Power management method and system adapted to extreme environment

Abstract

The invention provides a power management method and system adapted to an extreme environment. Wherein a power management scheme is generated according to the environmental sensor data and the historical energy consumption record, and the power management scheme is used for adapting to an extreme environment; all the medical devices are classified according to first-aid priorities, a decision analysis algorithm based on multiple criteria is adopted to balance the function importance of the different medical devices and the current task demand, and an energy-saving optimization configuration table is generated; based on the energy-saving optimization configuration table, applying an adaptive fault prediction and health management algorithm to monitor the health state of the power supply system, and generating early warning information and maintenance suggestions; and according to the early warning information and the maintenance suggestion, real-time visual management of the power supply state of the medical treatment box is realized through a security encryption communication link, and a remote management mechanism is generated. According to the technical scheme provided by the invention, the power utilization stability and efficiency of the medical treatment box in an extreme environment are remarkably improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of power management technology, and in particular, to a power management method and system that can adapt to extreme environments. Background Art

[0002] In extreme environments, such as remote sea areas, polar regions, or disaster sites, medical treatment kits need to ensure continuous and stable power supply for critical medical equipment. These environments are often accompanied by unpredictable climate conditions and limited energy resources, which place stringent demands on power management systems. Specifically, the system must be able to intelligently respond to changes in energy collection efficiency, ensure stable power supply even under adverse conditions such as continuous cloudy days or windless periods, and optimize power distribution to prioritize the operation of emergency equipment.

[0003] Current power management solutions for medical treatment boxes mainly rely on fixed charging and discharging strategies and simple fault detection mechanisms. These solutions adjust the charging and discharging of energy storage batteries through preset parameters, and lack the ability to dynamically adjust according to real-time environmental data. In addition, existing systems mostly use static equipment classification and power consumption configuration, fail to fully consider the differences between the functional importance and task requirements of different medical devices, and lack real-time monitoring and prediction of the health status of power components.

[0004] The main drawbacks of existing solutions are their lack of adaptability and flexibility. Fixed charging and discharging strategies cannot effectively cope with energy fluctuations under extreme climate conditions, resulting in inefficient energy utilization. Static equipment classification and power consumption configuration cannot flexibly respond to changes in actual mission requirements, which may result in insufficient power support for key equipment. In addition, the lack of real-time monitoring and prediction of the health status of power components makes it difficult to provide early warning of potential failures, increases the risk of system failures, and affects the reliability and timeliness of medical treatment. Summary of the invention

[0005] The embodiments of the present application provide a power management method and system that are adaptable to extreme environments, so as to solve the problems of energy waste and instability caused by fixed strategies in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a power management method adapted to extreme environments, including:

[0007] Based on environmental sensor data and historical energy consumption records, the changes in energy collection efficiency under extreme climate conditions are evaluated and processed, the charging and discharging strategies of energy storage batteries are intelligently adjusted, and a redundant power reserve mechanism is designed to cope with continuous cloudy or windless periods, and a power management solution is generated. The power management solution is used to adapt to extreme environments;

[0008] Using the power management solution, all medical devices are classified according to emergency priority, and a multi-criteria decision analysis algorithm is used to weigh the functional importance of different medical devices and current task requirements. The real-time energy consumption analysis technology is used to monitor the actual power consumption of each device and generate an energy-saving optimization configuration table.

[0009] Based on the energy-saving optimization configuration table, an adaptive fault prediction and health management algorithm is applied to monitor the health status of the power supply system. When a potential fault is detected in the main power supply or any backup power supply, a pre-set redundant power supply switching scheme is automatically activated, and the expected life of each power supply component is evaluated through the remaining service life analysis technology, and early warning information and maintenance suggestions are generated;

[0010] Based on the warning information and maintenance suggestions, real-time visual management of the power status of the medical treatment box is achieved through a secure encrypted communication link, so that authorized personnel can view the power configuration parameters and adjust the power configuration parameters according to the on-site conditions to generate a remote management mechanism. The remote management mechanism is used to maintain the operation of the life support system under poor communication conditions.

[0011] Optionally, the power management solution is used to classify all medical devices according to emergency priorities, and a multi-criteria decision analysis algorithm is used to weigh the functional importance of different medical devices and current task requirements, and real-time energy consumption analysis technology is used to accurately monitor the actual power consumption of each device to generate an energy-saving optimization configuration table, including:

[0012] Using the power management solution adapted to extreme environments, all medical devices are classified according to emergency priorities to obtain an emergency device list and a non-critical device list;

[0013] Based on the list of emergency equipment and the list of non-critical equipment, a multi-criteria decision analysis algorithm is used to quantitatively evaluate the importance of different equipment in combination with the functional importance of the equipment in the treatment process and the current task requirements, and an equipment priority score table is obtained;

[0014] According to the device priority scoring table and the real-time energy consumption analysis technology, the actual power consumption of each device is monitored to obtain a real-time energy consumption data stream;

[0015] By utilizing the real-time energy consumption data stream and combining it with the preset energy-saving target, the power consumption configuration of each device is optimized by dynamically adjusting the operating parameters and working mode of each device to generate an energy-saving optimization configuration table.

[0016] Optionally, the actual power consumption of each device is monitored according to the device priority scoring table and the real-time energy consumption analysis technology to obtain a real-time energy consumption data stream, including:

[0017] According to the device priority score table and real-time energy consumption analysis technology, the actual power consumption of each device is continuously and accurately monitored and processed to generate power consumption monitoring records;

[0018] By using the power consumption monitoring records and combining them with the equipment operation status monitoring, the power consumption changes of each equipment in different working modes are dynamically tracked and processed to generate a power consumption change trend graph;

[0019] Based on the power consumption trend graph, a machine learning algorithm is applied to predict the power consumption demand in the future, adjust the current energy-saving optimization configuration, and generate an estimated energy consumption report;

[0020] The estimated energy consumption report is used to evaluate the effectiveness of the existing power management strategy, plan emergency measures for energy shortages, implement emergency power management plans, and perform real-time energy consumption analysis on the optimized power configuration to obtain a real-time energy consumption data stream.

[0021] Optionally, the real-time energy consumption data stream is used in combination with a preset energy-saving target to optimize the power consumption configuration of each device by dynamically adjusting the operating parameters and working mode of each device to generate an energy-saving optimization configuration table, including:

[0022] Using the real-time energy consumption data stream, combined with the preset energy-saving target, the current power consumption status of each device is evaluated and processed to obtain an initial power consumption evaluation result;

[0023] According to the initial power consumption evaluation results, the power consumption efficiency of each device under different operating parameters and working modes is analyzed, the direction and scope of optimization adjustment are determined, and a power consumption efficiency analysis report is generated;

[0024] Based on the power consumption efficiency analysis report, the operating parameters and working modes of each device are dynamically adjusted to implement energy-saving optimization strategies to obtain an optimized device configuration solution;

[0025] By using the optimized device configuration scheme, combined with device performance and energy-saving effect, the power consumption configuration of each device is optimized to generate an energy-saving optimization configuration table.

[0026] Optionally, based on the energy-saving optimization configuration table, an adaptive fault prediction and health management algorithm is applied to monitor the health status of the power supply system. When a potential fault is detected in the main power supply or any backup power supply, a pre-set redundant power supply switching scheme is automatically activated, and the expected life of each power supply component is evaluated through the remaining service life analysis technology, and early warning information and maintenance suggestions are generated, including:

[0027] Based on the energy-saving optimization configuration table, an adaptive fault prediction and health management algorithm is applied to continuously monitor the health status of the power supply system to obtain a power supply system health assessment result;

[0028] Using the power system health assessment results, when a potential failure of the main power supply or any backup power supply is detected, a pre-set redundant power supply switching scheme is automatically activated to ensure the continuity of power supply and generate a failure response record;

[0029] Based on the fault response records, a detailed evaluation of the expected life of each power component is performed using a remaining service life analysis technique, and a power component life evaluation report is obtained by taking into account the historical usage of the component and the current working environment;

[0030] Based on the power component life assessment report, combined with the current equipment operating status and future power consumption demand forecast, early warning information and maintenance suggestions are generated.

[0031] Optionally, the expected life of each power component is evaluated in detail based on the fault response record by using a remaining service life analysis technology, and a power component life evaluation report is obtained by taking into account the historical usage of the component and the current working environment, including:

[0032] According to the fault response record, historical usage of each power component is collected and sorted to obtain a historical usage data set;

[0033] Using the historical usage data set and combining it with current working environment parameters, a detailed evaluation process is performed on the expected life of each power supply component through a remaining useful life analysis technique to generate a preliminary life evaluation result;

[0034] Based on the preliminary life assessment results, the assessment error is corrected by applying statistical analysis methods, taking into account changes in actual operating conditions, such as additional losses in extreme environments, to obtain corrected life assessment data;

[0035] Based on the corrected life assessment data, the health status of each power supply component is comprehensively analyzed to generate a power supply component life assessment report.

