an emergency situation inside the shelter, based on the optimized power distribution scheme, it immediately activates the emergency response mechanism, uses a priority allocation algorithm to re-plan the power supply order, performs energy consumption audits on all online devices, identifies and shuts down high-energy-consuming non-critical devices, and generates a power distribution adjustment record for the emergency situation, including: When the system detects an emergency situation inside the shelter, it immediately triggers the emergency response mechanism and generates an emergency situation detection report. Based on the emergency situation detection report, immediately activate the emergency response mechanism, invoke the pre-prepared emergency response plan, and generate an emergency response activation record; Using the optimized power allocation scheme and combined with the priority allocation algorithm, the power supply order is replanned to generate a replanned power supply order. Perform energy consumption audits on all online devices, collect real-time energy consumption data for each device, and generate energy consumption audit reports. Based on the energy consumption audit report, identify high-energy-consuming non-critical equipment and immediately shut down these devices, generating a high-energy-consuming non-critical equipment shutdown record; By combining the replanned power supply sequence and the shutdown records of high-energy-consuming non-critical equipment, an emergency power allocation adjustment record is generated.

6. The method according to claim 5, characterized in that, The process of using the optimized power allocation scheme, combined with a priority allocation algorithm, to re-plan the power supply order and generate a re-planned power supply order includes: Calculating the priority of each device Previously, it was necessary to collect and preprocess energy consumption data in real time, assess the criticality and importance of equipment, identify high-energy-consuming non-critical equipment, and consider the periodic fluctuations in equipment operating time to provide the necessary data support and parameter basis for priority calculation. Priority calculation formula: ； It is the first The priority of each device; It is the energy consumption weighting coefficient, with a value ranging from 0 to 1; It is the first Real-time energy consumption of each device is obtained from the energy consumption audit report; It is the key weighting coefficient, with a value ranging from 0 to 1; It is the first The key score for each device ranges from 0 to 1; It is the importance weighting coefficient, with a value ranging from 0 to 1; It is the first The importance score for each device ranges from 0 to 1. It is a high energy consumption penalty coefficient, with a value ranging from 0 to 1; It is the first The high energy consumption indicator for each device is 1 if the device is a high energy consumption non-critical device, and 0 otherwise. It is the time fluctuation coefficient, with a value ranging from 0 to 1; It is the first Operating time of each device; This is a time period parameter, with a value ranging from 0 to 1; After calculating the priority of each device Next, the equipment needs to be sorted, the system's energy consumption distribution and load allocation need to be evaluated, and the distance of the equipment to the power supply and the probability of failure need to be considered to generate the power supply order weights. Provide comprehensive evaluation criteria; Formula for adjusting the power supply order: ； : No. The power supply order weight of each device; : No. The priority of each device; The total number of online devices is obtained from the energy consumption audit report; The number of high-energy-consuming non-critical devices is obtained from the high-energy-consuming non-critical device shutdown records; : No. The energy consumption of high-energy-consuming non-critical equipment is obtained from the energy consumption audit report; Total energy consumption of all online devices, obtained from the energy audit report; Equipment distance weighting coefficient, with a value ranging from 0 to 1; : No. The distance from each device to the power source; Distance attenuation factor, with a value between 0 and 1; Failure probability weighting coefficient, with a value ranging from 0 to 1; : No. The probability of failure of each device; : No. The priority of each device; : No. The probability of failure of each device; After calculating the power supply order weight for each device Afterwards, the equipment needs to be reordered, the load distribution ratio of the main power supply and the backup power supply needs to be adjusted, simulation tests need to be conducted, and a new power supply sequence needs to be generated.

