A method and system for optimizing power supply of a shelter hospital based on micro-grid technology

Abstract

The application provides a shelter hospital power supply optimization method and system based on micro-grid technology, wherein, through monitoring the state information of each power consumption unit in the shelter hospital, the embodiment of the application analyzes the monitoring data by using a deep reinforcement learning algorithm, generates a dynamic adjustment strategy of a distributed power supply, evaluates and optimizes the charging and discharging strategy of an energy storage system by combining a multi-objective optimization algorithm, adjusts the power supply configuration scheme by using an adaptive fuzzy logic control system according to the internal environmental parameters of the shelter hospital, the working characteristics of the power consumption unit and the patient comfort requirement, forms an energy consumption control strategy, applies a time series prediction model to predict the power demand change trend in combination with historical power consumption data and weather forecast information, generates a scheduling plan, and records and verifies the power transaction process by using a blockchain technology to generate a power distribution scheme, and the application realizes the operation and management of the power system of the shelter hospital through comprehensive monitoring, intelligent analysis and adaptive control, and ensures reliable power supply.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of power supply technology for makeshift hospitals, and in particular to a power supply optimization method for makeshift hospitals based on microgrid technology. Background Technology

[0002] In modern enterprise operations, especially in e-commerce, finance, and marketing, data collection is a crucial task. Enterprises need to monitor market dynamics, competitor activities, and customer needs in real time to make timely and effective decisions. Specific requirements include: enterprises need to adjust the frequency, type, and scope of data collection according to business needs; data collection must be real-time to ensure the timeliness and accuracy of information; and the data collection process should be automated as much as possible to reduce labor costs and improve efficiency.

[0003] Existing solutions employ deep reinforcement learning algorithms to analyze monitoring data and generate dynamic adjustment strategies for distributed power sources. Multi-objective optimization algorithms are used to comprehensively evaluate the charging and discharging optimization strategies of the energy storage system, and a fast switching control algorithm is employed for adaptive adjustments in the event of external grid failures. Furthermore, an adaptive fuzzy logic control system adjusts the power configuration based on internal environmental parameters and patient comfort requirements. A time-series forecasting model is used to predict electricity demand trends for the energy consumption control strategy. Finally, blockchain technology is used to ensure transparency and security in the scheduling and planning process. However, these solutions face a series of challenges: First, the large amount of real-time data acquisition and processing involved leads to high computational resource consumption and long latency; second, although deep reinforcement learning and multi-objective optimization methods can provide relatively accurate results, they are less adaptable to new situations or outliers and may not be able to make optimal decisions in a timely manner; third, integrating various functional modules into a highly efficient and collaborative whole is quite difficult, and maintaining high reliability while ensuring seamless connection between each link presents challenges; finally, although blockchain technology has been introduced to enhance data security and integrity, further protection measures are still needed to resist potential attacks during actual deployment. Furthermore, the existing technologies are too specialized and lack an intuitive and easy-to-understand user interface, making them difficult for non-professionals to understand and use. Summary of the Invention

[0004] This invention provides a method and system for optimizing power supply in makeshift hospitals based on microgrid technology. This addresses several challenges in existing technologies, including high computational resource consumption and long latency due to the large amount of real-time data acquisition and processing involved; the difficulty in integrating various functional modules into a highly efficient and collaborative whole, particularly in ensuring seamless integration of each link while maintaining high reliability; insufficient security considerations: although blockchain technology has been introduced to enhance data security and integrity, further enhanced protection measures are still needed to defend against potential attacks during actual deployment; and many current technologies are overly specialized and lack intuitive user interfaces, making them difficult for non-professionals to understand and use.

[0005] In a first aspect, embodiments of the present invention provide a method for optimizing power supply in makeshift hospitals based on microgrid technology, comprising:

[0006] The actual power demand of each power-consuming unit in the makeshift hospital, the current operating status of the power supply system, and the status information of the external power grid are monitored to obtain monitoring data. The monitoring data is then analyzed using a deep reinforcement learning algorithm to obtain a dynamic adjustment strategy for the distributed power supply.

[0007] A multi-objective optimization algorithm is used to comprehensively evaluate the dynamic adjustment strategy and the current state of the energy storage system to obtain an energy storage system charging and discharging optimization strategy.

[0008] When an external power grid fault is detected, a fast switching control algorithm is used to adaptively adjust the charging and discharging optimization strategy of the energy storage system to obtain a power configuration scheme in islanded mode.

[0009] Based on the internal environmental parameters of the makeshift hospital, the working characteristics of the power consumption unit, and the comfort requirements of patients, an adaptive fuzzy logic control system is used to adjust the power configuration scheme to obtain an energy consumption control strategy.

[0010] By combining historical electricity consumption data and weather forecast information, a time series prediction model is applied to predict the trend of electricity demand changes in the energy consumption control strategy, resulting in a scheduling plan for distributed power sources and energy storage systems. Blockchain technology is used to record and verify the electricity trading process of the scheduling plan, generating an electricity allocation scheme.

[0011] Optionally, based on the internal environmental parameters of the makeshift hospital, the operating characteristics of the power consumption units, and the comfort requirements of patients, an adaptive fuzzy logic control system is used to adjust the power configuration scheme to obtain an energy consumption control strategy, including:

[0012] Environmental sensing sensors were used to monitor the internal environmental parameters, operating characteristics of power supply units, and patient comfort requirements of the makeshift hospital, resulting in an environmental and demand dataset.

[0013] The environmental and demand dataset is analyzed using an adaptive fuzzy logic control system. Combined with the power configuration scheme, the applicability of energy consumption modes under different conditions is evaluated, and a preliminary energy consumption assessment report is obtained.

[0014] The accuracy of the preliminary energy consumption assessment report is improved by using an ensemble learning algorithm to correct the accuracy of energy consumption pattern matching, resulting in a corrected energy consumption assessment report.

[0015] Using a genetic algorithm, based on the corrected energy consumption assessment report, the energy consumption patterns of different areas within the makeshift hospital are optimized, and optimization suggestions are obtained.

[0016] Based on the optimization suggestions, and combined with the flow of people and time distribution patterns in the makeshift hospital, the energy consumption allocation ratio of each area is dynamically adjusted using a scenario-aware algorithm to formulate an optimized energy consumption allocation plan.

[0017] Using digital twin technology, the optimized energy consumption allocation plan is simulated and tested to generate an energy consumption control strategy.

[0018] Optionally, based on the optimization suggestions and considering the flow and time distribution patterns of people within the makeshift hospital, a scenario-aware algorithm is used to dynamically adjust the energy consumption allocation ratio of each area, and an optimized energy consumption allocation plan is formulated, including:

[0019] Using a spatiotemporal prediction model, the trend of personnel flow in the preliminary energy consumption allocation plan is predicted, and the personnel flow prediction results are obtained.

[0020] Based on the population flow prediction results, the energy consumption demand changes in each region during different time periods are analyzed using machine learning algorithms to generate dynamic energy consumption demand predictions.

[0021] By combining the dynamic energy demand forecast with the preliminary energy allocation plan, and using a multi-objective optimization algorithm to re-evaluate the energy allocation ratio of each region, energy use is optimized while meeting the needs of personnel mobility, and a re-optimized energy allocation scheme is generated.

[0022] By utilizing augmented reality technology and combining it with the aforementioned optimized energy allocation scheme, an energy management interface is provided for the managers of makeshift hospitals, supporting real-time monitoring and manual intervention, and generating an optimized energy allocation plan.

[0023] Optionally, digital twin technology is used to simulate and test the optimized energy consumption allocation plan to generate an energy consumption control strategy, including:

[0024] The optimized energy consumption allocation plan was simulated and tested using digital twin technology, and the simulation test results were obtained.

[0025] The application effects of the simulation test results under different environmental conditions are simulated through a virtual simulation platform, generating energy consumption performance data under various environmental conditions.