[0036] Optionally, according to the warning information and maintenance suggestions, real-time visual management of the power status of the medical treatment box is achieved through a secure encrypted communication link, so that authorized personnel can view power configuration parameters and adjust the power configuration parameters according to on-site conditions, and generate a remote management mechanism, which is used to maintain the operation of the life support system under poor communication conditions, including:

[0037] According to the warning information and maintenance suggestions, the power status of the medical treatment box is managed and processed in real time and visualized to obtain a visual management interface;

[0038] Utilize secure encrypted communication links to ensure the security and integrity of data transmission, achieve remote access to the power status of the medical treatment box, and generate a secure remote access channel;

[0039] The visual management interface and the secure remote access channel are used to allow authorized personnel to view power configuration parameters, and adjust the power configuration parameters according to on-site conditions to obtain remote adjustment records;

[0040] When encountering an emergency, authorized personnel can quickly switch to emergency mode through preset operation procedures to ensure the continuous operation of basic life support systems and generate emergency operation logs;

[0041] According to the remote adjustment records and emergency operation logs, the effectiveness of remote management is analyzed and optimized to generate a reliable remote management mechanism.

[0042] In a second aspect, an embodiment of the present application provides a power management method adapted to extreme environments, including:

[0043] An evaluation module is used to evaluate and process changes in energy collection efficiency under extreme climate conditions based on environmental sensor data and historical energy consumption records, intelligently adjust the charging and discharging strategies of energy storage batteries, and design a redundant power reserve mechanism to cope with continuous cloudy days or windless periods, and generate a power management solution, which is used to adapt to extreme environments;

[0044] A monitoring module is used to classify all medical devices according to emergency priorities using the power management solution, use a multi-criteria decision analysis algorithm to weigh the functional importance of different medical devices and current task requirements, and use real-time energy consumption analysis technology to accurately monitor the actual power consumption of each device and generate an energy-saving optimization configuration table;

[0045] A monitoring module, for monitoring the health status of the power supply system by applying an adaptive fault prediction and health management algorithm based on the energy-saving optimization configuration table, automatically activating a pre-set redundant power supply switching scheme when a potential fault is detected in the main power supply or any backup power supply, evaluating the expected life of each power supply component by using a remaining service life analysis technique, and generating early warning information and maintenance suggestions;

[0046] The adjustment module is used to achieve real-time visual management of the power status of the medical treatment box through a secure encrypted communication link based on the warning information and maintenance suggestions, so that authorized personnel can view the power configuration parameters and adjust the power configuration parameters according to the on-site conditions to generate a remote management mechanism, which is used to maintain the operation of the life support system under poor communication conditions.

[0047] 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 a power management method that adapts to extreme environments as described in the first aspect.

[0048] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, a power management method adapted to extreme environments as described in the first aspect is implemented.

[0049] In the embodiment of the present application, based on the environmental sensor data and the historical energy consumption records, the energy collection efficiency changes under extreme climate conditions are evaluated and processed, the charging and discharging strategies of the energy storage battery are intelligently adjusted, and a redundant power reserve mechanism is designed to cope with continuous cloudy days or windless periods, and a power management plan is generated. The power management plan is used to adapt to extreme environments; using the power management plan, all medical equipment is classified according to the emergency priority, and a multi-criteria decision analysis algorithm is used to weigh the functional importance and current task requirements of different medical equipment, and the real-time energy consumption analysis technology is used to monitor the actual power consumption of each device to generate an energy-saving optimization configuration table; based on the energy-saving optimization Configuration table, using adaptive fault prediction and health management algorithm to monitor the health status of the power supply system. When a potential fault is detected in the main power supply or any backup power supply, the pre-set redundant power supply switching plan is automatically activated, and the expected life of each power supply component is evaluated through the remaining service life analysis technology, and early warning information and maintenance suggestions are generated; based on the early warning information and maintenance suggestions, real-time visual management of the power status of the medical treatment box is achieved through a secure encrypted communication link, so that authorized personnel can view the power supply configuration parameters and adjust the power supply configuration parameters according to the on-site situation, and a remote management mechanism is generated, which is used to maintain the operation of the life support system under poor communication conditions.

[0050] The technical solution of this application has the following beneficial effects:

[0051] By evaluating changes in energy collection efficiency under extreme climate conditions based on environmental sensor data and historical energy consumption records, the charging and discharging strategies of energy storage batteries can be intelligently adjusted to ensure continuous and stable power supply in various extreme environments. A redundant power reserve mechanism is designed to cope with continuous cloudy or windless periods, which effectively solves the problem of unstable energy supply under extreme conditions and enhances the reliability and adaptability of the system; a multi-criteria decision analysis algorithm is used to weigh the functional importance and current task requirements of different medical equipment to ensure that emergency equipment receives the highest power supply priority, optimize the power consumption of non-critical equipment, and improve the power efficiency and emergency capabilities during medical treatment; real-time energy consumption analysis technology is used to monitor the actual power consumption of each device and generate an energy-saving optimization configuration table to further save energy and extend system life; adaptive fault prediction and health management algorithms are used to monitor the health status of the power system and detect potential faults in a timely manner. It also automatically activates the redundant power switching scheme to ensure uninterrupted power supply; it evaluates the expected life of each power component through remaining service life analysis technology, provides early warning and makes maintenance suggestions, reduces the risk of failure, and improves maintenance efficiency and system reliability; it achieves real-time visual management of the power status of the medical treatment box through a secure encrypted communication link, and authorized personnel can view detailed power configuration parameters remotely and make flexible adjustments based on on-site conditions, enhancing remote management and emergency response capabilities; even under poor communication conditions, it can maintain basic life support system operations through a pre-set remote management mechanism to ensure that the medical treatment process is not affected, thereby improving the robustness and safety of the system.

[0052] Furthermore, by utilizing the power management scheme to classify all medical equipment according to the emergency priority, and using the multi-criteria decision analysis algorithm and the real-time energy consumption analysis technology to generate the energy-saving optimization configuration table, the present invention achieves the following significant beneficial effects: First, by classifying all medical equipment according to the emergency priority, a list of emergency equipment and a list of non-critical equipment are obtained, ensuring that critical equipment can obtain power support in priority in an emergency, greatly improving the response speed and efficiency of medical treatment. Secondly, based on the list of emergency equipment and non-critical equipment, combined with the functional importance of the equipment in the treatment process and the current task requirements, a multi-criteria decision analysis algorithm is used to quantitatively evaluate the importance of different equipment, and an equipment priority score table is generated, thereby achieving a more scientific and reasonable allocation of power resources and ensuring the effective use of resources. Thirdly, the actual power consumption of each device is accurately monitored through the real-time energy consumption analysis technology, and a real-time energy consumption data stream is obtained, ensuring the dynamic grasp of power consumption, avoiding energy waste and improving the energy efficiency of the system. Finally, by using the real-time energy consumption data stream combined with the pre-set energy-saving goals, the power consumption configuration of each device is optimized by dynamically adjusting the operating parameters and working modes of each device, and an energy-saving optimization configuration table is generated, which not only further saves energy, but also extends the overall battery life of the system and enhances the adaptability and reliability in extreme environments. In summary, this method significantly improves the power management efficiency of the medical treatment box in extreme environments, ensures the continuous and stable operation of key equipment, and reduces overall energy consumption, providing strong support for medical rescue.

[0053] Furthermore, by applying an adaptive fault prediction and health management algorithm based on an energy-saving optimization configuration table to monitor the health status of the power supply system, and automatically activating a redundant power supply switching scheme when a potential fault is detected, and at the same time evaluating the expected life of each power supply component through the remaining service life analysis technology, generating early warning information and maintenance suggestions, the present invention achieves the following significant beneficial effects: First, by continuously monitoring and processing the health status of the power supply system, an accurate power supply system health assessment result is obtained, ensuring real-time mastery of the health status of the power supply system, and improving the predictability and reliability of the system. Secondly, when a potential fault is detected in the main power supply or any backup power supply, the pre-set redundant power supply switching scheme can be automatically activated to ensure the continuity of power supply, avoid the risk of medical equipment downtime caused by power failure, and greatly improve the stability and emergency response capability of the system. Thirdly, according to the fault response record, the expected life of each power supply component is evaluated in detail through the remaining service life analysis technology, and the historical use of the component and the current working environment are considered to obtain a comprehensive power supply component life assessment report, so that the maintenance work is more targeted and the possibility of sudden failure is reduced. Finally, based on the power component life assessment report, combined with the current equipment operating status and future power consumption demand forecast, accurate early warning information and maintenance recommendations are generated, and necessary maintenance activities are planned in advance, which not only extends the service life of the equipment, but also ensures the stability of power supply during medical treatment, and enhances the overall reliability and operation and maintenance efficiency of the system. In summary, this method significantly improves the health monitoring and fault response capabilities of the power management system of the medical treatment box, ensures the continuous and stable operation of key equipment, and provides solid protection for medical rescue in extreme environments.

[0054] 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

[0055] 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.

[0056] Figure 1 A flowchart of a power management method adapted to extreme environments provided in an embodiment of the present application;

[0057] Figure 2 A schematic diagram of the structure of a power management system adapted to extreme environments provided in an embodiment of the present application;

[0058] Figure 3A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0059] 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.

