A high-altitude energy storage monitoring system

Abstract

This invention proposes a high-altitude energy storage monitoring system, comprising: an environmental data acquisition module, an intelligent temperature control analysis module, an intelligent energy dispatching module, a health monitoring module, an adaptive adjustment module, and a system optimization module. This invention provides an intelligent, environmentally adaptive energy storage system monitoring and maintenance strategy, capable of dynamically adjusting temperature control strategies, energy allocation, and maintenance plans based on real-time changes in the high-altitude environment, achieving efficient operation of the energy storage system and extending equipment lifespan. This requires utilizing sensor technology to collect multi-dimensional environmental data and combining it with intelligent algorithms to monitor equipment operating status in real time, predict equipment health status, optimize energy dispatching, and formulate adaptive maintenance strategies to reduce failure risks and maintenance costs, ensuring stable system operation in complex environments.

Description

Technical Field

[0001] This invention belongs to the field of energy storage monitoring, and in particular relates to a high-altitude energy storage monitoring system. Background Technology

[0002] The application of energy storage systems in high-altitude environments faces numerous challenges. High-altitude regions typically feature low air pressure, drastic temperature fluctuations, intense solar radiation, and high humidity, all of which significantly impact energy storage systems (especially batteries). First, low air pressure reduces air density, decreasing battery heat dissipation and increasing the risk of overheating. Second, drastic temperature changes can cause uneven internal battery temperatures, accelerating degradation and affecting battery health. Furthermore, intense solar radiation and high humidity exacerbate the heat dissipation burden on the battery. These environmental conditions significantly affect the lifespan and performance of energy storage systems, increasing the complexity of maintenance and management.

[0003] Existing energy storage system monitoring and maintenance technologies typically employ static temperature control strategies and periodic maintenance mechanisms when facing these extreme environments. However, static temperature control strategies cannot adjust to real-time environmental changes, easily leading to delayed temperature control responses or excessive energy consumption. Periodic maintenance strategies, on the other hand, do not fully consider the real-time operating status of the equipment and environmental stress, often resulting in prolonged operation of equipment in suboptimal conditions, increasing the risk of system failure. Traditional maintenance and monitoring methods struggle to adapt to the dynamic changes in high-altitude environments and cannot efficiently cope with the challenges posed by complex environments, ultimately leading to low operating efficiency of energy storage equipment, increased equipment wear and tear, high maintenance costs, and even system collapse. Summary of the Invention

[0004] The purpose of this invention is to propose a high-altitude energy storage monitoring system that dynamically adjusts temperature control strategies, energy allocation, and maintenance plans based on real-time changes in the high-altitude environment, thereby achieving efficient operation of the energy storage system and extending equipment lifespan. This requires utilizing sensor technology to collect multi-dimensional environmental data and combining it with intelligent algorithms to monitor equipment operating status in real time, predict equipment health status, optimize energy scheduling, and formulate adaptive maintenance strategies to reduce failure risks and maintenance costs, ensuring stable system operation in complex environments.

[0005] To achieve the above objectives, the present invention provides a high-altitude energy storage monitoring system, characterized in that the system comprises: an environmental data acquisition module, an intelligent temperature control analysis module, an intelligent energy dispatching module, a health monitoring module, an adaptive adjustment module, and a system optimization module; wherein,

[0006] The environmental data acquisition module is used to adaptively and dynamically collect environmental parameters in the high-altitude environment using a multi-dimensional sensor network, and preprocess them in the edge computing unit to obtain an environmental parameter dataset.

[0007] The intelligent temperature control analysis module is used to analyze and generate temperature control signals based on environmental parameter datasets to determine whether the energy storage device needs temperature control intervention; the temperature control signals include heating signals and cooling signals.

[0008] The intelligent energy dispatching module is used to optimize energy allocation based on the temperature control signal transmitted by the intelligent temperature control analysis module, combined with the power demand of the energy storage device and external environmental data.

[0009] The health monitoring module is used to perform health analysis in advance based on real-time power data and environmental feedback, and through intelligent prediction algorithms.

[0010] The adaptive adjustment module is used to receive the health analysis results from the health monitoring module and dynamically adjust the maintenance strategy of the energy storage device in combination with environmental factors.

[0011] The system optimization module is used to adaptively learn from the system's operational feedback and optimize maintenance strategies.

[0012] The specific analysis process for determining whether energy storage devices require temperature control intervention based on environmental parameter datasets is as follows:

[0013] Based on the battery operating temperature range of the energy storage device, a basic temperature control determination is made on the environmental parameter dataset acquired by the environmental data acquisition module.

[0014] The battery operating temperature range is corrected by introducing a pressure correction factor, a humidity correction term, and a solar radiation correction term to obtain the final temperature control judgment conditions.

[0015] The basic temperature control judgment is compared with the final temperature control judgment result. If the basic temperature control judgment is less than the minimum value of the final temperature control judgment result, a heating signal is generated. If the basic temperature control judgment is greater than the maximum value of the final temperature control judgment result, a cooling signal is generated.

[0016] Preferably, the multidimensional data includes temperature, air pressure, humidity, and solar radiation; the adaptive dynamic acquisition means that the sampling frequency is dynamically adjusted according to the rate of change of environmental parameters, as shown below:

[0017]

[0018] Among them, f i (t) is the sampling frequency, f0 is the basic sampling frequency; α is the adjustment coefficient, which controls the increase of the sampling frequency with the rate of change; It is environmental parameter X i,j The rate of change of (t) at time t, i.e.

[0019] Preferably, the battery operating temperature range of the energy storage device is set to [T]. min ,T max ];

[0020] The pressure correction factor is expressed as follows:

[0021]

[0022] in, It is the minimum value after adding the air pressure correction factor. This is the maximum value after adding the air pressure correction factor, where β(t) is the air pressure correction factor, reflecting the effect of air pressure on heat dissipation efficiency, defined as... γ is the pressure-temperature correction coefficient, used to adjust the effect of pressure changes on the temperature range; It is the air pressure at the current moment, P. std It is standard atmospheric pressure;

[0023] The pressure correction coefficient enables the temperature control operation to be triggered in advance when the pressure is lower than the standard pressure.