7. The method according to claim 5, characterized in that, The energy consumption audit of all online devices involves collecting real-time energy consumption data for each device and generating an energy consumption audit report, including: By using energy consumption monitoring devices installed on various devices, energy consumption data of each online device is collected in real time to generate a real-time energy consumption dataset; The real-time energy consumption dataset is cleaned and normalized to remove outliers and missing values, generating a preprocessed energy consumption dataset. Based on the preprocessed energy consumption dataset, and combined with the equipment's operating status and working mode, an energy consumption audit model is constructed. Using the energy consumption audit model, the real-time energy consumption data of each online device is analyzed to evaluate the energy consumption and energy efficiency of the device and generate energy consumption analysis results. Based on the energy consumption analysis results, the energy consumption of all online devices is summarized, and an energy consumption audit report is generated.

8. The method according to claim 1, characterized in that, After the extreme environmental conditions are alleviated, a comprehensive power system health check is performed using the power distribution adjustment records under the emergency situation. Based on the results of the comprehensive power system health check, a maintenance report is generated, including: Using environmental sensors, the extreme condition parameters of the environment in which the shelter is located are continuously monitored. When the extreme environmental conditions are detected to have eased, an environmental condition easing report is generated. Using the power distribution adjustment records under the emergency situation, all operational data of the power system during the emergency situation are collected to generate an emergency situation operation dataset. Based on the emergency operation dataset, and combined with the normal operating parameters and historical maintenance records of the power system, a power system health check model is constructed. Using the power system health check model, a comprehensive health check is performed on the power system to assess its operating status and potential problems during emergency situations and generate health check results. Based on the health check results, a comprehensive analysis of the power system's health status is conducted, necessary maintenance recommendations and improvement measures are proposed, and a maintenance report is generated.

9. A mobile cabin emergency power supply system suitable for extreme environments, characterized in that, include: The assessment module is used to detect and record extreme condition parameters of the environment in which the shelter is located using environmental sensors. By combining historical data with real-time monitoring data, it assesses the potential risks faced by the power system and generates an environmental risk assessment report. The adjustment module is used to optimize the power allocation strategy based on the environmental risk assessment report using an intelligent scheduling algorithm, dynamically adjust the load allocation ratio between the main power supply and the backup power supply, perform thermal management analysis on the internal circuit of the power system, and generate an optimized power allocation scheme. Based on the environmental risk assessment report, the process involves optimizing the power allocation strategy using an intelligent scheduling algorithm, dynamically adjusting the load distribution ratio between the main power supply and the backup power supply, performing thermal management analysis on the internal circuitry of the power system, and generating an optimized power allocation scheme, including: Using the extreme condition parameters provided in the environmental risk assessment report, combined with the historical power consumption data and real-time monitoring data of the makeshift hospital, these data are collected and integrated to generate a comprehensive dataset containing information on environmental conditions and power consumption trends. Based on the comprehensive dataset, the potential risks that the power system may face under the current environmental conditions are assessed, and an environmental risk assessment report is generated. Based on the aforementioned environmental risk assessment report, an intelligent scheduling algorithm is used, combined with the power demand forecast and power supply capacity assessment of the makeshift hospital, to optimize the load allocation ratio between the main power supply and the backup power supply, and generate a preliminary power allocation strategy. For the aforementioned preliminary power distribution strategy, thermal management analysis is performed on the internal circuitry of the power system to evaluate the temperature changes within the power system under different load distribution conditions and generate a thermal management analysis report. Based on the aforementioned thermal management analysis report, the preliminary power allocation strategy is further optimized to generate an optimized power allocation scheme. The planning module is used to immediately activate the emergency response mechanism based on the optimized power distribution scheme when the system detects an emergency situation in the cabin. It uses a priority allocation algorithm to re-plan the power supply order, performs energy consumption audits on all online devices, identifies and shuts down high-energy-consuming non-critical devices, and generates power distribution adjustment records under emergency conditions. The execution module is used to perform a comprehensive power system health check using the power distribution adjustment records under the emergency situation after the extreme environmental conditions have been alleviated, and to generate a maintenance report based on the results of the comprehensive power system health check.

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