[0060] 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.

[0061] 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.

[0062] Figure 1 A flowchart of a power management method adapted to extreme environments is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0063] 101. Based on environmental sensor data and historical energy consumption records, evaluate and process changes in energy collection efficiency under extreme climate conditions, intelligently adjust the charging and discharging strategies of energy storage batteries, and design a redundant power reserve mechanism to cope with continuous cloudy days or windless periods, and generate a power management plan, which is used to adapt to extreme environments;

[0064] In this step, environmental sensor data includes meteorological parameters such as temperature, humidity, and wind speed, as well as energy collection efficiency indicators such as light intensity and wind power generation; historical energy consumption records cover the charging and discharging of energy storage batteries over a period of time, power consumption statistics of various medical devices, etc. These data are used to evaluate changes in energy collection efficiency under extreme climate conditions, so as to intelligently adjust the charging and discharging strategy of energy storage batteries and design redundant power reserve mechanisms to cope with continuous cloudy or windless periods.

[0065] Brief explanation of the program process

[0066] In the embodiment of the present application, the system first predicts the change in energy collection efficiency in the future by analyzing environmental sensor data and historical energy consumption records. Based on this prediction result, the charging and discharging strategy of the energy storage battery is dynamically adjusted to ensure stable power supply under different weather conditions. In addition, in view of the possible continuous cloudy or windless periods, the system pre-designs a redundant power reserve mechanism to ensure continuous power supply in extreme environments.

[0067] Suppose that at a medical station on a remote island, the environmental sensor detects the coming of continuous rainy weather, and the system predicts that the charging capacity of the energy storage battery will be greatly reduced in the next few days based on historical energy consumption records. Therefore, the system automatically adjusts the charging and discharging strategy, increases the charging capacity of the energy storage battery when there is sufficient sunlight, and activates the redundant power reserve mechanism to reserve enough power in advance to cope with future adverse weather, ensuring that key medical equipment can obtain stable power supply under any circumstances.

[0068] 102. Using the power management solution, classify all medical devices according to emergency priority, use a multi-criteria decision analysis algorithm to weigh the functional importance of different medical devices and current task requirements, and use real-time energy consumption analysis technology to monitor the actual power consumption of each device and generate an energy-saving optimization configuration table;

[0069] In this step, the emergency priority classification is based on the role and importance of medical equipment in the treatment process. For example, emergency equipment such as cardiopulmonary resuscitation machines and ventilators are listed as the highest priority. The multi-criteria decision analysis algorithm comprehensively considers the functional importance of the equipment and the current task requirements, and quantitatively evaluates the importance of each device. Real-time energy consumption analysis technology is used to monitor the actual power consumption of each device and generate an energy-saving optimization configuration table.

[0070] In the embodiment of the present application, the system uses a power management solution to classify all medical devices according to the priority of emergency treatment, and obtains a list of emergency equipment and a list of non-critical equipment. Then, a multi-criteria decision analysis algorithm is used to combine the functional importance of the equipment and the current task requirements to quantitatively evaluate the importance of different equipment and generate an equipment priority score table. Subsequently, real-time energy consumption analysis technology is used to accurately monitor the actual power consumption of each device, generate an energy-saving optimization configuration table, and ensure the effective use of resources.

[0071] Assume that in an emergency medical rescue scenario, the system identifies the cardiopulmonary resuscitation machine and ventilator as emergency equipment and gives them the highest priority. Through the multi-criteria decision analysis algorithm, the system evaluates the importance of other equipment and generates a device priority score table. At the same time, the real-time energy consumption analysis technology monitors the high power consumption of some non-critical equipment. The system adjusts its working mode accordingly and generates an energy-saving optimization configuration table to ensure that emergency equipment is given priority under limited power.

[0072] 103. Based on the energy-saving optimization configuration table, an adaptive fault prediction and health management algorithm is applied to monitor the health status of the power supply system. When a potential fault is detected in the main power supply or any backup power supply, a pre-set redundant power supply switching scheme is automatically activated, and the expected life of each power supply component is evaluated through the remaining service life analysis technology, and early warning information and maintenance suggestions are generated;

[0073] In this step, the adaptive fault prediction and health management algorithm is used to monitor the health status of the power system, predict potential faults and activate redundant power switching solutions by analyzing the historical performance data and real-time operating status of power components. The remaining service life analysis technology predicts the expected life of power components by evaluating their historical usage and current working environment, and generates early warning information and maintenance recommendations.

[0074] In the embodiment of the present application, the system applies an adaptive fault prediction and health management algorithm based on the energy-saving optimization configuration table to continuously monitor the health status of the power supply system. Once a potential fault is detected in the main power supply or any backup power supply, the system automatically activates the pre-set redundant power supply switching plan to ensure the continuity of power supply and generate a fault response record. Furthermore, the system uses the remaining service life analysis technology to evaluate the expected life of each power component in detail, generate a power component life assessment report, provide early warning information and maintenance recommendations, and plan necessary maintenance activities in advance.

[0075] Suppose at a polar research station, the system detects abnormal voltage fluctuations in the main power supply and predicts that a failure may be imminent. The system immediately activates the redundant power supply switching solution and seamlessly switches to the backup power supply to ensure the continued operation of medical equipment. At the same time, the system evaluates the expected life of the main power supply through the remaining service life analysis technology, generates a detailed life assessment report, and makes replacement recommendations to ensure the timely implementation of subsequent maintenance work.

[0076] 104. Based on the warning information and maintenance suggestions, real-time visual management of the power status of the medical treatment box is achieved through a secure encrypted communication link, so that authorized personnel can view the power configuration parameters and adjust the power configuration parameters according to the on-site conditions to generate a remote management mechanism, which is used to maintain the operation of the life support system under poor communication conditions.

[0077] In this step, a secure encrypted communication link is used to ensure the safety and reliability of remote management and control. Authorized personnel can view detailed power configuration parameters through this link and make flexible adjustments based on on-site conditions. The remote management mechanism maintains the operation of the life support system through a preset emergency operation plan under poor communication conditions, ensuring that basic life support functions can be maintained even when the network is unstable.

[0078] In the embodiment of the present application, the system realizes real-time visual management of the power status of the medical treatment box through a secure encrypted communication link based on early warning information and maintenance suggestions. Authorized personnel can view detailed power configuration parameters remotely and flexibly adjust the settings according to changes in on-site conditions. It supports a one-button emergency operation mode to ensure that basic life support systems can be maintained even under poor communication conditions, and ultimately generate a reliable remote management mechanism.

[0079] Suppose at a disaster site, the communication conditions are extremely poor, but through a secure encrypted communication link, authorized personnel at the command center can view the power status of the medical treatment box in real time and adjust the power configuration parameters according to the actual situation. When encountering an emergency, authorized personnel quickly switch to emergency operation mode to ensure the continued operation of basic life support systems, and rely on the preset remote management mechanism to maintain the normal operation of key equipment even in the event of a network outage.

[0080] In order to solve the problem of uneven power distribution of medical devices in extreme environments, in some embodiments, the power management solution described in step 102 is used to classify all medical devices according to emergency priorities, and a multi-criteria decision analysis algorithm is used to weigh the functional importance and current task requirements of different medical devices, and real-time energy consumption analysis technology is used to accurately monitor the actual power consumption of each device to generate an energy-saving optimization configuration table, including:

[0081] Utilizing the power management solution adapted to extreme environments, all medical equipment is classified according to first aid priority to obtain a first aid equipment list and a non-critical equipment list; based on the first aid equipment list and the non-critical equipment list, a multi-criteria decision analysis algorithm is used, combined with the functional importance of the equipment in the treatment process and the current task requirements, to quantitatively evaluate the importance of different equipment and obtain an equipment priority score sheet; based on the equipment priority score sheet and real-time energy consumption analysis technology, the actual power consumption of each device is monitored to obtain a real-time energy consumption data stream; utilizing the real-time energy consumption data stream, combined with pre-set energy-saving targets, the power consumption configuration of each device is optimized by dynamically adjusting the operating parameters and working modes of each device to generate an energy-saving optimization configuration table.

[0082] In this embodiment, the emergency priority classification is based on the role played by the medical equipment in the treatment process and its importance. For example, emergency equipment such as cardiopulmonary resuscitation machines and ventilators are listed as the highest priority; the multi-criteria decision analysis algorithm comprehensively considers the functional importance of the equipment and the current task requirements, and quantitatively evaluates the importance of each device; real-time energy consumption analysis technology is used to monitor the actual power consumption of each device to ensure the effective use of resources.

[0083] In an embodiment of the present application, first, the system uses a power management solution that adapts to extreme environments to classify all medical equipment according to emergency priorities, and obtains a list of emergency equipment and a list of non-critical equipment; secondly, based on these lists, a multi-criteria decision analysis algorithm is used to combine the functional importance of the equipment and the current task requirements to quantitatively evaluate the importance of different equipment and generate an equipment priority score table; thirdly, according to the equipment priority score table and real-time energy consumption analysis technology, the actual power consumption of each device is continuously monitored to obtain a real-time energy consumption data stream; finally, using the real-time energy consumption data stream, combined with pre-set energy-saving goals, the power consumption configuration of each device is optimized by dynamically adjusting the operating parameters and working modes of each device, and finally an energy-saving optimization configuration table is generated.