[0024] The humidity correction term α H (t) and solar radiation correction term α R (t), represented as follows:

[0025]

[0026] in, It is the humidity correction for the temperature threshold, δ H H is the humidity-temperature correction factor. std Standard humidity, Current humidity; It is the correction of the temperature control threshold by solar radiation, δ R R is the radiation correction factor. std Standard solar radiation value, This represents the current solar radiation value. It is the minimum value of the final temperature threshold. It is the maximum value of the final temperature threshold.

[0027] Preferably, the energy is optimally allocated based on the temperature control signal transmitted by the intelligent temperature control analysis module, combined with the power demand of the energy storage device and external environmental data. The specific analysis process is as follows:

[0028] The total power requirement of the system is determined by the combined power requirements of the external load and the temperature control system.

[0029] The effective available power of the system is defined as the remaining power that the energy storage device can provide at the current moment. Then, a dynamic energy allocation strategy is designed to adjust the power allocation of the temperature control device in real time to maximize energy utilization, that is, to minimize the remaining power that the energy storage device can provide and the temperature control power demand. The temperature control power demand is determined by environmental factors and temperature control signals.

[0030] An environmental feedback correction mechanism is introduced for high-altitude environments. The power output of the temperature control equipment is dynamically adjusted according to the drastic fluctuations in environmental parameters. At the same time, an energy scheduling optimization algorithm is designed based on the adjusted power output to minimize the power loss of the energy storage equipment and ensure the normal operation of the temperature control equipment.

[0031] The final temperature-controlled power is output by the energy scheduling optimization algorithm that combines the temperature control signal.

[0032] Preferably, the environmental feedback correction mechanism is based on the corrected temperature control power. Including the environmental correction factor, it is expressed as follows:

[0033] P temp (t)'=P temp (t)·(1+θ(t))

[0034] Among them, P temp (t)' is the power required for heating or cooling in the temperature control equipment, and θ9t) is the environmental correction factor, reflecting the influence of environmental factors on the temperature control power; the formula for calculating the environmental correction factor θ(t0) is:

[0035]

[0036] Wherein, κ is the temperature correction coefficient, used to adjust the difference between the ambient temperature and the set target temperature; It is the ambient temperature, T set λ is the target temperature of the battery; λ is the pressure correction coefficient, used to adjust the impact of pressure on power demand. This is the current air pressure, P. std It is standard atmospheric pressure;

[0037] The energy scheduling optimization algorithm is expressed as follows:

[0038]

[0039] in, It is the final temperature control power;

[0040] When Heating9t) is true, the heating power is executed.

[0041] When Cooling(t) is true, the cooling power is applied.

[0042] Preferably, the step involves monitoring real-time power data and environmental feedback, and then using an intelligent prediction algorithm to perform health analysis in advance. The specific analysis process is as follows:

[0043] The status signals obtained by the intelligent energy dispatch module include total power output, available power, and power consumption of temperature control equipment. Combined with the environmental dataset, an operation dataset is formed, and the operation dataset is monitored in real time.

[0044] An adaptive health status prediction model is constructed, using historical and real-time operating datasets as inputs, to predict the health of energy storage devices.

[0045] Set a health threshold and determine whether the equipment has entered a high-risk zone for failure based on the predicted health status. If the health status is lower than the health threshold, it will enter the deterioration acceleration stage and a failure will occur soon. Then, based on the changing trend of the health status, design a dynamic maintenance optimization model and dynamically adjust the maintenance strategy according to the current health status and environmental pressure.

[0046] Finally, the system generates maintenance signals based on health status prediction, fault assessment, and maintenance optimization results.

[0047] Preferably, the adaptive health status prediction model processes time series data through a long short-term memory network to predict future health status, as shown below:

[0048] H(t)=LSTM(M(t-Δt,t),∈(t))

[0049] Where H(t) represents the health status of the system at time t, with a value range of [0,1], where 1 represents healthy and 0 represents failure; ∈(t) is the environment correction term, used to dynamically adjust the health prediction; LSTM is a Long Short-Term Memory network for processing time series data; the environment correction term ∈(t) is expressed as follows:

[0050]

[0051] Among them, T opt This is the optimal operating temperature of the battery, P std It is standard atmospheric pressure; H std It is standard humidity, R std α1, α2, α3, and α4 are standard radiation values; α1, α2, α3, and α4 are correction coefficients that reflect the weighting of the impact of different environmental factors on the battery's health status.

[0052] The dynamic maintenance optimization model is used to adjust the maintenance cycle, as shown below:

[0053]

[0054] Where T0 is the initial maintenance cycle; β is the adjustment factor based on the decline in equipment health status; γ is the adjustment factor based on the environmental correction term ∈(t); H init This is the initial health status of the equipment.

[0055] Preferably, the step of receiving the health analysis results from the health monitoring module and dynamically adjusting the maintenance strategy of the energy storage device in conjunction with environmental factors involves the following specific analysis process:

[0056] Maintenance is triggered immediately when the health status falls below a threshold. The model is optimized through an adaptive maintenance strategy, and the maintenance cycle is dynamically adjusted based on the health status and environmental correction items. At the same time, the priority and intensity of maintenance tasks are adjusted according to the health status of the equipment and environmental pressure. The final maintenance strategy is then output, executed, and a maintenance signal is output.

[0057] Preferably, the adaptive maintenance strategy optimization model is represented as follows:

[0058]

[0059] Where T0 is the initial maintenance cycle; H init β represents the initial health status of the equipment; β reflects the impact of changes in health status on the maintenance cycle; γ is the environmental correction coefficient, which adjusts the maintenance cycle in conjunction with environmental pressure; ∈(t) is the environmental correction term;

[0060] The adjustment of the priority and intensity of maintenance tasks based on the health status of the equipment and environmental stress is expressed as follows:

[0061]

[0062] Among them, P maint (t) is the maintenance task priority; δ is the environmental correction factor, reflecting the impact of environmental pressure on priority; H(t) is the current health status of the equipment;

[0063]

[0064] Among them, S maint (t) is the maintenance intensity, S0 is the initial maintenance intensity; λ is the environmental correction factor, which reflects the influence of environmental pressure on the maintenance intensity.

