Shelter emergency power supply processing method and system suitable for extreme environment

Abstract

The invention provides a shelter emergency power supply processing method and system suitable for an extreme environment. Wherein extreme condition parameters of the environment where the shelter is located are detected and recorded, and an environment risk assessment report is generated; dynamically adjusting a load distribution proportion between a main power supply and a standby power supply according to the environmental risk assessment report, performing thermal management analysis on an internal circuit of the power supply system, and generating an optimized power supply distribution scheme; when the system detects that an emergency occurs in the shelter, an emergency response mechanism is started immediately, and a power distribution adjustment record under the emergency is generated; and after the extreme environmental conditions are relieved, the power distribution adjustment record under the emergency condition is utilized to execute comprehensive power system health examination, and a detailed maintenance report is generated. According to the technical scheme provided by the invention, the reliability and the stability of the power supply system are improved, the emergency response capability is also improved, the power supply of key equipment is ensured, the maintenance cost is reduced, and the energy utilization efficiency is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of emergency power supply processing technology, and in particular, to a method and system for processing emergency power supply for shelters in extreme environments. Background Art

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

[0003] Existing shelter power management systems usually use traditional manual or semi-automatic methods to manage power distribution and emergency response. These systems adjust the load distribution ratio between the main power supply and the backup power supply through preset rules and simple algorithms. In an emergency, the system will re-plan the power supply order according to the preset emergency response plan and shut down non-critical equipment to save power. In addition, some systems are also equipped with basic thermal management functions to monitor temperature changes in the internal circuits of the power system.

[0004] Most existing power management systems rely on preset rules and static data, and are unable to monitor and evaluate the potential risks faced by the power system under extreme conditions in real time. This results in the system being unable to make the best decision in a timely manner in emergencies, which affects the stability and reliability of the power system. In emergencies, existing systems usually require manual intervention or rely on preset emergency response plans. This response method is slow and difficult to cope with complex and changing emergencies. In addition, the preset plans may not be able to adapt to all types of emergencies, resulting in poor emergency response results. The existing system is relatively extensive in terms of energy consumption management, lacking detailed energy consumption audits and refined management of online equipment. This results in the system being unable to effectively identify and shut down high-energy-consuming non-critical equipment in an emergency, thereby wasting precious power resources and affecting the normal operation of critical equipment. Summary of the invention

[0005] The embodiments of the present application provide a method and system for processing emergency power supply for a shelter in an extreme environment, so as to solve the problem that the prior art is difficult to cope with complex and changeable emergency situations.

[0006] In a first aspect, an embodiment of the present application provides a method for processing emergency power supply for a shelter in an extreme environment, comprising the following steps:

[0007] Use environmental sensors to detect and record the extreme condition parameters of the shelter's environment, combine historical data with real-time monitoring data, evaluate the potential risks faced by the power system, and generate an environmental risk assessment report;

[0008] According to the 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 circuit of the power supply system, and generate an optimized power distribution plan;

[0009] When the system detects an emergency in the shelter, based on the optimized power distribution plan, the emergency response mechanism is immediately activated, the power supply order is re-planned using the priority allocation algorithm, energy consumption audits are performed 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;

[0010] After the extreme environmental conditions are alleviated, a comprehensive health check of the power system is performed using the emergency power distribution adjustment record, and a detailed maintenance report is generated based on the results of the comprehensive health check of the power system.

[0011] Optionally, according to the 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 circuit of the power supply system, and generate an optimized power distribution plan, including:

[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 Fangcang shelter hospital, these data are collected and integrated to generate a comprehensive data set containing information on environmental conditions and power consumption trends;

[0013] Based on the comprehensive data set, potential risks that the power system may face under current environmental conditions are evaluated to generate an environmental risk assessment report;

[0014] Based on the environmental risk assessment report, an intelligent scheduling algorithm is used to optimize the load distribution ratio between the main power supply and the backup power supply in combination with the power demand forecast and power supply capacity assessment of the Fangcang Cabin Hospital, and a preliminary power distribution strategy is generated;

[0015] For the preliminary power distribution strategy, perform thermal management analysis on the internal circuit of the power system, evaluate the temperature change inside the power system under different load distribution conditions, and generate a thermal management analysis report;

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

[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 Fangcang Hospital, to optimize the load distribution ratio between the main power supply and the backup power supply, and generate a preliminary power distribution strategy, including:

[0018] When calculating the load sharing ratio L of 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 data set; pre-process the data, 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 of the power supply operation time, and provide data support for the calculation of the load distribution ratio;

[0019] Load distribution ratio optimization formula:

[0020]

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

[0022] After calculating the load distribution ratio L of each power supply i Finally, combined with the total power demand, the power distribution is preliminarily calculated; the demand fluctuation coefficient and risk adjustment coefficient are introduced to evaluate the failure probability and operating cost, and the power distribution strategy is further optimized to generate a preliminary power distribution strategy P i ;

[0023] Preliminary power distribution strategy generation formula:

[0024]

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

[0026] By calculating the load sharing ratio L of each power source i and the preliminary power distribution strategy P j , combining the total power demand and multiple evaluation factors to generate the final preliminary power allocation strategy.

[0027] Optionally, the step of evaluating potential risks that the power system may face under current environmental conditions based on the comprehensive data set to generate an environmental risk assessment report includes:

[0028] Using the comprehensive data set, cleaning and normalizing the data, removing outliers and missing values, and generating a preprocessed data set;

[0029] According to the preprocessed data set, using statistical analysis methods, correlation analysis is performed on various indicators to identify features closely related to potential risks of the power supply system and generate a list of features with high correlation;

[0030] Based on the list of features with high correlation, select features with high correlation as components of the input vector to construct a risk assessment model;

[0031] Using the input vector, training the risk assessment model, evaluating the generalization ability of the model through cross-validation, and generating a trained risk assessment model;

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

[0033] Optionally, the preliminary power distribution strategy is used to perform a thermal management analysis on the internal circuit of the power system, evaluate the temperature change inside the power system under different load distribution conditions, and generate a thermal management analysis report, including:

[0034] Using the preliminary power distribution strategy, operating data of the internal circuits of the power system under different load distribution conditions are collected to generate a load distribution data set;

[0035] Constructing a thermal management analysis model based on the load distribution data set and design parameters of the internal circuit of the power system;

[0036] calibrating and verifying the thermal management analysis model using the load distribution data set to generate a calibrated thermal management analysis model;

[0037] Based on the calibrated thermal management analysis model, the temperature change inside the power system under different load distribution conditions is simulated and analyzed, the thermal performance of the power system under various load distribution conditions is evaluated, and the temperature change analysis results are generated;

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

[0039] Optionally, when the system detects an emergency in the shelter, based on the optimized power distribution plan, the emergency response mechanism is immediately activated, the power supply order is re-planned using a priority allocation algorithm, energy consumption audits are performed on all online devices, high-energy-consuming non-critical devices are identified and shut down, and a power distribution adjustment record under emergency conditions is generated, including:

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

[0041] Based on the emergency detection report, immediately start the emergency response mechanism, call the pre-prepared emergency response plan, and generate an emergency response start record;

[0042] Re-planning the power supply sequence by using the optimized power distribution scheme in combination with a priority distribution algorithm to generate a re-planned power supply sequence;

[0043] Conduct 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 non-critical devices with high energy consumption, immediately shut down these devices, and generate a shutdown record of non-critical devices with high energy consumption;

[0045] The re-planned power supply sequence and the shutdown records of high-energy-consuming non-critical equipment are combined to generate a record of power distribution adjustment in emergency situations.