[0084] Here is a specific example:

[0085] Suppose that in a temporary medical station in a high-altitude mountainous area, the environmental conditions are harsh and energy is limited. The system first classifies all medical equipment according to the role of the equipment in treatment, identifies emergency equipment such as cardiopulmonary resuscitation machines and ventilators, and gives them the highest priority; secondly, based on these classifications, the system uses a multi-criteria decision analysis algorithm to evaluate the importance of other equipment and generate an equipment priority score table; thirdly, the system uses real-time energy consumption analysis technology to monitor the abnormal power consumption of a non-critical device, indicating that its working mode needs to be adjusted; finally, the system automatically adjusts the working parameters of the non-critical device based on the real-time energy consumption data stream and the pre-set energy-saving goals, reduces its power consumption, and ensures that more power resources are allocated to emergency equipment. Through the above steps, the system not only optimizes the allocation of power resources, but also improves the energy efficiency of the entire medical station and its ability to respond to emergencies.

[0086] In order to solve the problem that the existing power management system does not respond to changes in device power consumption in a timely manner, in one or more of the above embodiments, the actual power consumption of each device is monitored according to the device priority scoring table and the real-time energy consumption analysis technology to obtain a real-time energy consumption data stream, including:

[0087] According to the device priority scoring table and real-time energy consumption analysis technology, the actual power consumption of each device is continuously and accurately monitored to generate power consumption monitoring records; using the power consumption monitoring records, combined with equipment operation status monitoring, the power consumption changes of each device in different working modes are dynamically tracked and processed to generate a power consumption change trend chart; based on the power consumption change trend chart, a machine learning algorithm is used to predict the power consumption demand in the future, adjust the current energy-saving optimization configuration, and generate an estimated energy consumption report; using the estimated energy consumption report, the effectiveness of the existing power management strategy is evaluated, and emergency measures are planned for energy shortages, emergency power management plans are implemented, and real-time energy consumption analysis is performed on the optimized power configuration to obtain a real-time energy consumption data stream.

[0088] In this embodiment, the device priority score table includes the quantitative evaluation results of the functional importance and current task requirements of each medical device, which is used to guide the priority allocation of power resources; the real-time energy consumption analysis technology covers the continuous and accurate monitoring of the actual power consumption of each device to ensure the effective use of resources. The power consumption monitoring record records the power consumption of each device in detail, and the power consumption trend chart shows the changes in power consumption over time under different working modes, helping to predict future power consumption requirements.

[0089] In an embodiment of the present application, first, the system continuously and accurately monitors the actual power consumption of each device according to the device priority score table and real-time energy consumption analysis technology, and generates power consumption monitoring records; secondly, using the power consumption monitoring records, combined with the device operation status monitoring, the power consumption changes of each device in different working modes are dynamically tracked and processed, and a detailed power consumption change trend chart is generated; thirdly, based on the power consumption change trend chart, a machine learning algorithm is applied to predict the power consumption demand in the future period, and the current energy-saving optimization configuration is adjusted to generate an estimated energy consumption report; finally, the estimated energy consumption report is used to evaluate the effectiveness of the existing power management strategy, and emergency measures are planned for energy shortages, and emergency power management plans are implemented. At the same time, real-time energy consumption analysis is performed on the optimized power configuration to obtain a real-time energy consumption data stream.

[0090] Here is a specific example:

[0091] Suppose that in a polar research station, the environment is extremely harsh and the power resources are limited. First, the system continuously and accurately monitors the actual power consumption of all medical devices based on the equipment priority score table and real-time energy consumption analysis technology, and records the power consumption of each device; secondly, using these power consumption monitoring records, combined with equipment operation status monitoring, the system dynamically tracks the power consumption changes of each device in different working modes and generates a detailed power consumption change trend chart; thirdly, based on the trend chart, the system applies machine learning algorithms to predict the power consumption demand in the next few days, adjusts the current energy-saving optimization configuration, and generates an estimated energy consumption report; finally, the system uses the estimated energy consumption report to evaluate the effectiveness of the existing power management strategy, discover possible energy shortages, plan emergency measures in advance, such as starting the backup generator, and perform real-time energy consumption analysis on the optimized power configuration to ensure continuous and stable power supply. Through the above steps, the system not only realizes the accurate monitoring and prediction of the power consumption changes of medical equipment, but also optimizes the allocation of power resources and improves the ability to cope with extreme environments.

[0092] In order to solve the problem that the existing power management system is insufficient in optimizing the power consumption of devices, in some embodiments, the real-time energy consumption data stream is used in combination with a pre-set energy-saving target to dynamically adjust the operating parameters and working mode of each device to optimize the power consumption configuration of each device and generate an energy-saving optimization configuration table, including:

[0093] By utilizing the real-time energy consumption data stream and combining it with a pre-set energy-saving target, the current power consumption status of each device is evaluated and processed to obtain an initial power consumption evaluation result; based on the initial power consumption evaluation result, the power consumption efficiency of each device under different operating parameters and working modes is analyzed, the direction and scope of optimization adjustment are determined, and a power consumption efficiency analysis report is generated; based on the power consumption efficiency analysis report, by dynamically adjusting the operating parameters and working modes of each device, an energy-saving optimization strategy is implemented to obtain an optimized device configuration plan; by utilizing the optimized device configuration plan and combining the device performance and energy-saving effect, the power consumption configuration of each device is optimized to generate an energy-saving optimization configuration table.

[0094] In this embodiment, the real-time energy consumption data stream includes the actual power consumption monitoring record of each device, which is used to evaluate the current power consumption status; the energy saving target is a pre-set energy efficiency standard or power consumption upper limit, which guides the optimization direction of the system. The initial power consumption evaluation result is a preliminary evaluation of the current power consumption status of each device; the power consumption efficiency analysis report analyzes the power consumption efficiency under different operating parameters and working modes in detail, and determines the direction and scope of optimization adjustment; the optimized device configuration plan is the best configuration obtained by dynamically adjusting the operating parameters and working modes of each device based on the analysis; the energy-saving optimization configuration table finally generated combines the optimized configuration with the device performance and energy-saving effect to ensure that the system does not affect the device performance while achieving the energy-saving target.

[0095] In an embodiment of the present application, first, the system uses the real-time energy consumption data stream, combined with a pre-set energy-saving target, to evaluate and process the current power consumption status of each device to obtain an initial power consumption evaluation result; secondly, based on the initial power consumption evaluation result, the power consumption efficiency of each device under different operating parameters and working modes is analyzed, the direction and scope of optimization adjustment are determined, and a power consumption efficiency analysis report is generated; thirdly, based on the power consumption efficiency analysis report, the operating parameters and working modes of each device are dynamically adjusted to implement energy-saving optimization strategies to obtain an optimized device configuration plan; finally, using the optimized device configuration plan, combined with device performance and energy-saving effects, the power consumption configuration of each device is optimized to generate an energy-saving optimization configuration table.

[0096] Here is a specific example:

[0097] Suppose that in a temporary medical station in a remote desert area, the power resources are extremely limited. The system first uses the real-time energy consumption data stream, combined with the pre-set energy-saving goals, to evaluate and process the current power consumption status of all medical devices, and obtains the initial power consumption evaluation results; secondly, based on these evaluation results, the system analyzes the power consumption efficiency of each device under different operating parameters and working modes, and finds that a non-critical device can significantly reduce power consumption in low-power mode, thereby determining the direction and scope of optimization adjustment, and generating a power consumption efficiency analysis report; thirdly, based on the report, the system dynamically adjusts the operating parameters of the non-critical device, switches to low-power mode, implements energy-saving optimization strategies, and obtains the optimized device configuration plan; finally, the system uses the optimized configuration plan, combined with device performance and energy-saving effects, to further adjust the power consumption configuration of other devices, ensuring that the entire system maintains efficient operation while meeting energy-saving goals. Through the above steps, the system not only realizes the effective monitoring and optimization of the power consumption of each device, but also significantly improves the energy utilization efficiency, ensuring the stable operation of the medical station.

[0098] This application takes into account that in the prior art, due to the problem that static device classification and power consumption configuration cannot flexibly respond to changes in actual task requirements, key medical equipment does not receive sufficient power support. In addition, the lack of a comprehensive assessment of the importance of device functions and current task requirements makes power management less refined, affecting the stability and reliability of power supply during medical treatment. Therefore, the embodiment of the invention proposes this optional solution, which aims to dynamically evaluate the equipment through a multi-criteria decision analysis algorithm, and optimize the power configuration in combination with real-time data to solve the above problems and ensure that key equipment can obtain reliable power support under any circumstances.

