Optimized scheduling method and system for power management system

Abstract

The invention provides an optimal scheduling method and system for a power management system. Wherein a dynamic power distribution strategy and an equipment working mode switching rule are obtained based on the real-time geographic position, environmental parameters and task priority evaluation of the medical treatment box; generating an extra energy reserve decision and an energy-saving measure scheme by using the dynamic power distribution strategy and the equipment working mode switching rule; according to the extra energy reserve decision and the energy-saving measure scheme, optimizing prediction precision through a machine learning algorithm, implementing reliability engineering analysis, and evaluating reliability and stability of different power supply schemes under various conditions to generate a comprehensive stable power supply guarantee plan; and based on the comprehensive stable power supply guarantee plan, determining an optimal rescue path through a path optimization technology, and generating a rescue path plan. According to the technical scheme provided by the invention, the capability of providing stable power support by the medical treatment box under various conditions is remarkably improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of power management technology, and in particular to an optimization scheduling method and system for a power management system. Background Art

[0002] In modern medical rescue environments, especially in remote areas or disaster sites, intelligent medical treatment boxes play a vital role. These scenarios usually lack a stable power supply infrastructure, and the environmental conditions are complex and changeable, which places extremely high demands on the continuous operation of medical equipment. Therefore, for medical treatment boxes, a system that can intelligently manage power supply, optimize energy utilization, and ensure reliable power support in any situation is required.

[0003] Currently, most medical treatment boxes use a preset static power allocation strategy and a fixed device working mode. This solution relies on a built-in battery pack as the main power source, and may be combined with a renewable energy interface such as a solar panel for supplementary charging. However, such a power management system lacks flexibility and cannot make dynamic adjustments based on real-time mission requirements and external environmental changes.

[0004] Existing power management methods have several obvious limitations: first, they cannot effectively respond to fluctuations in power demand in emergency situations; second, they fail to fully utilize the collaborative potential between internal and external energy resources, resulting in energy waste or insufficient reserves; third, in the face of extreme weather or emergencies, the system's stability and reliability are low, making it difficult to ensure the continued operation of critical medical equipment. Summary of the invention

[0005] The embodiments of the present application provide an optimization scheduling method and system for a power management system, so as to solve the problems of insufficient flexibility and low energy utilization efficiency caused by static management in the prior art.

[0006] In a first aspect, an embodiment of the present application provides an optimization scheduling method for a power management system, comprising:

[0007] Based on the real-time geographic location, environmental parameters and task priority assessment of the medical treatment box, the urgency and expected power consumption of current and future tasks are intelligently judged and processed to obtain dynamic power allocation strategies and equipment working mode switching rules;

[0008] By utilizing the dynamic power allocation strategy and equipment working mode switching rules, combined with the cooperation mechanism between the built-in energy storage module and the external renewable energy interface, an adaptive energy allocation algorithm is applied to automatically match the optimal power supply mode, and time series prediction technology is used to estimate and analyze the energy supply and demand model, generating additional energy reserve decisions and energy-saving measures.

[0009] Based on the additional energy reserve decision and energy-saving measures, a rolling demand forecasting algorithm is used to make rolling updates to forecast future electricity demand trends. The historical energy consumption data and medical activity schedule are combined to optimize the forecast accuracy through machine learning algorithms. Reliability engineering analysis is performed to evaluate the reliability and stability of different power supply solutions under various conditions. The impact of extreme weather or emergencies is considered to generate a comprehensive stable power supply guarantee plan.

[0010] Based on the comprehensive stable power supply guarantee plan, when a main power failure or insufficient power is detected, the emergency response mechanism is activated, the backup battery pack is activated and a distress signal is sent to the nearest support site, and the optimal rescue route is determined through path optimization technology to generate a rescue path plan.

[0011] Optionally, the dynamic power allocation strategy and the equipment working mode switching rules are used in combination with the cooperation mechanism between the built-in energy storage module and the external renewable energy interface, and an adaptive energy allocation algorithm is applied to automatically match the optimal power supply mode, and the energy supply and demand model is estimated and analyzed using time series prediction technology to generate additional energy reserve decisions and energy-saving measures, including:

[0012] Based on the dynamic power allocation strategy and the equipment working mode switching rules, the current power demand of the medical treatment box and the working status of each device are evaluated to obtain a real-time power demand distribution map;

[0013] By using the real-time power demand distribution map, combined with the cooperation mechanism between the built-in energy storage module and the external renewable energy interface, an adaptive energy allocation algorithm is applied to intelligently dispatch the energy flow between different power sources to obtain a preliminary optimal power supply mode configuration;

[0014] Based on the preliminary optimal power supply mode configuration, use time series forecasting technology to estimate and analyze future energy supply and demand conditions, and generate a predicted energy supply and demand model by taking into account seasonal changes and weather forecast factors;

[0015] Based on the predicted energy supply and demand model, optimizing and calculating the energy reserve amount and the consumption rate to obtain the additional energy reserve decision;

[0016] By utilizing the additional energy reserve decision and taking environmental protection and cost-effectiveness principles into consideration, the operating parameters of all electrical equipment in the medical treatment box are adjusted to generate the energy-saving measure plan.

[0017] Optionally, the method of using time series forecasting technology to estimate and analyze future energy supply and demand conditions based on the preliminary optimal power supply mode configuration, and generating a predicted energy supply and demand model by taking into account seasonal changes and weather forecast factors, includes:

[0018] Based on the preliminary optimal power supply mode configuration, combined with historical power consumption data and medical activity schedules, a retrospective analysis of energy supply and demand conditions over a period of time in the past is performed to obtain historical energy supply and demand characteristics;

[0019] By using the historical energy supply and demand characteristics, combined with seasonal variation patterns and long-term meteorological data, and applying the ARIMA model or LSTM neural network algorithm in time series forecasting technology, the energy demand trend in the future period is modeled to obtain preliminary energy demand forecast results;

[0020] According to the preliminary energy demand forecast result, integrating real-time weather forecast information, revising the preliminary forecast result, and obtaining an adjusted energy demand forecast;

[0021] Based on the adjusted energy demand forecast, evaluating the expected power generation capacity of the external renewable energy interface, considering the change of power generation efficiency under different weather conditions, and generating an energy supply forecast report;

[0022] The energy supply forecast report and the adjusted energy demand forecast are used to comprehensively consider the power requirements of the equipment inside the medical treatment box and the urgency of future tasks, and a supply and demand balance calculation is performed to generate a predicted energy supply and demand model.

[0023] Optionally, the historical energy supply and demand characteristics are used in combination with seasonal variation patterns and long-term meteorological data, and the ARIMA model or LSTM neural network advanced algorithm in time series forecasting technology is applied to model the energy demand trend in the future period to obtain preliminary energy demand forecast results, including:

[0024] Based on the historical energy supply and demand characteristics, the past power consumption records of the medical treatment box are deeply analyzed to identify energy usage patterns related to seasonal fluctuations and daily activity patterns, and obtain a periodic energy usage template;

[0025] According to the periodic energy usage template, combined with the change trend of the target factors in the long-term meteorological data, the influence of the target factors on the energy demand is evaluated to generate a seasonal adjustment coefficient, wherein the target factors include: temperature, humidity and sunshine duration;

[0026] The seasonal adjustment coefficient and the ARIMA model or LSTM neural network algorithm in the time series prediction technology are used to mathematically model the periodic energy usage template, and the optimization selection of model parameters is considered to improve the prediction accuracy, so as to obtain an energy demand prediction model without weather correction;

[0027] Based on the energy demand forecasting model without weather correction, the output of the energy demand forecasting model without weather correction is verified and calibrated by simulating operation scenarios under different seasonal conditions to generate preliminary energy demand forecasting results.

[0028] Optionally, according to the additional energy reserve decision and energy-saving measures, a rolling demand forecasting algorithm is used to perform rolling update forecasts on future power demand trends, historical energy consumption data and medical activity schedules are combined, and the forecast accuracy is optimized through a machine learning algorithm. Reliability engineering analysis is performed to evaluate the reliability and stability of different power supply schemes under various conditions, and the impact of extreme weather or emergencies is considered to generate a comprehensive stable power supply guarantee plan, including:

[0029] Based on the additional energy reserve decision and energy-saving measure plan, the current energy reserve state of the medical treatment box and the execution effect of the energy-saving strategy are evaluated and processed to obtain a current energy management state report;

[0030] Using the current energy management status report, combined with historical energy consumption data and medical activity schedules, a rolling demand forecasting algorithm is applied to perform rolling update forecasts on power demand trends over a period of time in the future to generate a preliminary future power demand forecast;

[0031] According to the preliminary future electricity demand forecast, real-time and predicted medical activity information is integrated, and regression analysis or deep learning model in the machine learning algorithm is used to optimize the predicted energy supply and demand model parameters to improve the accuracy of the forecast and obtain a refined future electricity demand forecast;

[0032] Based on the refined future power demand forecast, reliability engineering analysis is performed to evaluate the reliability and stability of different power supply solutions under different load conditions, and a reliability evaluation report of the power supply solution is generated;

[0033] Using the reliability assessment report of the power supply scheme, combined with extreme weather forecast data and possible emergency scenarios, stress test the existing power supply scheme, evaluate the response capability and recovery speed of the existing power supply scheme under adverse conditions, and generate the evaluation results of the power supply scheme under extreme conditions;

[0034] Based on the evaluation results of the power supply plan under the extreme conditions, a comprehensive stable power supply guarantee plan is formulated. The comprehensive stable power supply guarantee plan is used to provide reliable power support for the medical treatment box under normal and extreme conditions and ensure the continuous operation of key medical equipment.