[0065] Preferably, the adaptive learning of the system's operational feedback to optimize the maintenance strategy involves the following specific analysis process:

[0066] Based on equipment feedback data, a maintenance effectiveness evaluation function is designed to quantify the improvement of equipment health by each maintenance, and adjustments are made in conjunction with an environmental stress correction term. A dynamic adaptive learning model is also designed to adjust the maintenance cycle and intensity based on historical maintenance effectiveness.

[0067] To address the unique impact of high-altitude environments on equipment health, a special environmental regularization term was introduced to smooth out drastic changes in health status and avoid misjudgments caused by short-term environmental fluctuations.

[0068] Finally, a global optimization model is designed to achieve a balance between maintenance costs, equipment health, and environmental stress in the long term.

[0069] The maintenance effect evaluation function is expressed as follows:

[0070]

[0071] Where H(t+Δt) is the health status of the equipment after maintenance; S maint (t) represents the current maintenance intensity; α is the weighting coefficient, representing the impact of environmental pressure on the maintenance effect; ∈(t) is the environmental correction term, used to reflect the impact of the external environment on equipment degradation.

[0072] The beneficial technical effects of the present invention are at least as follows:

[0073] (1) This invention proposes a multi-dimensional sensor network layout and dynamic acquisition strategy based on high-altitude environments. The system monitors key environmental parameters such as temperature, air pressure, humidity, and solar radiation in real time through multiple sensor nodes. It utilizes an adaptive acquisition frequency adjustment mechanism to ensure that the sampling frequency is increased when environmental changes are drastic and decreased when the environment is stable, thereby achieving accurate and real-time data collection. Through a multi-dimensional data fusion algorithm, the system can automatically correct sensor measurement errors, ensuring the accuracy of environmental data. This dynamic adaptive acquisition strategy significantly improves the real-time performance and accuracy of the data, providing a reliable foundation for subsequent temperature control and energy scheduling.

[0074] (2) To address the impact of complex factors such as air pressure, temperature, humidity, and solar radiation on the operation of energy storage systems in high-altitude environments, this invention designs an intelligent temperature control determination and energy dispatching system. This system not only dynamically adjusts the temperature control threshold based on real-time environmental data but also performs detailed calculations of temperature control requirements through environmental correction terms. Temperature control determination is based on adaptive adjustments of environmental parameters, ensuring the system can respond promptly to changes in factors such as temperature, air pressure, and humidity. Furthermore, the system optimizes energy dispatching based on the current energy storage status, power requirements, and temperature control requirements, dynamically allocating power to ensure the rational utilization of energy and effective heat dissipation of equipment in high-altitude environments.

[0075] (3) This invention proposes an adaptive health prediction model for energy storage devices, which combines environmental correction terms with historical operating data to dynamically predict the health status of the devices. The system predicts the future health status of the devices through intelligent algorithms (such as LSTM networks), and adjusts the weights in the prediction process based on real-time parameters such as temperature and air pressure in high-altitude environments, thereby achieving accurate prediction of the device's deterioration trend. In addition, the system dynamically adjusts the maintenance cycle and maintenance intensity based on the health status prediction results, avoiding the resource waste caused by traditional periodic maintenance. The adaptive maintenance strategy enables the devices to maintain optimal health status in complex environments, thereby extending equipment life, reducing failure rates, and improving operating efficiency. Attached Figure Description

[0076] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0077] Figure 1 This is a framework diagram of a high-altitude energy storage monitoring system according to the present invention. Detailed Implementation

[0078] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0079] like Figure 1 As shown in the figure, an embodiment of the present invention provides a high-altitude energy storage monitoring system, the system comprising: an environmental data acquisition module 101, an intelligent temperature control analysis module 102, an intelligent energy dispatching module 103, a health monitoring module 104, an adaptive adjustment module 105, and a system optimization module 106; wherein,

[0080] The environmental data acquisition module 101 is used to adaptively and dynamically collect environmental parameters in a high-altitude environment using a multi-dimensional sensor network, and preprocess them in an edge computing unit to obtain an environmental parameter dataset.

[0081] The intelligent temperature control analysis module 102 is used to analyze and generate temperature control signals based on environmental parameter datasets to determine whether the energy storage device needs temperature control intervention; the temperature control signals include heating signals and cooling signals.

[0082] The intelligent energy scheduling module 103 is used to optimize energy allocation based on the temperature control signal transmitted by the intelligent temperature control analysis module, combined with the power demand of the energy storage device and external environmental data.

[0083] The health monitoring module 104 is used to perform health analysis in advance based on real-time power data and environmental feedback, and through intelligent prediction algorithms.

[0084] The adaptive adjustment module 105 is used to receive the health analysis results of the health monitoring module and dynamically adjust the maintenance strategy of the energy storage device in combination with environmental factors.

[0085] The system optimization module 106 is used to adaptively learn from the system's operational feedback and optimize maintenance strategies.

[0086] Specifically, the method uses a multi-dimensional sensor network to adaptively and dynamically collect environmental parameters in high-altitude environments, and performs preprocessing in an edge computing unit to obtain an environmental parameter dataset. The detailed analysis is as follows:

[0087] To accurately capture various key parameters in high-altitude environments, this invention designs a multi-dimensional sensor network for real-time data acquisition of temperature, air pressure, humidity, and solar radiation. These sensor nodes are deployed both externally and internally of the energy storage device, particularly in critical areas (such as around the battery), to ensure comprehensive monitoring. Each sensor node P... i The set of environmental parameters collected at time t is as follows:

[0088] X i,j (t)={T i (t),P i (t),H i (t),R i (t)} (1)

[0089] Among them, T i (t) represents sensor node P i Temperature collected at time t; P i (t) represents air pressure; H i (t) represents humidity; R i (t represents solar radiation. These parameters reflect the real-time status of the energy storage system in a high-altitude environment, providing basic data for subsequent steps.)

[0090] Due to the drastic climate changes in high-altitude environments, environmental parameters such as temperature and air pressure can fluctuate significantly in a short period of time. Therefore, sensor nodes need to dynamically adjust their sampling frequency according to environmental changes. This invention designs an adaptive dynamic acquisition strategy with a sampling frequency f. i (t) Based on the rate of change of environmental parameters Dynamic adjustment:

[0091]

[0092] Where f0 is the basic sampling frequency; α is an adjustment coefficient that controls the increase in the sampling frequency as the rate of change increases; It is environmental parameter X i,j The rate of change of (t) at time t, i.e. This formula allows the sampling frequency to automatically increase when the environment changes rapidly, thus ensuring the real-time nature of the data; and to decrease the sampling frequency to save resources when the environment is stable.