[0099] Optionally, based on the first aid equipment list and the non-critical equipment list, a multi-criteria decision analysis algorithm is used to quantitatively evaluate the importance of different equipment by considering the functional importance of the equipment in the treatment process and the current specific task requirements, and obtain an equipment priority score table, including:

[0100] Before calculating the functional importance score of the device, the medical device is classified through a multi-criteria decision analysis algorithm, and its historical usage records, dependencies, and time sensitivity are evaluated to provide a data basis for subsequent FIS calculations;

[0101]

[0102] FIS i represents the functional importance score of the i-th device; E i represents the importance weight of the i-th device in the first aid equipment list; D i represents the dependency coefficient of the i-th device in the non-critical equipment list; γ represents the dependency influencing factor, which enhances the impact of dependency on the score; T i represents the time sensitivity of the device in the current task; λ represents the time sensitivity adjustment coefficient; θ represents the redundancy adjustment coefficient; ρ represents the redundancy impact factor; P i It represents the redundancy of the equipment, indicating whether the equipment has backup or alternative solutions; α and β represent adjustment coefficients, which respectively control the influence of different items on the final score;

[0103] After the FIS calculation is completed, the scoring weights are further adjusted in combination with dynamic factors such as the frequency of use, cost-effectiveness ratio, redundancy and reliability of the equipment to ensure a smooth transition from the functional importance score to the priority score (PIS), fully supporting the generation of the final score;

[0104]

[0105] PIS i represents the priority score of the i-th device; FIS i represents the importance score of the equipment function calculated according to Formula 1; U i represents the frequency of use of the device, indicating the number of times the device has been used in the past period of time; ζ represents the frequency of use influencing factor, which enhances the impact of the frequency of use on the score; C i represents the cost-effectiveness ratio of the equipment, that is, the ratio of the equipment cost to the expected benefit; μ, σ represent the mean and standard deviation of the cost-effectiveness ratio, which are used for standardization; δ,∈,η represent adjustment coefficients, which respectively control the influence of different items on the final score; V i Represents the reliability score of the equipment, reflecting the stability and failure rate of the equipment; S iIt represents the service life of the equipment, which means the time since the equipment was put into use; ω represents the service life influencing factor; φ represents the reliability adjustment coefficient;

[0106] After calculating the PIS, the equipment is sorted by score and a power configuration plan is developed to generate a visual report and emergency response plan to ensure that key equipment can continue to operate even in an emergency, improving the reliability and response speed of the system.

[0107] This formula is designed to achieve a comprehensive and dynamic evaluation of the importance of medical devices. This application introduces two core formulas: FIS (Functional Importance Score) and PIS (Priority Score). The FIS formula is used to quantitatively evaluate the functional importance of the device, taking into account factors such as the device's historical usage record, dependencies, and time sensitivity; the PIS formula further adjusts the scoring weights on this basis, combining dynamic factors such as the device's frequency of use, cost-effectiveness ratio, redundancy, and reliability to ensure a smooth transition from functional importance scoring to priority scoring, fully supporting the generation of the final score. This not only improves the accuracy of the evaluation, but also enhances the adaptability and flexibility of the system.

[0108] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0109]

[0110] This sub-item is used to emphasize the core role of first aid equipment and consider the dependence of the equipment. i The squaring process is performed to enhance the scores of key devices, while the scores of devices with strong dependencies are reduced through the dependency coefficient Di, ensuring that devices with strong independence and important functions receive higher priority.

[0111] λ·ln(1+T i ): This sub-item reflects the time sensitivity of the equipment, that is, the urgency of the equipment in the current task. Use the natural logarithm function to enhance the score of equipment with high time sensitivity, ensuring that the equipment that is urgently needed is appropriately improved in the score, thereby optimizing resource allocation and meeting urgent needs.

[0112] θ·exp(-ρ·P i ): This sub-item takes into account the redundancy of the device, that is, whether the device has a backup or alternative solution. The score of the device with redundant backup is reduced through an exponential decay function, ensuring that redundant devices occupy a higher position in the score, avoiding the risk of system failure due to a single failure.

[0113] The following is a brief introduction to how to obtain the parameters of the formula:

[0114] E iFrom the list of first aid equipment, determined by equipment type and function; D i Calculated through historical usage records and system dependency graphs; T i Determined according to current mission requirements and equipment scheduling; i It is obtained by checking whether the equipment has redundant backup or alternative solutions; α, β, λ, θ, ρ are adjustment coefficients set according to experience.

[0115] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0116]

[0117] This sub-item is based on the functional importance score (FIS) of the device. i , and combined with the device usage frequency U i By adjusting the impact of usage frequency, the scores of frequently used devices can be reduced to avoid waste of resources caused by excessive use, ensuring that resources are reasonably allocated to devices that are truly needed.

[0118] This sub-item takes into account the cost-effectiveness ratio of the equipment. i , using Gaussian distribution function to standardize the cost-effectiveness and give priority to cost-effective equipment. This not only improves resource utilization efficiency, but also ensures maximum economic benefits and optimal allocation of limited resources.

[0119] φ·V i ·sin(ω·S i ): This sub-item comprehensively considers the reliability and service life of the equipment. The score is adjusted by a sine function to increase the priority of new equipment and equipment with a short service life, ensure the long-term stable operation of the system and the reliability of the equipment, and avoid potential risks brought by old equipment.

[0120] The following is a brief introduction to how to obtain the parameters of the formula:

[0121] FIS i Calculated by the aforementioned FIS formula; U i Obtained by counting the number of times the device has been used over a period of time; C i Calculated based on the equipment purchase cost and expected service life; μ,σ are obtained by statistically analyzing the cost-effectiveness ratio of all equipment; δ,∈,η,φ,ω,ζ are adjustment coefficients set based on experience; V i Determined through the historical maintenance records and failure rate statistics of the equipment; S i Calculated from the date the equipment is put into service.

[0122] Suppose that at a polar research station, there are the following equipment: cardiopulmonary resuscitation machine (first aid equipment), ventilator (first aid equipment), ultrasound diagnostic instrument (non-critical equipment) and portable X-ray machine (non-critical equipment). First, according to the first aid equipment list and non-critical equipment list, the system classifies these devices and evaluates their historical usage records, dependencies and time sensitivity to provide a data basis for subsequent FIS calculations. Then, the functional importance score of each device is calculated:

[0123]

[0124]

[0125]

[0126]

[0127] After the FIS calculation is completed, the scoring weights are further adjusted based on dynamic factors such as the frequency of use, cost-effectiveness ratio, redundancy and reliability of the equipment to ensure a smooth transition from the functional importance score to the priority score (PIS), fully supporting the generation of the final score:

[0128]

[0129]

[0130]

[0131]

[0132] After calculating the PIS, the equipment is sorted by score and a power configuration plan is developed to generate a visual report and emergency response plan to ensure that key equipment can continue to operate even in an emergency, improving the reliability and response speed of the system.

[0133] Through the above steps, the system can accurately evaluate the importance and priority of medical equipment, ensuring the stability of power supply for key equipment in extreme environments. Assuming the threshold is set to 0.8, since the result is greater than the set threshold, it indicates that the cardiopulmonary resuscitation machine and ventilator are given the highest priority, ensuring that these key equipment can obtain reliable power support under any circumstances, significantly improving the reliability and response speed of the entire system.

[0134] In order to solve the problem that the existing power management system does not respond to potential faults in a timely manner, in some embodiments, the energy-saving optimization configuration table described in step 103 is used to apply an adaptive fault prediction and health management algorithm to monitor the health status of the power system. When a potential fault is detected in the main power supply or any backup power supply, a pre-set redundant power supply switching scheme is automatically activated, and the expected life of each power supply component is evaluated through the remaining service life analysis technology, and early warning information and maintenance suggestions are generated, including:

[0135] Based on the energy-saving optimization configuration table, an adaptive fault prediction and health management algorithm is used to continuously monitor and process the health status of the power supply system to obtain a power supply system health assessment result; using the power supply system health assessment result, when a potential fault is detected in the main power supply or any backup power supply, a pre-set redundant power supply switching plan is automatically activated to ensure the continuity of power supply and generate a fault response record; based on the fault response record, a detailed assessment of the expected life of each power supply component is performed through the remaining service life analysis technology, and the historical usage of the component and the current working environment are taken into consideration to obtain a power supply component life assessment report; based on the power supply component life assessment report, combined with the current equipment operating status and future power consumption demand forecast, early warning information and maintenance recommendations are generated.

[0136] In this embodiment, the energy-saving optimization configuration table includes the device power consumption configuration adjusted according to the real-time energy consumption data stream, which is used to guide power management; the adaptive fault prediction and health management algorithm predicts potential faults and evaluates the health status by continuously monitoring the operating parameters of the power system; the power system health assessment result is a comprehensive evaluation of the current health status of the power system based on algorithm analysis; the fault response record records the time, type and handling measures of the fault in detail; the power component life assessment report is based on the remaining service life analysis technology, considering the historical usage of the component and the current working environment, and assessing its expected life; early warning information and maintenance suggestions are preventive measures proposed based on the evaluation.

