New energy storage remote monitoring system based on cloud computing

Abstract

The invention discloses a new energy storage remote monitoring system based on cloud computing, and relates to the technical field of energy storage, and the system comprises a remote monitoring module, an environment early warning module, a data acquisition module, a health assessment module and a control module. Remote real-time monitoring is carried out on the environment state of the distributed energy storage equipment, an environment influence coefficient Xhj is constructed, and when the environment influence coefficient Xhj exceeds an influence threshold value Y, a health analysis instruction is sent out; after an instruction is received, the instantaneous current state in the charging and discharging process of the equipment is monitored in real time, the collected related operation state information data and related internal environment state information are analyzed, the comprehensive health assessment index Zjk is obtained through fitting in combination with the environment influence coefficient Xhj, and after comparison, a control instruction of a corresponding grade is generated and executed. Therefore, efficient operation of energy storage equipment is ensured and the service life is prolonged.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of energy storage, and particularly relates to a new energy storage remote monitoring system based on cloud computing. BACKGROUND

[0002] Energy storage technology is an important part of modern energy systems, and its core role is to balance energy supply and demand, improve energy utilization efficiency, and enhance power grid stability. In the traditional energy system, energy storage devices are mainly used for large-scale power regulation and backup power. However, with the rapid development of renewable energy, the field of new energy storage has gradually become the focus of the energy industry. The characteristics of new energy storage equipment are wide distribution and complex operating environment, and its performance directly affects the stability and sustainability of the entire energy network. However, since new energy storage equipment is usually distributed in different regions and exposed to high temperature, high humidity and strong electromagnetic interference, it is particularly important to monitor the operating state and environmental state of the equipment in real time. In this context, new energy storage remote monitoring technology has emerged.

[0003] However, the traditional monitoring and management of new energy storage still faces significant deficiencies in technical application. First, energy storage equipment is widely distributed and operates in complex environments, and traditional single-point collection and manual detection methods cannot cover key areas, resulting in incomplete and lagging environmental monitoring information. Second, the internal operating state of energy storage equipment, including electrolyte conductivity and electrode residual quality, cannot be obtained in real time through simple means, and the lack of data seriously affects the accuracy of health assessment. In addition, due to the limitation of data processing capacity, it is difficult to establish an effective correlation between various environmental parameters and equipment state data, and a comprehensive health assessment model cannot be formed. Therefore, the new energy storage remote monitoring system based on cloud computing can collect environmental and equipment operating data in real time through remote deployment of sensors and combine machine deep learning algorithms to quickly analyze and process monitoring data. This system compensates for the shortcomings of traditional monitoring methods, significantly improves monitoring efficiency and accuracy, and reduces the potential impact of environmental deterioration on equipment operation through early warning mechanisms, providing an innovative solution for efficient operation and extended service life of equipment. SUMMARY

[0004] In view of the deficiencies of the prior art, the application provides a new energy storage remote monitoring system based on cloud computing, which solves the problems in the background art.

[0005] To achieve the above purpose, the application realizes the following technical scheme: a new energy storage remote monitoring system based on cloud computing, comprising a remote monitoring module, an environmental early warning module, a data acquisition module, a health assessment module and a control module.

[0006] The remote monitoring module is used for monitoring the environmental state where the energy storage device is located, and obtaining relevant environmental state information data after data processing;

[0007] The environmental early warning module is used for analyzing the relevant environmental state information data, constructing an environmental influence coefficient Xhj after dimensionless processing and combining with machine deep learning algorithm, and issuing a health analysis instruction if the environmental influence coefficient Xhj exceeds a preset influence threshold Y;

[0008] The data acquisition module is used for monitoring the instantaneous current state of the energy storage device in the charging and discharging operation process in real time after receiving the health analysis instruction, obtaining instantaneous current information data, collecting relevant operation state information data after completing a complete charging and discharging operation cycle, and constructing relevant internal environmental state information according to the long-time charging and discharging use condition and relevant historical data of the energy storage device;

[0009] The health evaluation module is used for analyzing the instantaneous current information data, the relevant operation state information data and the relevant internal environmental state information, and associating with the environmental influence coefficient Xhj to construct a health comprehensive evaluation index Zjk;

[0010] The control module is used for comparing the health comprehensive evaluation index Zjk with a preset evaluation threshold P, generating a corresponding grade control instruction and executing.

[0011] Preferably, the remote monitoring module includes a deployment unit, an environmental monitoring unit and a preprocessing unit;

[0012] The deployment unit is used for deploying multiple sets of external environment monitoring devices in the external environment where the energy storage device is located according to the remote monitoring requirement of the energy storage device, and embedding multiple sets of micro sensors in the energy storage device, and combining with wireless network communication technology to wirelessly connect the multiple sets of external environment monitoring devices, the multiple sets of micro sensors and a cloud computing data platform; the multiple sets of external environment monitoring devices include temperature sensors, humidity sensors and electromagnetic interference sensors, and the multiple sets of micro sensors include high-precision current sensors, four-pole conductivity sensors, embedded micro mass sensors, micro resistance sensors and power meters; wherein the cloud computing data platform is used for storing the relevant data after preprocessing;

[0013] The environmental monitoring unit is used for monitoring the environmental state where the energy storage device is located in real time by using the multiple sets of remotely deployed external environment monitoring devices, and obtaining relevant environmental state information data, wherein the relevant environmental state information data includes temperature Twe, humidity Shd and electromagnetic intensity Edc at each monitoring time point in a monitoring period, and the length of the monitoring period is one day;

[0014] The preprocessing unit is used for data preprocessing of relevant data received by the cloud computing data platform, and the preprocessing includes removing noise, filling missing values and data smoothing operation, wherein the method for filling missing values includes mean filling, median filling, interpolation filling and regression filling, and the processed relevant environmental state information data is sent to the cloud computing data platform for storage.

[0015] Preferably, the environmental early warning module comprises an environmental analysis unit and a warning unit.

[0016] The environmental analysis unit is used for feature extraction of the acquired relevant environmental state information data, and combines a statistical mean algorithm to acquire temperature mean humidity mean and electromagnetic intensity mean in a monitoring period, respectively. By associating the temperature Twe of each monitoring time point in the monitoring period with the temperature mean in the monitoring period, the temperature fluctuation coefficient Xtw is acquired, and the temperature fluctuation coefficient Xtw is acquired by the following formula:

[0017]

[0018] In the formula, Twe i represents the temperature of the i-th monitoring time point in the monitoring period, i=1, 2, 3,..., n, and n represents the number of monitoring time points in the monitoring period.