[0026] By applying big data analytics, the energy consumption data is processed to identify key factors affecting energy efficiency and their degree of impact, thus generating a key factor impact report.

[0027] Based on the aforementioned key factor impact report, a deep learning algorithm is used to further optimize the optimized energy consumption allocation plan, generating a draft optimized energy consumption control strategy.

[0028] By applying a real-time feedback mechanism, the actual operating data is compared and analyzed with the optimized draft energy consumption control strategy, and the parameter settings in the draft energy consumption control strategy are dynamically adjusted to obtain the optimal draft energy consumption control strategy.

[0029] Visualization technology is used to demonstrate the implementation effect of the optimal energy consumption control strategy, thus completing the formulation of the energy consumption control strategy.

[0030] Optionally, big data analytics can be applied to process the energy consumption data, identify key factors affecting energy efficiency and their degree of influence, and generate a key factor impact report, including:

[0031] Big data analytics was used to process energy consumption data under various environmental conditions to obtain a preliminary list of key factors. These environmental conditions included: climate conditions, time periods, population density, equipment operating status, and hospital emergencies.

[0032] The factors in the preliminary list of key factors are analyzed in depth using an association rule mining algorithm to obtain the key factor interaction matrix.

[0033] Based on the interaction matrix of the key factors, a multi-dimensional data analysis method is used to evaluate the impact of different combinations of key factors and obtain a comprehensive impact assessment table.

[0034] Based on the comprehensive impact assessment table, an energy efficiency prediction model is established using machine learning algorithms to obtain an energy efficiency change trend chart;

[0035] The preliminary list of key factors, the key factor interaction matrix, the comprehensive impact assessment table, and the energy consumption efficiency change trend chart are integrated to form a key factor impact report.

[0036] Optionally, a multi-objective optimization algorithm is used to comprehensively evaluate the dynamic adjustment strategy and the current state of the energy storage system to obtain an energy storage system charging and discharging optimization strategy, including:

[0037] A multi-objective optimization algorithm is used to comprehensively evaluate the dynamic adjustment strategy and the current state of the energy storage system to obtain a preliminary charging and discharging optimization scheme.

[0038] The preliminary charge-discharge optimization scheme is optimized using machine learning algorithms to calculate battery health status, charge-discharge efficiency, and cost factors, and to generate a refined charge-discharge optimization scheme.

[0039] Using Internet of Things (IoT) technology, the operating status of the energy storage system in the refined charge and discharge optimization scheme is monitored in real time, and real-time monitoring data is generated.

[0040] Based on the real-time monitoring data, a predictive maintenance algorithm is used to predict potential failure points of the energy storage system and generate a preventive maintenance plan.

[0041] Based on the aforementioned preventative maintenance plan, a dynamic adjustment algorithm is used to adjust the refined charge-discharge optimization scheme in real time, generating a target charge-discharge optimization strategy.

[0042] Optionally, by combining historical electricity consumption data and weather forecast information, a time series forecasting model is applied to predict the electricity demand change trend of the energy consumption control strategy, resulting in a scheduling plan for distributed power sources and energy storage systems. Blockchain technology is then used to record and verify the electricity trading process of the scheduling plan, generating an electricity allocation scheme, including:

[0043] By combining historical electricity consumption data and weather forecast information, a time series prediction model is applied to predict the trend of electricity demand changes for the energy consumption control strategy, and preliminary electricity demand prediction results are obtained.

[0044] The preliminary electricity demand forecast results are analyzed using deep learning algorithms to calculate the impact of holidays and special events, and a detailed electricity demand forecast report is generated.

[0045] Based on the detailed electricity demand forecast report, a multi-objective optimization algorithm is used to optimize the scheduling plan of distributed power sources and energy storage systems, generating an optimized scheduling plan.

[0046] The optimized scheduling plan is simulated and verified using a simulation testing platform to evaluate its feasibility under different scenarios and generate a simulation test report.

[0047] Based on the simulation test report, the optimized scheduling plan is dynamically adjusted using a real-time feedback mechanism to generate the best scheduling plan.

[0048] By using blockchain technology, the power trading process of the optimal scheduling plan is recorded and verified to generate a power allocation scheme.

[0049] Secondly, embodiments of this application provide a power supply optimization method for mobile hospitals based on microgrid technology, including:

[0050] The monitoring module monitors the actual power demand of each power-consuming unit in the makeshift hospital, the current operating status of the power supply system, and the status information of the external power grid to obtain monitoring data. The monitoring data is then analyzed using a deep reinforcement learning algorithm to obtain a dynamic adjustment strategy for the distributed power supply.

[0051] The evaluation module uses a multi-objective optimization algorithm to comprehensively evaluate the dynamic adjustment strategy and the current state of the energy storage system, and obtains the energy storage system charging and discharging optimization strategy.

[0052] The adjustment module, when detecting an external power grid fault, uses a fast switching control algorithm to adaptively adjust the charging and discharging optimization strategy of the energy storage system to obtain a power configuration scheme in islanded mode.

[0053] The adjustment module, based on the internal environmental parameters of the makeshift hospital, the working characteristics of the power consumption unit, and the patient comfort requirements, uses an adaptive fuzzy logic control system to adjust the power configuration scheme to obtain an energy consumption control strategy.

[0054] The recording module combines historical electricity consumption data and weather forecast information, applies a time series prediction model to predict the trend of electricity demand changes in the energy consumption control strategy, obtains the scheduling plan of distributed power sources and energy storage systems, and uses blockchain technology to record and verify the electricity trading process of the scheduling plan, generating an electricity allocation scheme.

[0055] Thirdly, embodiments of the present invention provide a computing device, including a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a power supply optimization method for a mobile hospital based on microgrid technology as described in any of the first aspects.

[0056] Fourthly, embodiments of the present invention provide a computer storage medium storing computer program instructions, which, when executed by a processor, implement the power supply optimization method for makeshift hospitals based on microgrid technology as described in any one of the first aspects.

[0057] In this embodiment of the invention, the actual power demand of each power-consuming unit in the makeshift hospital, the current operating status of the power supply system, and the status information of the external power grid are monitored to obtain monitoring data. The monitoring data is then analyzed using a deep reinforcement learning algorithm to obtain a dynamic adjustment strategy for the distributed power supply.

[0058] A multi-objective optimization algorithm is used to comprehensively evaluate the dynamic adjustment strategy and the current state of the energy storage system to obtain an energy storage system charging and discharging optimization strategy.

[0059] When an external power grid fault is detected, a fast switching control algorithm is used to adaptively adjust the charging and discharging optimization strategy of the energy storage system to obtain a power configuration scheme in islanded mode.

[0060] Based on the internal environmental parameters of the makeshift hospital, the working characteristics of the power consumption unit, and the comfort requirements of patients, an adaptive fuzzy logic control system is used to adjust the power configuration scheme to obtain an energy consumption control strategy.

[0061] By combining historical electricity consumption data and weather forecast information, a time series prediction model is applied to predict the trend of electricity demand changes in the energy consumption control strategy, resulting in a scheduling plan for distributed power sources and energy storage systems. Blockchain technology is used to record and verify the electricity trading process of the scheduling plan, generating an electricity allocation scheme.

[0062] The technical solution provided by this invention enables intelligent management and optimization of the power supply system for makeshift hospitals, improving power utilization efficiency, enhancing system flexibility and reliability, improving patient experience, and ensuring the security and transparency of the power trading process. Specifically, it enhances transparency and security: the application of blockchain technology not only ensures transparency in the power trading process but also enhances data security and integrity, providing all participants with traceable and tamper-proof records, promoting the establishment of a fair and just power trading mechanism; it improves system robustness: when an external power grid fault is detected, the rapid switching control algorithm can quickly adjust the operating mode of the energy storage system to an islanded operation state, ensuring uninterrupted power supply to critical medical facilities within the makeshift hospital, and enhancing the reliability and stability of the entire power supply system.