[0137] In an embodiment of the present application, first, based on the energy-saving optimization configuration table, the system applies an adaptive fault prediction and health management algorithm to continuously monitor and process the health status of the power supply system to obtain a health assessment result of the power supply system; secondly, using the health assessment result, when a potential fault is detected in the main power supply or any backup power supply, a pre-set redundant power supply switching scheme is automatically activated to ensure the continuity of power supply and generate a detailed fault response record; thirdly, based on the fault response record, a detailed assessment of the expected life of each power supply component is performed through the remaining service life analysis technology, and a power supply component life assessment report is obtained by taking into account the historical usage of the component and the current working environment; finally, based on the life assessment report, combined with the current equipment operating status and future power consumption demand forecast, early warning information and maintenance suggestions are generated to plan necessary maintenance activities in advance.

[0138] Here is a specific example:

[0139] Suppose that in a medical station on a remote island, the power system relies on solar and wind power generation. First, based on the energy-saving optimization configuration table, the system applies the adaptive fault prediction and health management algorithm to continuously monitor the health status of the power system, finds that the voltage fluctuation of the main power supply is abnormal, and predicts that a failure may occur soon; secondly, the system immediately activates the pre-set redundant power switching scheme, seamlessly switches to the backup battery pack, ensures that all key medical equipment continues to operate stably, and generates detailed fault response records; thirdly, based on these fault response records, the system evaluates the expected life of the main power components (such as inverters) through the remaining service life analysis technology, considers its historical usage and the current humid working environment of the island, and obtains a detailed life assessment report; finally, based on the life assessment report, the system combines the current equipment operation status and the power consumption demand forecast for the next few days, and proposes early warning information and specific maintenance suggestions for replacing the inverter to ensure the timely implementation of subsequent maintenance work. Through the above steps, the system not only achieves a rapid response to potential faults, but also significantly improves the reliability and stability of the entire power system through detailed life assessment and maintenance suggestions.

[0140] In order to solve the problem of inaccurate component life assessment in the existing power management system, in some embodiments, the expected life of each power component is evaluated in detail based on the fault response record through the remaining service life analysis technology, and the historical use of the component and the current working environment are considered to obtain a power component life assessment report, including:

[0141] Based on the fault response records, the historical usage of each power component is collected and sorted to obtain a historical usage data set; using the historical usage data set, combined with the current working environment parameters, the expected life of each power component is evaluated in detail through the remaining service life analysis technology to generate a preliminary life assessment result; based on the preliminary life assessment result, the evaluation error is corrected using a statistical analysis method, taking into account changes in actual operating conditions, such as additional losses in extreme environments, to obtain corrected life assessment data; based on the corrected life assessment data, the health status of each power component is comprehensively analyzed to generate a power component life assessment report.

[0142] In this embodiment, the fault response record includes the time, type and handling measures of each fault for tracing and analysis; the historical usage data set collects and organizes the historical usage of each power component, such as operating hours, maintenance records, etc., for evaluating its aging degree; the current working environment parameters cover factors such as temperature, humidity, vibration, etc., which affect the service life of the component; the preliminary life assessment result is an initial expected life assessment generated based on the historical usage data set and the current working environment parameters; the corrected life assessment data is corrected for assessment errors through statistical analysis methods, taking into account changes in actual operating conditions, such as additional losses in extreme environments; the final generated power component life assessment report comprehensively analyzes the health status of each component and provides detailed life predictions and maintenance recommendations.

[0143] In an embodiment of the present application, first, the system collects and organizes the historical usage of each power component according to the fault response record to obtain a historical usage data set; secondly, using the historical usage data set, combined with the current working environment parameters, the remaining service life analysis technology is used to perform a detailed evaluation of the expected life of each power component to generate a preliminary life evaluation result; thirdly, based on the preliminary life evaluation result, a statistical analysis method is used to correct the evaluation error, taking into account changes in actual operating conditions, such as additional losses in extreme environments, to obtain corrected life evaluation data; finally, based on the corrected life evaluation data, a comprehensive analysis is performed on the health status of each power component to generate a power component life evaluation report.

[0144] Here is a specific example:

[0145] Assume that in a temporary medical station in a high-altitude mountainous area, the power system relies on solar and wind power generation. First, the system collects and organizes the historical usage of each power component (such as inverter, battery) based on the fault response record, and obtains a detailed historical usage data set; secondly, using these historical usage data sets, combined with the current low temperature and low pressure working environment parameters in the mountainous area, the remaining service life analysis technology is used to conduct a detailed evaluation of the expected life of each power component, and a preliminary life evaluation result is generated; thirdly, based on the preliminary life evaluation result, the system applies a statistical analysis method to correct the evaluation error, taking into account the additional losses in the extreme environment of the mountainous area, such as the degradation of battery performance caused by low temperature, and obtains the corrected life evaluation data; finally, based on the corrected life evaluation data, the system comprehensively analyzes the health status of each power component, generates a detailed power component life evaluation report, and puts forward specific maintenance suggestions, such as replacing severely aged inverters in advance and regularly checking battery performance. Through the above steps, the system not only achieves an accurate evaluation of the life of each power component, but also ensures the long-term stable operation of the system through detailed maintenance suggestions.

[0146] The present application takes into account that in the prior art, due to the problem that the static power management system cannot monitor and evaluate the health status of the power components in real time, it is difficult to warn of potential faults in advance, which increases the risk of system failures and affects the stability and reliability of power supply during medical treatment. In addition, the lack of a comprehensive assessment of the entire power system makes maintenance work less targeted and prone to missing key issues. Therefore, the invention embodiment proposes this optional solution, which aims to continuously monitor and process the health status of the power system through an adaptive fault prediction and health management algorithm, and obtain the health assessment results of the power system, so as to solve the above problems and ensure the continuous and stable operation of the system.

[0147] Optionally, based on the energy-saving optimization configuration table, the adaptive fault prediction and health management algorithm is applied to continuously monitor the health status of the power supply system to obtain a power supply system health assessment result, including:

[0148] Before calculating the health score of power components, we analyze the operating performance, collect user feedback and historical maintenance records, and calculate the fault repair time to provide necessary data support for subsequent HCS calculations to ensure that the true health status of each component is fully reflected;

[0149]

[0150] HCS j represents the health score of the jth power component; O j Indicates the operating performance index of the power component, reflecting its performance under different working conditions; refRepresents the reference operating performance index, which indicates the performance level under ideal working conditions; σ O Represents the standard deviation of operating performance, used for standardization; α H Indicates the operating performance adjustment factor; Q j Indicates the service quality score of the power supply component, based on user feedback and historical maintenance records; B j Indicates the fault repair time of the power supply component, which indicates the average repair time after each fault; β H ,γ H Represents the adjustment coefficient, which controls the influence of different items on the final score;

[0151] After the HCS calculation is completed, the health scores of all components are summarized and their dispersion is evaluated. Combined with the impact of continuous operation time and maintenance cost, a smooth transition is made from the health score of a single component to the health assessment index of the entire power system, comprehensively considering the overall operation and maintenance of the system;

[0152]

[0153] HSI stands for Health Assessment Index of Power System; HCS j represents the health score of each power component calculated according to Formula 1; N represents the total number of components in the power system; H med represents the median health score of all components; δ H represents the health score dispersion adjustment coefficient; T limit Indicates the maximum allowable continuous operating time of the power supply component; T op,j represents the actual continuous running time of the jth component; ∈ H Indicates the continuous running time influence coefficient; C j represents the cumulative maintenance cost of the jth component; C sys Represents the cumulative maintenance cost of the entire power system; H represents the maintenance cost adjustment factor;

[0154] After calculating the HSI, the components are sorted by score and detailed maintenance plans and emergency plans are developed. A visual report is generated to show the health status of each component and maintenance recommendations. An emergency response plan is prepared to deal with emergencies to ensure the continued stable operation of the system.

[0155] This formula aims to achieve a comprehensive and dynamic assessment of the health status of the power system. This application introduces two core formulas: HCS (power component health score) and HSI (power system health assessment index). The HCS formula is used to quantitatively assess the health status of each power component, taking into account factors such as operating performance, service quality score, and fault repair time; the HSI formula summarizes the health scores of all components and evaluates their discreteness on this basis, combining the impact of continuous operation time and maintenance costs, and smoothly transitions from the health score of a single component to the health assessment index of the entire power system, comprehensively considering the overall operation and maintenance of the system. This not only improves the accuracy of the assessment, but also enhances the adaptability and flexibility of the system.