[0019] By associating the temperature fluctuation coefficient Xtw, humidity mean and electromagnetic intensity mean , after dimensionless processing, and combining a machine deep learning algorithm, the environmental influence coefficient Xhj in the monitoring period is fitted and acquired, and the environmental influence coefficient Xhj is acquired by the following formula:

[0020]

[0021] In the formula, α1, α2 and α3 represent the weight values of the temperature fluctuation coefficient Xtw, humidity mean and electromagnetic intensity mean , respectively, and A represents the first correction constant.

[0022] Preferably, the warning unit is used for pre-setting an influence threshold Y, and comparing the acquired environmental influence coefficient Xhj with the preset influence threshold Y to determine whether to issue a health analysis instruction outward, and the specific content is as follows:

[0023] If the environmental influence coefficient Xhj > influence threshold Y, it is judged that the environmental state of the energy storage device in the current monitoring period has an influence on the normal operation of the energy storage device, at which time a health analysis instruction is sent out to the outside;

[0024] If the environmental influence coefficient Xhj ≤ influence threshold Y, it is judged that the environmental state of the energy storage device in the current monitoring period has no influence on the normal operation of the energy storage device, at which time no additional health analysis instruction is sent out, and the environmental state of the energy storage device is continuously monitored.

[0025] Preferably, the data acquisition module is used to receive the health analysis instruction, and then real-time monitoring of the instantaneous current state of the energy storage device in the charging and discharging operation process is performed to obtain instantaneous current information data, the instantaneous current information data including the instantaneous current value Iss in the charging and discharging operation time, based on the fact that the energy storage device completes a complete charging and discharging operation cycle, the charging and discharging operation state information thereof is collected to obtain relevant operation state information data, the relevant operation state information data including the resistance change rate Vbh and the charging and discharging efficiency Vcf of the energy storage device, according to the long-time charging and discharging use condition of the energy storage device, and in combination with the embedded multiple groups of micro sensors, relevant internal environmental state information data is obtained, the relevant internal environmental state information data including the electrolyte conductivity Vdd and the electrode residual mass Mdj.

[0026] Preferably, the health evaluation module includes a risk analysis state, a decay analysis unit and a health evaluation unit.

[0027] According to the state of charge before the charging and discharging operation of the energy storage device and the parameter content of the specification of the energy storage device, the initial state of charge proportion Soc0 and the nominal charge capacity Cr of the energy storage device are obtained respectively, and are associated with the instantaneous current information data to construct the current state of charge proportion Cdq, the current state of charge proportion Cdq being obtained through the following formula:

[0028]

[0029] In the formula, Soc0 represents the initial state of charge proportion, represents the integral of the instantaneous current Iss with respect to time t, the charge amount change in the charging and discharging operation time, and Cr represents the nominal charge capacity of the energy storage device;

[0030] The obtained current state of charge proportion Cdq is associated with the relevant operation state information data, and after dimensionless processing, the operation risk coefficient Xgf is fitted and obtained, the operation risk coefficient Xgf being obtained through the following formula:

[0031]

[0032] In the formula, Vbh represents the resistance change rate, Vcf represents the charge-discharge efficiency, β1, β2 and β3 represent the weight values of the current charge capacity proportion Cdq, the resistance change rate Vbh and the charge-discharge efficiency Vcf respectively, and B represents the second correction constant.

[0033] Preferably, the attenuation analysis unit is configured to obtain the electrolyte original conductivity Vdj and the electrode nominal initial mass Md according to the relevant factory parameter information of the energy storage device specification, and associate the relevant internal environment state information data, and obtain the attenuation degree coefficient Xsj after dimensionless processing, the attenuation degree coefficient Xsj is obtained by the following formula:

[0034]

[0035] In the formula, Vdd represents the electrolyte conductivity, Vd represents the electrolyte original conductivity, Mdj represents the electrode residual mass, Md represents the electrode nominal initial mass, and γ1 and γ2 represent the weight values.

[0036] Preferably, the health assessment unit is configured to input the environmental influence coefficient Xhj, the operation risk coefficient Xgf and the attenuation degree coefficient Xsj into the health assessment model, and obtain the health comprehensive assessment index Zjk after normalization processing, the health comprehensive assessment index Zjk is obtained by the following formula:

[0037]

[0038] In the formula, ω1, ω2 and ω3 represent the weight values of the environmental influence coefficient Xhj, the operation risk coefficient Xgf and the attenuation degree coefficient Xsj, and C represents the third correction constant.

[0039] Preferably, the control module comprises a comparison unit and an execution unit.

[0040] The comparison unit is configured to pre-set an evaluation threshold P, compare the evaluation threshold P with the health comprehensive assessment index Zjk, judge whether the current energy storage device is in a healthy state, and generate a corresponding level control instruction, and the specific content is as follows:

[0041] If the health comprehensive assessment index Zjk is less than the evaluation threshold P, that is, Zjk

[0042] If the health comprehensive assessment index Zjk is greater than or equal to the evaluation threshold P, that is, Zjk≥P, a secondary control instruction is obtained, indicating that the current energy storage device is in a healthy state, and there is no fault risk and life cycle attenuation, and a secondary control instruction is sent to the execution unit.

[0043] Preferably, the execution unit is used to execute corresponding means according to the received primary control instruction and secondary control instruction, and specifically execute the following contents:

[0044] When the primary control instruction is received, the execution content is to close the operation of the energy storage device through the remote control function in the cloud computing data platform, including stopping the charging and discharging operation and disconnecting the connection with the external load; in combination with the cloud computing data platform, relevant historical data and real-time acquisition information are called to locate the fault and attenuation reason of the energy storage device, the positioning result is pushed to the maintenance personnel in real time through the mode of email and short message, and a diagnosis report is generated according to the health comprehensive evaluation index Zjk, including suggesting to replace the worn components and optimize the charging and discharging strategy;

[0045] When the secondary control instruction is received, the execution content is to continue to monitor the environmental state and optimize the charging and discharging strategy of the energy storage device, specifically, in the discharging process, when the state of charge ratio is lower than 20%, the energy storage device is charged; in combination with the change trend of the health comprehensive evaluation index Zjk, the energy storage device operation health state report is generated regularly, and decision support is provided for subsequent energy storage device maintenance and optimization.