[0063] These or other aspects of the invention will become more apparent from the following description of the embodiments. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 A flowchart illustrating a power supply optimization method for a mobile hospital based on microgrid technology, provided as an embodiment of the present invention;

[0066] Figure 2 A schematic diagram of a power supply optimization system for a mobile hospital based on microgrid technology is provided for an embodiment of the present invention;

[0067] Figure 3 This is a schematic diagram of the structure of a computing device provided in an embodiment of the present invention. Detailed Implementation

[0068] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0069] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0070] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0071] In existing technologies, data acquisition systems rely on pre-defined rules for data collection, and these rules remain fixed throughout system operation. However, in practical applications, the characteristics of data sources, data requirements, and business logic change over time, demanding that the data acquisition system adapt to these changes. Therefore, this invention provides a power supply optimization method for makeshift hospitals based on microgrid technology, such as... Figure 1 ,include:

[0072] Step 101: Monitor the actual power demand of each power-consuming unit in the makeshift hospital, the current operating status of the power supply system, and the status information of the external power grid to obtain monitoring data. Analyze the monitoring data using a deep reinforcement learning algorithm to obtain a dynamic adjustment strategy for the distributed power supply.

[0073] This step involves real-time monitoring of the power consumption of all electrical equipment within the makeshift hospital, such as medical instruments and lighting systems, while simultaneously monitoring the operational status of the entire power supply system, including voltage, current, and frequency, as well as the health of the external power grid. This monitoring data provides a comprehensive understanding of the current power supply and demand. Next, a deep reinforcement learning algorithm is used to analyze this data. This algorithm learns power demand patterns from a large amount of historical data and generates a dynamic adjustment strategy to optimize the operation of distributed power sources, such as solar panels and wind turbines, ensuring a balance between power supply and demand.

[0074] For example, in a makeshift field hospital, smart meters and sensor networks are installed to collect electricity consumption data from various wards and public areas. Simultaneously, information about the external power grid is obtained in cooperation with the local power company. Then, a software platform based on deep reinforcement learning analyzes this data, predicts renewable energy generation for the next few hours based on weather changes, and automatically adjusts the output power of distributed power sources to meet actual needs.

[0075] Step 102: Use a multi-objective optimization algorithm to comprehensively evaluate the dynamic adjustment strategy and the current state of the energy storage system to obtain the energy storage system charging and discharging optimization strategy;

[0076] In this step, after determining the dynamic adjustment strategy for distributed power sources, it is necessary to further consider how to effectively manage energy storage systems, such as battery packs. Multi-objective optimization algorithms play a crucial role in this process, simultaneously considering multiple factors such as cost-effectiveness, energy efficiency, and environmental impact to find the optimal charging and discharging scheme for the energy storage system. This helps ensure a stable power supply even during peak or off-peak electricity demand periods.

[0077] Step 103: When an external power grid fault is detected, a fast switching control algorithm is used to adaptively adjust the charging and discharging optimization strategy of the energy storage system to obtain a power configuration scheme in islanded mode.

[0078] In this step, if an external power grid failure is detected, the fast switching control algorithm will be activated immediately, putting the makeshift hospital into "island" mode, meaning it will operate independently entirely relying on its internal distributed power sources and energy storage systems. At this point, the previously established energy storage system charging and discharging strategies will be reassessed and adjusted to ensure that critical equipment can continue to operate normally without external power support.

[0079] Step 104: Based on the internal environmental parameters of the makeshift hospital, the working characteristics of the power consumption unit, and the patient comfort requirements, an adaptive fuzzy logic control system is used to adjust the power configuration scheme to obtain an energy consumption control strategy.

[0080] In this step, to further refine the management of power resources, an adaptive fuzzy logic control system is introduced to fine-tune the power supply configuration scheme. This system can handle uncertainties and nonlinear relationships, flexibly adjusting the energy consumption of facilities such as air conditioning and lighting based on changes in environmental parameters such as temperature and humidity, as well as the operating modes of power-consuming units at different times. At the same time, it takes into account the comfort needs of patients, achieving more humanized energy management.

[0081] For example, during the high temperatures of summer, the adaptive fuzzy logic control system of the makeshift hospital will automatically adjust the air conditioning settings based on the data from the indoor temperature and humidity sensors, while reducing the lighting brightness in unnecessary areas, thus saving energy and ensuring a good treatment environment.

[0082] Step 105: Combining historical electricity consumption data and weather forecast information, apply a time series prediction model to predict the trend of electricity demand changes in the energy consumption control strategy, obtain the scheduling plan of distributed power sources and energy storage systems, and use blockchain technology to record and verify the electricity trading process of the scheduling plan to generate an electricity allocation scheme.

[0083] The final step in this process involves using historical electricity consumption records and the latest weather forecasts to accurately estimate future electricity demand using a time-series forecasting model. Based on this forecast, a more rational scheduling plan for distributed power sources and energy storage systems can be developed. Furthermore, blockchain technology is used to record and verify all scheduling decisions and their execution, ensuring the entire process is transparent and trustworthy.

[0084] For example, combining electricity consumption data from the past year with weather forecasts for the next week provided by the meteorological bureau, time series models predict continuous rainy weather next week, which may significantly reduce photovoltaic power generation. Therefore, the scheduling plan recommends increasing the charging capacity of the energy storage system in advance and appropriately reducing power supply to certain equipment in makeshift hospitals during non-emergency situations. All these decisions will be publicly released through a blockchain platform for review and oversight by relevant stakeholders.

[0085] The beneficial effects achieved by the embodiments of the present invention through the above steps are as follows:

[0086] By monitoring the actual power demand, power supply system status, and external power grid information of each electrical unit within the makeshift hospital, deep reinforcement learning algorithms are used to analyze the data and generate dynamic adjustment strategies. These strategies are then combined with multi-objective optimization algorithms to evaluate the charging and discharging strategies of the energy storage system. In the event of an external power grid failure, a fast switching control algorithm ensures stable power supply in islanded mode. An adaptive fuzzy logic control system adjusts the power configuration based on environmental parameters and patient comfort requirements, optimizing energy consumption. A time series forecasting model, combining historical data and weather forecasts, predicts trends in power demand and generates scheduling plans. These steps significantly improve the stability and efficiency of power supply, reduce operating costs, enhance system flexibility and reliability, and ensure the efficient operation of the makeshift hospital under various conditions.

[0087] Based on this, the present invention provides a specific embodiment, wherein step 104, receiving user-inputted collection rule parameters, the collection rule parameters including collection frequency, collection type, and collection range, specifically includes the following steps:

[0088] Step 401: Use environmental sensing sensors to monitor the internal environmental parameters of the makeshift hospital, the operating characteristics of the power supply units, and the comfort requirements of patients to obtain an environmental and demand dataset.

[0089] In this step, various environmental sensors, such as temperature, humidity, and light intensity sensors, are deployed to collect real-time environmental parameters inside the makeshift hospital. Simultaneously, the operating status and performance indicators of different electrical units are monitored, such as the operating efficiency of the air conditioning system and the energy consumption of lighting equipment. Furthermore, patient comfort feedback, such as preferences for temperature and lighting, is considered. All this information constitutes a comprehensive dataset for subsequent analysis.

[0090] For example, a makeshift hospital installed various types of sensors, including temperature and humidity sensors, carbon dioxide concentration detectors, and photosensors. These sensors not only continuously record the physical environment within the wards but also collect patient satisfaction ratings regarding the current environmental conditions through an intelligent questionnaire system. All this data is aggregated into a central database, forming a detailed environmental and needs dataset.

[0091] Step 402: Analyze the environmental and demand dataset using an adaptive fuzzy logic control system, and evaluate the applicability of energy consumption modes under different conditions in conjunction with the power configuration scheme to obtain a preliminary energy consumption assessment report.

[0092] In this step, the adaptive fuzzy logic control system is a control method based on fuzzy logic theory. It can handle uncertainties and nonlinear relationships and is suitable for complex and ever-changing environments. By analyzing environmental and demand datasets, the system can combine existing power supply configuration schemes to evaluate the effectiveness and applicability of various energy consumption modes under different environmental conditions and generate a preliminary energy consumption assessment report.