[0046] Optionally, the utilizing the optimized power distribution scheme in combination with a priority distribution algorithm to re-plan the power supply sequence and generate a re-planned power supply sequence includes:

[0047] When calculating the priority P of each device dev,i Previously, it was necessary to collect and pre-process energy consumption data in real time, evaluate the criticality and importance of equipment, identify non-critical equipment with high energy consumption, and consider the periodic fluctuations of equipment operation time to provide the necessary data support and parameter basis for priority calculation;

[0048] Priority calculation formula:

[0049]

[0050] P dev,i is the priority of the ith device; α is the energy consumption weight coefficient, ranging from 0 to 1; E i is the real-time energy consumption of the ith device, obtained from the energy consumption audit report; β is the critical weight coefficient, ranging from 0 to 1; K i 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 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 is the high energy consumption flag of the i-th device. If the device is a high energy consumption non-critical device, it is 1, otherwise it is 0; φ is the time fluctuation coefficient, ranging from 0 to 1; O i is the operating time of the ith device; τ is the time period parameter, ranging from 0 to 1;

[0051] After calculating the priority P of each device dev,i After that, it is necessary to sort the devices, evaluate the energy consumption distribution and load distribution of the system, consider the distance from the device to the power supply and the probability of failure, and generate the power supply sequence weight S dev,i Provide a comprehensive basis for evaluation;

[0052] Electricity supply order adjustment formula:

[0053]

[0054] S dev,i : power supply sequence weight of the ith device; P dev,i : the priority of the ith device; N: the total number of online devices, obtained from the energy consumption audit report; M: the number of high-energy-consuming non-critical devices, obtained from the shutdown records of high-energy-consuming non-critical devices; H k: The energy consumption of the kth high-energy-consuming non-critical device, obtained from the energy consumption audit report; T: The total energy consumption of all online devices, obtained from the energy consumption audit report; η: The device distance weight coefficient, ranging from 0 to 1; L i : the distance from the ith device to the power supply; θ: distance attenuation factor, ranging from 0 to 1; ψ: fault probability weight coefficient, ranging from 0 to 1; F i : Failure probability of the i-th device; P dev,j : The priority of the jth device; F j : Failure probability of the jth device;

[0055] After calculating the power supply sequence weight S of each device dev,i After that, it is necessary to reorder the equipment, adjust the load distribution ratio of the main power supply and the backup power supply, conduct simulation tests, and generate a re-planned power supply order.

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

[0057] Using the energy consumption monitoring devices installed on each device, the energy consumption data of each online device is collected in real time to generate a real-time energy consumption data set;

[0058] Cleaning and normalizing the real-time energy consumption data set, removing outliers and missing values, and generating a preprocessed energy consumption data set;

[0059] According to the preprocessed energy consumption data set, combined with the operating status and working mode of the equipment, an energy consumption audit model is constructed;

[0060] The energy consumption audit model is used to analyze the real-time energy consumption data of each online device, evaluate the energy consumption and energy efficiency performance 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 to generate an energy consumption audit report.

[0062] Optionally, after the extreme environmental condition is alleviated, a comprehensive power system health check is performed using the power distribution adjustment record under emergency conditions, and a detailed maintenance report is generated based on the results of the comprehensive power system health check, including:

[0063] Use environmental sensors to continuously monitor the extreme condition parameters of the shelter's environment, and generate an environmental condition mitigation report when it is detected that the extreme environmental conditions have been alleviated;

[0064] Using the emergency power distribution adjustment record, all operation data of the power supply system during the emergency is collected to generate an emergency operation data set;

[0065] Building a power system health check model based on the emergency operation data set and in combination with normal operating parameters and historical maintenance records of the power system;

[0066] Using the power system health check model, a comprehensive health check is performed on the power system, the operating status and potential problems of the power system during an emergency are evaluated, and a health check result is generated;

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

[0068] In a second aspect, an embodiment of the present application provides a shelter emergency power supply processing system suitable for use in extreme environments, including:

[0069] The evaluation module is used to detect and record the extreme condition parameters of the shelter's environment using environmental sensors, combine historical data with real-time monitoring data, evaluate the potential risks faced by the power system, and generate an environmental risk assessment report;

[0070] An adjustment module is used to optimize the power distribution strategy by using an intelligent scheduling algorithm according to 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 supply system, and generate an optimized power distribution plan;

[0071] The planning module is used to immediately start the emergency response mechanism based on the optimized power distribution plan when the system detects an emergency in the shelter, re-plan the power supply order using the priority allocation algorithm, 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 conditions;

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

[0073] In an embodiment of the present application, environmental sensors are used to detect and record extreme condition parameters of the environment in which the cabin is located, and historical data and real-time monitoring data are combined to evaluate the potential risks faced by the power supply system and generate an environmental risk assessment report; based on the 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 circuit of the power supply system, and generate an optimized power distribution plan; when the system detects an emergency in the cabin, based on the optimized power distribution plan, an emergency response mechanism is immediately activated, the power supply order is re-planned using a priority allocation algorithm, energy consumption audits are performed on all online devices, high-energy consumption non-critical equipment is identified and shut down, and a record of power distribution adjustments under emergency conditions is generated; after the extreme environmental conditions are alleviated, the power distribution adjustment record under emergency conditions is used to perform a comprehensive power system health check, and a detailed maintenance report is generated based on the results of the comprehensive power system health check.

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

[0075] By using environmental sensors to monitor and record the extreme condition parameters of the environment in which the shelter is located in real time, and combining historical data with real-time monitoring data, it is possible to timely assess the potential risks faced by the power supply system and generate an environmental risk assessment report. This helps to take preventive 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, an intelligent scheduling algorithm is 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 can ensure that the power supply system can operate efficiently and stably under different environmental conditions, avoiding system failures caused by load imbalance; when the system detects an emergency in the shelter, it immediately activates the emergency response mechanism, uses the priority allocation algorithm to re-plan the power supply order, conducts energy consumption audits on all online devices, and identifies and shuts down high-energy-consuming non-critical devices. This can quickly reduce power consumption, ensure the normal operation of key equipment, and improve the emergency response and survivability of the system; through the priority allocation algorithm, it can give priority to the power supply of key equipment in an emergency, ensure the normal operation of important facilities such as medical equipment, and thus ensure the life safety of patients and the continuity of medical services; after the extreme environmental conditions are alleviated, the power distribution adjustment records under emergency conditions are used to perform a comprehensive power system health check and generate a detailed maintenance report. This helps to timely discover and fix potential problems, extend the service life of equipment, and reduce downtime and maintenance costs caused by sudden failures; by conducting energy consumption audits on all online devices, identifying and shutting down high-energy non-critical equipment, it can effectively reduce overall energy consumption, improve energy utilization efficiency, and reduce operating costs; the generated environmental risk assessment report, optimized power distribution plan, emergency power distribution adjustment records, and detailed maintenance reports provide managers with comprehensive data support to help them make more scientific and reasonable decisions and improve management efficiency; through intelligent scheduling algorithms and priority allocation algorithms, the system can flexibly adjust according to different environmental conditions and emergency conditions, enhancing the adaptability and flexibility of the system and improving the ability to cope with various complex situations.