[0156] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0157]

[0158] Operational performance adjustment Reflects the performance of power components under different working conditions, uses Gaussian distribution function to standardize operating performance, and enhances the score of components with good performance; service quality score β H Q j / (1+γ H ·B j ): Based on user feedback and historical maintenance records, reduce the score of components with long fault repair time to ensure that reliable components have higher priority;

[0159] The following is a brief introduction to how to obtain the parameters of the formula:

[0160] O j Derived from analysis of operating performance data; ref and σ O According to the performance level and standard deviation under ideal working conditions; Q j It is obtained by collecting user feedback and historical maintenance records; B j Obtained by counting the average repair time after each failure; α H ,β H ,γ H It is an adjustment factor set based on experience;

[0161] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0162]

[0163] Health score dispersion adjustment Evaluate the discreteness of all component health scores, enhance the scores of systems with large discreteness, and ensure a true reflection of the health status of the system; continuous running time impact ∈ H·log(T limit / T op,j ): Consider the maximum allowed continuous operation time and the actual continuous operation time, reduce the score of long-operating components, and avoid overuse; maintenance cost affects ζ H ·C j / C sys : Combine the accumulated maintenance cost and reduce the score of high maintenance cost components to ensure economy;

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

[0165] HCS j Calculated by the above HCS formula; N represents the total number of components in the power system; H med The median health score of all components; T limit and T op,j Respectively represent the maximum allowable continuous operation time and the actual continuous operation time of the power supply component; C j and C sys denote the cumulative maintenance cost of the jth component and the entire power system respectively; δ H ,∈ H ,ζ H It is an adjustment factor set based on experience;

[0166] Assume that at a medical station on a remote island, the power system includes the following components: main battery pack, backup battery pack, inverter, and charge controller. First, the system analyzes operating performance, collects user feedback and historical maintenance records, and calculates fault repair time to provide necessary data support for subsequent HCS calculations, ensuring that the true health status of each component is fully reflected. Then, the health score of each power component is calculated:

[0167]

[0168]

[0169]

[0170]

[0171] After the HCS calculation is completed, the health scores of all components are summarized and their dispersion is evaluated. Combined with the impact of continuous operation time and maintenance cost, a smooth transition is made from the health score of a single component to the health assessment index of the entire power system:

[0172]

[0173] After calculating the HSI, the components are sorted by score and detailed maintenance plans and emergency plans are developed. A visual report is generated to show the health status of each component and maintenance recommendations. An emergency response plan is prepared to deal with emergencies to ensure the continued stable operation of the system.

[0174] Through the above steps, the system realizes a comprehensive and dynamic assessment of the health status of power components, ensuring the continuous and stable operation of the system. Assuming that the threshold is set to 0.7, since the result is greater than the set threshold, it indicates that the overall health of the power system is good, but some components such as the charging controller have low scores, indicating that these components need to be paid special attention to and maintained, thereby significantly improving the reliability and response speed of the system.

[0175] In order to solve the problem that the existing remote management system cannot effectively maintain the operation of the life support system under poor communication conditions, in some embodiments, the step 104 implements real-time visual management of the power status of the medical treatment box through a secure encrypted communication link based on the warning information and maintenance suggestions, so that authorized personnel can view the power configuration parameters and adjust the power configuration parameters according to the on-site conditions to generate a remote management mechanism, which is used to maintain the operation of the life support system under poor communication conditions, including:

[0176] According to the warning information and maintenance suggestions, the power status of the medical treatment box is managed and processed in real time and visualized to obtain a visual management interface; a secure encrypted communication link is used to ensure the security and integrity of data transmission, remote access to the power status of the medical treatment box is achieved, and a secure remote access channel is generated; the visual management interface and the secure remote access channel are used to enable authorized personnel to view power configuration parameters, and adjust the power configuration parameters according to on-site conditions to obtain remote adjustment records; when encountering an emergency, the authorized personnel quickly switch to emergency mode through a preset operating process to ensure the continuous operation of the basic life support system and generate an emergency operation log; based on the remote adjustment record and the emergency operation log, the effectiveness of remote management is analyzed and optimized to generate a reliable remote management mechanism.

[0177] In this embodiment, the early warning information and maintenance suggestions include early warnings of potential power system failures and specific maintenance measures to guide remote management; the visual management interface provides an intuitive power status display to help authorized personnel quickly understand the current status; the secure encrypted communication link ensures the security and integrity of data transmission to prevent unauthorized access or data tampering; the remote adjustment record records the time, content and results of each remote adjustment in detail for subsequent analysis; the emergency operation log records the operating procedures and handling measures under emergency conditions to ensure traceability; the reliable remote management mechanism is a management strategy optimized based on the analysis of remote adjustment records and emergency operation logs, which improves the reliability and response speed of the system.

[0178] In the embodiment of the present application, firstly, the system performs real-time visual management and processing of the power status of the medical treatment box according to the warning information and maintenance suggestions, and obtains a visual management interface; secondly, a secure encrypted communication link is used to ensure the security and integrity of data transmission, realize remote access to the power status of the medical treatment box, and generate a secure remote access channel; thirdly, the visual management interface and the secure remote access channel are used to enable authorized personnel to view the power configuration parameters and adjust them according to the on-site conditions to obtain remote adjustment records; finally, when encountering an emergency, the authorized personnel quickly switch to the emergency mode through the preset operation process to ensure the continuous operation of the basic life support system and generate an emergency operation log. Based on the remote adjustment records and emergency operation logs, the system analyzes the effectiveness of remote management and optimizes it to generate a reliable remote management mechanism.

[0179] Here is a specific example:

[0180] Assume that in a remote polar research station, the communication conditions are extremely unstable. First, the system performs real-time visual management and processing of the power status of the medical treatment box according to the early warning information and maintenance suggestions, and generates an intuitive visual management interface; secondly, by using a secure encrypted communication link, the security and integrity of data transmission are ensured, remote access to the power status of the medical treatment box is realized, and a secure remote access channel is generated; thirdly, through the visual management interface and the secure remote access channel, the authorized personnel of the command center can view the power configuration parameters in real time, and flexibly adjust the settings according to the on-site conditions, and obtain detailed remote adjustment records; finally, when encountering emergencies such as communication interruption caused by extreme weather, the authorized personnel quickly switch to the emergency mode through the preset operation process to ensure the continuous operation of the basic life support system and generate a complete emergency operation log. Through the above steps, the system not only realizes the real-time monitoring and remote management of the power status of the medical treatment box, but also analyzes the effectiveness of remote management and optimizes it through detailed remote adjustment records and emergency operation logs, generating a reliable remote management mechanism.

[0181] Figure 2 A schematic diagram of a power management system adapted to extreme environments is provided for the present application embodiment. Figure 2 As shown, the device comprises:

[0182] An evaluation module 21 is used to evaluate and process changes in energy collection efficiency under extreme climate conditions based on environmental sensor data and historical energy consumption records, intelligently adjust the charging and discharging strategies of energy storage batteries, and design a redundant power reserve mechanism to cope with continuous cloudy days or windless periods, and generate a power management plan, wherein the power management plan is used to adapt to extreme environments;

[0183] The monitoring module 22 is used to classify all medical devices according to emergency priorities by using the power management solution, to weigh the functional importance of different medical devices and current task requirements by using a multi-criteria decision analysis algorithm, and to accurately monitor the actual power consumption of each device by using real-time energy consumption analysis technology to generate an energy-saving optimization configuration table;

[0184] A monitoring module 23 is used to monitor the health status of the power supply system based on the energy-saving optimization configuration table by applying an adaptive fault prediction and health management algorithm. When a potential fault is detected in the main power supply or any backup power supply, a preset redundant power supply switching scheme is automatically activated, and the expected life of each power supply component is evaluated by the remaining service life analysis technology, and early warning information and maintenance suggestions are generated;

[0185] The adjustment module 24 is used to achieve real-time visual management of the power status of the medical treatment box through a secure encrypted communication link based on the warning information and maintenance suggestions, so that authorized personnel can view the power configuration parameters and adjust the power configuration parameters according to the on-site conditions to generate a remote management mechanism, which is used to maintain the operation of the life support system under poor communication conditions.

[0186] Figure 2 The power management system adapted to extreme environments can be implemented Figure 1 The implementation principle and technical effect of the power management method adapted to extreme environments described in the illustrated embodiment will not be described in detail. The specific manner in which each module and unit performs operations in the power management system adapted to extreme environments in the above embodiment has been described in detail in the embodiment of the method, and will not be described in detail here.

[0187] In one possible design, Figure 2 A power management system adapted to extreme environments in the illustrated embodiment may 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;

[0188] 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 .

[0189] The processing component 32 is used to: evaluate and process the change of energy collection efficiency under extreme climate conditions based on environmental sensor data and historical energy consumption records, intelligently adjust the charging and discharging strategy of the energy storage battery, and design a redundant power reserve mechanism to cope with continuous cloudy or windless periods, and generate a power management plan, which is used to adapt to extreme environments; use the power management plan to classify all medical devices according to emergency priority, use a multi-criteria decision analysis algorithm to weigh the functional importance and current task requirements of different medical devices, and use real-time energy consumption analysis technology to monitor the actual power consumption of each device and generate an energy-saving optimization configuration table; based on the energy-saving priority The system uses a self-adaptive fault prediction and health management algorithm to monitor the health status of the power supply system. When a potential fault is detected in the main power supply or any backup power supply, the system automatically activates the preset redundant power supply switching plan, evaluates the expected life of each power supply component through the remaining service life analysis technology, and generates early warning information and maintenance suggestions. Based on the early warning information and maintenance suggestions, the system realizes real-time visual management of the power status of the medical treatment box through a secure encrypted communication link, so that authorized personnel can view the power supply configuration parameters and adjust the power supply configuration parameters according to the on-site situation, thereby generating a remote management mechanism, which is used to maintain the operation of the life support system under poor communication conditions.