[0035] Optionally, based on the refined future power demand forecast, reliability engineering analysis is performed to evaluate the reliability and stability of different power supply schemes under different load conditions, and a power supply scheme reliability evaluation report is generated, including:

[0036] Based on the refined future power demand forecast, the power consumption pattern of the medical treatment box in different time periods in the future is analyzed in detail to obtain the power demand distribution in different time periods;

[0037] By using the time-divided power demand distribution, combined with the existing power supply configuration and potential power supply improvement solutions, multiple hypothetical scenarios are constructed to obtain multiple power supply solution models, wherein one hypothetical scenario represents one power supply strategy;

[0038] According to the multiple power supply scheme models, reliability engineering analysis methods are applied to set performance indicators to quantify evaluation standards and generate a reliability evaluation index system;

[0039] Based on the reliability evaluation index system, a simulation test is performed on each power supply scheme model in a simulation environment, and the response time and recovery capability under different load conditions are recorded to obtain an original simulation data set;

[0040] Using the original simulation data set, generating load adaptability analysis results through statistical analysis and comparative research;

[0041] According to the load adaptability analysis results, each power supply scheme is evaluated by comprehensively considering the long-term operating cost, maintenance convenience and environmental adaptability factors of each power supply scheme to obtain a final reliability score;

[0042] Based on the final reliability score, a power supply scheme reliability assessment report is prepared, and the power supply scheme reliability assessment report can provide stable and reliable power support under various expected operating conditions.

[0043] Optionally, based on the comprehensive stable power supply guarantee plan, when a main power failure or insufficient power is detected, an emergency response mechanism is started, a backup battery pack is activated, and a distress signal is sent to the nearest support site, and an optimal rescue route is determined through path optimization technology to generate a rescue path plan, including:

[0044] Based on the comprehensive stable power supply guarantee plan, the power system of the medical treatment box is monitored in real time, the main power supply status and the remaining power level are monitored, and a real-time power status report is obtained;

[0045] When a main power failure is detected or the power level is lower than a preset threshold, an emergency response mechanism is immediately triggered based on the real-time power status report, and power is automatically switched to the backup battery pack to ensure the continuous operation of critical medical equipment, and emergency power supply confirmation information is generated;

[0046] According to the emergency power supply confirmation information, the communication module is activated to send a distress signal including the current location and emergency details to the nearest pre-set support site, and the current power status and estimated power consumption are uploaded to obtain a distress signal sending record;

[0047] Based on the distress signal sending record, combined with the road network data and traffic condition information in the geographic information system, the path optimization technology is applied to calculate the cost and time of all possible paths from the support site to the location of the medical treatment box, and generate a list of candidate rescue paths;

[0048] By using the candidate rescue path list, taking into account the type of support vehicles, weather conditions and potential obstacles, the practical feasibility of each path is screened and evaluated, the optimal rescue route is selected, and a rescue path plan is generated.

[0049] In a second aspect, an embodiment of the present application provides an optimization scheduling system for a power management system, including:

[0050] The processing module is used to make intelligent judgments and processing on the urgency and expected power consumption of current and future tasks based on the real-time geographic location, environmental parameters and task priority evaluation of the medical treatment box, and obtain dynamic power allocation strategies and equipment working mode switching rules;

[0051] A matching module is used to utilize the dynamic power allocation strategy and the equipment working mode switching rules, combine the cooperation mechanism between the built-in energy storage module and the external renewable energy interface, apply the adaptive energy allocation algorithm to automatically match the optimal power supply mode, and use the time series prediction technology to estimate and analyze the energy supply and demand model, so as to generate additional energy reserve decisions and energy-saving measures;

[0052] A prediction module, which is used to use a rolling demand forecasting algorithm to perform rolling update forecasts on future power demand trends based on the additional energy reserve decision and energy-saving measures, combine historical energy consumption data with medical activity schedules, optimize forecast accuracy through machine learning algorithms, implement reliability engineering analysis, evaluate the reliability and stability of different power supply solutions under various conditions, and consider the impact of extreme weather or emergencies to generate a comprehensive stable power supply guarantee plan;

[0053] The activation module is used to start the emergency response mechanism based on the comprehensive stable power supply guarantee plan, activate the backup battery pack and send a distress signal to the nearest support site when a main power failure or insufficient power is detected, and determine the optimal rescue route through path optimization technology to generate a rescue path plan.

[0054] In a third aspect, an embodiment of the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an optimization scheduling method for a power management system as described in the first aspect.

[0055] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, which, when executed by a computer, implements an optimization scheduling method for a power management system as described in the first aspect.

[0056] In the embodiment of the present application, based on the real-time geographic location, environmental parameters and task priority evaluation of the medical treatment box, the urgency and expected power consumption of current and future tasks are intelligently judged and processed to obtain a dynamic power allocation strategy and equipment working mode switching rules; using the dynamic power allocation strategy and equipment working mode switching rules, combined with the collaboration mechanism between the built-in energy storage module and the external renewable energy interface, an adaptive energy allocation algorithm is applied to automatically match the optimal power supply mode, and the energy supply and demand model is estimated and analyzed using time series prediction technology to generate additional energy reserve decisions and energy-saving measures; according to the additional energy reserve decision and energy-saving measures, a rolling demand forecasting algorithm is used to perform rolling update forecasts on future power demand trends, combined with historical energy consumption data and medical activity schedules, the prediction accuracy is optimized through a machine learning algorithm, reliability engineering analysis is implemented, the reliability and stability of different power supply schemes under various conditions are evaluated, and the impact of extreme weather or emergencies is considered to generate a comprehensive stable power supply guarantee plan; based on the comprehensive stable power supply guarantee plan, when a main power failure or insufficient power is detected, an emergency response mechanism is activated, the backup battery pack is activated, and a distress signal is sent to the nearest support site, and the optimal rescue route is determined through path optimization technology to generate a rescue path plan.

[0057] The technical solution of this application has the following beneficial effects:

[0058] This method can intelligently judge and process the urgency and expected power consumption of current and future tasks based on the real-time geographic location, environmental parameters and task priority assessment of the medical treatment box. This allows the system to more accurately predict power demand and dynamically adjust power allocation strategies to ensure that critical equipment has sufficient power supply at all times; by applying adaptive energy allocation algorithms and time series prediction technology, this method can effectively manage the collaboration between the built-in energy storage module and the external renewable energy interface, thereby achieving automatic matching of the optimal power supply mode. This not only improves energy efficiency, but also reduces unnecessary energy consumption and extends battery life; the method takes into account the impact of extreme weather or emergencies and generates a comprehensive stable power supply guarantee plan. It can ensure reliable power support for the medical treatment box under various conditions. In particular, in emergency situations, it can quickly start the emergency response mechanism, activate the backup battery pack and send a distress signal to ensure that medical activities are not affected. By analyzing and predicting future energy supply and demand, the method can make additional energy reserve decisions and energy-saving measures in advance, which helps to reduce operating costs while meeting environmental protection requirements. When a main power failure or insufficient power is detected, the method can quickly determine the optimal rescue route to ensure that the support team can arrive at the scene in the shortest time and improve rescue efficiency.

[0059] Furthermore, by utilizing dynamic power allocation strategies and equipment working mode switching rules, and combining the cooperation mechanism between the built-in energy storage module and the external renewable energy interface, this method uses an adaptive energy allocation algorithm to realize the intelligent scheduling of energy flows between different power sources, thereby obtaining a preliminary optimal power supply mode configuration. Furthermore, the time series prediction technology is used to estimate and analyze the future energy supply and demand, and an accurate energy supply and demand model is generated by considering seasonal changes and weather forecast factors. Based on this model, the energy reserve and consumption rate are optimized and calculated to form an additional energy reserve decision, and the operating parameters of the electrical equipment in the medical treatment box are adjusted accordingly to generate energy-saving measures. This method not only improves the utilization efficiency of power resources and ensures the continuous and stable operation of key medical equipment under any conditions, but also significantly enhances the reliability and responsiveness of the system through advance planning and intelligent adjustment, while reducing operating costs and environmental impact.

[0060] Furthermore, by adopting a rolling demand forecasting algorithm combined with historical energy consumption data and medical activity schedules, and using a machine learning algorithm to optimize the forecasting accuracy, this method can dynamically update future power demand trends and ensure the accuracy and real-time nature of the forecast. Based on the additional energy reserve decision and energy-saving measures plan, the system generates a current energy management status report, and accordingly makes a refined forecast of future power demand. Furthermore, reliability engineering analysis is implemented to evaluate the reliability and stability of different power supply schemes under various load conditions, and a detailed power supply scheme reliability assessment report is generated. In addition, combined with extreme weather forecast data and emergency scenarios, the existing power supply scheme is stress tested to evaluate its response capability and recovery speed, and finally a comprehensive stable power supply guarantee plan is formulated. This method not only improves the predictability and adaptability of power supply, enhances the reliability and stability of the system, but also effectively responds to power challenges under extreme conditions, ensuring that the medical treatment box can provide continuous and stable power support for key medical equipment under any circumstances, thereby significantly improving the efficiency and safety of medical rescue work.

[0061] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0063] Figure 1 A flowchart of an optimization scheduling method for a power management system provided in an embodiment of the present application;

[0064] Figure 2 A schematic diagram of the structure of an optimization scheduling system for a power management system provided in an embodiment of the present application;

[0065] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0066] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0067] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0068] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0069] Figure 1 A flowchart of an optimization scheduling method for a power management system is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0070] 101. Based on the real-time geographic location, environmental parameters and task priority assessment of the medical treatment box, the urgency and estimated power consumption of current and future tasks are intelligently judged and processed to obtain dynamic power allocation strategies and equipment working mode switching rules;

[0071] In this step, based on the real-time geographic location of the medical treatment box, environmental parameters (such as temperature, humidity, etc.) and task priority assessment (determined by task type and urgency), the system makes intelligent judgments on the urgency and expected power consumption of current and future tasks. These data are used to generate dynamic power allocation strategies and equipment working mode switching rules to ensure that power resources can be used most efficiently.

[0072] In an embodiment of the present application, the system collects and analyzes real-time information of the medical treatment box, including but not limited to geographic location, environmental conditions, and details of the task to be performed, to determine how to best allocate available power and set appropriate operating modes for different types of equipment, thereby ensuring that critical tasks receive priority power supply.

[0073] Suppose that after an earthquake in a remote mountainous area, the medical treatment kits carried by rescuers need to support multiple emergency operations at the same time. The system will automatically adjust the power management strategy based on the fact that the current location is close to the epicenter, severe weather conditions, and high-priority life-sustaining mission requirements to ensure that key equipment such as cardiopulmonary resuscitation equipment obtains sufficient power first.