[0093] The data collected by each sensor node may be noisy due to measurement errors or external interference. To improve data accuracy, this invention proposes a multi-dimensional data fusion algorithm that processes the data through weighted summation and the introduction of a regularization term. For sensor node P... i Parameter X collected at time t i,j (t), its fused parameters Calculated using the following formula:

[0094]

[0095] Among them, w i It is sensor node P i The weight of the node is adaptively adjusted based on the node's historical accuracy and current environmental changes, satisfying ∑w i =1; λ is the coefficient of the regularization term; Reg(X) i,j (t) is a regularization term used to smooth out data fluctuations, and its form is:

[0096]

[0097] This regularization term controls the second derivative of the data, suppressing drastic fluctuations, especially short-term abnormal fluctuations in air pressure and temperature, to ensure data stability.

[0098] Furthermore, to reduce data transmission latency and ensure rapid system response in high-altitude environments, this invention introduces an edge computing unit (EPU). The EPU is responsible for local data processing and compression, reducing the amount of data transmitted and ensuring high-frequency transmission during critical moments. The dataset after adaptive fusion processing... The data will be further processed by EPU and output as a complete environmental dataset D(t), including temperature, air pressure, humidity, and radiation.

[0099]

[0100] The fused environmental data D(t) will become the input for subsequent temperature control and energy scheduling, ensuring that the system can be optimized and adjusted based on accurate and real-time data in high-altitude environments.

[0101] Specifically, the step of analyzing environmental parameter datasets to generate temperature control signals to determine whether the energy storage device requires temperature control intervention; the temperature control signals include heating signals and cooling signals, and the specific analysis is as follows:

[0102] First, based on the battery operating temperature range of the energy storage system [T] min ,T max [This involves] performing a basic temperature control determination. This invention needs to ensure that the energy storage device maintains efficient operation within this temperature range. The preliminary determination formula is:

[0103]

[0104] Among them, T min and T max These are the minimum and maximum operating temperatures of the energy storage device, respectively. This is the current temperature output from step 1 after data fusion processing. This judgment provides the basic conditions for temperature control triggering, but relying solely on temperature for temperature control determination is insufficient, especially in high-altitude environments where factors such as air pressure, humidity, and radiation have a significant impact on temperature control requirements. Therefore, this invention further incorporates dynamic correction.

[0105] Furthermore, in high-altitude areas, changes in air pressure affect air density and battery heat dissipation efficiency, especially at lower air pressures where heat dissipation becomes more difficult. Therefore, relying solely on temperature thresholds for temperature control may be inaccurate. This invention introduces an air pressure correction coefficient β(t) to dynamically adjust the temperature threshold for temperature control. The temperature range after air pressure correction is... The calculation formula is:

[0106]

[0107] Where β(t) is the air pressure correction coefficient, reflecting the influence of air pressure on heat dissipation efficiency, defined as... γ is the pressure-temperature correction coefficient, used to adjust the effect of pressure changes on the temperature range; It is the air pressure at the current moment, P. std This is the standard atmospheric pressure. The atmospheric pressure correction makes the temperature control threshold more stringent when the atmospheric pressure is below the standard pressure, triggering temperature control operations earlier. For example, in low-pressure environments, the battery is prone to overheating; the lower the atmospheric pressure, the narrower the temperature threshold.

[0108] Furthermore, under conditions of high humidity and strong solar radiation, the battery's heat dissipation efficiency decreases further. Therefore, the temperature control system needs to comprehensively consider humidity. and solar radiation Additional dynamic adjustments are made. To this end, the present invention introduces a humidity correction term α. H (t) and solar radiation correction term α R(t) is used to fine-tune the temperature control threshold. The adjusted temperature range is:

[0109]

[0110] in, It is the humidity correction for the temperature threshold, δ H H is the humidity-temperature correction factor. std Standard humidity; It is the correction of the temperature control threshold by solar radiation, δ R R is the radiation correction factor. std These are standard solar radiation values. These corrections ensure that the temperature control system can adjust its operation more sensitively to environmental changes under conditions of high humidity and strong radiation. For example, under intense solar radiation, the external temperature of the system may rise rapidly, requiring the temperature control system to activate the cooling system in advance.

[0111] Furthermore, combining the temperature thresholds corrected for air pressure, humidity, and solar radiation, the final temperature control determination formula is:

[0112]

[0113] This means that when the current temperature Below the minimum temperature after multiple corrections When the temperature is above a certain level, the system activates the heating function; conversely, when the temperature is above a certain level, the system activates the heating function. When necessary, the system activates its cooling function. These criteria are adjusted based on real-time environmental changes to ensure that the energy storage device can receive timely temperature control support under any climatic conditions.

[0114] Furthermore, based on the final temperature control determination result, the system generates a temperature control trigger signal:

[0115] like Generate a heating signal Heating(t);

[0116] like Generate a cooling signal Cooling(t).

[0117] These trigger signals will serve as inputs to the subsequent intelligent energy dispatch system, used to determine the rational allocation and management of energy during heating or cooling. The output of this step ensures that the energy storage system maintains the optimal operating temperature of the battery through precise temperature control strategies in the extreme environment of high altitudes.

[0118] Through the aforementioned intelligent temperature control determination scheme, this invention extracts current temperature, air pressure, humidity, radiation, and other information from the environmental dataset D(t), and achieves accurate determination of temperature control requirements by dynamically adjusting the temperature control threshold. This scheme, through multiple corrections based on air pressure, humidity, and radiation, ensures that the system can respond rapidly and take appropriate temperature control measures in high-altitude environments. All determination signals serve as the basis for subsequent energy scheduling, ensuring a close integration of temperature control operations and energy management, forming an efficient integrated temperature control and energy management scheme.