[0046] The application provides a new energy storage remote monitoring system based on cloud computing, which has the following beneficial effects:

[0047] (1) Through the cooperative work of the remote monitoring module, the environment early warning module, the data acquisition module, the health assessment module and the control module, the whole process closed-loop management of the energy storage equipment from environment monitoring to running state assessment, to health management and maintenance decision is realized, the intelligence and reliability of the system are improved, the remote monitoring module realizes real-time and accurate monitoring of the environment where the energy storage equipment is located through multiple groups of sensors deployed in different geographical locations, obtains temperature, humidity, electromagnetic interference environment information, and ensures the integrity and accuracy of the data after preprocessing to remove noise and fill missing values, the environment early warning module analyzes the influence of environment data on equipment operation through dimensionless processing and machine deep learning algorithm, generates environment influence coefficient Xhj, and compares with preset threshold Y, issues health analysis instruction in time in abnormal situation, perceives environmental risk in advance and responds quickly, the data acquisition module further combines instantaneous current data, historical operation records and internal environment information in the equipment running process, collects resistance change rate Vbh, charge and discharge efficiency Vcf, electrolyte conductivity Vdd and electrode residual mass Mdj indexes, ensures dynamic monitoring and analysis of running state, the health assessment module integrates related running state information data, related internal environment state information data and environment influence coefficient Xhj, constructs health comprehensive evaluation index Zjk, realizes quantitative evaluation of equipment health state, provides reliable basis for intelligent maintenance decision, finally, the control module compares health comprehensive evaluation index Zjk with evaluation threshold P, generates grade control instruction, including optimization operation strategy, emergency shutdown and notification of maintenance personnel for repair operation, the system covers the running environment and health state of the energy storage equipment, improves the running safety, reliability and service life of the energy storage equipment, reduces manual intervention and maintenance cost through intelligent management, and provides strong technical support for sustainable development of new energy storage field.

[0048] (2) Through the cooperative work of the remote monitoring module and the environment early warning module, the deficiencies of the traditional monitoring system in environment monitoring of distributed energy storage equipment are effectively made up, the remote monitoring module uses the remote distributed sensor network to monitor the temperature, humidity and electromagnetic interference environment state around the energy storage equipment in real time, ensures the comprehensiveness and timeliness of the environment state information, the received environment data are preprocessed, key features are extracted through dimensionless processing and deep learning algorithm, environment influence coefficient Xhj is generated, the environment early warning module further quantitatively evaluates the environment state using the environment influence coefficient Xhj, compares with the preset influence threshold Y, and issues health analysis instruction in time, this early warning mechanism not only can perceive the environmental risk affecting the equipment operation in advance, but also can dynamically adapt to the environmental needs of different energy storage equipment, avoids the hidden trouble caused by lack of environment data in the traditional system, through efficient environment monitoring and early warning function, the adaptability and running safety of the energy storage equipment in complex environment are improved.

[0049] (3) The health assessment module combines multi-dimensional data analysis of instantaneous current, operating state and internal environment information, significantly improving the accuracy of the evaluation of the operating health state of the energy storage device. The data acquisition module monitors the instantaneous current state of the device during the charging and discharging cycle, obtains instantaneous current information data, and extracts internal environment data in combination with long-term use records of the device. After these multi-dimensional data are processed by the health assessment module, a health comprehensive evaluation index Zjk is dynamically constructed. By comparing the health comprehensive evaluation index Zjk with a preset evaluation threshold P, the health level of the device can be accurately judged, and corresponding level control instructions are generated. The control module executes maintenance operations according to the instruction content, such as optimizing the charging and discharging strategy, notifying the operation and maintenance personnel to replace the key components and adjusting the operating environment. Compared with the traditional method of relying on manual inspection and static evaluation, the system realizes the scientificity and initiative of device maintenance through accurate data analysis and intelligent decision mechanism, reduces the maintenance cost, prolongs the service life of the device, and improves the efficiency and reliability of the overall management of the energy storage device. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 A block diagram of a new energy storage remote monitoring system based on cloud computing is provided. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0052] Embodiment 1

[0053] Please refer to Figure 1 The present application provides a new energy storage remote monitoring system based on cloud computing, which comprises a remote monitoring module, an environment early warning module, a data acquisition module, a health assessment module and a control module.

[0054] The remote monitoring module is used for monitoring the environment state of the remotely distributed energy storage device. After data processing, relevant environment state information data are obtained.

[0055] The environment early warning module is used for analyzing the relevant environment state information data. After dimensionless processing, an environment influence coefficient Xhj is constructed in combination with a machine deep learning algorithm. If the environment influence coefficient Xhj exceeds a preset influence threshold Y, a health analysis instruction is sent out.

[0056] The data acquisition module is used to monitor the instantaneous current state of the energy storage device in real time during the charging and discharging operation after receiving the health analysis instruction, to obtain the instantaneous current information data, to collect the related operation state information data after completing a complete charging and discharging operation cycle, and to construct the related internal environment state information according to the long-time charging and discharging use condition and the related historical data of the energy storage device.

[0057] The health evaluation module is used to analyze the instantaneous current information data, the related operation state information data and the related internal environment state information, and to associate them with the environmental influence coefficient Xhj to construct the health comprehensive evaluation index Zjk.

[0058] The control module is used to compare the health comprehensive evaluation index Zjk with the preset evaluation threshold P, to generate the corresponding grade control instruction and to execute it.

[0059] In this embodiment, through the implementation of the new energy storage remote monitoring system based on cloud computing, the problems of poor real-time performance, limited monitoring range and insufficient data analysis capability of the traditional monitoring system are effectively solved, and the operation safety, efficiency and service life of the energy storage device are significantly improved. The combination of the remote monitoring module and the environmental early warning module not only realizes the dynamic monitoring of the environmental state of the distributed energy storage device, but also quickly identifies potential risks through dimensionless processing and deep learning algorithms, issues health analysis instructions in advance, and makes up for the defect that the traditional system cannot timely perceive environmental changes. When the data acquisition module obtains the operation state and internal environment information, it combines historical data to ensure the comprehensiveness and long-term analysis capability of the monitoring, effectively avoiding evaluation errors caused by incomplete and delayed data. The health evaluation module quantitatively associates the device operation state, internal environment information and external environmental influence by constructing the health comprehensive evaluation index Zjk, realizes accurate evaluation of the device health condition, and provides a scientific basis for subsequent maintenance and optimization. The control module further solves the problems of frequent manual intervention and decision lag in the traditional system through an intelligent grade instruction generation mechanism, can adjust the operation strategy, maintenance planning and emergency shutdown in real time according to the actual device state, and ensures the stability and reliability of the energy storage device in complex environments. Overall, through the introduction of intelligentization and deep analysis capability, this system realizes closed-loop management from data acquisition to evaluation decision, improves monitoring efficiency and prediction accuracy, provides strong support for stable operation and efficient management of new energy storage devices, reduces maintenance cost and reduces fault risk, fully embodies the application value in the field of new energy storage.