[0076] Furthermore, through real-time monitoring and historical data analysis, the potential risks faced by the power supply system under current environmental conditions can be identified and evaluated in a timely manner, so as to take preventive measures, reduce the occurrence of failures, and improve the overall reliability of the system; the intelligent scheduling algorithm is adopted, combined 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 to ensure the efficient operation of the power supply system under different environmental conditions and avoid system failures caused by load imbalance; by conducting thermal management analysis on the internal circuits of the power supply system and evaluating the temperature changes under different load distribution conditions, the power distribution strategy can be further optimized so that the system can maintain optimal performance under different working conditions, enhancing the adaptability and flexibility of the system; thermal management analysis helps to control the temperature inside the power supply system and prevent equipment damage caused by overheating, thereby extending the service life of the equipment and reducing maintenance costs; the optimized power distribution strategy can allocate power resources more reasonably, reduce unnecessary energy consumption, improve energy utilization efficiency, and reduce operating costs; the generated environmental risk assessment report and thermal management analysis report provide managers with comprehensive data support to help them make more scientific and reasonable decisions and improve management efficiency; through intelligent scheduling algorithms and thermal management analysis, it is ensured that key equipment can also obtain a stable power supply in an emergency, and ensure the continuity of medical services and patient safety in the Fangcang Hospital.

[0077] Furthermore, the system can quickly detect and respond to emergencies, immediately activate the emergency response mechanism, ensure that effective measures are taken in the shortest time, and reduce the impact of emergencies on the shelter hospital; re-plan the order of power supply through the priority allocation algorithm to ensure that key medical equipment and infrastructure obtain stable power supply in emergency situations, and ensure the continuity of medical services and patient safety; by conducting energy consumption audits on all online devices, identifying and shutting down high-energy non-critical devices, the overall energy consumption can be significantly reduced, energy utilization efficiency can be improved, and the use time of backup power can be extended; the optimized power distribution scheme combined with the priority allocation algorithm can dynamically adjust the power supply in an emergency situation, ensure the stable operation of the system under different conditions, and reduce failures caused by insufficient power; generate detailed records such as emergency detection reports, emergency response startup records, energy consumption audit reports, and high-energy non-critical equipment shutdown records, which provide a basis for subsequent analysis and improvement, and help managers better understand and evaluate the effectiveness of emergency response; through intelligent scheduling and priority allocation, the system can flexibly adjust the power supply strategy according to different emergency situations, enhance the adaptability and flexibility of the system, and improve the ability to cope with complex situations; by timely shutting down high-energy non-critical equipment, unnecessary power consumption is reduced, maintenance costs are reduced, and the service life of the equipment is extended.

[0078] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0080] Figure 1 A flowchart of a method for processing emergency power supply for shelters in extreme environments provided in an embodiment of the present application;

[0081] Figure 2 A schematic diagram of a structural diagram of a shelter emergency power supply processing system suitable for use in extreme environments provided in an embodiment of the present application;

[0082] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0083] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0084] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0085] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0086] Figure 1 A flowchart of a method for processing emergency power supply for a shelter in an extreme environment is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

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

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

[0089] Extreme condition parameters: refers to environmental parameters that may affect the power system under extreme environments, such as high temperature, low temperature, high humidity, strong wind, etc.

[0090] Historical data: Data records of the cabin’s environment over a period of time, including environmental parameters and the operating status of the power system.

[0091] Real-time monitoring data: Real-time data of the current environment of the cabin, used to dynamically evaluate the status of the power system.

[0092] Environmental risk assessment report: Based on historical data and real-time monitoring data, the potential risks that the power system may face under current environmental conditions are assessed and a detailed report is generated.

[0093] Brief explanation of the program process:

[0094] Environmental sensors are used to collect real-time parameters of the extreme conditions of the cabin's environment. Real-time monitoring data is integrated with historical data to form a comprehensive data set. The comprehensive data set is used to assess the potential risks that the power system may face under current environmental conditions. Based on the risk assessment results, an environmental risk assessment report is generated to provide a basis for subsequent steps.

[0095] Assume that the Fangcang Hospital is located in an area that is 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] At the same time, historical data showed that the power system had experienced multiple failures under similar extreme weather conditions in the past few years. After integrating this data, the system assessed that the power system was at high risk under current environmental conditions, especially due to possible circuit short circuits and equipment damage caused by high humidity and strong winds. Ultimately, the system generated a detailed environmental risk assessment report, pointing out the need to strengthen waterproofing measures and circuit protection, and recommending the addition of backup power to cope with possible power outages.

[0100] 102. Based on the 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 circuit of the power supply system, and generate an optimized power distribution plan;

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

[0102] According to the environmental risk assessment report, the specific risks faced by the power supply system under the current environmental conditions are analyzed, and an intelligent scheduling algorithm is used 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. The internal circuits of the power supply system are subjected to thermal management analysis to evaluate the temperature changes under different load distribution conditions. The power supply distribution strategy is further optimized based on the results of the thermal management analysis to generate an optimized power supply distribution plan.

[0103] Optionally, according to the 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 circuit of the power supply system, and generate an optimized power distribution plan, including:

[0104] 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 Fangcang Hospital, these data are collected and integrated to generate a comprehensive data set containing information on environmental conditions and power consumption trends; based on the comprehensive data set, the potential risks that the power supply system may face under current environmental conditions are evaluated to generate an environmental risk assessment report; 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 Fangcang Hospital, to optimize the load distribution ratio between the main power supply and the backup power supply, and generate a preliminary power distribution strategy; for the preliminary power distribution strategy, a thermal management analysis is performed on the internal circuits of the power supply system, the temperature changes inside the power supply system under different load distribution conditions are evaluated, and a thermal management analysis report is generated; based on the thermal management analysis report, the preliminary power distribution strategy is further optimized to generate an optimized power distribution plan.

[0105] Wherein, the potential risks that the power system may face under the current environmental conditions are evaluated based on the comprehensive data set, and an environmental risk assessment report is generated, including:

[0106] Using the comprehensive data set, the data is cleaned and normalized to remove outliers and missing values, and a preprocessed data set is generated; based on the preprocessed data set, a statistical analysis method is used to perform correlation analysis on various indicators, identify features that are closely related to potential risks of the power supply system, and generate a feature list with high correlation; based on the feature list with high correlation, features with high correlation are selected as components of the input vector to construct a risk assessment model; using the input vector, the risk assessment model is trained, the generalization ability of the model is evaluated through cross-validation, and a trained risk assessment model is generated; based on the trained risk assessment model, the potential risks that the power supply system may face under current environmental conditions are evaluated, and a detailed environmental risk assessment report is generated.

[0107] The preliminary power distribution strategy is used to perform thermal management analysis on the internal circuit of the power system, evaluate the temperature change inside the power system under different load distribution conditions, and generate a thermal management analysis report, including:

[0108] Utilizing the preliminary power distribution strategy, the operating data of the internal circuits of the power supply system under different load distribution conditions are collected to generate a load distribution data set; based on the load distribution data set and in combination with the design parameters of the internal circuits of the power supply system, a thermal management analysis model is constructed; utilizing the load distribution data set, 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 supply system under different load distribution conditions are simulated and analyzed, the thermal performance of the power supply system under various load distribution conditions is evaluated, and a temperature change analysis result is generated; based on the temperature change analysis result, the thermal management performance of the power supply system under different load distribution conditions is comprehensively evaluated to generate a thermal management analysis report.

[0109] Assume that a shelter hospital is located in an area that is often affected by extreme weather (such as high temperature and high humidity). The hospital needs to ensure the stability and reliability of the power supply system in extreme environments to ensure the normal operation of medical equipment.

[0110] First, deploy temperature sensors, humidity sensors, wind speed sensors, etc. to monitor and record the extreme condition parameters of the shelter's environment in real time. Collect the electricity consumption data of the shelter hospital in the past year, including power consumption in different time periods, equipment operation status, etc. Through smart meters and monitoring systems, collect the current electricity consumption data of the shelter hospital in real time. Integrate and process the above data to generate a comprehensive data set containing information on environmental conditions and power consumption trends.