[0119] Specifically, based on the temperature control signal transmitted by the intelligent temperature control analysis module, and combined with the power demand of the energy storage device and external environmental data, energy is optimally allocated. The specific analysis is as follows:

[0120] Because the energy of energy storage devices is limited, the system must dynamically schedule power supply between external loads and internal temperature control requirements. The total power requirement of the system, P... total (t) is caused by external load P sys (t) and temperature control power requirement P temp (t) are jointly determined. The expression for the initial power demand is:

[0121] P total (t)=P sys 9t)+P temp (t) (11)

[0122] Among them, P sys (t) represents the current external load demand; P temp (t) is the power required by the temperature control device (heating or cooling), which is adaptively calculated based on environmental data when the temperature control signal Heating(t) or Cooling(t) is activated.

[0123] At this point, it is necessary to determine the system's current energy reserve E. avail 9t) is used for scheduling to ensure that the power output does not exceed the energy limit of the energy storage system.

[0124] Furthermore, in order to cope with the dynamic changes in energy of energy storage devices in high-altitude environments (such as changes in heat dissipation performance caused by drastic temperature differences or air pressure fluctuations), this invention introduces a dynamic energy allocation strategy. This strategy adjusts the power allocation of the temperature control device in real time to ensure maximum energy utilization.

[0125] Define the effective available power P of the system avail (t) represents the remaining power that the energy storage device can provide at the current moment. The dynamic allocation strategy will optimize the energy distribution between the temperature control device and the external load:

[0126] P temp (t)=min(P avail(t),P req (t)) (12)

[0127] Among them, P avail 9t) represents the surplus power that the energy storage device can provide; P req 9t) is the power required by the temperature control equipment, which is determined by environmental factors and the temperature control signal.

[0128] Furthermore, to adapt to the complex environment of high-altitude areas, the system also needs to perform adaptive energy scheduling based on environmental changes. External factors such as temperature, air pressure, humidity, and radiation directly affect temperature control requirements. Therefore, this invention designs an environmental feedback correction mechanism to enable the system to dynamically adjust the power of the temperature control equipment. An environmental correction coefficient θ(t) is introduced to dynamically adjust the power output of the temperature control equipment based on drastic fluctuations in environmental parameters. The corrected temperature control power... The expression is:

[0129]

[0130] Where θ(t) is the environmental correction coefficient, reflecting the impact of environmental factors on the temperature control power; the formula for calculating θ(t) is:

[0131]

[0132] Wherein, κ is the temperature correction coefficient, used to adjust the difference between the ambient temperature and the set target temperature; It is the ambient temperature, T set λ is the target temperature of the battery; λ is the pressure correction coefficient, used to adjust the impact of pressure on power demand. This is the current air pressure, P. std This is standard atmospheric pressure. The correction factor θ(t) provides an adaptive power regulation method based on real-time environmental feedback, enabling temperature control equipment to adjust power output more flexibly according to environmental changes. For example, when the temperature drops sharply or the atmospheric pressure decreases sharply, the power output will increase accordingly to ensure the stable operation of the energy storage system.

[0133] Furthermore, to ensure efficient energy utilization of the entire system under complex environments, this invention designs an energy scheduling optimization algorithm aimed at minimizing power loss in the energy storage system and ensuring the normal operation of the temperature control equipment. The energy scheduling objective is to maximize temperature control efficiency while minimizing excessive consumption of the energy storage equipment. The final temperature control power... The calculation formula is:

[0134]

[0135] This formula ensures that the temperature control power does not exceed the available power P of the energy storage device. availThe algorithm calculates the temperature control power in real time and adaptively adjusts the power output based on the environmental correction coefficient θ(t). It also considers the current environmental data and the remaining power of the energy storage device to ensure efficient operation of the temperature control equipment in extreme environments.

[0136] Furthermore, based on the final calculated power allocation, the system will execute the power output of the temperature control device:

[0137] When Heating(t) is true, the heating power is applied.

[0138] When Cooling(t) is true, the cooling power is applied.

[0139] The system will also output the current power status signal S status (t), including total power output, available energy, temperature-controlled power consumption, etc., are used for subsequent system health management and further optimization.

[0140] Specifically, based on real-time power data and environmental feedback, and through intelligent prediction algorithms, health analysis is performed in advance, as detailed below:

[0141] The power state signal S obtained from the above module status (t), including the total power output P of the energy storage system total (t), available power P avail (t) Power consumption of temperature control equipment These data are related to the environmental dataset provided in step 1. Combined, these elements constitute the operational dataset M(t) of the energy storage device. This dataset is used for real-time monitoring of the device's performance and health status. The operational monitoring dataset is defined as follows:

[0142]

[0143] This dataset encompasses device power allocation and environmental data, providing ample input for subsequent health status predictions. It not only reflects the current performance status of the devices but also dynamically adjusts the weights of health assessments based on changes in the external environment.

[0144] Furthermore, the performance of energy storage devices (especially batteries) gradually deteriorates over time, and environmental stress (such as low air pressure at high altitudes and drastic temperature changes) accelerates this deterioration. Therefore, it is necessary to construct a dynamic prediction model by combining historical data with real-time status to determine the health status of the energy storage system. This invention designs an Adaptive Health Status Prediction Model (AHSPM), which dynamically adjusts the prediction process based on historical operating data of the energy storage device and real-time input. This model corrects the deterioration rate of the energy storage device through environmental parameters, making the health prediction more accurate. The model's input is time series data M(t-Δt,t), and the output is the health status H(t) at the current time t. The model processes the time series data through a Long Short-Term Memory (LSTM) network to predict the future health status H(t), as shown in the following formula:

[0145] H(t)=LSTM(M(t-Δt,t),∈(t)) (17)

[0146] Where H(t) represents the health status of the system at time t, with a value range of [0,1], where 1 represents health and 0 represents failure; ∈(t) is the environmental correction term, used to dynamically adjust the health prediction; LSTM is a long short-term memory network for processing time series data.

[0147] The innovation of this invention lies in the environmental correction term ∈(t), which dynamically adjusts the health prediction of the energy storage device based on different weights of environmental pressure. High-altitude areas experience severe environmental fluctuations, with temperature differences and air pressure significantly impacting the battery. Therefore, the correction term ∈(t) depends on environmental data.

[0148]

[0149] Among them, T opt This is the optimal operating temperature for the battery; P std It is standard atmospheric pressure; H std It is standard humidity, R std α1, α2, α3, and α4 are standard radiation values; α1, α2, α3, and α4 are correction coefficients, reflecting the weights of different environmental factors on the battery's health status. The environmental correction term ∈(t) is dynamically adjusted with environmental changes. By introducing environmental influences into the health prediction process, the model ensures that it can accurately reflect the true health status of energy storage devices under extreme environments.