[0060] Embodiment 2

[0061] Please refer to Figure 1 , specifically: the remote monitoring module includes a deployment unit, an environment monitoring unit and a preprocessing unit;

[0062] The deployment unit is used to deploy a plurality of external environment monitoring devices according to the remote monitoring needs of the energy storage device, and to embed a plurality of micro sensors inside the energy storage device, and to wirelessly connect the plurality of external environment monitoring devices, the plurality of micro sensors and a cloud computing data platform by using wireless network communication technology; the plurality of external environment monitoring devices include temperature sensors, humidity sensors and electromagnetic interference sensors, and the plurality of micro sensors include high-precision current sensors, four-pole conductivity sensors, embedded micro mass sensors, micro resistance sensors and power meters; wherein the cloud computing data platform is used to store the relevant data after preprocessing;

[0063] The environment monitoring unit is used to monitor the environment state of the energy storage device in real time by using the plurality of externally deployed environment monitoring devices, and to obtain relevant environmental state information data, including temperature Twe, humidity Shd and electromagnetic intensity Edc at each monitoring time point in the monitoring period, wherein the length of the monitoring period is one day.

[0064] The preprocessing unit is used to preprocess the relevant data received by the cloud computing data platform, including noise removal, missing value filling and data smoothing operation, wherein the missing value filling method includes mean filling, median filling, interpolation filling and regression filling, and the processed relevant environmental state information data is sent to the cloud computing data platform for storage.

[0065] In this embodiment, through the cooperative work of the deployment unit, the environment monitoring unit and the preprocessing unit of the remote monitoring module, the problems of monitoring blind area, incomplete data and insufficient real-time in traditional new energy storage monitoring are effectively solved, the accuracy and timeliness of monitoring are improved, the deployment unit deploys temperature, humidity and electromagnetic interference sensors in the external environment of the storage equipment, and embeds high-precision four-pole conductivity sensors, micro mass sensors, resistance sensors and power meters inside, realizes the monitoring from the external environment to the internal state, combines with wireless network communication technology, the monitoring data can be transmitted to the cloud computing data platform in real time for processing, solves the limitations of traditional manual inspection difficult to cover remote equipment and insufficient multi-dimensional data integration capability, the environment monitoring unit dynamically collects the environmental state of temperature, humidity and electromagnetic intensity day and night, ensures the comprehensiveness and time sequence continuity of the environmental information, provides a reliable data basis for subsequent analysis, at the same time, the preprocessing unit improves the accuracy and consistency of data through denoising, missing value filling and data smoothing operation, avoids the deviation of evaluation result caused by original data quality problem, in general, the remote monitoring module makes up for the defects of traditional system in environmental state monitoring and data processing with intelligent, multi-dimensional and high-time-efficiency monitoring means, supports more accurate evaluation of the running state and health condition of the storage equipment, and provides a solid guarantee for the stability and efficiency of the new energy storage system.

[0066] Embodiment 3

[0067] Please refer to Figure 1 , specifically: the environment early warning module includes an environment analysis unit and a warning unit;

[0068] The environment analysis unit is used for feature extraction on the acquired related environmental state information data, and combines with statistical mean value algorithm to respectively acquire temperature mean value humidity mean value and electromagnetic intensity mean value By associating the temperature Twe of each monitoring time point in the monitoring period with the temperature mean value in the monitoring period, the temperature fluctuation coefficient Xtw is obtained, and the temperature fluctuation coefficient Xtw is obtained by the following formula:

[0069]

[0070] In the formula, Twe i represents the temperature of the i-th monitoring time point in the monitoring period, i=1, 2, 3,..., n, and n represents the number of monitoring time points in the monitoring period;

[0071] By associating the temperature fluctuation coefficient Xtw, the humidity mean value and the electromagnetic intensity mean value The correlation is associated, is processed after being dimensionless, and is fitted to obtain the environmental influence coefficient Xhj in the monitoring period by combining a machine deep learning algorithm, the environmental influence coefficient Xhj is obtained by the following formula:

[0072]

[0073] In the formula, α1, α2 and α3 respectively represent weight values of the temperature fluctuation coefficient Xtw, the humidity mean value and the electromagnetic intensity mean value , and A represents a first correction constant.

[0074] Specifically, the early warning unit is used to pre-set an influence threshold Y, and compare the obtained environmental influence coefficient Xhj with the pre-set influence threshold Y to determine whether to issue a health analysis instruction outward, and the specific content is as follows:

[0075] If the environmental influence coefficient Xhj is greater than the influence threshold Y, it is judged that the environmental state of the energy storage device in the current monitoring period has an influence on the normal operation of the energy storage device, and a health analysis instruction is issued outward at this time.

[0076] If the environmental influence coefficient Xhj is less than or equal to the influence threshold Y, it is judged that the environmental state of the energy storage device in the current monitoring period has no influence on the normal operation of the energy storage device, and no additional health analysis instruction is issued at this time, and the environmental state of the energy storage device is continuously monitored.