[0111] Secondly, the comprehensive data set is cleaned and normalized to remove outliers and missing values ​​to generate a preprocessed data set. Using statistical analysis methods, correlation analysis is performed on various indicators to identify features that are closely related to potential risks of the power system, such as temperature, humidity, wind speed, etc. Features with high correlation are selected as components of the input vector to build a risk assessment model. For example, machine learning algorithms such as support vector machines (SVM) or random forests are used. The risk assessment model is trained using the preprocessed data set, and the generalization ability of the model 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 evaluated to generate a detailed environmental risk assessment report.

[0112] Furthermore, a genetic algorithm is used to combine the power demand forecast and power supply capacity assessment of the Fangcang Hospital to dynamically adjust the load distribution ratio between the main power supply and the backup power supply to generate a preliminary power distribution strategy. Time series analysis methods (such as the ARIMA model) are used to predict power demand in the future. The power supply capacity of the main power supply and the backup power supply is evaluated, considering their maximum output power and stability. Based on the results of the intelligent scheduling algorithm, a preliminary power distribution strategy is generated to ensure the efficient operation of the power supply system under different environmental conditions.

[0113] Furthermore, using the preliminary power distribution strategy, the operating data of the internal circuits of the power system under different load distribution conditions are collected to generate a load distribution data set. Combined with the design parameters of the internal circuits of the power system (such as resistors, capacitors, heat sinks, etc.), a thermal management analysis model is constructed. Finite element analysis (FEA) or computational fluid dynamics (CFD) methods can be used. The load distribution data set is used to calibrate and verify the thermal management analysis model 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 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, the thermal management performance of the power system under different load distribution conditions is comprehensively evaluated to generate a thermal management analysis report.

[0114] Finally, based on the thermal management analysis report, the preliminary power distribution strategy is further optimized to generate an optimized power distribution plan. 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 the high-load area and increase the power distribution in the low-load area to balance the overall temperature distribution.

[0115] Through the above steps, the Fangcang Cabin Hospital can ensure the stability and reliability of the power supply system in extreme environments, improve emergency response efficiency, and ensure the normal operation of key medical equipment.

[0116] This application takes into account that in extreme environments, the power supply system of the square cabin hospital needs to be able to operate efficiently and stably to ensure the normal operation of medical equipment and other critical facilities. In order 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 distribution strategy. By comprehensively considering environmental conditions, historical power consumption data, real-time monitoring data, power demand forecasts, power supply capacity assessments, risk assessments and other factors, a more reasonable power distribution plan 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 Fangcang Hospital, to optimize the load distribution ratio between the main power supply and the backup power supply, and generate a preliminary power distribution strategy, including:

[0118] When calculating the load sharing ratio L of 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 data set; pre-process the data, 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 of the power supply operation time, and provide data support for the calculation of the load distribution ratio;

[0119] Load distribution ratio optimization formula:

[0120]

[0121] Among them, L i is the load distribution ratio of the i-th power source; α is the power demand weight coefficient, ranging from 0 to 1; D i is the power demand forecast of the i-th power source; β is the power supply capacity weight coefficient, ranging from 0 to 1; S i is the power supply capacity assessment of the i-th power source; γ is the risk weight coefficient, ranging from 0 to 1; R i is the risk assessment value of the i-th power supply; δ is the high energy consumption penalty coefficient, ranging from 0 to 1; H i is the high energy consumption flag of the i-th power supply, which is 1 if the power supply is high energy consumption, otherwise it is 0; φ is the time fluctuation coefficient, which ranges from 0 to 1; T i is the operating time of the ith 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 of each power supply i Finally, combined with the total power demand, the power distribution is preliminarily calculated; the demand fluctuation coefficient and risk adjustment coefficient are introduced to evaluate the failure probability and operating cost, and the power distribution strategy is further optimized to generate a preliminary power distribution strategy P i ;

[0123] Preliminary power distribution strategy generation formula:

[0124]

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

[0126] By calculating the load sharing ratio L of each power source i and the preliminary power distribution strategy P i , combining the total power demand and multiple evaluation factors to generate the final preliminary power allocation strategy.

[0127] The scheme aims to comprehensively consider multiple factors, including power demand, supply capacity, risk assessment, high energy consumption penalty, time fluctuation, etc., to generate the optimal load distribution ratio; by introducing demand fluctuation coefficient, risk adjustment coefficient, failure probability weight coefficient and cost weight coefficient, the power distribution strategy can be dynamically adjusted according to actual conditions to improve the adaptability and reliability of the system.

[0128] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0129]

[0130] The design purpose is to consider multiple factors to calculate the load sharing ratio L of each power supply. i These factors include power demand, power supply capacity, risk assessment value, high energy consumption penalty, and time fluctuation. By weighted summation, the comprehensive performance of each power supply under current environmental conditions can be more comprehensively reflected.

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

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

[0133] Electricity Demand Forecast i Predicting electricity demand in the future through historical data and time series analysis methods (such as ARIMA model); assessing electricity supply capacity i Through the maximum output power and stability assessment of the equipment; risk assessment value R i Based on environmental risk assessment reports and historical data, using statistical analysis methods (such as correlation analysis); High energy consumption mark H i According to the energy consumption characteristics of the power supply, if the power supply is high energy consumption, it is 1, otherwise it is 0; the running time T i Real-time data collection through sensors and monitoring systems; the time period parameter τ is usually set according to actual conditions, and the value range is between 0 and 1; weight coefficients α, β, γ, δ, φ: these coefficients are usually set according to experience and actual needs, and the value range is between 0 and 1;

[0134] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0135]

[0136] The product of load distribution ratio and total power demand: This term reflects the contribution of each power source to the total power demand. 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 fluctuation coefficient: This term takes into account the fluctuation of power demand. By introducing the demand fluctuation coefficient η and the exponential function, the power distribution strategy can be adjusted to cope with the changes in power demand. The exponential function is used to smooth the impact of demand fluctuations.

[0138] Risk Adjustment Factor: This item takes into account the risk assessment value of the power supply. By introducing the ratio of the risk adjustment coefficient θ and the risk assessment value, the allocation ratio of high-risk power supplies can be reduced and the reliability of the system can be improved.

[0139] Failure probability weight coefficient: This term takes into account the failure probability of the power supply. By introducing the failure probability weight coefficient ψ and the ratio of the failure probability, the power distribution strategy can be adjusted to reduce the impact of the failure on the system.

[0140] Cost weight factor: This item takes into account the operating cost of the power supply. By introducing the cost weight coefficient λ and the ratio of the operating cost, the allocation ratio of high-cost power supplies can be reduced and the economy of the system can be improved.

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

[0142] L i represents the load distribution ratio obtained through the previous calculation; T represents the total power demand, which is obtained by summarizing the power demand forecasts of all devices; 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 represents the power demand forecast of the i-th power source; Represents the average power demand of all power sources, calculated through statistical analysis; σ D represents the standard deviation of electricity demand, which is calculated through statistical analysis; θ represents the risk adjustment coefficient, which ranges from 0 to 1 and is set according to actual conditions; R i represents the risk assessment value of the ith power source, which is obtained based on the environmental risk assessment report and historical data using statistical analysis methods (such as correlation analysis); represents the sum of the risk assessment values ​​of all power supplies; ψ represents the failure probability weight coefficient, which ranges from 0 to 1 and is set according to the actual situation; F i represents the failure probability of the i-th power supply, which is calculated based on historical failure data and equipment maintenance records; represents the sum of the failure probabilities of all power supplies; λ represents the cost weight coefficient, which ranges from 0 to 1 and is set according to the actual situation; C i represents the operating cost of the ith power source, which is calculated based on the operating cost data of the equipment; Represents the sum of the operating costs of all power supplies.