[0150] Furthermore, based on the predicted health state H(t), the system determines whether the device has entered a high-risk fault zone. This invention sets a health threshold H. thresh When H(t) falls below this threshold, it indicates that the equipment has entered an accelerated degradation phase and may be about to fail. Failure risk assessment formula:

[0151] IfH(t) <H thresh, trigger maintenance alert. (19)

[0152] Among them, H thresh It is a health threshold that is adaptively adjusted based on historical equipment data and current operating status; when H(t) <H thresh When this happens, the system issues a maintenance alert, prompting that equipment checks and possible replacements are needed.

[0153] In addition, the fault risk assessment also incorporates temperature control power consumption. Prolonged high-load temperature control operation will accelerate battery degradation. Therefore, the system will further correct the health status of the equipment based on the fluctuation of temperature control power.

[0154] Furthermore, when the equipment's health status falls below a set threshold, the system adjusts the maintenance strategy based on the trend of health status changes. To extend equipment lifespan and reduce failure rates, this invention introduces a Dynamic Maintenance Optimization Model (DMOM), which adjusts the frequency and intensity of maintenance based on the current health status and environmental pressure. Maintenance cycle T maint (t) is dynamically adjusted using the following formula:

[0155]

[0156] Where T0 is the initial maintenance cycle; β is the adjustment factor based on the decline in equipment health status; γ is the adjustment factor based on the environmental correction term ∈(t); H init This is the initial health status of the equipment.

[0157] When the health status H(t) drops significantly, or the environmental pressure is high (∈(t) is high), the system will automatically shorten the maintenance cycle to schedule maintenance operations in advance; when the equipment is in good condition and the environmental impact is small, the maintenance cycle will be appropriately extended to reduce unnecessary maintenance.

[0158] Finally, based on the results of health status prediction, fault assessment, and maintenance optimization, the system generates a maintenance signal S. maint (t). This signal contains maintenance recommendations, estimated maintenance time, and operational instructions to help the system perform adaptive maintenance. The maintenance signal is expressed as follows:

[0159] S maint (t)={H(t),T maint (t),Maintenance Alert} (21)

[0160] This signal is used to alert operators or automatically trigger maintenance procedures, and provides data support for subsequent maintenance feedback.

[0161] Specifically, the health analysis results received from the health monitoring module are combined with environmental factors to dynamically adjust the maintenance strategy of the energy storage equipment. The specific analysis is as follows:

[0162] Based on the received health status signal S maint (t) includes the current health status of the equipment H(t) and the maintenance cycle T. maint (t) and maintenance alarm. First, the system determines whether H(t) is lower than the maintenance threshold H. thresh If the value falls below this level, maintenance will be triggered immediately.

[0163] IfH(t) <H thresh , trigger immediate maintenance. (22)

[0164] Where H(t) is the current health status of the device, with a value range of [0,1], where 1 represents healthy and 0 represents failure; H thresh This represents the health status threshold, determined based on historical device data. When H(t) ≥ H... thresh Dynamic optimization that enters the maintenance cycle.

[0165] Furthermore, the maintenance cycle T is dynamically adjusted based on the health state H(t) and the environmental correction term ∈(t) using the Adaptive Maintenance Strategy Optimization Model (AMSO). maint (t).

[0166]

[0167] Where T0 represents the initial maintenance cycle; H init β represents the initial health status of the equipment; β represents the impact of changes in health status on the maintenance cycle; γ represents the environmental correction coefficient, which adjusts the maintenance cycle in conjunction with environmental pressure; ∈(t) represents the environmental correction term, which considers external factors such as temperature, air pressure, and humidity.

[0168]

[0169] in, T represents the current ambient temperature. opt For optimal operating temperature; P represents the current air pressure. std Standard atmospheric pressure; H represents the current humidity. std Standard humidity; R represents the current solar radiation. std α1 represents standard radiation; α2, α3, and α4 represent environmental weighting coefficients.

[0170] The system further adjusts the priority and intensity of maintenance tasks, dynamically adjusting them based on the equipment's health status H(t) and environmental pressure ∈(t). Maintenance priority P maint (t):

[0171]

[0172] Among them, P maint H(t) represents the maintenance task priority; δ represents the environmental correction factor, reflecting the impact of environmental pressure on the priority; H(t) represents the current health status of the equipment.

[0173] Maintenance strength S maint (t):

[0174]

[0175] Where S0 represents the initial maintenance intensity; λ represents the environmental correction factor, which reflects the impact of environmental pressure on the maintenance intensity.

[0176] Furthermore, based on the optimization results, the system outputs a maintenance signal S. maint (t) is used to perform actual maintenance operations and record the maintenance results.

[0177]

[0178] This signal feedback is used for optimization in subsequent maintenance cycles.

[0179] Specifically, the adaptive learning of the system's operational feedback to optimize maintenance strategies is analyzed as follows:

[0180] Based on the acquired maintenance signal S maint The feedback dataset is formed by combining the equipment health status H(t) and the environmental correction term ∈(t):

[0181]

[0182] Where H(t) represents the current health status of the device, and its value ranges from [0,1], where 1 indicates that the device is healthy and 0 indicates that the device is close to failure; Indicates the adjusted maintenance cycle; P maint (t) represents the current maintenance priority; S maint (t) represents the maintenance intensity, indicating the current resource input for maintenance; ∈(t) represents the environmental pressure correction term, reflecting the impact of the high-altitude environment on equipment degradation. These feedback data are the core inputs for the system's optimization learning and are used for subsequent adjustments to the maintenance strategy.

[0183] Furthermore, the system maintains the effect evaluation function E. maint(t) is used to quantify the improvement in equipment health caused by each maintenance, and adjusted in conjunction with environmental stress correction items:

[0184]

[0185] Where H(t+Δt) represents the health status of the equipment after maintenance; S maint (t) represents the current maintenance intensity; α represents the weighting coefficient, which represents the impact of environmental pressure on the maintenance effect; ∈(t) represents the environmental correction term, which is used to reflect the impact of the external environment on equipment degradation.