[0077] In the embodiment, the close cooperation of the environmental analysis unit and the early warning unit provides the energy storage device with intelligent and dynamic environmental monitoring and early warning capabilities, effectively making up for the deficiencies of traditional monitoring systems in environmental state evaluation and abnormal response, the environmental analysis unit extracts core features from the monitoring data by combining statistical algorithms and dimensionless processing methods, including the temperature fluctuation coefficient Xtw, the humidity mean value and the electromagnetic intensity mean value The indicators not only reflect the real-time state of the environmental parameters, but also generate a quantitative indicator of environmental impact, the environmental impact coefficient Xhj, through the weight combination algorithm and deep learning fitting. This comprehensive coefficient simplifies the complex multi-dimensional environmental information into an intuitive and usable evaluation result, improving the efficiency and accuracy of environmental analysis. By setting an impact threshold Y, the early warning unit can quickly determine whether the environmental impact coefficient Xhj exceeds the safe range of device operation. If it exceeds the impact threshold Y, a health analysis instruction is immediately issued to start further monitoring and analysis functions of the system. If it does not exceed, it remains in normal monitoring to ensure continuous operation of the system under low energy consumption conditions. Compared to traditional monitoring systems that rely solely on single parameter monitoring, this module accurately captures environmental changes that affect energy storage devices through multi-parameter correlation analysis, especially in complex environments, including dramatic fluctuations in temperature and humidity and strong electromagnetic interference conditions. Traditional monitoring methods often struggle to respond in a timely manner due to insufficient data processing capabilities. However, this module, with its deep learning algorithm and multi-parameter dynamic correlation analysis, can shorten the warning time and reduce the false alarm rate. Overall, the introduction of the environmental early warning module not only improves the adaptability of energy storage devices in complex environments, but also effectively reduces the risk of device failure caused by environmental abnormalities, providing a key guarantee for the safety, stability, and efficient operation of the system.

[0078] Embodiment 4

[0079] For details, please refer to Figure 1 Specifically, the data acquisition module is used to receive the health analysis instruction and monitor the instantaneous current state of the energy storage device in real time during the charging and discharging process to obtain instantaneous current information data. The instantaneous current information data includes the instantaneous current value Iss during the charging and discharging operation time. Based on the completion of a complete charging and discharging cycle of the energy storage device, the charging and discharging state information is collected to obtain related operation state information data. The related operation state information data includes the resistance change rate Vbh of the energy storage device and the charging and discharging efficiency Vcf. According to the long-term charging and discharging use of the energy storage device, and in combination with the embedded multiple micro sensors, the related internal environment state information data is obtained, including the electrolyte conductivity Vdd and the electrode residual mass Mdj.

[0080] It should be noted that the instantaneous current value Iss is collected in real time by a high-precision current sensor installed in the energy storage device circuit, such as a Hall effect current sensor or a shunt resistance current sensor. The significance is that it provides key data for dynamically monitoring the operation state of the energy storage device, which can be used to calculate the state of charge, assess the charging and discharging efficiency, detect overcurrent abnormalities, and optimize the control strategy of the energy storage device.

[0081] The resistance change rate Vbh is obtained by an embedded micro-resistance sensor, which measures the impedance response of the energy storage device at different frequencies by applying an alternating current signal, analyzes the equivalent circuit model, and calculates the dynamic change of the internal resistance. The DC internal resistance measurement module uses the voltage difference generated by the instantaneous current change to obtain the change rate by calculating the current internal resistance and comparing it with the initial value, which can reflect the efficiency and attenuation degree of the internal electrochemical reaction of the energy storage device, and is of great significance for evaluating the health status of the device and optimizing the charging and discharging strategy. The charge-discharge efficiency Vcf is obtained by embedded calculation, which is calculated by recording the ratio of charging input energy and discharging output energy. The charge-discharge efficiency Vcf reflects the energy conversion ability of the energy storage device, which can be used to judge the energy loss and system efficiency of the device, and provides key data for operation optimization.

[0082] The electrolyte conductivity Vdd is obtained by embedding a four-pole conductivity sensor in the device, which measures the relationship between current and voltage by applying an alternating current in the electrolyte, calculates the resistance of the electrolyte and converts it into conductivity. The electrolyte conductivity Vdd can directly reflect the concentration and conductivity of ions in the electrolyte, evaluate its chemical activity and health status, and is an important indicator for judging the internal aging and degradation of the device. The electrode residual mass Mdj is obtained by an embedded micro-mass sensor, which is embedded in the electrode structure and directly measures the residual mass of the electrode. The residual mass of the electrode material in the energy storage device after a certain number of charge and discharge cycles reflects the actual working capacity and consumption state of the electrode material, and is an important indicator for evaluating the health status and remaining life of the battery.

[0083] In this embodiment, by accurately collecting the operating state and internal environment information of the energy storage device, the shortcomings of traditional monitoring methods in data integrity, dynamics and depth analysis ability are made up, and a reliable foundation is provided for device health assessment. First, after receiving the health analysis instruction, the instantaneous current value Iss during the charging and discharging process of the device is monitored in real time, and the dynamic change during the operation process is captured. This real-time effectively solves the problem of lag in traditional system for instantaneous state monitoring, which helps to find early signs of operation abnormalities. Secondly, by collecting the resistance change rate Vbh and the charging and discharging efficiency Vcf after completing a complete charging and discharging cycle, the dynamic change trend of device performance can be quantified, thereby providing accurate data support for long-term operation analysis of the energy storage device. At the same time, combined with the embedded multiple micro sensors, the related internal environment state information data of the device is further obtained, including the electrolyte conductivity Vdd and the electrode residual mass Mdj. The introduction of these parameters solves the limitations of traditional methods in collecting internal environment data of the device, and can more accurately assess the health status and degradation level of the battery internal. Through multi-level data collection and analysis, not only the comprehensiveness of device state monitoring is improved, but also a solid foundation is laid for subsequent health assessment and optimization control, effectively avoiding the problems of device failure and performance loss caused by data loss or misjudgment, providing protection for the efficient operation and long-term reliability of the energy storage device.

[0084] Embodiment 5

[0085] Please refer to Figure 1 , specifically: the health assessment module includes risk analysis state, attenuation analysis unit and health assessment unit;

[0086] According to the state of charge before the charging and discharging operation of the energy storage device and the parameter content of the specification book of the energy storage device, the initial charge capacity ratio Soc0 and the nominal charge capacity Cr of the energy storage device are obtained respectively, and are associated with the instantaneous current information data, to construct the current charge capacity ratio Cdq. The current charge capacity ratio Cdq is obtained by the following formula:

[0087]

[0088] In the formula, Soc0 represents the initial charge capacity ratio, represents the integral of the instantaneous current Iss with respect to time t, the charge capacity change in the charging and discharging operation time, and Cr represents the nominal charge capacity of the energy storage device;

[0089] The obtained current charge capacity ratio Cdq is associated with the related operating state information data, and after dimensionless processing, the operating risk coefficient Xgf is fitted and obtained. The operating risk coefficient Xgf is obtained by the following formula:

[0090]

[0091] In the formula, Vbh represents the resistance change rate, Vcf represents the charge-discharge efficiency, β1, β2 and β3 respectively represent the weight values of the current charge capacity proportion Cdq, the resistance change rate Vbh and the charge-discharge efficiency Vcf, and B represents the second correction constant.