[0143] Assuming that a certain shelter hospital has three power sources (main power source 1, backup power source 2, and backup power source 3), it is necessary to optimize the power distribution strategy in extreme environments to ensure the normal operation of key medical equipment. The system detects the current environmental conditions through environmental sensors and combines historical power consumption data with real-time monitoring data to generate a comprehensive data set. Next, the intelligent scheduling algorithm is used to optimize the load distribution ratio between the main power source and the backup power source to generate a preliminary power distribution strategy.

[0144] Data preparation:

[0145] Total number of 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 value R1 = 0.8, R2 = 0.6, R3 = 0.4; High energy consumption flag 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] Power demand weight coefficient α = 0.4; power supply capacity weight coefficient β = 0.3; risk weight 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 weight coefficient ψ = 0.1; cost weight 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 preliminary 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 preliminary power allocation strategy for power source 1, P1 ≈ 482.74 kW, indicates that it will bear the largest load because of its higher power demand and stronger supply capability.

[0174] The preliminary power allocation strategy for power source 2, P2 ≈ 400.24 kW, indicates that it will bear the second highest load because its power demand is moderate and its supply capacity is strong.

[0175] The preliminary power allocation strategy for power source 3, P3 ≈ 320.06 kW, indicates that it will bear the smallest load because of its lower power demand and weaker supply capability.

[0176] Assume that a threshold of 400kW is set to determine whether a power source is the main power source. According to the calculation results, the preliminary power allocation strategies of power sources 1 and 2 are both over 400kW, so they will be used as the main power source; while the preliminary power allocation strategy of power source 3 is less than 400kW, so it will be used as the auxiliary power source.

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

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

[0179] The emergency response mechanism refers to a series of automatic or semi-automatic response measures that are immediately initiated when an emergency situation (such as power outage or equipment failure) is detected in the cabin; the priority allocation algorithm is an algorithm that re-plans the order of power supply according to the criticality and importance of the equipment; energy consumption audit is the process of monitoring and evaluating the real-time energy consumption of all online equipment; high-energy non-critical equipment refers to equipment that consumes a lot of power but is not essential for critical tasks; the record of power distribution adjustment under emergency conditions is a document that records all operations and results of power distribution adjustment under emergency conditions.

[0180] When the system detects an emergency in the cabin, it immediately activates the emergency response mechanism and calls the pre-prepared emergency response plan; uses the priority allocation algorithm to re-plan the power supply order to ensure that key equipment gets priority power supply; conducts energy consumption audits on all online equipment, collects real-time energy consumption data, and identifies high-energy consumption non-critical equipment, and immediately shuts down these devices to reduce power consumption; finally, generates emergency power distribution adjustment records, and records all operations and results in detail.

[0181] Optionally, when the system detects an emergency in the shelter, based on the optimized power distribution plan, the emergency response mechanism is immediately activated, the power supply order is re-planned using a priority allocation algorithm, energy consumption audits are performed on all online devices, high-energy-consuming non-critical devices are identified and shut down, and a power distribution adjustment record under emergency conditions is generated, including:

[0182] When the system detects an emergency in the cabin, the emergency response mechanism is immediately triggered and an emergency detection report is generated; based on the emergency detection report, the emergency response mechanism is immediately started, the pre-prepared emergency response plan is called, and an emergency response start record is generated; the optimized power distribution scheme is used in combination with the priority allocation algorithm to re-plan the power supply order and generate a re-planned power supply order; energy consumption audits are performed on all online devices, real-time energy consumption data of each device is collected, and an energy consumption audit report is generated; based on the energy consumption audit report, high-energy-consuming non-critical devices are identified and immediately shut down these devices to generate high-energy-consuming non-critical device shutdown records; the re-planned power supply order and high-energy-consuming non-critical device shutdown records are combined to generate emergency power distribution adjustment records.

[0183] The energy consumption audit of all online devices is performed, the real-time energy consumption data of each device is collected, and an energy consumption audit report is generated, including:

[0184] By using the energy consumption monitoring device installed on each device, the energy consumption data of each online device is collected in real time to generate a real-time energy consumption data set; the real-time energy consumption data set is cleaned and normalized to remove outliers and missing values ​​to generate a preprocessed energy consumption data set; based on the preprocessed energy consumption data set, in combination with the operating status and working mode of the device, 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, the energy consumption and energy efficiency performance of the device are evaluated, and an energy consumption analysis result is generated; based on the energy consumption analysis result, the energy consumption of all online devices is summarized to generate an energy consumption audit report.

[0185] Suppose a shelter hospital is suddenly hit by a strong storm at night, causing the main power supply to be partially damaged, and the backup power supply needs to be activated immediately to ensure the normal operation of key medical equipment. At this time, the system detects the emergency and immediately activates the emergency response mechanism.

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

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

[0188] Furthermore, the system uses the optimized power distribution scheme and the priority distribution algorithm to re-plan the power supply order. For example, it prioritizes the power supply of key medical equipment such as ventilators and monitors to ensure their normal operation; it generates a new power supply order to ensure that key equipment is given priority in power supply, and non-key equipment is delayed or reduced in power supply.

[0189] Furthermore, the smart meters installed on each device are used to collect the energy consumption data of each online device in real time to generate a real-time energy consumption data set; the real-time energy consumption data set is cleaned and normalized to remove outliers and missing values ​​to generate a preprocessed energy consumption data set; an energy consumption audit model is constructed in combination with the operating status and working mode of the equipment. For example, a cluster analysis method is used to identify the energy consumption patterns of different devices; the energy consumption audit model is used to analyze the real-time energy consumption data of each online device, evaluate the energy consumption and energy efficiency performance of the equipment, and generate energy consumption analysis results; based on the energy consumption analysis results, the energy consumption of all online devices is summarized to generate an energy consumption audit report, which includes the energy consumption, energy efficiency performance and recommended measures of each device.

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

[0191] Finally, the re-planned power supply order and the shutdown records of high-energy-consuming non-critical equipment are comprehensively recorded to generate emergency power distribution adjustment records, record all operations and results, and provide a basis for subsequent maintenance and improvement; through the above steps, the Fangcang Cabin Hospital can respond quickly in emergency situations, ensure the normal operation of key medical equipment, and effectively manage power resources to improve the reliability and stability of the system.

[0192] Through the above steps, the Fangcang Cabin Hospital can respond quickly in emergency situations and ensure the normal operation of key medical equipment, while effectively managing power resources and improving the reliability and stability of the system.

[0193] This application takes into account that in extreme environments, Fangcang shelter hospitals need to ensure the normal operation of key medical equipment. To achieve this goal, the system needs to re-plan the power supply order based on real-time energy consumption data, the criticality and importance of equipment, and the identification of high-energy non-critical equipment. Through the priority allocation algorithm, the power supply strategy can be optimized to ensure that key equipment has priority in power supply while reducing overall energy consumption.