[0074] 102. Utilize the dynamic power allocation strategy and equipment working mode switching rules, combine the cooperation mechanism between the built-in energy storage module and the external renewable energy interface, apply the adaptive energy allocation algorithm to automatically match the optimal power supply mode, and use the time series prediction technology to estimate and analyze the energy supply and demand model, and generate additional energy reserve decisions and energy-saving measures;

[0075] In this step, the system applies an adaptive energy allocation algorithm to determine the optimal power supply mode, combining the cooperation mechanism between the built-in energy storage module (such as a battery pack) and the external renewable energy interface (such as a solar panel). This step also involves using time series forecasting technology to estimate future energy supply and demand to form additional energy reserve decisions and energy-saving measures.

[0076] In the embodiment of the present application, the system utilizes existing energy reserves and renewable energy that may be obtained, and calculates the optimal energy allocation plan through an intelligent algorithm to ensure that limited power resources are not wasted and that upcoming mission requirements can be met.

[0077] Assuming that in the earthquake rescue scenario above, the medical treatment box is not only powered by the internal battery, but also obtains additional power through the solar panels installed on it. The system will optimize the ratio of internal and external energy according to the sunshine hours and expected mission volume of the day, ensure sufficient power reserves at night or on cloudy days, and take necessary energy-saving measures to reduce unnecessary consumption.

[0078] 103. Based on the additional energy reserve decision and energy-saving measures, a rolling demand forecasting algorithm is used to make rolling updates to forecast future electricity demand trends. The historical energy consumption data and medical activity schedule are combined to optimize the forecast accuracy through machine learning algorithms. Reliability engineering analysis is performed to evaluate the reliability and stability of different power supply schemes under various conditions, and the impact of extreme weather or emergencies is considered to generate a comprehensive stable power supply guarantee plan.

[0079] In this step, a rolling demand forecasting algorithm is used to continuously update the forecast of future power demand trends, combining historical energy consumption data (past power consumption records) with medical activity schedules (planned surgeries or other treatment activities), and using machine learning algorithms to improve forecast accuracy. In addition, the impact of extreme weather or emergencies is also considered to generate a comprehensive stable power supply guarantee plan.

[0080] In the embodiment of the present application, the system regularly updates the forecast of future electricity demand, integrates all relevant historical and planned information, and adjusts the forecast parameters through advanced machine learning models to ensure a reliable power supply even in unforeseen circumstances.

[0081] As the rescue work progresses, the medical team learns that more injured people will arrive in the next few days and the forecast shows that there may be continuous rainfall. The system adjusts the power demand forecast accordingly, increases the charging frequency of the backup battery, and develops an emergency power supply plan to ensure that critical medical services can be maintained even in adverse weather conditions.

[0082] 104. Based on the comprehensive stable power supply guarantee plan, when a main power failure or insufficient power is detected, the emergency response mechanism is activated, the backup battery pack is activated, and a distress signal is sent to the nearest support site. The optimal rescue route is determined through path optimization technology, and a rescue path plan is generated.

[0083] In this step, when a main power failure or insufficient power is detected, the system immediately starts the emergency response mechanism, activates the backup battery pack and sends a distress signal to the nearest support site. At the same time, the optimal rescue route is determined through path optimization technology to ensure that fast and effective assistance can arrive at the scene in time.

[0084] In the embodiment of the present application, if a problem with the power supply is found under any circumstances, the system will quickly switch to the backup power supply and notify nearby support units, while planning the shortest route for the support team to go there, ensuring that the medical treatment box is always in the best operating state.

[0085] If a medical treatment box suddenly loses power during a rescue operation, the system immediately uses the backup battery to continue powering it and sends an alert to the nearest support station. At the same time, it also calculates the fastest route from the support station to the scene and guides the rescue vehicle along this route to ensure that medical equipment is not interrupted.

[0086] In summary, steps 101 to 104 cover the entire process from intelligent judgment processing to the final emergency response, aiming to provide an intelligent, flexible and reliable medical treatment box power management system to meet the demand for stable power supply in complex and changing rescue environments.

[0087] In order to solve the problem of effective allocation and optimal utilization of power resources, in some embodiments, the step 102 uses the dynamic power allocation strategy and the equipment working mode switching rules, combines the cooperation mechanism between the built-in energy storage module and the external renewable energy interface, applies the adaptive energy allocation algorithm to automatically match the optimal power supply mode, and uses the time series prediction technology to estimate and analyze the energy supply and demand model, and generates additional energy reserve decisions and energy-saving measures, including:

[0088] Based on the dynamic power allocation strategy and equipment working mode switching rules, the current power demand of the medical treatment box and the working status of each device are evaluated to obtain a real-time power demand distribution map; using the real-time power demand distribution map, combined with the collaboration mechanism between the built-in energy storage module and the external renewable energy interface, an adaptive energy allocation algorithm is applied to intelligently schedule the energy flow between different power sources to obtain a preliminary optimal power supply mode configuration; based on the preliminary optimal power supply mode configuration, the future energy supply and demand situation is estimated and analyzed using time series prediction technology, and a predicted energy supply and demand model is generated by taking into account seasonal changes and weather forecast factors; based on the predicted energy supply and demand model, the energy reserve amount and consumption rate are optimized and calculated to obtain the additional energy reserve decision; using the additional energy reserve decision, considering the principles of environmental protection and cost-effectiveness, the operating parameters of all electrical equipment in the medical treatment box are adjusted to generate the energy-saving measures plan.

[0089] In this embodiment, based on the dynamic power allocation strategy and equipment working mode switching rules, the system evaluates the current power demand of the medical treatment box and the working status of each device, and generates a real-time power demand distribution map; the distribution map is used to guide the intelligent scheduling of energy flow between different power sources to obtain a preliminary optimal power supply mode configuration; then, based on the preliminary configuration, combined with seasonal changes and weather forecast factors, time series prediction technology is used to estimate and analyze future energy supply and demand situations to form a predicted energy supply and demand model; finally, based on this model, the energy reserve amount and consumption rate are optimized and calculated to obtain additional energy reserve decisions, and the operating parameters of the electrical equipment are adjusted accordingly to generate energy-saving measures.

[0090] In the embodiment of the present application, first, the system will generate a real-time power demand distribution map that reflects the current power demand status of all equipment; secondly, based on this map and taking into account the availability of internal and external energy resources, the system will intelligently schedule energy flows and determine a preliminary optimal power supply mode; thirdly, by introducing seasonal and meteorological data, the system will make accurate predictions about energy supply and demand in the future; finally, based on these prediction results, the system calculates the optimal energy reserve strategy, adjusts equipment operating parameters according to environmental protection and cost-effectiveness principles, and formulates energy-saving measures.

[0091] Here is a specific example:

[0092] Suppose that at a temporary medical station in a remote area, the rescue team needs to ensure that its medical treatment box can operate stably for a long time. To achieve this, the system first analyzes the real-time power demand of all connected devices and draws a detailed power demand distribution map; secondly, based on this map and the status of solar panels and battery packs, the system intelligently adjusts the energy distribution between different power sources and preliminarily determines the most effective power supply mode; thirdly, considering the local seasonal characteristics (such as short sunshine hours in winter) and the latest weather forecast (such as upcoming storms), the system predicts the energy supply and demand trends in the next few days; finally, based on these predictions, the system calculates the additional energy reserves and adjusts the working parameters of non-critical equipment accordingly, such as reducing the brightness of the lighting system in non-emergency situations, thereby formulating energy-saving measures. Through the above steps, the system not only improves the efficiency of energy use, but also ensures the continuous and stable operation of key medical equipment, enhancing the success rate of the entire rescue operation.

[0093] In order to solve the problem of inaccurate prediction of future energy supply and demand and further improve the accuracy and efficiency of power resource management, in some embodiments, according to the preliminary optimal power supply mode configuration, the future energy supply and demand situation is estimated and analyzed using time series prediction technology, and seasonal changes and weather forecast factors are taken into consideration to generate a predicted energy supply and demand model, including:

[0094] Based on the preliminary optimal power supply mode configuration, combined with historical power consumption data and medical activity schedules, a retrospective analysis is performed on the energy supply and demand conditions in the past period of time to obtain historical energy supply and demand characteristics; using the historical energy supply and demand characteristics, combined with seasonal variation patterns and long-term meteorological data, the ARIMA model or LSTM neural network algorithm in time series prediction technology is applied to model the energy demand trend in the future period of time to obtain preliminary energy demand forecast results; based on the preliminary energy demand forecast results, real-time weather forecast information is integrated to correct the preliminary forecast results to obtain an adjusted energy demand forecast; based on the adjusted energy demand forecast, the expected power generation capacity of the external renewable energy interface is evaluated, and the changes in power generation efficiency under different weather conditions are considered to generate an energy supply forecast report; using the energy supply forecast report and the adjusted energy demand forecast, the power requirements of the equipment inside the medical treatment box and the urgency of future tasks are comprehensively considered to perform supply and demand balance calculations to generate a predicted energy supply and demand model.

[0095] In this embodiment, based on the preliminary optimal power supply mode configuration, the system combines historical power consumption data (such as past power usage records) and medical activity schedules (such as surgery plans, examination arrangements, etc.) to conduct a retrospective analysis of the energy supply and demand status in the past period of time to identify the historical energy supply and demand characteristics; these characteristics are used to build a prediction model for future energy demand trends. In addition, the system uses seasonal variation patterns (such as power consumption patterns in different seasons of spring, summer, autumn and winter) and long-term meteorological data (such as temperature, humidity, sunshine duration, etc.), and applies ARIMA models or LSTM neural network algorithms to model the energy demand trend in the future period of time to obtain preliminary energy demand prediction results; then, the system integrates real-time weather forecast information to correct the preliminary prediction results to ensure that the prediction is closer to the actual situation, thereby obtaining an adjusted energy demand forecast. Finally, the system evaluates the expected power generation capacity of the external renewable energy interface (such as solar panels), considers the changes in power generation efficiency under different weather conditions, and generates an energy supply forecast report; and based on this report and the adjusted energy demand forecast, the system comprehensively considers the power requirements of the internal equipment of the medical treatment box and the urgency of future tasks, performs supply and demand balance calculations, and finally generates a predicted energy supply and demand model.