[0186] This formula is used to evaluate the efficiency of maintenance operations, ensuring that maintenance at high altitudes can offset the effects of environmental stress. The system is based on E... maint (t) is used to determine whether the maintenance has achieved the expected results.

[0187] Furthermore, the system uses a Dynamic Adaptive Learning Model (DALM) to analyze historical maintenance performance E. maint (t) Adjust the maintenance cycle and intensity. The adaptive maintenance cycle adjustment formula is:

[0188]

[0189] in, This indicates the optimal cycle for the next maintenance. Indicates the adjustment cycle after the current maintenance; κ represents the learning rate, which controls the adjustment range of the cycle; E maint (t) represents the current maintenance effectiveness evaluation; E target This indicates the desired maintenance effect.

[0190] When E maint (t) is greater than E target If the maintenance is effective, the maintenance cycle can be extended; otherwise, the cycle should be shortened.

[0191] The maintenance intensity adjustment formula is:

[0192]

[0193] in, This represents the optimal intensity for the next maintenance; μ represents the learning step size, which controls the magnitude of intensity adjustment.

[0194] When the maintenance effect is lower than expected, the system will increase the maintenance intensity; when the maintenance effect is higher than expected, the maintenance intensity will be reduced to save resources.

[0195] Furthermore, to address the unique impact of high-altitude environments on equipment health, this invention introduces a special environment regularization term λ. env(t) is used to smooth out drastic changes in health status and avoid misjudgments caused by short-term environmental fluctuations.

[0196]

[0197] Where, λ env (t) represents the regularization term for a special environment; β i The weighting coefficients represent the environmental factors and reflect the impact of different environmental factors. The second derivative represents the health status of the equipment, reflecting the accelerated change in health status. This regularization term is used to smooth out drastic changes in health status, avoiding misjudgments and over-maintenance caused by short-term environmental fluctuations.

[0198] Finally, through the global optimization model G(t), the system achieves a balance between maintenance costs, equipment health, and environmental stress in the long run. The global optimization objective function is:

[0199]

[0200] Among them, C maint (t) represents the maintenance cost, which varies with the maintenance intensity S. maint (t) increases; λ represents the weight of health status loss, reflecting the impact of health status on the long-term benefits of the system; λ env (t) represents a special environment regularization term used to smooth the impact of environmental fluctuations on health status. This global optimization model ensures optimal maintenance strategies and energy utilization efficiency under harsh environments by considering the system's operating costs and health management over the long term.

[0201] Unless otherwise specifically stated, the relative steps, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application.

[0202] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0203] In the description of this application, it should be noted that the terms "upper" and "lower" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use. They are only used to facilitate the description of this application and to simplify the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0204] In the description of this application, it should also be noted that, unless otherwise expressly specified and limited, the terms "set up," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0205] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A high altitude energy storage monitoring system, characterized by, The system includes: an environmental data acquisition module, an intelligent temperature control analysis module, an intelligent energy dispatching module, a health monitoring module, an adaptive adjustment module, and a system optimization module; wherein, The environmental data acquisition module is used to adaptively and dynamically collect environmental parameters in the high-altitude environment using a multi-dimensional sensor network, and preprocess them in the edge computing unit to obtain an environmental parameter dataset. The intelligent temperature control analysis module is used to analyze and generate temperature control signals based on environmental parameter datasets to determine whether the energy storage device needs temperature control intervention; the temperature control signals include heating signals and cooling signals. The intelligent energy dispatching module is used to optimize energy allocation based on the temperature control signal transmitted by the intelligent temperature control analysis module, combined with the power demand of the energy storage device and external environmental data. The health monitoring module is used to perform health analysis in advance based on real-time power data and environmental feedback, and through intelligent prediction algorithms. The adaptive adjustment module is used to receive the health analysis results from the health monitoring module and dynamically adjust the maintenance strategy of the energy storage device in combination with environmental factors. The system optimization module is used to adaptively learn from the system's operational feedback and optimize maintenance strategies. The specific analysis process for determining whether energy storage devices require temperature control intervention based on environmental parameter datasets is as follows: Based on the battery operating temperature range of the energy storage device, a basic temperature control determination is made on the environmental parameter dataset acquired by the environmental data acquisition module. The battery operating temperature range is corrected by introducing a pressure correction factor, a humidity correction term, and a solar radiation correction term to obtain the final temperature control judgment conditions. The basic temperature control judgment is compared with the final temperature control judgment result. If the basic temperature control judgment is less than the minimum value of the final temperature control judgment result, a heating signal is generated. If the basic temperature control judgment is greater than the maximum value of the final temperature control judgment result, a cooling signal is generated.

2. A high altitude energy storage monitoring system as claimed in claim 1, wherein, The environmental parameters include temperature, air pressure, humidity, and solar radiation; the adaptive dynamic acquisition means that the sampling frequency is dynamically adjusted according to the rate of change of the environmental parameters, as shown below: ； in, It is the sampling frequency. It is the basic sampling frequency; It is an adjustment coefficient that controls the increase in the sampling frequency as the rate of change increases; Environmental parameters In time The rate of change of , i.e. .

3. The high altitude energy storage monitoring system of claim 1, wherein, The battery operating temperature range of the energy storage device is set to ; The pressure correction factor is expressed as follows: ； in, It is the minimum value after adding the air pressure correction factor. It is the maximum value after adding the air pressure correction factor. It is the air pressure correction factor, which reflects the effect of air pressure on heat dissipation efficiency, and is defined as follows: ; It is the pressure-temperature correction coefficient, used to adjust the effect of pressure changes on the temperature range; It is the air pressure at the current moment. It is standard atmospheric pressure; The pressure correction coefficient enables the temperature control operation to be triggered in advance when the pressure is lower than the standard pressure. The humidity correction item and solar radiation correction term , means as follows: ； in, It is a correction for the temperature threshold caused by humidity. Humidity-temperature correction factor Standard humidity, Current humidity; It is the correction of the temperature control threshold by solar radiation. This is the radiation correction factor. Standard solar radiation value, This represents the current solar radiation value. It is the minimum value of the final temperature threshold. It is the maximum value of the final temperature threshold.