[0194] Optionally, the utilizing the optimized power distribution scheme in combination with a priority distribution algorithm to re-plan the power supply sequence and generate a re-planned power supply sequence includes:

[0195] When calculating the priority P of each device dev,i Previously, it was necessary to collect and pre-process energy consumption data in real time, evaluate the criticality and importance of equipment, identify non-critical equipment with high energy consumption, and consider the periodic fluctuations of equipment operation time to provide the necessary data support and parameter basis for priority calculation;

[0196] Priority calculation formula:

[0197]

[0198] P dev,i is the priority of the ith device; α is the energy consumption weight coefficient, ranging from 0 to 1; E i is the real-time energy consumption of the ith device, obtained from the energy consumption audit report; β is the critical weight coefficient, ranging from 0 to 1; K i 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 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 is the high energy consumption flag of the i-th device. If the device is a high energy consumption non-critical device, it is 1, otherwise it is 0; φ is the time fluctuation coefficient, ranging from 0 to 1; O i is the operating time of the ith device; τ is the time period parameter, ranging from 0 to 1;

[0199] After calculating the priority P of each device dev,i After that, it is necessary to sort the devices, evaluate the energy consumption distribution and load distribution of the system, consider the distance from the device to the power supply and the probability of failure, and generate the power supply sequence weight S dev,i Provide a comprehensive basis for evaluation;

[0200] Electricity supply order adjustment formula:

[0201]

[0202] S dev,i : power supply sequence weight of the ith device; P dev,i : the priority of the ith device; N: the total number of online devices, obtained from the energy consumption audit report; M: the number of high-energy-consuming non-critical devices, obtained from the shutdown records of high-energy-consuming non-critical devices; H k: The energy consumption of the kth high-energy-consuming non-critical device, obtained from the energy consumption audit report; T: The total energy consumption of all online devices, obtained from the energy consumption audit report; η: The device distance weight coefficient, ranging from 0 to 1; L i : the distance from the ith device to the power supply; θ: distance attenuation factor, ranging from 0 to 1; ψ: fault probability weight coefficient, ranging from 0 to 1; F i : Failure probability of the i-th device; P dev,j : The priority of the jth device; F j : Failure probability of the jth device;

[0203] After calculating the power supply sequence weight S of each device dev,i After that, it is necessary to reorder the equipment, adjust the load distribution ratio of the main power supply and the backup power supply, conduct simulation tests, and generate a re-planned power supply order.

[0204] The solution aims to re-plan the power supply order by calculating the priority and power supply order weight of each device to ensure priority power supply to key equipment; comprehensively consider factors such as the equipment's energy consumption, criticality, importance, high energy consumption penalties, time fluctuations, etc. to ensure the comprehensiveness and rationality of the power supply strategy; dynamically adjust the power supply order according to real-time data to improve the adaptability and flexibility of the system.

[0205] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0206]

[0207] α·E i Design reason: High energy consumption equipment may require more power resources in emergency situations, so its energy consumption should be considered in the priority calculation. By introducing the energy consumption weight coefficient α, the impact of energy consumption on priority can be adjusted. If you want to lower the priority of high energy consumption equipment, you can set a higher α value.

[0208] β·K i Design reason: Critical equipment should be given priority in power supply in emergency situations to ensure the normal operation of important functions. By introducing the criticality weight coefficient β, the impact of criticality on priority can be adjusted. If you want to increase the priority of critical equipment, you can set a higher β value.

[0209] γ·I i Design reason: Important equipment should also be given priority in power supply in emergency situations to ensure the execution of critical tasks. By introducing the importance weight coefficient γ, the impact of importance on priority can be adjusted. If you want to increase the priority of important equipment, you can set a higher γ value.

[0210] δ·log(1+Gi ) Design reason: High energy consumption non-critical equipment should be restricted or shut down in emergency situations to save power resources. By introducing the high energy consumption penalty coefficient δ, the degree of penalty for high energy consumption equipment can be adjusted. If you want to strictly punish high energy consumption equipment, you can set a higher δ value.

[0211] Design reason: The operation requirements of the equipment may be different in different time periods. By introducing the time fluctuation coefficient φ and the sine function, the periodic fluctuation of the equipment operation time can be considered. This helps to dynamically adjust the priority of the equipment to make it more in line with the actual operation situation.

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

[0213] Energy consumption weight 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 consumption monitoring devices (such as smart meters) installed on each device, and obtained from energy consumption audit reports.

[0215] Criticality weight coefficient β: set according to actual conditions, with a value range between 0 and 1.

[0216] Criticality score K i : Based on the criticality assessment results of the equipment, the value range is between 0 and 1. For example, the criticality score of a ventilator is 0.9, and the criticality score of general lighting equipment is 0.2.

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

[0218] Importance Rating I i : Based on the importance evaluation result of the device, the value range is between 0 and 1. For example, the importance score of a monitor is 0.8, and the importance score of an ordinary printer is 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 mark G i :Set according to the energy consumption characteristics of the equipment. If the equipment is a high-energy-consuming non-critical equipment, it is 1, otherwise it is 0. For example, the high-energy consumption flag of air-conditioning equipment is 1, and the high-energy consumption flag of general lighting equipment is 0.

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

[0222] Running time i:Real-time collection of equipment operating time through sensors and monitoring systems.

[0223] Time cycle 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 operation cycle of the equipment.

[0224] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0225]

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

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

[0228] Distance weight factor Consider the impact of the distance between the device and the power source on the power supply order. Devices that are closer will be given priority to reduce power transmission losses and improve system stability.

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

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

[0231] Priority P dev,i Calculated by 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 shutdown records of high-energy-consuming non-critical devices; the energy consumption of high-energy-consuming non-critical devices G 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 weight coefficient η is set according to the actual situation, and the value range is between 0 and 1; the distance L from the device to the power supply i Obtained through physical measurement or layout diagram; the distance attenuation factor θ is set according to the actual situation, and the value range is between 0 and 1; the failure probability weight coefficient ψ is set according to the actual situation, and the value range is between 0 and 1; the failure probability F of the equipment i Calculated based on historical failure data and equipment maintenance records; It is the sum of the failure probabilities of all online devices.

[0232] Assume that the Fangcang Hospital has 5 devices (N=5), and the parameters are as follows:

[0233] Equipment 1:

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

[0235] Device 2:

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

[0237] Device 3:

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

[0239] Device 4:

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

[0241] Device 5:

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

[0243] α=0.4: energy consumption weight coefficient; β=0.3: criticality weight coefficient; γ=0.2: importance weight coefficient; δ=0.1: high energy consumption penalty coefficient; φ=0.1: time fluctuation coefficient; τ=1 hour: time cycle parameter; priority calculation formula

[0244]

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

[0246] Equipment 1:

[0247]

[0248] Device 2:

[0249]

[0250] Device 3:

[0251]

[0252] Device 4:

[0253]

[0254] Device 5:

[0255]

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

[0257] Device 1: Priority is about 1.15.

[0258] Explanation: It has a high energy consumption and criticality score, is not a high energy consumption device, and has a moderate running time, so it is given a higher priority.

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

[0260] Device 2: Priority is about 0.80.

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

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

[0263] Device 3: Priority is about 1.46.

[0264] Specific explanation: Although the energy consumption is high, the priority is higher because the criticality and importance scores are low and it is a high energy consumption device.

[0265] Threshold analysis: Priority 1.46 is greater than 1, which is a high priority. This indicates that device 3 should be given priority in power supply in an emergency.

[0266] Device 4: Priority is approximately 0.75.

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

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

[0269] Device 5: Priority is approximately 1.32.

[0270] Specific explanation: The energy consumption is moderate, the criticality and importance scores are moderate, and it is not a high-energy consumption device, so it has a higher priority.

[0271] Threshold analysis: Priority 1.32 is greater than 1, which is a high priority. This indicates that device 5 should be given priority in power supply in an emergency.

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

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

[0274] After the extreme environmental conditions are alleviated, a comprehensive health check of the shelter hospital's power system is performed using the power distribution adjustment records generated during the emergency to ensure that all equipment and circuits are restored to normal operation. The health check includes detailed inspection of each part of the power system to identify potential problems and failure points. Based on the results of the health check, a detailed maintenance report is generated to record the problems found during the inspection and provide specific maintenance recommendations and improvement measures to ensure that the power system can operate more stably and reliably in the future.