[0096] In the embodiment of the present application, first, the system will review the energy supply and demand conditions in the past period of time and extract the historical energy supply and demand characteristics; secondly, the system will combine these characteristics with seasonal changes and long-term meteorological data, and use advanced time series prediction technology to model future energy demand trends to obtain preliminary energy demand forecasts; thirdly, by integrating the latest weather forecast information, the preliminary forecast results are refined to ensure that the forecast results are more in line with reality; finally, the system evaluates the power generation potential of the external renewable energy interface, generates an energy supply forecast report after considering the impact of weather, and performs supply and demand balance calculations based on this to complete the prediction of the energy supply and demand model.

[0097] Here is a specific example: suppose that in a temporary medical station in a remote mountainous area, the rescue team needs to plan the power supply for the next week in advance. First, the system reviewed all the power consumption records and medical activity arrangements of the medical station in the past month, and extracted the historical energy supply and demand characteristics from them; secondly, combined with the local seasonal characteristics (such as increased heating demand due to low temperatures in winter) and long-term meteorological data, the system used the LSTM neural network algorithm to model the energy demand trend for the next week and obtained preliminary forecast results; thirdly, the system introduced the latest weather forecast information, especially for the upcoming rainy weather, and adjusted the preliminary forecast results to obtain a more accurate energy demand forecast; finally, the system evaluated the power generation efficiency of the solar panels installed in the medical station under different weather conditions, generated an energy supply forecast report, and calculated the supply and demand balance based on this report and the adjusted energy demand forecast, and finally completed the forecast of the energy supply and demand model. Through the above steps, the rescue team can better prepare for the power demand in the next week and ensure that all key medical equipment can obtain stable and reliable power support.

[0098] In order to solve the problem of inaccurate energy demand forecasting and further improve the accuracy of energy demand trend forecasting in the future, in some embodiments, the historical energy supply and demand characteristics are used, combined with seasonal variation patterns and long-term meteorological data, and the ARIMA model or LSTM neural network advanced algorithm in time series forecasting technology are applied to model the energy demand trend in the future to obtain preliminary energy demand forecasting results, including:

[0099] Based on the historical energy supply and demand characteristics, the past power consumption records of the medical treatment box are deeply analyzed to identify the energy usage patterns related to seasonal fluctuations and daily activity patterns, and obtain a periodic energy usage template; according to the periodic energy usage template, combined with the changing trend of the target factors in the long-term meteorological data, the impact of the target factors on the energy demand is evaluated to generate a seasonal adjustment coefficient, wherein the target factors include: temperature, humidity and sunshine duration; using the seasonal adjustment coefficient, as well as the ARIMA model or LSTM neural network algorithm in the time series prediction technology, mathematical modeling is performed on the periodic energy usage template, and the optimization selection of model parameters is considered to improve the prediction accuracy, so as to obtain an energy demand prediction model without weather correction; based on the energy demand prediction model without weather correction, the output of the energy demand prediction model without weather correction is verified and calibrated by simulating the operating scenarios under different seasonal conditions to generate a preliminary energy demand prediction result.

[0100] In this embodiment, based on the historical energy supply and demand characteristics, the system deeply analyzes the past power consumption records of the medical treatment box, identifies the energy usage patterns related to seasonal fluctuations and daily activity patterns, and obtains periodic energy usage templates; these templates reflect the typical electricity usage patterns in different time periods. According to the periodic energy usage template, combined with the changing trend of long-term meteorological data (such as temperature, humidity, sunshine duration, etc.), the impact of the target factor on energy demand is evaluated, and a seasonal adjustment coefficient is generated. Using the seasonal adjustment coefficient and the ARIMA model or LSTM neural network algorithm, the periodic energy usage template is mathematically modeled, and the model parameters are optimized to improve the prediction accuracy, and finally an energy demand prediction model without weather correction is obtained. Finally, by simulating the operating scenarios under different seasonal conditions, the output of the energy demand prediction model without weather correction is verified and calibrated to generate a preliminary energy demand prediction result.

[0101] In an embodiment of the present application, first, the system conducts an in-depth analysis of the historical power consumption records of the medical treatment box, identifies the energy usage patterns related to seasonality and daily activities, and forms a periodic energy usage template; secondly, combined with long-term meteorological data, the impact of factors such as temperature, humidity, and sunshine duration on energy demand is evaluated to generate a seasonal adjustment coefficient; thirdly, the seasonal adjustment coefficient and advanced algorithms in time series prediction technology are used to model the periodic energy usage template, and the model parameters are optimized to improve the prediction accuracy, thereby obtaining an energy demand prediction model without weather correction; finally, by simulating operating scenarios under different seasonal conditions, the model output is verified and calibrated to generate preliminary energy demand prediction results.

[0102] Here is a specific example:

[0103] Suppose that in a temporary medical station located in a high-latitude area, the rescue team needs to plan the power supply for the next few months in advance. First, the system conducted a detailed analysis of the power consumption records of the medical station in the past year, identified seasonal fluctuation patterns such as the surge in power demand during the winter heating period and the stable demand during the summer cooling period, as well as the peak of daytime power consumption caused by daily diagnosis and treatment activities, and formed a periodic energy use template; secondly, the system combined local long-term meteorological data to evaluate the impact of factors such as temperature, humidity and sunshine duration on energy demand, and generated corresponding seasonal adjustment coefficients; thirdly, using these coefficients and the LSTM neural network algorithm, the system modeled the periodic energy use template and optimized the model parameters to ensure that the prediction results are as accurate as possible, and obtained an energy demand prediction model without weather correction; finally, the system simulated the operating scenarios under different seasonal conditions, especially the situation under extreme cold weather, verified and calibrated the model output, and generated preliminary energy demand forecast results. Through the above steps, the rescue team can better prepare for the power demand in the next few months and ensure that all key medical equipment can obtain stable and reliable power support.

[0104] The present application takes into account that, in the prior art, due to the lack of intelligent scheduling and processing of energy flows between different power sources, traditional power management systems are difficult to dynamically adapt to the real-time power demand changes of the internal equipment of the medical treatment box. In addition, the cooperation mechanism between the built-in energy storage module and the external renewable energy interface has not been fully considered, resulting in low energy utilization efficiency and failure to ensure the continuous and stable operation of key medical equipment under any conditions. Therefore, the embodiment of the present invention proposes this optional solution to solve the above problems and provide an intelligent, flexible and efficient energy allocation mechanism to ensure that the medical treatment box can obtain the best power support in various situations.

[0105] Optionally, the real-time power demand distribution map is used in combination with the cooperation mechanism between the built-in energy storage module and the external renewable energy interface, and an adaptive energy allocation algorithm is applied to intelligently schedule the energy flow between different power sources to obtain a preliminary optimal power supply mode configuration, including:

[0106] The system first analyzes the real-time power demand distribution map, evaluates the power consumption patterns and priorities of the equipment, combines the status of the built-in energy reserves and external renewable energy, predicts future power demand and power generation potential, and formulates a preliminary energy allocation strategy to calculate W i Be prepared;

[0107]

[0108] W i represents the energy allocation weight of the i-th device; P req，i represents the real-time power demand of the i-th device; Ereserve Indicates the current power of the built-in energy storage module; G renewable represents the expected power generation of the external renewable energy interface; n represents the total number of devices; D j Represents the working difficulty coefficient of the jth device; R j represents the redundancy coefficient of the jth device; α represents the time decay factor; T i represents the task start time of the i-th device; T avg represents the average task start time;

[0109] Based on the calculated W i , optimizes the energy flow between different power sources through adaptive energy allocation algorithm, and adjusts the power output ratio considering actual availability and loss compensation to form a comprehensive score S config ,Evaluate the effect of power supply mode configuration;

[0110]

[0111] S config表示 Initial score of the best power supply mode configuration; W i represents the energy allocation weight of the i-th device; E reserve Indicates the current power of the built-in energy storage module; E min Indicates the minimum safe power threshold of the built-in energy storage module; G renewable表示 The expected electricity generation from external renewable energy interfaces; G min represents the minimum expected power generation of renewable energy; β represents the loss impact factor; P loss，i represents the power transmission loss of the i-th device; P req，i represents the real-time power demand of the i-th device;

[0112] We conduct comparative analysis on different configuration solutions, select the configuration with the highest score as the best candidate, verify its feasibility and reliability, and continuously optimize it based on feedback. Finally, we verify the configuration effect through simulation tests and pilot applications to ensure that the selected configuration can run stably and efficiently in a real environment.

[0113] This formula is designed to achieve accurate energy allocation and optimization. The system needs to evaluate the power consumption patterns and priorities of the equipment based on the real-time power demand distribution map, and combine the status of built-in energy reserves and external renewable energy to predict future power demand and power generation potential, and formulate a preliminary energy allocation strategy. By introducing an adaptive energy allocation algorithm, the system can intelligently adjust the energy flow between different power sources, while taking into account actual availability and loss compensation, to form a comprehensive score S configTo evaluate the effect of power supply mode configuration. By comparing and analyzing different configuration schemes, the configuration with the highest score is selected as the candidate best solution, and then continuously optimized to ensure that the selected configuration can run stably and efficiently in the real environment.

[0114] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0115]

[0116] This sub-item is used to calculate the basis of the energy allocation weight for each device. It combines the real-time power demand of a single device with its reliance on built-in energy reserves and external renewable energy, and divides it by the weighted sum of the demand of all devices (taking into account the difficulty coefficient and redundancy of the work), thereby ensuring that high-priority and mission-critical devices can get more energy allocation. This approach not only takes into account the current energy availability, but also comprehensively evaluates the importance and operational complexity of the equipment, providing a basic weight for subsequent energy allocation.

[0117] exp(-α·|T i -T avg |): This sub-item introduces a time decay factor to adjust the impact of the difference between the device startup time and the average startup time on energy allocation. Through the exponential decay function, the system can dynamically adjust the energy allocation priority of tasks that are about to start or have already started, ensuring that urgent tasks can immediately obtain sufficient power support when needed, while preventing tasks that wait for a long time from occupying too many resources. This helps optimize the overall energy efficiency and improve the responsiveness and flexibility of the system.