[0093] For example, in makeshift hospitals, an adaptive fuzzy logic control system assessed the rationality of the current air conditioning system settings based on collected data, such as trends in temperature and humidity within wards and patient feedback on the environment. The system found that during certain periods, excessively high air conditioning setpoints led to energy waste and patient discomfort. Based on this, the system proposed adjustments to the air conditioning setpoint and generated a preliminary energy consumption assessment report incorporating these findings.

[0094] Step 403: The preliminary energy consumption assessment report is corrected using an ensemble learning algorithm to improve the accuracy of energy consumption pattern matching, resulting in a corrected energy consumption assessment report;

[0095] In this step, ensemble learning algorithms are machine learning methods that improve the accuracy and robustness of overall predictions by combining the predictions of multiple models. Here, ensemble learning algorithms are used to further analyze and correct the data in the preliminary energy consumption assessment report, thereby improving the match between energy consumption patterns and actual needs, and ultimately generating a more accurate energy consumption assessment report.

[0096] For example, the initial energy consumption assessment report of the makeshift hospital was reanalyzed using various ensemble learning algorithms such as random forest and gradient boosting machine. By comparing the results of different algorithms, it was found that the energy consumption pattern estimation in some areas was biased. After correction, the new energy consumption assessment report provided more accurate energy consumption pattern suggestions, especially with significant improvements in energy consumption management during nighttime and early morning hours.

[0097] Step 404: Apply a genetic algorithm to optimize the energy consumption patterns of different areas within the makeshift hospital based on the corrected energy consumption assessment report, and obtain optimization suggestions;

[0098] In this step, the genetic algorithm, an optimization algorithm that simulates the natural selection process, iterates to find the optimal solution to the problem. Based on the corrected energy consumption assessment report, the genetic algorithm optimizes the energy consumption patterns of each area within the makeshift hospital to find the best energy allocation strategy, thereby reducing unnecessary energy consumption.

[0099] For example, genetic algorithms were applied to optimize the energy consumption patterns of different functional areas in makeshift hospitals, such as wards, operating rooms, and public rest areas. For instance, the optimization algorithm determined that solar power should be prioritized during peak daytime hours, while relying more on energy storage systems during off-peak hours. These recommendations aim to maximize the use of renewable energy while ensuring a stable power supply for critical medical facilities.

[0100] Step 405: Based on the optimization suggestions, and combined with the flow of people and time distribution patterns in the makeshift hospital, use the scenario-aware algorithm to dynamically adjust the energy consumption allocation ratio of each area and formulate an optimized energy consumption allocation plan.

[0101] In this step, the scenario-aware algorithm can automatically adjust its decisions based on changes in conditions within a specific scenario. Specifically, it considers the flow of people and their time distribution patterns within the makeshift hospital, dynamically adjusting the energy allocation ratio for each area to meet the actual needs at different times, thereby developing a more flexible and efficient energy allocation plan.

[0102] For example, based on optimization suggestions and combined with the entry and exit records of personnel and daily activity schedules within the makeshift hospital, the scenario-aware algorithm dynamically adjusted the energy consumption allocation of each area. For instance, during peak patient visit times, ventilation and lighting intensity in public areas were increased; while at night or when there was less foot traffic, energy consumption in these areas was appropriately reduced to save electricity.

[0103] Step 406: Using digital twin technology, simulate and test the optimized energy consumption allocation plan to generate an energy consumption control strategy;

[0104] In this step, digital twin technology is used to simulate and analyze physical entities by creating virtual copies. At this stage, digital twin technology was used to build a virtual model of a makeshift hospital and test an optimized energy consumption allocation plan within it. Through simulation testing, the effectiveness of the plan can be verified, necessary adjustments can be made, and ultimately a complete energy consumption control strategy can be generated.

[0105] The beneficial effects achieved by the embodiments of the present invention through the above steps are as follows:

[0106] Environmental sensors monitor internal environmental parameters, electrical unit operating characteristics, and patient comfort requirements within the makeshift hospital, acquiring a comprehensive dataset. An adaptive fuzzy logic control system analyzes this data to assess the applicability of energy consumption patterns under different conditions, generating a preliminary energy consumption assessment report. An ensemble learning algorithm further refines the report's accuracy, improving the precision of energy consumption pattern matching. A genetic algorithm optimizes the energy consumption pattern design for different areas, providing specific optimization suggestions. A context-aware algorithm, combining personnel flow and time distribution patterns, dynamically adjusts energy allocation ratios to formulate an optimized energy allocation plan. Digital twin technology is used for simulation testing, generating the final energy consumption control strategy. These steps significantly improve the accuracy and flexibility of energy management, ensuring patient comfort, reducing energy waste, and enhancing overall operational efficiency.

[0107] Based on this, the present invention provides a specific embodiment. Step 405, according to the optimization suggestions, combined with the personnel flow and time distribution patterns within the makeshift hospital, uses a scenario-aware algorithm to dynamically adjust the energy consumption allocation ratio of each area and formulate an optimized energy consumption allocation plan, specifically including the following steps:

[0108] Step 411: Using a spatiotemporal prediction model, predict the trend of personnel flow in the preliminary energy consumption allocation plan to obtain the personnel flow prediction results;

[0109] In this step, the spatiotemporal prediction model is a forecasting method that combines time series analysis and spatial distribution characteristics. It considers not only the trend of data changes over time but also the interactions between different locations. In this step, the spatiotemporal prediction model is used to predict the flow of people in different time periods and areas within the makeshift hospital, thereby helping to understand future activity patterns.

[0110] For example, in a makeshift hospital, historical visitor flow data was collected using people counters and cameras installed at the entrance. This data, combined with information from the patient appointment system, was used to construct a spatiotemporal prediction model. This model can predict the number of patients arriving each day for the next week and the duration of their stay in various areas, such as wards and treatment areas. For instance, the model might predict that 9:00 AM to 11:00 AM is the peak time for patient visits, while visitor flow gradually decreases after 3:00 PM.

[0111] Step 412: Based on the population flow prediction results, analyze the changes in energy consumption demand in each region during different time periods using machine learning algorithms to generate dynamic energy consumption demand predictions;

[0112] In this step, machine learning algorithms can learn patterns and make predictions from large amounts of data. Based on the results of population flow predictions, the machine learning algorithms can further analyze changes in energy demand in different regions over different time periods, thereby generating a dynamic energy demand forecast. This helps to more accurately estimate energy consumption in the future.

[0113] For example, machine learning algorithms such as support vector machines or random forests can be used to train the system on historical energy consumption data and predicted patient flow data from makeshift hospitals. For instance, if the model discovers that the energy consumption of the air conditioning system increases whenever the number of patients increases, the system can be adjusted in advance to accommodate higher energy demands when a large influx of patients is anticipated. Ultimately, the resulting dynamic energy demand forecasts can help managers better plan power supply.

[0114] Step 413: Combine the dynamic energy demand forecast with the preliminary energy allocation plan, and use a multi-objective optimization algorithm to re-evaluate the energy allocation ratio of each region, so as to optimize energy use while meeting the needs of personnel flow, and generate a re-optimized energy allocation plan.

[0115] In this step, the multi-objective optimization algorithm aims to simultaneously optimize multiple objective functions, such as cost minimization and efficiency maximization. In this step, the multi-objective optimization algorithm combines dynamic energy demand forecasting with the preliminary energy allocation plan, reassessing the energy allocation ratio of each region to ensure that the needs of population movement are met while maximizing energy conservation.

[0116] For example, suppose a preliminary energy allocation plan has determined the basic energy consumption configuration for each area. By incorporating dynamic energy demand forecasts, a multi-objective optimization algorithm recalculates the optimal energy consumption ratio for each area. For instance, during periods when a higher number of patients is expected, the intensity of lighting and ventilation in public areas might be increased; conversely, when fewer patients are expected, energy consumption in these areas might be reduced. In this way, the optimized plan achieves optimal energy use while maintaining service quality.