4. The high altitude energy storage monitoring system of claim 1, wherein, The energy is optimized and allocated based on the temperature control signal transmitted by the intelligent temperature control analysis module, combined with the power demand of the energy storage device and external environmental data. The specific analysis process is as follows: The total power requirement of the system is determined by the combined power requirements of the external load and the temperature control system. The effective available power of the system is defined as the remaining power that the energy storage device can provide at the current moment. Then, a dynamic energy allocation strategy is designed to adjust the power allocation of the temperature control device in real time to maximize energy utilization, i.e., minimize the remaining power that the energy storage device can provide and the temperature control power demand. The temperature control power demand is determined by environmental factors and the temperature control signal. Here, the effective available power of the system is defined. This indicates the remaining power that the energy storage device can provide at the current moment; the dynamic allocation strategy will optimize the energy distribution between the temperature control device and the external load. in, This indicates the surplus power that the energy storage device can provide; The required power of the temperature control equipment is determined by environmental factors and the temperature control signal. For temperature control power requirements; An environmental feedback correction mechanism is introduced for high-altitude environments. The power output of the temperature control equipment is dynamically adjusted according to the drastic fluctuations in environmental parameters. At the same time, an energy scheduling optimization algorithm is designed based on the adjusted power output to minimize the power loss of the energy storage equipment and ensure the normal operation of the temperature control equipment. The final temperature-controlled power is output by the energy scheduling optimization algorithm that combines the temperature control signal.

5. A high-altitude energy storage monitoring system according to claim 4, characterized in that, The environmental feedback correction mechanism is to correct the temperature control power The environmental correction coefficient is added, and is expressed as follows: ； in, It is the power required for heating or cooling in temperature control equipment. This is the environmental correction factor, reflecting the impact of environmental factors on temperature control power; environmental correction factor The calculation formula is: ； in, It is a temperature correction factor, used to adjust for the difference between the ambient temperature and the set target temperature; It is the ambient temperature. This is the target temperature of the battery; It is the air pressure correction factor, used to adjust the impact of air pressure on power demand; This is the current air pressure. It is standard atmospheric pressure; The energy scheduling optimization algorithm is expressed as follows: ； wherein, is the final temperature control power; When the heating power is executed ; When the cooling power is executed when .

6. The high altitude energy storage monitoring system of claim 1, wherein, Based on real-time power data and environmental feedback, and through intelligent prediction algorithms, a health analysis is performed in advance. The specific analysis process is as follows: The status signals obtained by the intelligent energy dispatch module include total power output, available power, and power consumption of temperature control equipment. Combined with the environmental dataset, an operation dataset is formed, and the operation dataset is monitored in real time. An adaptive health status prediction model is constructed, using historical and real-time operating datasets as inputs, to predict the health of energy storage devices. Set a health threshold and determine whether the equipment has entered a high-risk zone for failure based on the predicted health status. If the health status is lower than the health threshold, it will enter the deterioration acceleration stage and a failure will occur soon. Then, based on the changing trend of the health status, design a dynamic maintenance optimization model and dynamically adjust the maintenance strategy according to the current health status and environmental pressure. Finally, the system generates maintenance signals based on health status prediction, fault assessment, and maintenance optimization results.

7. A high altitude energy storage monitoring system according to claim 6, wherein, The adaptive health status prediction model processes time-series data through a long short-term memory network to predict future health status, as shown below: ； wherein, represents the health state of the system at time , taking the value range , 1 represents health, and 0 represents failure; These are environmental correction items used to dynamically adjust health predictions; It is a long short-term memory network for processing time series data; among which, the environment correction term It is expressed as follows: in, This is the optimal operating temperature for the battery. It is standard atmospheric pressure; It is standard humidity. It is the standard radiation value; It is a correction factor that reflects the weight of the impact of different environmental factors on the battery's health status; The dynamic maintenance optimization model is used to adjust the maintenance cycle, as shown below: ； in, This is the initial maintenance cycle; It is an adjustment factor based on the decline in the health status of the equipment; Based on environmental modification items Adjustment coefficient; This is the initial health status of the equipment.

8. A high-altitude energy storage monitoring system according to claim 7, characterized in that, The process of receiving the health analysis results from the health monitoring module and dynamically adjusting the maintenance strategy of the energy storage equipment in conjunction with environmental factors is as follows: Maintenance is triggered immediately when the health status falls below a threshold. The maintenance cycle is dynamically adjusted based on the health status and environmental correction items through a dynamic maintenance optimization model. At the same time, the priority and intensity of maintenance tasks are adjusted according to the health status of the equipment and environmental pressure. The final maintenance strategy is then output, executed, and a maintenance signal is output.

9. A high altitude energy storage monitoring system according to claim 8, wherein, The dynamic maintenance optimization model is represented as follows: ； in, This is the initial maintenance cycle; This is the initial health status of the equipment; It reflects the impact of changes in health status on the maintenance cycle; It is an environmental correction factor, which is used to adjust the maintenance cycle in conjunction with environmental pressure; It is an environmental remediation item; The adjustment of the priority and intensity of maintenance tasks based on the health status of the equipment and environmental stress is expressed as follows: ； in, It is about maintaining task priorities; It is an environmental correction factor, reflecting the impact of environmental pressure on priority. This indicates the current health status of the equipment; ； wherein, is the maintenance intensity, is the initial maintenance intensity; is an environmental correction factor, reflecting the influence of the environmental pressure on the maintenance intensity.

10. The high altitude energy storage monitoring system of claim 1, wherein, The specific analysis process for adaptively learning the system's operational feedback and optimizing maintenance strategies is as follows: Based on equipment feedback data, a maintenance effectiveness evaluation function is designed to quantify the improvement of equipment health by each maintenance, and adjustments are made in conjunction with an environmental stress correction term. A dynamic adaptive learning model is also designed to adjust the maintenance cycle and intensity based on historical maintenance effectiveness. To address the unique impact of high-altitude environments on equipment health, a special environmental regularization term was introduced to smooth out drastic changes in health status and avoid misjudgments caused by short-term environmental fluctuations. Finally, a global optimization model is designed to achieve a balance between maintenance costs, equipment health, and environmental stress in the long term. The maintenance effect evaluation function is expressed as follows: ； in, It refers to the health status of the equipment after maintenance; This represents the current maintenance intensity. It is a weighting coefficient, representing the impact of environmental pressure on maintenance effectiveness; This is an environmental correction item used to reflect the impact of the external environment on equipment degradation.

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