[0118] The following is a brief introduction to how to obtain the parameters of the formula:

[0119] P req，i : Read directly from real-time monitoring data; E reserve : Obtained through the built-in battery management system; G renewable : Prediction based on weather forecast and historical power generation data; D j and R j : defined based on equipment specifications and operating manual; α: determined by experimental tests; T i and T avg : Recorded by the task scheduling system.

[0120] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0121]

[0122] This sub-item reflects the ratio of the current power of the built-in energy reserve module to its minimum safe power threshold. By comparing the actual power with the minimum safe power, the system can ensure that there is no over-discharge under any circumstances and maintain safe operation of the battery. At the same time, this ratio is also an important factor in evaluating the configuration effect of the power supply mode, ensuring that there is enough remaining energy to support the continuous operation of key equipment even under extreme conditions.

[0123] This sub-item represents the proportion of the expected power generation of the external renewable energy interface relative to its minimum expected power generation. It evaluates the availability and reliability of external energy sources to ensure that the system will not affect the overall power supply due to insufficient power generation caused by weather or other factors when utilizing renewable energy. In this way, the system can flexibly adjust the power source combination under different weather conditions, maximize the use of clean energy, and reduce dependence on limited built-in energy.

[0124] This sub-item takes into account the loss during power transmission. By introducing the loss impact factor β and the ratio of actual loss to total demand, the system can quantify and compensate for the energy loss caused by transmission loss. This step is crucial to improving the energy efficiency of the entire system, because it not only reduces unnecessary waste, but also increases the proportion of actual available energy, ensuring that more electricity can be effectively delivered to various devices, especially critical medical equipment.

[0125] The following is a brief introduction to how to obtain the parameters of the formula:

[0126] E reserve and E min : Obtained through the built-in battery management system; G renewable and G min : Prediction based on weather forecast and historical power generation data; β: Determined by experimental test; P loss,i i is measured by power monitoring equipment; P req,i iRead directly from real-time monitoring data.

[0127] Assume that at a temporary medical station in a remote area, the rescue team needs to ensure that their medical treatment kit can provide stable power support in the face of changing environments. First, the system analyzes the real-time power demand distribution map of all connected devices, evaluates the power usage pattern and priority of the devices, and combines the built-in battery (E reserve =500kWh) and solar panels (G renewable =300kWh), predicted the power demand and power generation potential for the next day, and formulated a preliminary energy allocation strategy. req,1 =100kW, D 1 =1.2, R1 =0.8), its energy allocation weight W 1 The calculation is as follows:

[0128]

[0129] Based on the calculated W i The system optimizes the energy flow between different power sources through an adaptive energy allocation algorithm, forming a comprehensive score S config :

[0130]

[0131] By comparing and analyzing different configuration schemes, a configuration with a comprehensive score of 0.94 was selected as the candidate best solution, and its feasibility and reliability were verified through simulation tests. The results show that when the threshold is set to 0.9, since the result is greater than the set threshold, it shows that this configuration scheme can significantly improve the utilization efficiency of power resources and ensure the continuous and stable operation of key medical equipment, thereby solving the problems existing in the existing technology and improving the overall performance and reliability of the system.

[0132] In order to solve the problem of inaccurate power demand forecasting and insufficient reliability assessment of power supply schemes, and to further improve the accuracy of future power demand trend forecasting and the reliability and stability of different power supply schemes under various conditions, in some embodiments, according to the additional energy reserve decision and energy-saving measures scheme described in step 103, a rolling demand forecasting algorithm is used to perform rolling update forecasting of future power demand trends, and historical energy consumption data and medical activity schedules are combined to optimize the forecast accuracy through a machine learning algorithm, implement reliability engineering analysis, evaluate the reliability and stability of different power supply schemes under various conditions, and consider the impact of extreme weather or emergencies to generate a comprehensive stable power supply guarantee plan, including:

[0133] Based on the additional energy reserve decision and energy-saving measures plan, the current energy reserve status of the medical treatment box and the execution effect of the energy-saving strategy are evaluated and processed to obtain a current energy management status report; using the current energy management status report, combined with historical energy consumption data and medical activity schedules, a rolling demand forecasting algorithm is used to perform rolling update forecasts on the power demand trend in the future period of time, and a preliminary future power demand forecast is generated; based on the preliminary future power demand forecast, real-time and predicted medical activity information is integrated, and regression analysis or deep learning models in machine learning algorithms are used to optimize the predicted energy supply and demand model parameters to improve the accuracy of the forecast, so as to obtain a refined future power demand forecast; based on the refined Refine the forecast of future electricity demand, implement reliability engineering analysis, evaluate the reliability and stability of different power supply schemes under different load conditions, and generate a power supply scheme reliability assessment report; use the power supply scheme reliability assessment report, combined with extreme weather forecast data and possible emergency scenarios, to stress test the existing power supply scheme, evaluate the response capability and recovery speed of the existing power supply scheme under adverse conditions, and generate the evaluation results of the power supply scheme under extreme conditions; based on the evaluation results of the power supply scheme under extreme conditions, formulate a comprehensive and stable power supply guarantee plan, which is used to provide reliable power support for medical treatment boxes under conventional and extreme conditions, and ensure the continuous operation of key medical equipment.

[0134] In this embodiment, based on the additional energy reserve decision and energy-saving measures, the system evaluates and processes the current energy reserve state of the medical treatment box and the execution effect of the energy-saving strategy to obtain a current energy management status report; this report is used to reflect the current power resource management and use. Using the current energy management status report, combined with historical energy consumption data (such as past power consumption records) and medical activity schedules (such as surgery, examinations, etc.), a rolling demand forecasting algorithm is used to perform rolling update forecasts on the power demand trend in the future period of time, and a preliminary future power demand forecast is generated; these forecasts help plan future power distribution. According to the preliminary future power demand forecast, real-time and predicted medical activity information is integrated, and regression analysis or deep learning models in machine learning algorithms are used to optimize the predicted energy supply and demand model parameters to improve the accuracy of the forecast and obtain a refined future power demand forecast; the refined forecast provides a more reliable basis for subsequent decision-making. Based on the refined future power demand forecast, reliability engineering analysis is implemented to evaluate the reliability and stability of different power supply schemes under different load conditions, and a power supply scheme reliability assessment report is generated; this report helps select the optimal power supply scheme. Using the power supply plan reliability assessment report, combined with extreme weather forecast data and possible emergency scenarios, the existing power supply plan is stress tested to evaluate its response capability and recovery speed under adverse conditions, and generate the evaluation results of the power supply plan under extreme conditions; finally, based on the evaluation results of the power supply plan under extreme conditions, a comprehensive and stable power supply guarantee plan is formulated to ensure that medical treatment boxes can obtain reliable power support under conventional and extreme conditions, and to ensure the continuous operation of key medical equipment.

[0135] In the embodiment of the present application, first, the system evaluates the current energy reserve status of the medical treatment box and the execution effect of the energy-saving strategy, and generates a current energy management status report; secondly, in combination with historical energy consumption data and medical activity schedules, a rolling demand forecasting algorithm is applied to perform rolling update forecasts on future electricity demand trends, and a preliminary future electricity demand forecast is generated; thirdly, real-time and predicted medical activity information is integrated, and a machine learning algorithm is used to optimize the predicted energy supply and demand model parameters to obtain a refined future electricity demand forecast; finally, reliability engineering analysis is implemented to evaluate the reliability and stability of different power supply schemes under different load conditions, generate a power supply scheme reliability assessment report, and combine extreme weather forecast data and emergency scenarios for stress testing, and finally formulate a comprehensive and stable power supply guarantee plan.

[0136] Here is a specific example:

[0137] Suppose that in a temporary medical station located in a tropical area, the rescue team needs to plan the power supply for the next month in advance. First, the system evaluates the current energy reserve status of the medical station and the effectiveness of energy-saving measures, and generates a detailed current energy management status report, showing the current use of power resources and the effectiveness of energy-saving strategies; secondly, combined with the historical energy consumption data of the past few months and the upcoming medical activity schedule, the system applies the rolling demand forecasting algorithm to make a rolling update forecast of the power demand trend for the next month, generates a preliminary future power demand forecast, and reveals the high demand period that may occur in the next few weeks; thirdly, the system integrates real-time and predicted medical activity information, and uses regression analysis in the machine learning algorithm to optimize the parameters of the predicted energy supply and demand model, improves the accuracy of the forecast, obtains a refined future power demand forecast, and ensures that the forecast results are closer to the actual situation; finally, the system implements reliability engineering analysis, evaluates the reliability and stability of different power supply schemes under different load conditions, generates a power supply scheme reliability assessment report, and combines the local upcoming rainy season weather forecast data and possible emergency scenarios to conduct stress tests, evaluates the response capability and recovery speed of the existing power supply scheme under adverse conditions, and finally formulates a comprehensive stable power supply guarantee plan. By taking the above steps, the rescue team will be better prepared to deal with the power needs in the next month and ensure that all critical medical equipment has stable and reliable power support.

[0138] In order to solve the problem of insufficient reliability and stability evaluation of different power supply schemes under complex load conditions and further improve the scientificity and rationality of power supply scheme selection, in some embodiments, based on the refined future power demand forecast, reliability engineering analysis is implemented to evaluate the reliability and stability of different power supply schemes under different load conditions, and a power supply scheme reliability evaluation report is generated, including:

[0139] Based on the refined future power demand forecast, the power consumption pattern of the medical treatment box in different time periods in the future is analyzed in detail to obtain the power demand distribution in different time periods; using the power demand distribution in different time periods, combined with the existing power supply configuration and potential power supply improvement solutions, multiple hypothetical scenarios are constructed to obtain multiple power supply solution models, wherein one hypothetical scenario represents a power supply strategy; according to the multiple power supply solution models, the reliability engineering analysis method is applied to set performance indicators to quantify the evaluation standards and generate a reliability evaluation index system; based on the reliability evaluation index system, each power supply solution model is simulated and tested in a simulation environment, and the response time and recovery capability under different load conditions are recorded to obtain the original simulation data set; using the original simulation data set, through statistical analysis and comparative research, the load adaptability analysis results are generated; according to the load adaptability analysis results, the long-term operating cost, maintenance convenience and environmental adaptability of each power supply solution are comprehensively considered, and each power supply solution is evaluated to obtain the final reliability score; based on the final reliability score, a power supply solution reliability evaluation report is compiled, and the power supply solution reliability evaluation report can provide stable and reliable power support under various expected operating conditions.