[0117] Step 414: Using augmented reality technology, combined with the aforementioned optimized energy consumption allocation scheme, provide an energy consumption management interface for the management personnel of the makeshift hospital, supporting real-time monitoring and manual intervention, and generating an optimized energy consumption allocation plan;

[0118] In this step, augmented reality (AR) technology overlays virtual information onto the real world, allowing users to see and interact with it intuitively. In this instance, AR technology is used to create an energy management interface, enabling managers to view energy consumption in real time and make manual adjustments as needed. This visualization tool improves management efficiency and facilitates more flexible energy management strategies.

[0119] For example, an AR-based mobile application could be developed to display the real-time energy consumption status of various areas in a makeshift hospital. Managers could scan a specific area using a smartphone or tablet camera, and the screen would display the area's current energy consumption level, temperature, humidity, and recommended energy adjustments. Furthermore, managers could directly interact with the interface, such as adjusting air conditioning temperature or changing lighting brightness, enabling immediate manual intervention. Such an interface not only provides real-time data feedback but also enhances the transparency and controllability of the decision-making process.

[0120] The beneficial effects achieved by the embodiments of the present invention through the above steps are as follows:

[0121] By using a spatiotemporal prediction model to predict the flow of people in makeshift hospitals, and combining this with machine learning algorithms to analyze changes in energy demand in different areas over different time periods, dynamic energy demand forecasts are generated. A multi-objective optimization algorithm comprehensively evaluates and adjusts the energy allocation ratio of each area, ensuring that energy use is optimized while meeting the needs of personnel flow, generating a further optimized energy allocation plan. Augmented reality technology provides managers with an intuitive energy management interface, supporting real-time monitoring and manual intervention, ultimately generating an optimized energy allocation plan. These steps significantly improve the accuracy and flexibility of energy management, enhance system response speed and efficiency, reduce energy waste, and simultaneously improve patient comfort and operational management levels.

[0122] Based on this, the present invention provides a specific embodiment. Step 406, according to the optimization suggestions, combined with the personnel flow and time distribution patterns within the makeshift hospital, uses a scenario-aware algorithm to dynamically adjust the energy consumption allocation ratio of each area and formulate an optimized energy consumption allocation plan, specifically including the following steps:

[0123] Step 421: Using digital twin technology, simulate and test the optimized energy consumption allocation plan to obtain simulation test results;

[0124] In this step, digital twin technology is a technique that simulates the behavior of a physical entity by creating a virtual copy. In this case, digital twins are used to build an accurate virtual model of a makeshift hospital and its internal systems to test an optimized energy allocation plan without actual operation. Simulation testing allows for the evaluation of the plan's performance under different conditions and the collection of data for further analysis.

[0125] Step 422: Using a virtual simulation platform, simulate the application effect of the simulation test results under different environmental conditions to generate energy consumption performance data under various environmental conditions;

[0126] In this step, the virtual simulation platform is a software tool used to simulate real-world conditions, allowing users to change input parameters and observe changes in output results. Here, the virtual simulation platform is used to simulate the application effect of the optimized energy consumption allocation plan under different environmental conditions, such as different weather conditions and seasonal changes, thereby generating a series of energy consumption performance data.

[0127] For example, based on the digital twin model established above, a virtual simulation platform was used to simulate the impact of different seasons and weather conditions on the energy consumption of makeshift hospitals. For instance, in hot summer weather, the air conditioning system requires more electricity to maintain a comfortable indoor temperature; while in winter, heating demand may need to be considered. Through these simulations, energy consumption performance data under different environmental conditions were generated, including key indicators such as hourly energy consumption and peak load.

[0128] Step 423: Apply big data analytics to process the energy consumption performance data, identify the key factors affecting energy efficiency and their degree of influence, and generate a key factor impact report;

[0129] In this step, big data analytics refers to the process of extracting valuable information from large, complex datasets. In this instance, big data analytics is used to process energy performance data generated by virtual simulations to identify which factors significantly impact energy efficiency and quantify the extent of these impacts. Ultimately, this results in a detailed report on the key factors influencing energy consumption.

[0130] Step 424: Based on the key factor impact report, use a deep learning algorithm to optimize the optimized energy consumption allocation plan and generate an optimized draft energy consumption control strategy;

[0131] In this step, deep learning algorithms, a machine learning method, are used to automatically learn features and make predictions from large amounts of data. At this stage, the deep learning algorithm, combined with information from the key factors impact report, further optimizes the existing energy allocation plan to generate a more efficient draft energy control strategy that is more adaptable to changes in the actual environment.

[0132] For example, based on findings from the key factor impact report, the energy allocation plan was further optimized using a deep neural network. For instance, the algorithm automatically generated a strategy for dynamically adjusting air conditioning setpoints in response to changes in outdoor temperature; and for pedestrian flow patterns, the algorithm designed an intelligent lighting control system to reduce unnecessary energy waste. Ultimately, an optimized energy control strategy draft was formed, ready for the next stage of validation.

[0133] Step 425: Apply a real-time feedback mechanism to compare and analyze the actual operating data with the optimized draft energy consumption control strategy, dynamically adjust the parameter settings in the draft energy consumption control strategy, and obtain the optimal draft energy consumption control strategy.

[0134] In this step, the real-time feedback mechanism refers to the process of continuously collecting data during system operation and adjusting system parameters in real time based on this data. In this step, by comparing actual operating data with the optimized draft energy consumption control strategy, the parameter settings in the draft are continuously adjusted until the optimal energy consumption control scheme is found.

[0135] For example, in the actual operation of makeshift hospitals, various sensors were installed to collect real-time energy consumption data. This data was transmitted to the central control system and compared with the optimized draft energy consumption control strategy. If the actual energy consumption in certain areas was found to be higher than expected, the system would automatically adjust relevant parameters, such as lowering the air conditioning temperature or reducing lighting brightness. After a period of operation and adjustment, a more accurate and efficient draft energy consumption control strategy was finally obtained.

[0136] More specifically, the present invention also provides a formula for optimization using the following formula:

[0137] P opt =α·E pred +β·C env +γ·F sys +δ·T real +η·H hist +θ·W weather

[0138] +λ·M maint

[0139] Among them, P opt This represents the optimized parameter settings, used to adjust specific parameters in the draft energy consumption control strategy; E pred This represents the predicted energy demand, a preliminary electricity demand forecast generated based on a time series forecasting model; C env This represents the environmental impact value, based on the degree of impact of environmental factors identified in the key factor impact report; F sys T represents the system performance factor, based on the system performance metrics evaluated in the simulation test report. real This represents actual operating data, obtained through a real-time feedback mechanism, used for dynamically adjusting parameter settings; H hist Represents historical electricity consumption data, based on historical electricity consumption records, used to predict electricity consumption patterns; W weather This represents weather forecast information, used to adjust energy consumption control strategies based on weather forecast data; M maintλ represents the impact value of the maintenance plan, based on a preventative maintenance plan, used to consider the impact of maintenance on energy consumption; α represents the weighting coefficient of the predicted energy demand, reflecting the importance of energy demand prediction in parameter optimization; β represents the weighting coefficient of the impact value of environmental conditions, reflecting the importance of environmental factors in parameter optimization; γ represents the weighting coefficient of the system performance factor, reflecting the importance of system performance in parameter optimization; δ represents the weighting coefficient of the actual operating data, reflecting the importance of the actual operating data in parameter optimization; η represents the weighting coefficient of the historical electricity consumption data, reflecting the importance of the historical electricity consumption data in parameter optimization; θ represents the weighting coefficient of the weather forecast information, reflecting the importance of the weather forecast in parameter optimization; λ represents the weighting coefficient of the impact value of the maintenance plan, reflecting the importance of the maintenance plan in parameter optimization.