[0275] After the extreme environmental conditions are alleviated, the system uses the power distribution adjustment records generated during the emergency to initiate a comprehensive power system health check, and conduct detailed inspections of all parts of the power system, including the main power supply, backup power supply, circuit connections, and key equipment. Through the inspection results, any problems and potential risks in the power system are identified and recorded. Finally, based on the results of the health check, a detailed maintenance report is generated, recording all the problems found, and specific maintenance recommendations and improvement measures are proposed to ensure that the power system can operate more stably and reliably in the future.

[0276] Optionally, after the extreme environmental condition is alleviated, a comprehensive power system health check is performed using the power distribution adjustment record under emergency conditions, and a detailed maintenance report is generated based on the results of the comprehensive power system health check, including:

[0277] Use environmental sensors to continuously monitor the extreme condition parameters of the shelter's environment, and generate an environmental condition mitigation report when it is detected that the extreme environmental conditions have been alleviated;

[0278] Using the emergency power distribution adjustment record, all operation data of the power supply system during the emergency is collected to generate an emergency operation data set;

[0279] Building a power system health check model based on the emergency operation data set and in combination with normal operating parameters and historical maintenance records of the power system;

[0280] Using the power system health check model, a comprehensive health check is performed on the power system, the operating status and potential problems of the power system during an emergency are evaluated, and a health check result is generated;

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

[0282] Figure 2 The present application provides a schematic diagram of a structure of a shelter emergency power supply processing system suitable for use in extreme environments, such as Figure 2 As shown, the device comprises:

[0283] An evaluation module 21 is used to detect and record the extreme condition parameters of the environment in which the shelter is located by using environmental sensors, and to evaluate the potential risks faced by the power supply system by combining historical data with 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 according to 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 supply system, and generate an optimized power distribution plan;

[0285] The planning module 23 is used to immediately start the emergency response mechanism based on the optimized power distribution plan when the system detects an emergency in the cabin, re-plan the power supply order using the priority allocation algorithm, 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 conditions;

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

[0287] Figure 2 The emergency power supply processing system for shelters in extreme environments can be implemented Figure 1 The implementation principle and technical effect of the method for processing emergency power supply for shelters in extreme environments described in the embodiment shown are not described in detail. The specific manner in which each module and unit performs operations in the above embodiment of a shelter emergency power supply processing system suitable for extreme environments has been described in detail in the embodiment of the method, and will not be described in detail here.

[0288] In one possible design, Figure 2 The embodiment shown in the figure is a shelter emergency power supply processing system suitable for use in extreme environments and 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 called and executed by the processing component 32 .

[0290] The processing component 32 is used to: use environmental sensors to detect and record extreme condition parameters of the environment in which the cabin is located, combine historical data with real-time monitoring data, evaluate the potential risks faced by the power supply system, and generate an environmental risk assessment report; according to the environmental risk assessment report, use an intelligent scheduling algorithm 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 circuit of the power supply system, and generate an optimized power distribution plan; when the system detects an emergency in the cabin, based on the optimized power distribution plan, immediately start the emergency response mechanism, use the priority allocation algorithm to re-plan the power supply order, perform energy consumption audits on all online devices, identify and shut down high-energy consumption non-critical devices, and generate emergency power distribution adjustment records; after the extreme environmental conditions are alleviated, use the emergency power distribution adjustment records 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 method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

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

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

[0294] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0295] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

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

[0297] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment is a method for processing emergency power supply for a shelter in an extreme environment.

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

[0299] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0300] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0301] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for processing emergency power supply for shelters in extreme environments, characterized in that: The following steps are involved: Use environmental sensors to detect and record the extreme condition parameters of the shelter's environment, combine historical data with real-time monitoring data, evaluate the potential risks faced by the power system, and generate an environmental risk assessment report; According to the 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 circuit of the power supply system, and generate an optimized power distribution plan; When the system detects an emergency in the shelter, based on the optimized power distribution plan, the emergency response mechanism is immediately activated, the power supply order is re-planned using the priority allocation algorithm, energy consumption audits are performed 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; After the extreme environmental conditions are alleviated, a comprehensive health check of the power system is performed using the emergency power distribution adjustment record, and a detailed maintenance report is generated based on the results of the comprehensive health check of the power system.

2. The method according to claim 1, characterized in that According to the 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 circuit of the power supply system, and generate an optimized power distribution plan, 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 Fangcang shelter hospital, these data are collected and integrated to generate a comprehensive data set containing information on environmental conditions and power consumption trends; Based on the comprehensive data set, potential risks that the power system may face under current environmental conditions are evaluated to generate an environmental risk assessment report; Based on the environmental risk assessment report, an intelligent scheduling algorithm is used to optimize the load distribution ratio between the main power supply and the backup power supply in combination with the power demand forecast and power supply capacity assessment of the Fangcang Cabin Hospital, and a preliminary power distribution strategy is generated; For the preliminary power distribution strategy, perform thermal management analysis on the internal circuit of the power system, evaluate the temperature change inside the power system under different load distribution conditions, and generate a thermal management analysis report; Based on the thermal management analysis report, the preliminary power distribution strategy is further optimized to generate an optimized power distribution solution.

3. The method according to claim 2, characterized in that Based on the environmental risk assessment report, the intelligent scheduling algorithm is used, combined with the power demand forecast and power supply capacity assessment of the Fangcang Hospital, to optimize the load distribution ratio between the main power supply and the backup power supply, and generate a preliminary power distribution strategy, including: When calculating the load sharing ratio L of 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 data set; pre-process the data, 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 of the power supply operation time, and provide data support for the calculation of the load distribution ratio; Load distribution ratio optimization formula: Among them, L i is the load distribution ratio of the i-th power source; α is the power demand weight coefficient, ranging from 0 to 1; D i is the power demand forecast of the i-th power source; β is the power supply capacity weight coefficient, ranging from 0 to 1; S i is the power supply capacity assessment of the i-th power source; γ is the risk weight coefficient, ranging from 0 to 1; R i is the risk assessment value of the i-th power supply; δ is the high energy consumption penalty coefficient, ranging from 0 to 1; H i is the high energy consumption flag of the i-th power supply, which is 1 if the power supply is high energy consumption, otherwise it is 0; φ is the time fluctuation coefficient, which ranges from 0 to 1; T i is the operating time of the ith power supply; τ is the time period parameter, ranging from 0 to 1; N is the total number of power supplies; After calculating the load distribution ratio L of each power supply i Finally, combined with the total power demand, the power distribution is preliminarily calculated; the demand fluctuation coefficient and risk adjustment coefficient are introduced to evaluate the failure probability and operating cost, and the power distribution strategy is further optimized to generate a preliminary power distribution strategy P i ; Preliminary power distribution strategy generation formula: Among them, P i is the preliminary power allocation strategy of the i-th power supply; L i is the load distribution ratio of the i-th power source; T is the total power demand; η is the demand fluctuation coefficient, ranging from 0 to 1; D i is the power demand forecast of the ith power source; is the average power demand of all power sources; σ D is the standard deviation of electricity demand; θ is the risk adjustment coefficient, ranging from 0 to 1; R i is the risk assessment value of the ith power supply; ψ is the failure probability weight coefficient, ranging from 0 to 1; F i is the failure probability of the i-th power supply; λ is the cost weight coefficient, ranging from 0 to 1; C i is the operating cost of the ith power source; L j is the load distribution ratio of the jth power source; R j is the risk assessment value of the jth power supply; F j is the failure probability of the jth power supply; C j is the operating cost of the jth power supply; By calculating the load sharing ratio L of each power source i and the preliminary power distribution strategy P i , combining the total power demand and multiple evaluation factors to generate the final preliminary power allocation strategy.