[0140] In this embodiment, based on the refined prediction of future power demand, the system analyzes the power consumption pattern of the medical treatment box in different time periods in the future in detail, and obtains the power demand distribution in different time periods; these data are used to simulate future power demand more accurately. Using the power demand distribution in different time periods, combined with the existing power supply configuration and potential power supply improvement solutions, multiple hypothetical scenarios are constructed to obtain multiple power supply solution models; each hypothetical scenario represents a power supply strategy, covering from the existing configuration to various possible improvement measures. According to the multiple power supply solution models, the reliability engineering analysis method is applied, the performance indicators are set to quantify the evaluation criteria and generate a reliability evaluation index system; this system provides a clear evaluation standard for subsequent simulation tests. Based on the reliability evaluation index system, each power supply solution model is simulated and tested in a simulation environment, and the response time and recovery capability under different load conditions are recorded to obtain the original simulation data set; these data reflect the performance of each solution in actual operation. Using the original simulation data set, the load adaptability analysis results are generated through statistical analysis and comparative research; this result reveals the advantages and disadvantages of each power supply solution under different load conditions. According to the results of load adaptability analysis, each power supply scheme is evaluated by comprehensively considering the long-term operating cost, maintenance convenience and environmental adaptability factors of each power supply scheme to obtain the final reliability score; finally, based on the final reliability score, a power supply scheme reliability assessment report is compiled to ensure that the report can provide stable and reliable power support under various expected operating conditions.

[0141] In the embodiment of the present application, first, the system conducts a detailed analysis of the power consumption pattern of the medical treatment box in different time periods in the future to obtain the power demand distribution in different time periods; secondly, based on the existing power supply configuration and potential power supply improvement plans, multiple hypothetical scenarios are constructed to obtain multiple power supply plan models; thirdly, according to the multiple power supply plan models, the reliability engineering analysis method is applied, performance indicators are set, and a reliability evaluation index system is generated; finally, each power supply plan model is simulated and tested in a simulation environment, the response time and recovery capability are recorded, the original simulation data set is obtained, and the load adaptability analysis results are generated through statistical analysis and comparative research, and the long-term operating cost, maintenance convenience and environmental adaptability factors of each power supply plan are comprehensively considered to obtain the final reliability score, and a power supply plan reliability evaluation report is compiled.

[0142] Here is a specific example:

[0143] Assume that in a temporary medical station in a remote area, the rescue team needs to evaluate the reliability of its power system to ensure that it can provide stable power support in the face of different load conditions. First, based on the refined future power demand forecast, the system conducts a detailed analysis of the power consumption pattern in different time periods in the next week, obtains the power demand distribution in different time periods, and shows the specific demand in peak and valley periods; secondly, combined with the existing diesel generator and solar panel power supply configuration and possible improvement schemes (such as adding energy storage batteries), five hypothetical scenarios are constructed, each of which represents a different power supply strategy; thirdly, based on these five power supply scheme models, the reliability engineering analysis method is applied, performance indicators such as response time, recovery speed and continuous power supply capacity are set, and a reliability evaluation index system is generated; finally, each power supply scheme model is simulated and tested in a simulation environment, the response time and recovery capacity under different load conditions are recorded, the original simulation data set is obtained, and the load adaptability analysis results are generated through statistical analysis and comparative research. Based on these results, the system comprehensively considers the long-term operating cost, maintenance convenience and environmental adaptability factors of each power supply scheme, evaluates each scheme, obtains the final reliability score, and compiles a power supply scheme reliability evaluation report. Through the above steps, the rescue team can better understand the performance of different power supply solutions under various operating conditions, so as to select the most suitable solution and ensure that all critical medical equipment can obtain stable and reliable power support.

[0144] This application takes into account that in the prior art, due to the lack of dynamic evaluation of the current energy management status and accurate prediction of future power demand trends, the traditional power management system is difficult to adapt to the changes in the real-time energy consumption of the internal equipment of the medical treatment box, resulting in low energy allocation efficiency, especially when facing emergency tasks or high-priority medical activities, it is impossible to ensure that key equipment obtains sufficient power support. Therefore, the embodiment of the present invention proposes this optional solution to solve the above problems and provide an intelligent, flexible and reliable rolling power demand prediction mechanism to ensure that the medical treatment box can provide stable and efficient power support under various conditions.

[0145] Optionally, the current energy management status report is used in combination with historical energy consumption data and medical activity schedules to apply a rolling demand forecasting algorithm to perform rolling update forecasts on the power demand trend in the future period to generate a preliminary future power demand forecast, including:

[0146] The system analyzes the current energy management status report, combines historical energy consumption data and medical activity schedules, identifies periodic and trend characteristics and the impact of high-priority tasks, and builds a comprehensive energy consumption impact model to calculate C roll (t) make preparations;

[0147]

[0148] C roll (t) represents the rolling forecast correction coefficient at time point t; H(τ) represents the average value of historical energy consumption data within the time interval τ; A(t-τ) represents the influencing factor of the medical activity schedule within the time interval t-τ; γ represents the matching degree attenuation factor; M(t) represents the management status at time point t recorded in the current energy management status report; M(τ) represents the historical management status within the time interval τ;

[0149] The adaptive adjustment mechanism is used to integrate the current energy reserve and the real-time energy consumption factor of medical equipment, dynamically adjust the power demand estimate according to the rolling forecast correction coefficient, and introduce a variable index to reflect short-term fluctuations to form a preliminary future power demand forecast P. pred (t+Δt);

[0150]

[0151] P pred (t+Δt) represents the predicted electricity demand at the future time t+Δt; E current Indicates the total energy reserve in the current energy management status report; F i (t) represents the energy consumption factor of the i-th medical device at time point t; N represents the total number of medical devices; C roll(t) represents the rolling forecast correction coefficient; λ represents the change rate influencing factor; V(t) represents the variability index at time point t; η represents the seasonal fluctuation factor; ω represents the seasonal cycle frequency; Δt represents the time increment of the forecast;

[0152] Compare and analyze the forecast results under different scenarios, select the most appropriate forecasting scheme, and adjust the model to improve the reliability of long-term forecasts by considering factors such as seasonality. Optimize the forecasting method based on actual feedback, and verify and fine-tune the forecasting parameters through simulation tests, and finally generate accurate and practical preliminary future power demand forecasts to guide energy management and scheduling decisions.

[0153] This formula is designed to achieve accurate power demand forecasting. The system needs to analyze the current energy management status report, combine historical energy consumption data and medical activity schedules, identify periodic and trend characteristics and the impact of high-priority tasks, and build a comprehensive energy consumption impact model. By introducing the rolling forecast correction factor C roll (t), the system can integrate the current energy reserve and the real-time energy consumption factor of medical equipment according to the adaptive adjustment mechanism, dynamically adjust the estimated power demand, and introduce variable indicators to reflect short-term fluctuations to form a preliminary future power demand forecast P pred (t+Δt). By comparing and analyzing the forecast results under different scenarios, selecting the most appropriate forecasting scheme, and optimizing the forecasting method based on actual feedback, we can ultimately generate an accurate and practical preliminary forecast of future electricity demand to guide energy management and scheduling decisions.

[0154] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0155]

[0156] e -γ·|M(t)-M(τ)| :This sub-item is used to measure the similarity between the current energy management state M(t) and the historical management state M(τ). By introducing the matching decay factor γ and the exponential decay function, the system can quantify the difference between the two and make weighted adjustments based on the impact of similarity on historical energy consumption data. This approach not only takes into account the distance in time, but also comprehensively evaluates the changes in management status in different time periods, making the prediction closer to the actual situation and improving the adaptability and accuracy of the model.

[0157] The following is a brief introduction to how to obtain the parameters of the formula:

[0158] H(τ): extracted from the historical energy consumption database; A(t-τ): obtained from the medical activity scheduling system; γ: determined by experimental tests; M(t) and M(τ): recorded by the energy management monitoring system.

[0159] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0160]

[0161] 1+λ·V(t): This sub-item reflects the impact of short-term fluctuations on power demand forecasts. By introducing the variability index V(t) and the change rate influence factor λ, the system can dynamically adjust the power demand estimate to cope with uncertainties and fluctuations that may occur in the short term. This mechanism ensures that the forecast model can maintain flexibility and responsiveness in the face of emergencies or high-priority tasks, enhancing the stability and reliability of the system.

[0162] F i (t)·C roll (t): This item combines the real-time energy consumption factor F of the i-th medical device at time point t i (t) and rolling prediction correction factor C roll (t) is used to dynamically adjust the power demand estimate of each device. By combining the real-time energy consumption of the device with the overall rolling forecast correction factor, the system can more accurately reflect the actual power demand of each device and make appropriate adjustments based on historical and current status to ensure that key devices receive sufficient power support while optimizing overall energy distribution.

[0163] 1+η·sin(ω·Δt): This sub-item introduces the seasonal fluctuation factor η and the seasonal cycle frequency ω to reflect the impact of seasonal changes on electricity demand. By simulating periodic fluctuations through the sine function, the system can take seasonal factors into account in long-term forecasts, improving the reliability and accuracy of forecasts. This approach not only helps to better plan future power supply, but also can respond to seasonal peaks and troughs in advance, ensuring the rational allocation and effective use of power resources.

[0164] The following is a brief introduction to how to obtain the parameters of the formula:

[0165] E current : Read from the energy management status report; Obtained through real-time monitoring of equipment energy consumption; C roll (t): calculated by formula 1; λ: determined by experimental test; V(t): obtained from the real-time monitoring system; η and ω: set according to historical data analysis; Δt: user-defined or system default.