[0140] Step 426: Use visualization technology to demonstrate the implementation effect of the optimal energy consumption control strategy, and complete the formulation of the energy consumption control strategy;

[0141] In this step, visualization technology, a technique that transforms data into graphical or image representations, helps users more easily understand and analyze complex information. In this final step, visualization technology is used to demonstrate the effectiveness of the optimal energy control strategy, allowing managers to visually see the benefits of the strategy, thus completing the entire energy control strategy development process.

[0142] For example, a web-based visualization platform was developed that displays real-time energy consumption, historical energy consumption trends, and the energy-saving effects of optimized energy control strategies in various areas of the makeshift hospital. Managers can view the energy distribution of each area through charts, heat maps, and other formats, and can also view specific energy savings over specific time periods. Furthermore, the platform provides an interactive interface, allowing users to select different time periods and areas for in-depth analysis. This approach not only improves management efficiency but also enhances the transparency and support for decision-making.

[0143] The beneficial effects achieved by the embodiments of the present invention through the above steps are as follows:

[0144] By utilizing digital twin technology and a virtual simulation platform, the optimized energy allocation plan for makeshift hospitals was simulated and tested under various environmental conditions, generating detailed energy consumption performance data. Big data analytics identified key factors affecting energy efficiency and their impact, generating a key factor impact report. Based on this report, deep learning algorithms were used to further optimize the energy allocation plan, generating a draft energy control strategy. A real-time feedback mechanism compared actual operational data with the draft, dynamically adjusting parameter settings to ensure the optimal energy control strategy was formulated. These steps significantly improved the accuracy and adaptability of energy management, reduced energy waste, enhanced system stability and reliability, and improved management transparency and decision support through visualization technology.

[0145] Based on this, the present invention provides a specific embodiment. Step 102, which uses a multi-objective optimization algorithm to comprehensively evaluate the dynamic adjustment strategy and the current state of the energy storage system to obtain an energy storage system charging and discharging optimization strategy, specifically includes the following steps:

[0146] Step 201: Use a multi-objective optimization algorithm to comprehensively evaluate the dynamic adjustment strategy and the current state of the energy storage system to obtain a preliminary charging and discharging optimization scheme;

[0147] In this step, the multi-objective optimization algorithm is a mathematical optimization method that aims to simultaneously optimize multiple objective functions, such as cost minimization and efficiency maximization. In this step, the multi-objective optimization algorithm is used to evaluate the current state and dynamic adjustment strategies of the energy storage system to find a charging and discharging scheme that meets power demand while optimizing battery lifespan and economics.

[0148] Step 202: Optimize the preliminary charge and discharge optimization scheme using machine learning algorithms, calculate battery health status, charge and discharge efficiency and cost factors, and generate a refined charge and discharge optimization scheme;

[0149] In this step, machine learning algorithms learn patterns from a large amount of historical data and make predictions and decisions based on these patterns. Further, the machine learning algorithms optimize the initial charge / discharge scheme by analyzing battery health, charge / discharge efficiency, and cost factors to generate a more refined and efficient optimized charge / discharge scheme.

[0150] Step 203: Using Internet of Things (IoT) technology, monitor the operating status of the energy storage system in the refined charge and discharge optimization scheme in real time and generate real-time monitoring data;

[0151] In this step, IoT technology connects various devices and sensors to enable real-time data acquisition and transmission. In this process, IoT technology is used to continuously monitor the operating status of the energy storage system, including key parameters such as battery voltage, current, and temperature, ensuring the system operates as expected and providing real-time data to support subsequent analysis and adjustments.

[0152] Step 204: Based on the real-time monitoring data, a predictive maintenance algorithm is used to predict potential failure points of the energy storage system and generate a preventive maintenance plan;

[0153] In this step, predictive maintenance algorithms are data analytics-based methods that can identify potential equipment failures in advance and take preventative measures. Specifically, these algorithms utilize real-time monitoring data to analyze the operational trends of the energy storage system, predict possible failures, and develop preventative maintenance plans to reduce downtime and repair costs.

[0154] Step 205: Based on the preventive maintenance plan, use a dynamic adjustment algorithm to adjust the refined charge-discharge optimization scheme in real time to generate a target charge-discharge optimization strategy;

[0155] In this step, the dynamic adjustment algorithm is an algorithm that can automatically adjust the strategy based on actual conditions during operation. In this step, the dynamic adjustment algorithm, combined with a preventative maintenance plan, adjusts the existing refined charge-discharge optimization scheme in real time, ensuring that the energy storage system maintains efficient operation while also coping with potential fault risks, thereby generating the final target charge-discharge optimization strategy.

[0156] The beneficial effects achieved by the embodiments of the present invention through the above steps are as follows:

[0157] By processing energy consumption data under various environmental conditions using big data analytics, a preliminary list of key factors is generated. Association rule mining algorithms are then used to analyze these factors in depth, forming a key factor interaction matrix. Multi-dimensional data analysis methods are employed to evaluate the impact of different combinations of key factors, generating a comprehensive impact assessment table. Combining these results, machine learning algorithms are used to build an energy efficiency prediction model and plot energy efficiency change trends. Finally, a key factor impact report is compiled. These steps significantly improve the understanding and management of energy consumption influencing factors, enhance the accuracy of energy consumption prediction, optimize energy use efficiency, reduce operating costs, and provide scientific data support and decision-making basis for energy consumption management in makeshift hospitals.

[0158] Based on this, the present invention provides a specific embodiment. Step 105, combining historical electricity consumption data and weather forecast information, applies a time series prediction model to predict the electricity demand change trend of the energy consumption control strategy, obtains the scheduling plan for distributed power sources and energy storage systems, and uses blockchain technology to record and verify the electricity trading process of the scheduling plan to generate an electricity allocation scheme. Specifically, it includes the following steps:

[0159] Step 501: Combining historical electricity consumption data and weather forecast information, apply a time series forecasting model to predict the electricity demand change trend of the energy consumption control strategy and obtain preliminary electricity demand forecast results.

[0160] In this step, time series forecasting models are a method that uses historical data to predict data trends over a future period. By combining historical electricity consumption data from makeshift hospitals with future weather forecasts, this step allows for the prediction of future electricity demand trends. These predictions will serve as the basis for developing energy consumption control strategies.

[0161] Step 502: Analyze the preliminary electricity demand forecast results using deep learning algorithms, calculate the impact of holidays and special events, and generate a detailed electricity demand forecast report;

[0162] In this step, deep learning algorithms, a sophisticated machine learning method, are used to automatically extract features and make predictions from large amounts of data. Specifically, deep learning algorithms are used to further analyze the preliminary electricity demand forecast results, particularly considering the impact of holidays and special events on electricity demand, thereby generating a more refined and accurate electricity demand forecast report.

[0163] Step 503: Based on the refined power demand forecast report, use a multi-objective optimization algorithm to optimize the scheduling plan of distributed power sources and energy storage systems, and generate an optimized scheduling plan;

[0164] In this step, the multi-objective optimization algorithm aims to simultaneously optimize multiple objective functions, such as cost minimization and efficiency maximization. Based on refined electricity demand forecasting reports, the algorithm optimizes the scheduling planning of distributed power sources and energy storage systems to ensure that electricity demand is met while maximizing economic benefits and minimizing environmental impact.

[0165] Step 504: Simulate and verify the optimized scheduling plan using a simulation testing platform, evaluate its feasibility under different scenarios, and generate a simulation test report;

[0166] In this step, the simulation testing platform is a tool used to simulate real-world conditions, allowing users to change input parameters and observe the changes in output results. In this step, the simulation testing platform is used to verify the feasibility and effectiveness of the optimized scheduling plan under different scenarios, thereby generating a detailed simulation test report.

[0167] Step 505: Based on the simulation test report, use the real-time feedback mechanism to dynamically adjust the optimized scheduling plan and generate the best scheduling plan;

[0168] In this step, the real-time feedback mechanism refers to the process of continuously collecting data during system operation and adjusting system parameters in real time based on this data. In this step, the optimized scheduling plan is continuously adjusted by comparing simulation test reports with actual operating data until the best solution is found.