[0092] The energy storage device specification is a technical document of the energy storage device, which describes the key performance parameters, technical requirements and operating conditions of the device, wherein the nominal charge capacity, electrolyte conductivity, electrode initial mass and performance information of the device in special environment are contained. The content of the energy storage device specification provides a scientific basis for the operation and optimization of the energy storage device, and is also an important technical document for evaluating the quality and performance of the device.

[0093] It should be noted that the current charge capacity proportion Cdq directly indicates the remaining energy level of the energy storage device. If it is lower than a certain proportion for a long time, it will cause deep discharge of the battery and increase the risk of aging. The resistance change rate Vbh represents the change of the internal electrochemical impedance of the device. High impedance indicates that ion transmission is blocked or the performance of the electrolyte is decreased, which may cause overheating and low efficiency. The charge-discharge efficiency Vcf measures the energy conversion capacity of the device. Efficiency decline is often accompanied by increased internal resistance and active material loss, which will further exacerbate performance degradation. By correlating and analyzing the three parameters, the operation risk coefficient Xgf can comprehensively identify the potential problems of the energy storage device under specific load and environmental conditions, provide a scientific basis for real-time optimization and risk warning of the device, and prevent efficiency reduction, thermal runaway and premature failure risks during operation.

[0094] In this embodiment, through the synergistic effect of the risk analysis state, the attenuation analysis unit and the health assessment unit, a precise and efficient health state evaluation mechanism is provided for the energy storage device, which makes up for the shortcomings of traditional monitoring systems in dynamic risk assessment and long-term attenuation analysis. First, the risk analysis state can calculate the current charge capacity ratio Cdq based on instantaneous current information data, and dynamically reflect the energy change in the charging and discharging process of the device using the charge capacity change formula. This function improves the real-time monitoring capability of the system for the state of the energy storage device, effectively solving the problem of lagging behind in identifying operational risks in traditional systems. Second, by correlating the current charge capacity ratio Cdq with the resistance change rate Vbh and the charging and discharging efficiency Vcf, and using dimensionless processing and weighted fitting algorithm, the operational risk coefficient Xgf is constructed, which comprehensively evaluates the potential risks of the energy storage device in operation. Not only can it quantify the real-time state of device performance degradation, but also provide risk trend prediction of the device under specific load and environmental conditions. In addition, through the weight adjustment mechanism, the different influence weights of charge capacity, resistance change and charging and discharging efficiency on the health state of the device are reflected, enhancing the pertinence and reliability of the evaluation. This multi-dimensional and multi-parameter dynamic evaluation method not only solves the problem that traditional methods cannot effectively correlate operational state and health condition, but also provides more intelligent decision-making basis, reducing errors caused by human judgment. The introduction of the health assessment module reduces the probability of device failure due to untimely detection of operational risks and degradation, and provides strong data support and scientific basis for the long-term operation and maintenance optimization of the energy storage device.

[0095] Embodiment 6

[0096] For details, please refer to Figure 1 Specifically, the attenuation analysis unit is configured to obtain the original electrolyte conductivity Vdj and the electrode nominal initial mass Md according to the relevant factory parameter information of the energy storage device specification book, and associate them with relevant internal environment state information data. After dimensionless processing, the attenuation degree coefficient Xsj is obtained. The attenuation degree coefficient Xsj is obtained by the following formula:

[0097]

[0098] In the formula, Vdd represents the electrolyte conductivity, Vd represents the original electrolyte conductivity, Mdj represents the electrode remaining mass, Md represents the electrode nominal initial mass, and γ1 and γ2 represent weight values.

[0099] Specifically, the health assessment unit is configured to input the environmental influence coefficient Xhj, the operational risk coefficient Xgf and the attenuation degree coefficient Xsj into the health assessment model, and after normalization processing, the health comprehensive evaluation index Zjk is fitted and constructed. The health comprehensive evaluation index Zjk is obtained by the following formula:

[0100]

[0101] In the formula, ω1, ω2 and ω3 respectively represent the weight values of the environmental influence coefficient Xhj, the operation risk coefficient Xgf and the attenuation degree coefficient Xsj, and C represents the third correction constant.

[0102] In the embodiment, the combination of the attenuation analysis unit and the health assessment unit provides a highly accurate and scientific analysis means for the comprehensive assessment of the health state of the energy storage device, effectively solves the deficiencies of the traditional system in attenuation state monitoring and health assessment, and through the analysis of the internal environment state information, the attenuation analysis unit uses dimensionless processing to construct the attenuation degree coefficient Xsj, reflecting the degradation degree of the internal material performance of the battery. The formula compares the change amount of the key indicators with the initial state, and adjusts through the weight value, so that it can more accurately capture the influence of different parameters on attenuation. The health assessment unit further inputs the environmental influence coefficient Xhj, the operation risk coefficient Xgf and the attenuation degree coefficient Xsj into the health assessment model, generates the health comprehensive assessment index Zjk through normalization processing and weighted fitting. This index is based on the integrated analysis of multi-dimensional parameters, provides the quantitative results of the current health state of the device, and through the weight configuration, accurately reflects the different contributions of environment, operation risk and material attenuation to the overall health of the device. Not only improves the comprehensiveness and accuracy of the assessment, but also realizes the effective prediction of the long-term operation trend of the energy storage device, makes up for the defects of the traditional system in single-dimensional evaluation and complex correlation analysis. According to the health comprehensive assessment index Zjk, it can quickly judge whether the device is in the best operating state or there is potential risk, and provide scientific guidance for subsequent maintenance optimization, reduce the device failure rate, prolong the service life, and provide important support for the efficient and reliable operation of the energy storage system.