[0166] Assume that at a temporary medical station in a remote area, the rescue team needs to ensure that the power supply of its medical treatment box is stable and reliable in the next week. First, the system analyzes the current energy management status report, combines the historical energy consumption data of the past month and the medical activity schedule for the next week, identifies the periodic and trend characteristics and the impact of high-priority tasks (such as emergency surgery), and constructs a comprehensive energy consumption impact model. For a certain day at time t, the rolling forecast correction factor C roll (t) is calculated as follows:

[0167]

[0168] Based on this, the system uses an adaptive adjustment mechanism to integrate the current energy reserve (E current =500kWh) and real-time energy consumption factor of medical equipment The estimated power demand was dynamically adjusted according to the rolling forecast correction coefficient, and a variability index (V(t) = 0.08) was introduced to reflect short-term fluctuations, forming a preliminary future power demand forecast P pred (t+1day):

[0169]

[0170] By comparing and analyzing the prediction results under different scenarios, the most appropriate prediction scheme was selected, and the prediction method was continuously optimized based on actual feedback. The results showed that when the threshold was set to 750kW, since the result was greater than the set threshold, this configuration scheme could significantly improve the accuracy of power demand prediction and ensure that all key medical equipment can obtain stable and reliable power support in the next week, thus solving the problems existing in the existing technology and improving the overall performance and reliability of the system.

[0171] In order to solve the emergency response problem when the main power supply fails or the power is insufficient, ensure the continuous operation of key medical equipment, and quickly obtain external support, in some embodiments, the step 104 described in the comprehensive stable power supply guarantee plan, when the main power supply fails or the power is insufficient, starts the emergency response mechanism, activates the backup battery pack and sends a distress signal to the nearest support site, and determines the optimal rescue route through the path optimization technology, and generates the rescue path planning, including:

[0172] Based on the comprehensive stable power supply guarantee plan, the power system of the medical treatment box is monitored in real time, the main power status and the remaining power level are monitored, and a real-time power status report is obtained; when a main power failure is detected or the power level is lower than a preset threshold, the emergency response mechanism is immediately triggered based on the real-time power status report, and the power supply is automatically switched to the backup battery pack to ensure the continuous operation of key medical equipment, and emergency power supply confirmation information is generated; according to the emergency power supply confirmation information, the communication module is activated, and a distress signal containing the current location and emergency details is sent to the pre-set nearest support site, and the current power status and expected power consumption are uploaded to obtain a distress signal sending record; based on the distress signal sending record, combined with the road network data and traffic condition information in the geographic information system, the path optimization technology is applied to calculate the cost and time of all possible paths from the support site to the location of the medical treatment box, and a list of candidate rescue paths is generated; using the candidate rescue path list, considering the type of support vehicle, weather conditions and potential obstacles, the actual feasibility of each path is screened and evaluated, the optimal rescue route is selected, and a rescue path plan is generated.

[0173] In this embodiment, based on the comprehensive stable power supply guarantee plan, the system monitors the power system of the medical treatment box in real time, monitors the main power supply status and the remaining power level, and obtains a real-time power status report; these data are used to evaluate the health of the current power system. When the main power failure is detected or the power level is lower than the preset threshold, the system immediately triggers the emergency response mechanism, automatically switches to the backup battery pack for power supply, ensures the continuous operation of key medical equipment, and generates emergency power supply confirmation information; this information is used to record key operations in the emergency response process. According to the emergency power supply confirmation information, the system activates the communication module, sends a distress signal containing the current location and emergency details to the pre-set nearest support site, and uploads the current power status and expected power consumption to obtain a distress signal sending record; these records help track the rescue process. Based on the distress signal sending record, combined with the road network data and traffic status information in the geographic information system (GIS), the path optimization technology is applied to calculate the cost and time of all possible paths from the support site to the location of the medical treatment box, and generate a candidate rescue path list; this list provides multiple feasible rescue route options. Using the list of candidate rescue paths, taking into account the type of support vehicles, weather conditions and potential obstacles, the feasibility of each path is screened and evaluated, the optimal rescue route is selected, and a rescue path plan is generated; this plan guides the most effective rescue operation.

[0174] In the embodiment of the present application, first, the system monitors the power system of the medical treatment box in real time, monitors the main power status and the remaining power level, and generates a real-time power status report; secondly, when a main power failure or insufficient power is detected, the system immediately triggers the emergency response mechanism, automatically switches to the backup battery pack for power supply, ensures the continuous operation of key medical equipment, and records the emergency power supply confirmation information; thirdly, the system activates the communication module, sends a distress signal to the nearest support site, and uploads the current power status and expected power consumption, and records the rescue process; finally, the system combines the road network data and traffic status information in the geographic information system, applies path optimization technology to generate a list of candidate rescue paths, and considers the type of support vehicles, weather conditions and potential obstacles, selects the optimal rescue route, and generates a rescue path plan.

[0175] Here is a specific example:

[0176] Consider a makeshift medical station in a remote mountain area. The rescue team needs to ensure that their medical treatment kits can be quickly restored and receive external support if they encounter power problems. First, the system monitors the power system of the medical treatment box in real time, monitors the main power status and the remaining power level, and generates a detailed real-time power status report showing the current operating status of the power system; second, when the system detects a main power failure or the power level is lower than the preset threshold, it immediately triggers the emergency response mechanism and automatically switches to the backup battery pack to ensure the continuous operation of key medical equipment such as the cardiopulmonary resuscitation device, and generates emergency power supply confirmation information and records the switching process; third, the system activates the communication module and sends a distress signal to the pre-set nearest support station. The signal contains the current location of the medical station and the details of the emergency, and uploads the current power status and expected power consumption. The distress signal sending record is obtained to ensure that the support team can obtain the required information in a timely manner; finally, the system combines the road network data and real-time traffic status information in the geographic information system, and uses the path optimization technology to calculate the cost and time of all possible paths from the support station to the medical station, generates a list of candidate rescue paths, and considers the type of support vehicles, local weather conditions (such as road waterlogging caused by heavy rain) and potential obstacles. Select the optimal rescue route and generate a rescue path plan. Through the above steps, the rescue team can respond quickly in the event of a main power failure or low power, ensure the continued operation of critical medical equipment, and guide the support team to arrive at the scene as quickly as possible to provide assistance.

[0177] Figure 2 A schematic diagram of the structure of an optimization scheduling system for a power management system is provided for an embodiment of the present application, such as Figure 2 As shown, the device comprises:

[0178] The processing module 21 is used to intelligently judge the urgency and estimated power consumption of current and future tasks based on the real-time geographic location, environmental parameters and task priority evaluation of the medical treatment box, and obtain a dynamic power allocation strategy and equipment working mode switching rules;

[0179] A matching module 22 is used to utilize the dynamic power allocation strategy and the equipment working mode switching rules, combine the cooperation mechanism between the built-in energy storage module and the external renewable energy interface, apply the adaptive energy allocation algorithm to automatically match the optimal power supply mode, and use the time series prediction technology to estimate and analyze the energy supply and demand model, and generate additional energy reserve decisions and energy-saving measures.

[0180] The prediction module 23 is used to use a rolling demand forecasting algorithm to perform rolling update forecasts on future power demand trends based on the additional energy reserve decision and energy-saving measures, combine historical energy consumption data with medical activity schedules, optimize forecast accuracy through machine learning algorithms, perform reliability engineering analysis, evaluate the reliability and stability of different power supply solutions under various conditions, and consider the impact of extreme weather or emergencies to generate a comprehensive stable power supply guarantee plan;

[0181] The activation module 24 is used to start the emergency response mechanism based on the comprehensive stable power supply guarantee plan, activate the backup battery pack and send a distress signal to the nearest support site when a main power failure or insufficient power is detected, and determine the optimal rescue route through path optimization technology to generate a rescue path plan.

[0182] Figure 2 The optimization scheduling system of the power management system can execute Figure 1 The implementation principle and technical effect of the optimization scheduling method of a power management system described in the embodiment are not repeated here. The specific way in which each module and unit performs operations in the optimization scheduling system of a power management system in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0183] In one possible design, Figure 2 An optimization scheduling system of a power management system of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0184] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0185] The processing component 32 is used to: based on the real-time geographic location, environmental parameters and task priority evaluation of the medical treatment box, intelligently judge and process the urgency and expected power consumption of current and future tasks to obtain a dynamic power allocation strategy and equipment working mode switching rules; using the dynamic power allocation strategy and equipment working mode switching rules, combined with the cooperation mechanism between the built-in energy storage module and the external renewable energy interface, apply an adaptive energy allocation algorithm to automatically match the optimal power supply mode, and use time series prediction technology to estimate and analyze the energy supply and demand model to generate additional energy reserve decisions and energy-saving measures; based on the additional energy reserve decision and energy-saving measures, use a rolling demand prediction algorithm to perform rolling update predictions on future power demand trends, combine historical energy consumption data with medical activity schedules, optimize prediction accuracy through machine learning algorithms, implement reliability engineering analysis, evaluate the reliability and stability of different power supply schemes under various conditions, and consider the impact of extreme weather or emergencies to generate a comprehensive stable power supply guarantee plan; based on the comprehensive stable power supply guarantee plan, when a main power failure or insufficient power is detected, start the emergency response mechanism, activate the backup battery pack and send a distress signal to the nearest support site, and determine the optimal rescue route through path optimization technology to generate a rescue path plan.

[0186] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0187] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0188] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0189] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0190] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0191] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0192] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 An optimization scheduling method for a power management system according to the illustrated embodiment.

[0193] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0194] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0195] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0196] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An optimization scheduling method for a power management system, characterized in that: include: Based on the real-time geographic location, environmental parameters and task priority assessment of the medical treatment box, the urgency and expected power consumption of current and future tasks are intelligently judged and processed to obtain dynamic power allocation strategies and equipment working mode switching rules; By utilizing the dynamic power allocation strategy and equipment working mode switching rules, combined with the cooperation mechanism between the built-in energy storage module and the external renewable energy interface, an adaptive energy allocation algorithm is applied to automatically match the optimal power supply mode, and time series prediction technology is used to estimate and analyze the energy supply and demand model, generating additional energy reserve decisions and energy-saving measures. Based on the additional energy reserve decision and energy-saving measures, a rolling demand forecasting algorithm is used to make rolling updates to forecast future electricity demand trends. The historical energy consumption data and medical activity schedule are combined to optimize the forecast accuracy through machine learning algorithms. Reliability engineering analysis is performed to evaluate the reliability and stability of different power supply solutions under various conditions. The impact of extreme weather or emergencies is considered to generate a comprehensive stable power supply guarantee plan. Based on the comprehensive stable power supply guarantee plan, when a main power failure or insufficient power is detected, the emergency response mechanism is activated, the backup battery pack is activated and a distress signal is sent to the nearest support site, and the optimal rescue route is determined through path optimization technology to generate a rescue path plan.