[0169] Step 506: Using blockchain technology, record and verify the power trading process of the optimal scheduling plan to generate a power allocation scheme;

[0170] The beneficial effects achieved by the embodiments of the present invention through the above steps are as follows:

[0171] By combining multi-objective optimization algorithms with machine learning techniques, the charging and discharging strategies of energy storage systems were refined and optimized. This approach not only considered factors such as battery health, efficiency, and cost, but also utilized IoT technology to achieve real-time monitoring of the system's operational status. Based on the collected data, predictive maintenance algorithms were used to effectively predict potential failure points and develop preventative maintenance plans, further ensuring the system's stability and reliability. This comprehensive approach significantly improves the management efficiency of energy storage systems, extends equipment lifespan, reduces operating costs, and enhances the flexibility and sustainability of energy use, laying a solid foundation for building a more intelligent and efficient energy management system.

[0172] By combining historical electricity consumption data with weather forecast information, and applying time series forecasting models and deep learning algorithms, the accuracy of electricity demand forecasting was significantly improved, providing a solid data foundation for energy consumption control in makeshift hospitals. Multi-objective optimization algorithms and real-time feedback mechanisms ensured optimal scheduling of distributed power sources and energy storage systems, reducing energy waste, lowering operating costs, and enhancing system stability and reliability. Detailed electricity demand forecast reports and simulation test reports provided managers with comprehensive information support, improving the scientific rigor and quality of decision-making. Furthermore, a dynamic adjustment mechanism enabled the system to flexibly respond to changes in actual operation, further optimizing resource allocation. Overall, these steps not only improved the efficiency and economy of power supply but also enhanced the system's adaptability and stability, ensuring the efficient operation of makeshift hospitals under various conditions.

[0173] Figure 2 This application provides a schematic diagram of a power supply optimization system for a mobile hospital based on microgrid technology, as shown in the embodiment of the present application. Figure 2 As shown, the system includes:

[0174] Monitoring module 21 monitors the actual power demand of each power-consuming unit in the makeshift hospital, the current operating status of the power supply system, and the status information of the external power grid to obtain monitoring data. It then uses a deep reinforcement learning algorithm to analyze the monitoring data and obtain a dynamic adjustment strategy for the distributed power supply.

[0175] Evaluation module 22 uses a multi-objective optimization algorithm to comprehensively evaluate the dynamic adjustment strategy and the current state of the energy storage system, and obtains the energy storage system charging and discharging optimization strategy.

[0176] When an external power grid fault is detected, the adjustment module 23 uses a fast switching control algorithm to adaptively adjust the charging and discharging optimization strategy of the energy storage system to obtain a power configuration scheme in islanded mode.

[0177] The adjustment module 24, based on the internal environmental parameters of the makeshift hospital, the working characteristics of the power consumption unit, and the patient comfort requirements, uses an adaptive fuzzy logic control system to adjust the power configuration scheme to obtain an energy consumption control strategy.

[0178] The recording module 25 combines historical electricity consumption data and weather forecast information, applies a time series prediction model to predict the trend of electricity demand changes in the energy consumption control strategy, obtains the scheduling plan of distributed power sources and energy storage systems, and uses blockchain technology to record and verify the electricity trading process of the scheduling plan, generating an electricity allocation scheme.

[0179] Figure 2 The aforementioned power supply optimization system for mobile hospitals based on microgrid technology can perform... Figure 1 The implementation principle and technical effects of the microgrid-based power supply optimization method for makeshift hospitals described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the microgrid-based power supply optimization system for makeshift hospitals described in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0180] Figure 2 The power supply optimization system for a mobile hospital based on microgrid technology, as shown in the embodiment, can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0181] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0182] The processing component 32 is used to monitor the actual power demand of each power-consuming unit in the makeshift hospital, the current operating status of the power supply system, and the status information of the external power grid to obtain monitoring data. A deep reinforcement learning algorithm is used to analyze the monitoring data to obtain a dynamic adjustment strategy for the distributed power source. A multi-objective optimization algorithm is used to comprehensively evaluate the dynamic adjustment strategy and the current status of the energy storage system to obtain an energy storage system charging and discharging optimization strategy. When an external power grid fault is detected, a fast switching control algorithm is used to adaptively adjust the energy storage system charging and discharging optimization strategy to obtain a power configuration scheme in islanded mode. Based on the internal environmental parameters of the makeshift hospital, the operating characteristics of the power-consuming units, and the patient comfort requirements, an adaptive fuzzy logic control system is used to adjust the power configuration scheme to obtain an energy consumption control strategy. Combining historical power consumption data and weather forecast information, a time series prediction model is applied to predict the power demand change trend of the energy consumption control strategy to obtain a scheduling plan for the distributed power source and energy storage system. Blockchain technology is used to record and verify the power trading process of the scheduling plan to generate a power allocation scheme.

[0183] The processing component 32 includes one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component can be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DPs), digital signal processing devices (DPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0184] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (RAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0185] Computing devices also include other components such as input / output interfaces, display components, and communication components.

[0186] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices or input devices.

[0187] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0188] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0189] This invention also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown monitors the actual power demand of each power-consuming unit within the makeshift hospital, the current operating status of the power supply system, and the status information of the external power grid to obtain monitoring data. A deep reinforcement learning algorithm is used to analyze the monitoring data to obtain a dynamic adjustment strategy for distributed power sources. A multi-objective optimization algorithm is used to comprehensively evaluate the dynamic adjustment strategy and the current status of the energy storage system to obtain an energy storage system charging and discharging optimization strategy. When an external power grid fault is detected, a fast switching control algorithm is used to adaptively adjust the energy storage system charging and discharging optimization strategy to obtain a power configuration scheme in islanded mode. Based on the internal environmental parameters of the makeshift hospital, the operating characteristics of the power-consuming units, and the patient comfort requirements, an adaptive fuzzy logic control system is used to adjust the power configuration scheme to obtain an energy consumption control strategy. Combining historical power consumption data and weather forecast information, a time series prediction model is applied to predict the power demand change trend of the energy consumption control strategy to obtain a scheduling plan for distributed power sources and the energy storage system. Blockchain technology is used to record and verify the power trading process of the scheduling plan, generating a power allocation scheme method and system.