[0103] Embodiment 7

[0104] Please refer to Figure 1 , specifically: the control module includes a comparison unit and an execution unit;

[0105] The comparison unit is used to pre-set an evaluation threshold P, compare it with the health comprehensive assessment index Zjk to judge whether the current energy storage device is in a healthy state, and generate the corresponding level control instruction. The specific content is as follows:

[0106] If the health comprehensive assessment index Zjk is less than the evaluation threshold P, that is, Zjk < P, the first level control instruction is obtained, indicating that the current energy storage device is not in a healthy state, indicating that there is a risk of failure and life cycle attenuation. At this time, the first level control instruction is sent to the execution unit;

[0107] If the health comprehensive evaluation index Zjk is greater than or equal to the evaluation threshold P, that is, Zjk≥P, a secondary control instruction is obtained, indicating that the current energy storage device is in a healthy state, indicating no fault risk and life cycle attenuation, and a secondary control instruction is issued to the execution unit.

[0108] Specifically, the execution unit is configured to execute corresponding means according to the received primary control instruction and secondary control instruction, and specifically execute the following contents:

[0109] When receiving the primary control instruction, the execution content is to close the operation of the energy storage device through the remote control function in the cloud computing data platform, including stopping the charging and discharging operation and disconnecting the connection with the external load; in combination with the cloud computing data platform, relevant historical data and real-time collected information are called to locate the fault and attenuation reason of the energy storage device, the positioning result is pushed to the maintenance personnel in real time through the email and short message mode, and a diagnosis report is generated according to the health comprehensive evaluation index Zjk, including the suggestion of replacing the worn components and optimizing the charging and discharging strategy;

[0110] When receiving the secondary control instruction, the execution content is to continue to monitor the environmental state and optimize the charging and discharging strategy of the energy storage device, specifically, in the discharging process, when the state of charge ratio is less than 20%, the energy storage is charged; in combination with the change trend of the health comprehensive evaluation index Zjk, the energy storage device operation health state report is generated regularly, providing decision support for subsequent energy storage device maintenance and optimization.

[0111] In this embodiment, through the coordinated operation of the comparison unit and the execution unit, an efficient and intelligent solution is provided for the operation control and fault maintenance of the energy storage device, effectively solving the problems of lagging fault response, relying on manual judgment for maintenance decision and insufficient operation optimization of traditional energy storage systems; the comparison unit compares the health comprehensive evaluation index Zjk with the preset evaluation threshold P in real time, accurately judges whether the device is in a healthy state, and generates corresponding first or second control instructions; the triggering mechanism of the first control instruction ensures that the system can quickly stop the device operation remotely through the cloud computing platform when detecting device fault risk and significant life cycle degradation, preventing further damage, and at the same time, combining historical data and real-time acquisition information, the fault cause and degradation condition are accurately located, through the generation of a diagnostic report, the maintenance personnel can be guided to replace the worn components and optimize the operation strategy in time, which significantly reduces the device failure rate and maintenance cost; on the other hand, when the second control instruction is generated, the system confirms that the energy storage device is in a healthy state, at this time the execution unit optimizes the charging and discharging strategy, including automatically triggering the charging operation when the low charge level, further improving the energy efficiency of the device, and at the same time, combining the change trend of the health comprehensive evaluation index Zjk, generating a health status report regularly, providing a scientific basis for subsequent device maintenance; this hierarchical control mechanism not only improves the fault response and maintenance efficiency, but also optimizes the device operation process through intelligent strategy adjustment, prolongs the service life of the device, improves the stability and reliability of the energy storage system, and at the same time, significantly reduces the possibility of manual intervention and misjudgment, laying a solid foundation for the long-term and efficient operation of the energy storage device.

[0112] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A cloud computing-based new energy storage remote monitoring system, characterized in that: The remote monitoring module is used for monitoring the environment state where the energy storage equipment is located, and obtaining relevant environment state information data after data processing. The environment warning module is used for analyzing the relevant environment state information data, and constructing an environment influence coefficient Xhj after dimensionless processing and combining with machine deep learning algorithm. The data acquisition module is used for receiving the health analysis instruction, and monitoring the instantaneous current state of the energy storage equipment in the charging and discharging operation process in real time to obtain instantaneous current information data. The health evaluation module is used for analyzing the instantaneous current information data, the relevant running state information data and the relevant internal environment state information, and associating with the environment influence coefficient Xhj to construct a health comprehensive evaluation index Zjk. The control module is used for comparing the health comprehensive evaluation index Zjk with a preset evaluation threshold P, generating a corresponding grade control instruction and executing. The remote monitoring module includes a deployment unit, an environment monitoring unit and a preprocessing unit.

2. The new energy storage remote monitoring system based on cloud computing according to claim 1, characterized in that: The deployment unit is used for deploying multiple sets of external environment monitoring devices in the external environment where the energy storage equipment is located according to the remote monitoring requirements of the energy storage equipment, and embedding multiple sets of micro sensors in the energy storage equipment. The environment monitoring unit is used for monitoring the environment state of the energy storage equipment in real time by using the remotely deployed multiple sets of external environment monitoring devices to obtain relevant environment state information data. The preprocessing unit is used for data preprocessing on the relevant data received by the cloud computing data platform, including removing noise, filling missing values and data smoothing operation. The environment warning module includes an environment analysis unit and a warning unit. 3.The new energy storage remote monitoring system based on cloud computing according to claim 2, characterized in that: The environment analysis unit is used for analyzing the relevant environment state information data, and constructing an environment influence coefficient Xhj after dimensionless processing and combining with machine deep learning algorithm. The warning unit is used for issuing a health analysis instruction to the outside if the environment influence coefficient Xhj exceeds a preset influence threshold Y. The environmental analysis unit is used to extract features from the acquired environmental status information data and, in conjunction with a statistical averaging algorithm, obtain the average temperature value within the monitoring period. Average humidity and mean electromagnetic intensity By comparing the temperature Twe at each monitoring time point within the monitoring period with the average temperature within the monitoring period... Correspondingly, the temperature fluctuation coefficient Xtw is obtained, which is obtained by the following formula: wherein Twe i T represents the temperature at the i-th monitoring time point in the monitoring period, i = 1, 2, 3,..., n, n represents the number of monitoring time points in the monitoring period; By correlating the temperature fluctuation coefficient Xtw, the humidity average value and the electromagnetic intensity average value After non-dimensional processing, combined with machine deep learning algorithm, the environmental influence coefficient Xhj in the monitoring period is fitted and obtained, and the environmental influence coefficient Xhj is obtained by the following formula: In the formula, α1, α2, and α3 represent the temperature fluctuation coefficient Xtw, the mean humidity, and the mean temperature fluctuation coefficient, respectively. and mean electromagnetic intensity The weight value is A, which is the first correction constant.