4. The method according to claim 2, characterized in that: Based on the comprehensive data set, potential risks that the power system may face under current environmental conditions are evaluated to generate an environmental risk assessment report, including: Using the comprehensive data set, cleaning and normalizing the data, removing outliers and missing values, and generating a preprocessed data set; According to the preprocessed data set, using statistical analysis methods, correlation analysis is performed on various indicators to identify features closely related to potential risks of the power supply system and generate a list of features with high correlation; Based on the list of features with high correlation, select features with high correlation as components of the input vector to construct a risk assessment model; Using the input vector, training the risk assessment model, evaluating the generalization ability of the model through cross-validation, and 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, and a detailed environmental risk assessment report is generated.

5. The method according to claim 2, characterized in that: The preliminary power distribution strategy is used to perform thermal management analysis on the internal circuit of the power system, evaluate the temperature change inside the power system under different load distribution conditions, and generate a thermal management analysis report, including: Using the preliminary power distribution strategy, operating data of the internal circuits of the power system under different load distribution conditions are collected to generate a load distribution data set; Constructing a thermal management analysis model based on the load distribution data set and design parameters of the internal circuit of the power system; calibrating and verifying the thermal management analysis model using the load distribution data set to generate a calibrated thermal management analysis model; Based on the calibrated thermal management analysis model, the temperature change inside the power system under different load distribution conditions is simulated and analyzed, the thermal performance of the power system under various load distribution conditions is evaluated, and the temperature change analysis results are generated; Based on the temperature change analysis results, a comprehensive evaluation is performed on the thermal management performance of the power system under different load distribution conditions, and a thermal management analysis report is generated.

6. The method according to claim 1, characterized in that When the system detects an emergency in the shelter, based on the optimized power distribution plan, the emergency response mechanism is immediately activated, the power supply order is re-planned using the priority allocation algorithm, energy consumption audits are performed on all online devices, high-energy-consuming non-critical devices are identified and shut down, and power distribution adjustment records under emergency conditions are generated, including: When the system detects an emergency in the shelter, it immediately triggers the emergency response mechanism and generates an emergency detection report; Based on the emergency detection report, immediately start the emergency response mechanism, call the pre-prepared emergency response plan, and generate an emergency response start record; Re-planning the power supply sequence by using the optimized power distribution scheme in combination with a priority distribution algorithm to generate a re-planned power supply sequence; Conduct 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 non-critical devices with high energy consumption, immediately shut down these devices, and generate a shutdown record of non-critical devices with high energy consumption; The re-planned power supply sequence and the shutdown records of high-energy-consuming non-critical equipment are combined to generate a record of power distribution adjustment in emergency situations.

7. The method according to claim 6, characterized in that The method of utilizing the optimized power distribution scheme in combination with a priority distribution algorithm to re-plan the power supply sequence and generate a re-planned power supply sequence includes: When calculating the priority P of each device dev,i Previously, it was necessary to collect and pre-process energy consumption data in real time, evaluate the criticality and importance of equipment, identify non-critical equipment with high energy consumption, and consider the periodic fluctuations of equipment operation time to provide the necessary data support and parameter basis for priority calculation; Priority calculation formula: P dev,i is the priority of the ith device; α is the energy consumption weight coefficient, ranging from 0 to 1; E i is the real-time energy consumption of the ith device, obtained from the energy consumption audit report; β is the critical weight coefficient, ranging from 0 to 1; K i 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 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 is the high energy consumption flag of the i-th device. If the device is a high energy consumption non-critical device, it is 1, otherwise it is 0; φ is the time fluctuation coefficient, ranging from 0 to 1; O i is the operating time of the ith device; τ is the time period parameter, ranging from 0 to 1; After calculating the priority P of each device dev,i After that, it is necessary to sort the devices, evaluate the energy consumption distribution and load distribution of the system, consider the distance from the device to the power supply and the probability of failure, and generate the power supply sequence weight S dev,i Provide a comprehensive basis for evaluation; Electricity supply order adjustment formula: S dev,i : power supply sequence weight of the ith device; P dev,i : the priority of the ith device; N: the total number of online devices, obtained from the energy consumption audit report; M: the number of high-energy-consuming non-critical devices, obtained from the shutdown record of high-energy-consuming non-critical devices; G k : The energy consumption of the kth high-energy-consuming non-critical device, obtained from the energy consumption audit report; T: The total energy consumption of all online devices, obtained from the energy consumption audit report; η: The device distance weight coefficient, ranging from 0 to 1; L i : the distance from the ith device to the power supply; θ: distance attenuation factor, ranging from 0 to 1; ψ: fault probability weight coefficient, ranging from 0 to 1; F i : Failure probability of the i-th device; P dev,i : The priority of the jth device; F j : Failure probability of the jth device; After calculating the power supply sequence weight S of each device dev,i After that, it is necessary to reorder the equipment, adjust the load distribution ratio of the main power supply and the backup power supply, conduct simulation tests, and generate a re-planned power supply order.

8. The method according to claim 6, characterized in that The energy consumption audit is performed on all online devices, real-time energy consumption data of each device is collected, and an energy consumption audit report is generated, including: Using the energy consumption monitoring devices installed on each device, the energy consumption data of each online device is collected in real time to generate a real-time energy consumption data set; Cleaning and normalizing the real-time energy consumption data set, removing outliers and missing values, and generating a preprocessed energy consumption data set; According to the preprocessed energy consumption data set, combined with the operating status and working mode of the equipment, an energy consumption audit model is constructed; The energy consumption audit model is used to analyze the real-time energy consumption data of each online device, evaluate the energy consumption and energy efficiency performance 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 to generate an energy consumption audit report.

9. 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 record under emergency conditions, and a detailed maintenance report is generated based on the results of the comprehensive power system health check, including: Use environmental sensors to continuously monitor the extreme condition parameters of the cabin's environment, and generate an environmental condition mitigation report when it is detected that the extreme environmental conditions have been alleviated; Using the emergency power distribution adjustment record, all operation data of the power supply system during the emergency is collected to generate an emergency operation data set; Building a power system health check model based on the emergency operation data set and in combination with normal operating parameters and historical maintenance records of the power system; Using the power system health check model, a comprehensive health check is performed on the power system, the operating status and potential problems of the power system during an emergency are evaluated, and a health check result is generated; Based on the health check results, a comprehensive analysis of the health status of the power system is conducted, necessary maintenance suggestions and improvement measures are put forward, and a detailed maintenance report is generated.

10. A shelter emergency power supply processing system suitable for use in extreme environments, characterized in that: include: The evaluation module is used to detect and record the extreme condition parameters of the shelter's environment using environmental sensors, combine historical data with real-time monitoring data, evaluate the potential risks faced by the power system, and generate an environmental risk assessment report; An adjustment module is used to optimize the power distribution strategy by using an intelligent scheduling algorithm according to 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 supply system, and generate an optimized power distribution plan; The planning module is used to immediately start the emergency response mechanism based on the optimized power distribution plan when the system detects an emergency in the shelter, re-plan the power supply order using the priority allocation algorithm, 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 conditions; The execution module is used to perform a comprehensive power system health check using the power distribution adjustment record under the emergency situation after the extreme environmental conditions are alleviated, and generate a detailed maintenance report based on the results of the comprehensive power system health check.

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