[0190] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0191] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0192] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0193] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing power supply of a shelter hospital based on microgrid technology, characterized in that, The method comprises the following steps: monitoring the actual power demand of each electrical unit in the shelter hospital, the operating state of the current power supply system, and the state information of the external power grid to obtain monitoring data, analyzing the monitoring data using a deep reinforcement learning algorithm to obtain a dynamic adjustment strategy for the distributed power supply; comprehensively evaluating the dynamic adjustment strategy and the current state of the energy storage system using a multi-objective optimization algorithm to obtain an energy storage system charge-discharge optimization strategy; when an external power grid failure is detected, a fast switching control algorithm is used to adaptively adjust the energy storage system charge-discharge optimization strategy to obtain a power supply configuration scheme in island mode; adjusting the power supply configuration scheme using an adaptive fuzzy logic control system according to the internal environmental parameters of the shelter hospital, the working characteristics of the electrical units, and the patient comfort requirements to obtain an energy consumption control strategy; combining historical power consumption data and weather forecast information, applying a time series prediction model to predict the power demand trend of the energy consumption control strategy, obtaining a scheduling plan for the distributed power supply and the energy storage system, and using blockchain technology to record and verify the power transaction process of the scheduling plan to generate a power distribution scheme; wherein, according to the internal environmental parameters of the shelter hospital, the working characteristics of the electrical units, and the patient comfort requirements, the adaptive fuzzy logic control system is used to adjust the power supply configuration scheme to obtain an energy consumption control strategy, which comprises: monitoring the internal environmental parameters of the shelter hospital, the working characteristics of the electrical units, and the patient comfort requirements using environmental perception sensors to obtain environmental and demand data sets; using an adaptive fuzzy logic control system to analyze the environmental and demand data sets, combining the power supply configuration scheme to evaluate the applicability of energy consumption modes under different conditions, and obtaining a preliminary energy consumption evaluation report; correcting the accuracy of energy consumption mode matching by integrating learning algorithm to correct the preliminary energy consumption evaluation report, obtaining a corrected energy consumption evaluation report; applying a genetic algorithm based on the corrected energy consumption evaluation report to optimize the energy consumption mode in different areas of the shelter hospital, obtaining optimization suggestions; according to the optimization suggestions, combining the personnel flow and time distribution law in the shelter hospital, using a scene perception algorithm to dynamically adjust the energy consumption allocation ratio of each area, and formulating an optimized energy consumption allocation plan; using digital twin technology to simulate and test the optimized energy consumption allocation plan to generate an energy consumption control strategy; wherein, using digital twin technology to simulate and test the optimized energy consumption allocation plan to generate an energy consumption control strategy, comprises: The optimized energy consumption allocation plan is simulated and tested by using digital twin technology to obtain simulation test results. The application effects of the simulation test results under different environmental conditions are simulated through a virtual simulation platform to generate energy consumption performance data under various environmental conditions. The energy consumption performance data is processed by applying big data analysis technology to identify key factors affecting energy consumption efficiency and the degree of influence, forming a key factor influence report. The optimized energy consumption allocation plan is optimized by using a deep learning algorithm in combination with the key factor influence report to generate an optimized energy consumption control strategy draft. Real-time feedback mechanisms are applied to compare and analyze actual operation data with the optimized energy consumption control strategy draft, dynamically adjusting parameter settings in the energy consumption control strategy draft to obtain an optimal energy consumption control strategy draft. The implementation effects of the optimal energy consumption control strategy are displayed by using visualization technology to complete the formulation of the energy consumption control strategy.

2. The method of claim 1, wherein, According to the optimization suggestion, the energy consumption allocation proportion of each area is dynamically adjusted by using a scene perception algorithm in combination with the personnel flow situation and time distribution law in the shelter hospital to formulate an optimized energy consumption allocation plan, including: The personnel flow situation in the preliminary energy consumption allocation plan is trend forecasted by using a spatiotemporal prediction model to obtain personnel flow prediction results. Based on the personnel flow prediction results, the energy consumption demand changes of each area in different time periods are analyzed by a machine learning algorithm to generate a dynamic energy consumption demand prediction. The dynamic energy consumption demand prediction is combined with the preliminary energy consumption allocation plan, and a multi-objective optimization algorithm is used to re-evaluate the energy consumption allocation proportion of each area to optimize energy use while meeting personnel flow demand, generating a re-optimized energy consumption allocation scheme. An augmented reality technology is used in combination with the re-optimized energy consumption allocation scheme to provide an energy consumption management interface for shelter hospital managers, supporting real-time monitoring and manual intervention to generate an optimized energy consumption allocation plan.

3. The method of claim 1, wherein, Big data analysis technology is applied to process the energy consumption performance data to identify key factors affecting energy consumption efficiency and the degree of influence, forming a key factor influence report, including: The energy consumption performance data under various environmental conditions is processed by using big data analysis technology to obtain a preliminary key factor list, wherein the various environmental conditions include climate conditions, time periods, personnel density, and equipment operation status and hospital emergencies. The factors in the preliminary key factor list are deeply analyzed by using an association rule mining algorithm to obtain a key factor interaction matrix. Based on the key factor interaction matrix, a multi-dimensional data analysis method is used to evaluate the influence of different key factor combinations to obtain a comprehensive influence evaluation table. In combination with the comprehensive influence evaluation table, an energy consumption efficiency prediction model is established by using a machine learning algorithm to obtain an energy consumption efficiency trend graph. The preliminary key factor list, key factor interaction matrix, comprehensive influence evaluation table, and energy consumption efficiency trend graph are integrated to form a key factor influence report.

4. The method of claim 1, wherein, comprehensive evaluation of the dynamic adjustment strategy and the current state of the energy storage system is performed using a multi-objective optimization algorithm to obtain an energy storage system charging and discharging optimization strategy, including: comprehensive evaluation of the dynamic adjustment strategy and the current state of the energy storage system is performed using a multi-objective optimization algorithm to obtain an energy storage system charging and discharging optimization strategy, including: optimization of the preliminary charging and discharging optimization scheme is performed through a machine learning algorithm, battery health state, charging and discharging efficiency and cost factors are calculated, and a refined charging and discharging optimization scheme is generated; real-time monitoring of the operating state of the energy storage system in the refined charging and discharging optimization scheme is performed using Internet of Things technology to generate real-time monitoring data; based on the real-time monitoring data, a predictive maintenance algorithm is used to predict potential failure points of the energy storage system to generate a preventive maintenance plan; in combination with the preventive maintenance plan, a dynamic adjustment algorithm is used to perform real-time adjustment of the refined charging and discharging optimization scheme to generate a target charging and discharging optimization strategy.

5. The method of claim 1, wherein, in combination with historical power consumption data and weather forecast information, a time series prediction model is applied to predict the power demand trend of the energy consumption control strategy to obtain a scheduling plan for the distributed power source and energy storage system, and a blockchain technology is used to record and verify the power transaction process of the scheduling plan to generate a power distribution scheme, including: in combination with historical power consumption data and weather forecast information, a time series prediction model is applied to predict the power demand trend of the energy consumption control strategy to obtain a preliminary power demand prediction result; a deep learning algorithm is used to analyze the preliminary power demand prediction result to calculate the impact of holidays and special events and generate a refined power demand prediction report; based on the refined power demand prediction report, a multi-objective optimization algorithm is used to optimize the scheduling plan for the distributed power source and energy storage system to generate an optimized scheduling plan; the optimized scheduling plan is simulated and verified through a simulation test platform to evaluate its feasibility in different scenarios and generate a simulation test report; in combination with the simulation test report, a real-time feedback mechanism is used to dynamically adjust the optimized scheduling plan to generate an optimal scheduling plan; a blockchain technology is used to record and verify the power transaction process of the optimal scheduling plan to generate a power distribution scheme.

6. A microgrid-based power supply optimization system for a shelter hospital, configured to perform the microgrid-based power supply optimization method according to any one of claims 1-5. including: a monitoring module that monitors the actual power demand of each power consumption unit in the shelter hospital, the operating state of the current power supply system, and the state information of the external power grid to obtain monitoring data, and uses a deep reinforcement learning algorithm to analyze the monitoring data to obtain a dynamic adjustment strategy for the distributed power source; an evaluation module that uses a multi-objective optimization algorithm to comprehensively evaluate the dynamic adjustment strategy and the current state of the energy storage system to obtain an energy storage system charging and discharging optimization strategy; an adjustment module that, when an external power grid failure is detected, uses a fast switching control algorithm to adaptively adjust the energy storage system charging and discharging optimization strategy to obtain a power supply configuration scheme in island mode; An adjusting module adjusts the power supply configuration scheme according to the internal environment parameters of the shelter hospital, the working characteristics of the power-consuming units, and the patient comfort requirements, and obtains an energy consumption control strategy by using an adaptive fuzzy logic control system; A recording module combines historical power consumption data and weather forecast information, applies a time series prediction model to predict the power demand trend of the energy consumption control strategy, obtains a scheduling plan of the distributed power supply and energy storage system, records and verifies the power transaction process of the scheduling plan by using a blockchain technology, and generates a power distribution scheme.

7. A computing device, comprising: The method comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to realize the method for optimizing power supply of a shelter hospital based on micro-grid technology according to any one of claims 1-5.

8. A computer storage medium, characterized in that The computer program is stored in a computer and is executed to realize the method for optimizing power supply of a shelter hospital based on micro-grid technology according to any one of claims 1-5.

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