4. The new energy storage remote monitoring system based on cloud computing according to claim 3, characterized in that: The pre-warning unit is used for setting an influence threshold Y in advance, and comparing the acquired environmental influence coefficient Xhj with the preset influence threshold Y to determine whether to send a health analysis instruction outward, and the specific content is as follows: If the environmental influence coefficient Xhj is greater than the influence threshold Y, it is determined that the environmental state of the energy storage device in the current monitoring period has an influence on the normal operation of the energy storage device, and at this time a health analysis instruction is sent outward; If the environmental influence coefficient Xhj is less than or equal to the influence threshold Y, it is determined that the environmental state of the energy storage device in the current monitoring period has no influence on the normal operation of the energy storage device, and at this time no additional health analysis instruction is sent, and the environmental state of the energy storage device is continuously monitored.

5. The new energy storage remote monitoring system based on cloud computing according to claim 4, characterized in that: The data acquisition module is used for receiving the health analysis instruction, and monitoring the instantaneous current state of the energy storage device in the charging and discharging operation process in real time to acquire instantaneous current information data, the instantaneous current information data including an instantaneous current value Iss in the charging and discharging operation time, acquiring related operation state information data based on the charging and discharging operation state information of the energy storage device after completing a complete charging and discharging operation cycle, the related operation state information data including an energy storage device resistance change rate Vbh and a charging and discharging efficiency Vcf, acquiring related internal environmental state information data according to the long-time charging and discharging use condition of the energy storage device and in combination with the embedded multiple micro sensors, the related internal environmental state information data including an electrolyte conductivity Vdd and an electrode residual mass Mdj. 6.The new energy storage remote monitoring system based on cloud computing according to claim 5, wherein: The health evaluation module includes a risk analysis state, a decay analysis unit and a health evaluation unit; According to the state of charge before the charging and discharging operation of the energy storage device and the parameter content of the energy storage device specification book, an initial charge capacity proportion Soc0 and a nominal charge capacity Cr of the energy storage device are acquired respectively, and are associated with the instantaneous current information data to construct a current charge capacity proportion Cdq, the current charge capacity proportion Cdq being acquired through the following formula: In the formula, Soc0 represents the initial state of charge ratio, represents the integral of the instantaneous current Iss with respect to time t, the change in charge quantity within the charging and discharging operation time, and Cr represents the nominal charge capacity of the energy storage device. The acquired current charge capacity proportion Cdq is associated with the related operation state information data, and after dimensionless processing, an operation risk coefficient Xgf is fitted and acquired, the operation risk coefficient Xgf being acquired through the following formula: In the formula, Vbh represents the resistance change rate, Vcf represents the charging and discharging efficiency, β1, β2 and β3 represent weight values of the current charge capacity proportion Cdq, the resistance change rate Vbh and the charging and discharging efficiency Vcf respectively, and B represents the second correction constant.

7. The new energy storage remote monitoring system based on cloud computing according to claim 6, characterized in that: The decay analysis unit is used for acquiring an electrolyte original conductivity Vdj and an electrode nominal initial mass Md according to the related factory parameter information of the energy storage device specification book, and associating the electrolyte original conductivity Vdj and the electrode nominal initial mass Md with the related internal environmental state information data, and after dimensionless processing, a decay degree coefficient Xsj is acquired, the decay degree coefficient Xsj being acquired through the following formula: In the formula, Vdd represents the electrolyte conductivity, Vd represents the electrolyte original conductivity, Mdj represents the electrode residual mass, Md represents the electrode nominal initial mass, and γ1 and γ2 represent weight values. 8.The new energy storage remote monitoring system based on cloud computing according to claim 6, wherein: The health assessment unit is configured to input the environmental influence coefficient Xhj, the operation risk coefficient Xgf and the attenuation degree coefficient Xsj into a health assessment model, perform normalization processing, and fit and construct a health comprehensive assessment index Zjk, which is obtained by the following formula: In the formula, ω1, ω2 and ω3 represent the weight values of the environmental influence coefficient Xhj, the operation risk coefficient Xgf and the attenuation degree coefficient Xsj respectively, and C represents a third correction constant. 9.The new energy storage remote monitoring system based on cloud computing according to claim 8, characterized in that: The control module comprises a comparison unit and an execution unit. The comparison unit is configured to pre-set an evaluation threshold P, compare the evaluation threshold P with the health comprehensive assessment index Zjk, determine whether the current energy storage device is in a healthy state, and generate a corresponding level control instruction, and the specific content is as follows: If the health comprehensive assessment index Zjk is less than the evaluation threshold P, that is, Zjk < P, a first-level control instruction is obtained, indicating that the current energy storage device is not in a healthy state, indicating that there is a risk of failure and life cycle attenuation, and the first-level control instruction is sent to the execution unit at this time; If the health comprehensive assessment index Zjk is greater than or equal to the evaluation threshold P, that is, Zjk ≥ P, a second-level control instruction is obtained, indicating that the current energy storage device is in a healthy state, indicating that there is no risk of failure and life cycle attenuation, and the second-level control instruction is sent to the execution unit at this time. 10.The new energy storage remote monitoring system based on cloud computing according to claim 9, wherein: The execution unit is configured to execute corresponding means according to the received first-level control instruction and second-level control instruction, and the specific execution content is as follows: When the first-level control instruction is received, the execution content is to close the operation of the energy storage device through the remote control function in the cloud computing data platform, including stopping the charging and discharging operation and disconnecting the connection with the external load; in combination with the cloud computing data platform, relevant historical data and real-time collected information are called to locate the failure and attenuation reasons of the energy storage device, the positioning result is pushed to the maintenance personnel in real time through the ways of email and short message, and a diagnosis report is generated according to the health comprehensive assessment index Zjk, including suggestions for replacing the worn components and optimizing the charging and discharging strategy; When the second-level control instruction is received, the execution content is to continue to monitor the environmental state and optimize the charging and discharging strategy of the energy storage device, specifically, in the discharging process, when the state of charge is less than 20%, the energy storage device is charged; in combination with the change trend of the health comprehensive assessment index Zjk, a health state report of the energy storage device is generated regularly, providing decision support for subsequent maintenance and optimization of the energy storage device.

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