2. The method according to claim 1, characterized in that The dynamic power allocation strategy and equipment working mode switching rules are used, combined with the cooperation mechanism between the built-in energy storage module and the external renewable energy interface, and the adaptive energy allocation algorithm is used to automatically match the optimal power supply mode, and the time series prediction technology is used to estimate and analyze the energy supply and demand model, and generate additional energy reserve decisions and energy-saving measures, including: Based on the dynamic power allocation strategy and the equipment working mode switching rules, the current power demand of the medical treatment box and the working status of each device are evaluated to obtain a real-time power demand distribution map; By using the real-time power demand distribution map, combined with the cooperation mechanism between the built-in energy storage module and the external renewable energy interface, an adaptive energy allocation algorithm is applied to intelligently dispatch the energy flow between different power sources to obtain a preliminary optimal power supply mode configuration; Based on the preliminary optimal power supply mode configuration, use time series forecasting technology to estimate and analyze future energy supply and demand conditions, and generate a predicted energy supply and demand model by taking into account seasonal changes and weather forecast factors; Based on the predicted energy supply and demand model, optimizing and calculating the energy reserve amount and the consumption rate to obtain the additional energy reserve decision; By utilizing the additional energy reserve decision and taking environmental protection and cost-effectiveness principles into consideration, the operating parameters of all electrical equipment in the medical treatment box are adjusted to generate the energy-saving measure plan.

3. The method according to claim 2, characterized in that The method uses time series forecasting technology to estimate and analyze future energy supply and demand conditions based on the preliminary optimal power supply mode configuration, and generates a predicted energy supply and demand model by taking into account seasonal changes and weather forecast factors, including: Based on the preliminary optimal power supply mode configuration, combined with historical power consumption data and medical activity schedules, a retrospective analysis of energy supply and demand conditions over a period of time in the past is performed to obtain historical energy supply and demand characteristics; By using the historical energy supply and demand characteristics, combined with seasonal variation patterns and long-term meteorological data, and applying the ARIMA model or LSTM neural network algorithm in time series forecasting technology, the energy demand trend in the future period is modeled to obtain preliminary energy demand forecast results; According to the preliminary energy demand forecast result, integrating real-time weather forecast information, revising the preliminary forecast result, and obtaining an adjusted energy demand forecast; Based on the adjusted energy demand forecast, evaluating the expected power generation capacity of the external renewable energy interface, considering the change of power generation efficiency under different weather conditions, and generating an energy supply forecast report; The energy supply forecast report and the adjusted energy demand forecast are used to comprehensively consider the power requirements of the equipment inside the medical treatment box and the urgency of future tasks, and a supply and demand balance calculation is performed to generate a predicted energy supply and demand model.

4. The method according to claim 3, characterized in that The above-mentioned historical energy supply and demand characteristics are used in combination with seasonal variation patterns and long-term meteorological data, and the ARIMA model or LSTM neural network advanced algorithm in time series forecasting technology are applied to model the energy demand trend in the future period of time, and obtain preliminary energy demand forecast results, including: Based on the historical energy supply and demand characteristics, the past power consumption records of the medical treatment box are deeply analyzed to identify energy usage patterns related to seasonal fluctuations and daily activity patterns, and obtain a periodic energy usage template; According to the periodic energy usage template, combined with the change trend of the target factors in the long-term meteorological data, the influence of the target factors on the energy demand is evaluated to generate a seasonal adjustment coefficient, wherein the target factors include: temperature, humidity and sunshine duration; The seasonal adjustment coefficient and the ARIMA model or LSTM neural network algorithm in the time series prediction technology are used to mathematically model the periodic energy usage template, and the optimization selection of model parameters is considered to improve the prediction accuracy, so as to obtain an energy demand prediction model without weather correction; Based on the energy demand forecasting model without weather correction, the output of the energy demand forecasting model without weather correction is verified and calibrated by simulating operation scenarios under different seasonal conditions to generate preliminary energy demand forecasting results.

5. The method according to claim 1, characterized in that According to the additional energy reserve decision and energy-saving measures, a rolling demand forecasting algorithm is used to make rolling updates and forecasts on future power demand trends. The historical energy consumption data and medical activity schedule are combined to optimize the forecast accuracy through machine learning algorithms. Reliability engineering analysis is performed to evaluate the reliability and stability of different power supply schemes under various conditions, and the impact of extreme weather or emergencies is considered to generate a comprehensive stable power supply guarantee plan, including: Based on the additional energy reserve decision and energy-saving measure plan, the current energy reserve state of the medical treatment box and the execution effect of the energy-saving strategy are evaluated and processed to obtain a current energy management state report; Using the current energy management status report, combined with historical energy consumption data and medical activity schedules, a rolling demand forecasting algorithm is applied to perform rolling update forecasts on power demand trends over a period of time in the future to generate a preliminary future power demand forecast; According to the preliminary future electricity demand forecast, real-time and predicted medical activity information is integrated, and regression analysis or deep learning model in the machine learning algorithm is used to optimize the predicted energy supply and demand model parameters to improve the accuracy of the forecast and obtain a refined future electricity demand forecast; Based on the refined future power demand forecast, reliability engineering analysis is performed to evaluate the reliability and stability of different power supply solutions under different load conditions, and a reliability evaluation report of the power supply solution is generated; Using the reliability assessment report of the power supply scheme, combined with extreme weather forecast data and possible emergency scenarios, stress test the existing power supply scheme, evaluate the response capability and recovery speed of the existing power supply scheme under adverse conditions, and generate the evaluation results of the power supply scheme under extreme conditions; Based on the evaluation results of the power supply plan under the extreme conditions, a comprehensive stable power supply guarantee plan is formulated. The comprehensive stable power supply guarantee plan is used to provide reliable power support for the medical treatment box under normal and extreme conditions and ensure the continuous operation of key medical equipment.

6. The method according to claim 5, characterized in that Based on the refined future power demand forecast, reliability engineering analysis is performed to evaluate the reliability and stability of different power supply schemes under different load conditions, and a power supply scheme reliability evaluation report is generated, including: Based on the refined future power demand forecast, the power consumption pattern of the medical treatment box in different time periods in the future is analyzed in detail to obtain the power demand distribution in different time periods; By using the time-divided power demand distribution, combined with the existing power supply configuration and potential power supply improvement solutions, multiple hypothetical scenarios are constructed to obtain multiple power supply solution models, wherein one hypothetical scenario represents one power supply strategy; According to the multiple power supply scheme models, reliability engineering analysis methods are applied to set performance indicators to quantify evaluation standards and generate a reliability evaluation index system; Based on the reliability evaluation index system, a simulation test is performed on each power supply scheme model in a simulation environment, and the response time and recovery capability under different load conditions are recorded to obtain an original simulation data set; Using the original simulation data set, generating load adaptability analysis results through statistical analysis and comparative research; According to the load adaptability analysis results, each power supply scheme is evaluated by comprehensively considering the long-term operating cost, maintenance convenience and environmental adaptability factors of each power supply scheme to obtain a final reliability score; Based on the final reliability score, a power supply scheme reliability assessment report is prepared, and the power supply scheme reliability assessment report can provide stable and reliable power support under various expected operating conditions.

7. The method according to claim 1, characterized in that Based on the comprehensive stable power supply guarantee plan, when a main power failure or insufficient power is detected, the emergency response mechanism is started, the backup battery pack is activated, and a distress signal is sent to the nearest support site, and the optimal rescue route is determined through path optimization technology to generate a rescue path plan, including: Based on the comprehensive stable power supply guarantee plan, the power system of the medical treatment box is monitored in real time, the main power supply status and the remaining power level are monitored, and a real-time power status report is obtained; When a main power failure is detected or the power level is lower than a preset threshold, an emergency response mechanism is immediately triggered based on the real-time power status report, and power is automatically switched to the backup battery pack to ensure the continuous operation of critical medical equipment, and emergency power supply confirmation information is generated; According to the emergency power supply confirmation information, the communication module is activated to send a distress signal including the current location and emergency details to the nearest pre-set support site, and the current power status and estimated power consumption are uploaded to obtain a distress signal sending record; Based on the distress signal sending record, combined with the road network data and traffic condition information in the geographic information system, the path optimization technology is applied to calculate the cost and time of all possible paths from the support site to the location of the medical treatment box, and generate a list of candidate rescue paths; By using the candidate rescue path list, taking into account the type of support vehicles, weather conditions and potential obstacles, the practical feasibility of each path is screened and evaluated, the optimal rescue route is selected, and a rescue path plan is generated.

8. An optimization scheduling system for a power management system, characterized in that: include: The processing module is used to make intelligent judgments and processing on the urgency and expected power consumption of current and future tasks based on the real-time geographic location, environmental parameters and task priority evaluation of the medical treatment box, and obtain dynamic power allocation strategies and equipment working mode switching rules; A matching module is used to utilize the dynamic power allocation strategy and the equipment working mode switching rules, combine the cooperation mechanism between the built-in energy storage module and the external renewable energy interface, apply the adaptive energy allocation algorithm to automatically match the optimal power supply mode, and use the time series prediction technology to estimate and analyze the energy supply and demand model, so as to generate additional energy reserve decisions and energy-saving measures; A prediction module, which is used to use a rolling demand forecasting algorithm to perform rolling update forecasts on future power demand trends based on the additional energy reserve decision and energy-saving measures, combine historical energy consumption data with medical activity schedules, optimize forecast accuracy through machine learning algorithms, implement reliability engineering analysis, evaluate the reliability and stability of different power supply solutions under various conditions, and consider the impact of extreme weather or emergencies to generate a comprehensive stable power supply guarantee plan; The activation module is used to start the emergency response mechanism based on the comprehensive stable power supply guarantee plan, activate the backup battery pack and send a distress signal to the nearest support site when a main power failure or insufficient power is detected, and determine the optimal rescue route through path optimization technology to generate a rescue path plan.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an optimization scheduling method for a power management system as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, an optimization scheduling method for a power management system as claimed in any one of claims 1 to 7 is implemented.

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