[0190] 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.

[0191] 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.

[0192] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0193] 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.

[0194] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0195] 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.

[0196] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment is a power management method adapted to extreme environments.

[0197] 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.

[0198] 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.

[0199] 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 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, which 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.

[0200] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A power management method adapted to extreme environments, characterized in that: include: Based on environmental sensor data and historical energy consumption records, the changes in energy collection efficiency under extreme climate conditions are evaluated and processed, the charging and discharging strategies of energy storage batteries are intelligently adjusted, and a redundant power reserve mechanism is designed to cope with continuous cloudy or windless periods, and a power management solution is generated. The power management solution is used to adapt to extreme environments; Using the power management solution, all medical devices are classified according to emergency priority, and a multi-criteria decision analysis algorithm is used to weigh the functional importance of different medical devices and current task requirements. The real-time energy consumption analysis technology is used to monitor the actual power consumption of each device and generate an energy-saving optimization configuration table. Based on the energy-saving optimization configuration table, an adaptive fault prediction and health management algorithm is applied to monitor the health status of the power supply system. When a potential fault is detected in the main power supply or any backup power supply, a pre-set redundant power supply switching scheme is automatically activated, and the expected life of each power supply component is evaluated through the remaining service life analysis technology, and early warning information and maintenance suggestions are generated; Based on the warning information and maintenance suggestions, real-time visual management of the power status of the medical treatment box is achieved through a secure encrypted communication link, so that authorized personnel can view the power configuration parameters and adjust the power configuration parameters according to the on-site conditions to generate a remote management mechanism. The remote management mechanism is used to maintain the operation of the life support system under poor communication conditions.

2. The method according to claim 1, characterized in that The power management solution is used to classify all medical devices according to the emergency priority, and a multi-criteria decision analysis algorithm is used to weigh the functional importance of different medical devices and the current task requirements. The real-time energy consumption analysis technology is used to accurately monitor the actual power consumption of each device and generate an energy-saving optimization configuration table, including: Using the power management solution adapted to extreme environments, all medical devices are classified according to emergency priorities to obtain an emergency device list and a non-critical device list; Based on the list of emergency equipment and the list of non-critical equipment, a multi-criteria decision analysis algorithm is used to quantitatively evaluate the importance of different equipment in combination with the functional importance of the equipment in the treatment process and the current task requirements, and an equipment priority score table is obtained; According to the device priority scoring table and the real-time energy consumption analysis technology, the actual power consumption of each device is monitored to obtain a real-time energy consumption data stream; By utilizing the real-time energy consumption data stream and combining it with the preset energy-saving target, the power consumption configuration of each device is optimized by dynamically adjusting the operating parameters and working mode of each device to generate an energy-saving optimization configuration table.

3. The method according to claim 2, characterized in that According to the device priority scoring table and the real-time energy consumption analysis technology, the actual power consumption of each device is monitored to obtain a real-time energy consumption data stream, including: According to the device priority score table and real-time energy consumption analysis technology, the actual power consumption of each device is continuously and accurately monitored and processed to generate power consumption monitoring records; By using the power consumption monitoring records and combining them with the equipment operation status monitoring, the power consumption changes of each equipment in different working modes are dynamically tracked and processed to generate a power consumption change trend graph; Based on the power consumption trend graph, a machine learning algorithm is applied to predict the power consumption demand in the future, adjust the current energy-saving optimization configuration, and generate an estimated energy consumption report; The estimated energy consumption report is used to evaluate the effectiveness of the existing power management strategy, plan emergency measures for energy shortages, implement emergency power management plans, and perform real-time energy consumption analysis on the optimized power configuration to obtain a real-time energy consumption data stream.

4. The method according to claim 2, characterized in that: The real-time energy consumption data stream is used in combination with a preset energy-saving target to optimize the power consumption configuration of each device by dynamically adjusting the operating parameters and working mode of each device to generate an energy-saving optimization configuration table, including: Using the real-time energy consumption data stream, combined with the preset energy-saving target, the current power consumption status of each device is evaluated and processed to obtain an initial power consumption evaluation result; According to the initial power consumption evaluation results, the power consumption efficiency of each device under different operating parameters and working modes is analyzed, the direction and scope of optimization adjustment are determined, and a power consumption efficiency analysis report is generated; Based on the power consumption efficiency analysis report, the operating parameters and working modes of each device are dynamically adjusted to implement energy-saving optimization strategies to obtain an optimized device configuration solution; By using the optimized device configuration scheme, combined with device performance and energy-saving effect, the power consumption configuration of each device is optimized to generate an energy-saving optimization configuration table.

5. The method according to claim 1, characterized in that Based on the energy-saving optimization configuration table, the adaptive fault prediction and health management algorithm is applied to monitor the health status of the power supply system. When a potential fault is detected in the main power supply or any backup power supply, the pre-set redundant power supply switching scheme is automatically activated, and the expected life of each power supply component is evaluated through the remaining service life analysis technology, and early warning information and maintenance suggestions are generated, including: Based on the energy-saving optimization configuration table, an adaptive fault prediction and health management algorithm is applied to continuously monitor the health status of the power supply system to obtain a power supply system health assessment result; Using the power system health assessment results, when a potential failure of the main power supply or any backup power supply is detected, a pre-set redundant power supply switching scheme is automatically activated to ensure the continuity of power supply and generate a failure response record; Based on the fault response records, a detailed evaluation of the expected life of each power component is performed using a remaining service life analysis technique, and a power component life evaluation report is obtained by taking into account the historical usage of the component and the current working environment; Based on the power component life assessment report, combined with the current equipment operating status and future power consumption demand forecast, early warning information and maintenance suggestions are generated.

6. The method according to claim 5, characterized in that According to the fault response record, the expected life of each power supply component is evaluated in detail through the remaining service life analysis technology, and the historical use of the component and the current working environment are considered to obtain a power supply component life assessment report, including: According to the fault response record, historical usage of each power component is collected and sorted to obtain a historical usage data set; Using the historical usage data set and combining it with current working environment parameters, a detailed evaluation process is performed on the expected life of each power supply component through a remaining useful life analysis technique to generate a preliminary life evaluation result; Based on the preliminary life assessment results, the assessment error is corrected by applying statistical analysis methods, taking into account changes in actual operating conditions, such as additional losses in extreme environments, to obtain corrected life assessment data; Based on the corrected life assessment data, the health status of each power supply component is comprehensively analyzed to generate a power supply component life assessment report.

7. The method according to claim 1, characterized in that According to the warning information and maintenance suggestions, the real-time visual management of the power status of the medical treatment box is realized through a secure encrypted communication link, so that the authorized personnel can view the power configuration parameters and adjust the power configuration parameters according to the on-site conditions, and generate a remote management mechanism, which is used to maintain the operation of the life support system under poor communication conditions, including: According to the warning information and maintenance suggestions, the power status of the medical treatment box is managed and processed in real time and visualized to obtain a visual management interface; Utilize secure encrypted communication links to ensure the security and integrity of data transmission, achieve remote access to the power status of the medical treatment box, and generate a secure remote access channel; The visual management interface and the secure remote access channel are used to allow authorized personnel to view power configuration parameters, and adjust the power configuration parameters according to on-site conditions to obtain remote adjustment records; When encountering an emergency, authorized personnel can quickly switch to emergency mode through preset operation procedures to ensure the continuous operation of basic life support systems and generate emergency operation logs; According to the remote adjustment records and emergency operation logs, the effectiveness of remote management is analyzed and optimized to generate a reliable remote management mechanism.

8. A power management method adapted to extreme environments, characterized in that: include: An evaluation module is used to evaluate and process changes in energy collection efficiency under extreme climate conditions based on environmental sensor data and historical energy consumption records, intelligently adjust the charging and discharging strategies of energy storage batteries, and design a redundant power reserve mechanism to cope with continuous cloudy days or windless periods, and generate a power management solution, which is used to adapt to extreme environments; A monitoring module is used to classify all medical devices according to emergency priorities using the power management solution, use a multi-criteria decision analysis algorithm to weigh the functional importance of different medical devices and current task requirements, and use real-time energy consumption analysis technology to accurately monitor the actual power consumption of each device and generate an energy-saving optimization configuration table; A monitoring module, for monitoring the health status of the power supply system by applying an adaptive fault prediction and health management algorithm based on the energy-saving optimization configuration table, automatically activating a pre-set redundant power supply switching scheme when a potential fault is detected in the main power supply or any backup power supply, evaluating the expected life of each power supply component by using a remaining service life analysis technique, and generating early warning information and maintenance suggestions; The adjustment module is used to achieve real-time visual management of the power status of the medical treatment box through a secure encrypted communication link based on the warning information and maintenance suggestions, so that authorized personnel can view the power configuration parameters and adjust the power configuration parameters according to the on-site conditions to generate a remote management mechanism, which is used to maintain the operation of the life support system under poor communication conditions.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a power management method adapted to extreme environments 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, a power management method adapted to extreme environments as claimed in any one of claims 1 to 7 is implemented.

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