Method and system for monitoring and early warning over-temperature risk of energy storage system

Abstract

The invention provides an energy storage system overtemperature risk monitoring and early warning method and system, and the method comprises the steps: collecting temperature time series data of preselected equipment in an energy storage system, extracting the highest temperature of the preselected equipment on that day as the characteristic temperature of the preselected equipment, and formulating the characteristic temperature time series data of the preselected equipment; performing curve fitting on the characteristic temperature time sequence data of the pre-selected equipment in a preset time window, and calculating temperature rising trend data of the pre-selected equipment; predicting the overtemperature time of the pre-selected equipment by using the time sequence prediction model; and according to the current temperature, the temperature rising trend and the overtemperature time of the pre-selected equipment, the pre-selected equipment is divided into a plurality of health states, and corresponding early warning and maintenance schemes are formulated. By predicting the overtemperature time of the equipment, an early warning signal can be sent in advance, damage or safety accidents caused by overtemperature of the equipment are avoided, a targeted early warning and maintenance scheme is formulated according to the health state of the equipment, and the maintenance efficiency and reliability of the equipment are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of operation and maintenance control of battery energy storage power stations, and specifically relates to a monitoring and early warning method and system for over-temperature risk of an energy storage system. Background Art

[0002] At present, with the advancement of energy-saving goals, the proportion of new energy power continues to increase, and electrochemical energy storage plays an increasingly important role in balancing power supply and demand and optimizing energy structure. The electrochemical energy storage market has shown a significant growth trend in the near future. It is expected that the electrochemical energy storage market will face greater challenges in the near future.

[0003] Although the technology of electrochemical energy storage has made significant progress, electrochemical energy storage still faces many technical risks, especially when it is applied on a large scale. The core equipment of the electrochemical energy storage system often faces over-temperature failures. In the prior art, the over-temperature alarm of the equipment itself generally adopts a fixed value alarm strategy, that is, when the equipment exceeds the preset temperature threshold, the system sends an alarm signal. However, for equipment with a significant temperature rise trend and an over-temperature risk, it is often impossible to give an early warning in a timely and accurate manner. The technical problems it brings not only affect the stability of the energy storage system, but may also cause safety accidents. Therefore, with the rapid expansion of the market scale, it is particularly important to effectively manage and control a large number of energy storage devices to ensure their safe operation. In summary, in the current electrochemical energy storage system, due to the use of a fixed value alarm strategy, when facing equipment with a significant temperature rise trend and an over-temperature risk, it is impossible to give an early warning in a timely and accurate manner, affecting the stability and safety factor of the energy storage system. Summary of the invention

[0004] The present invention provides a monitoring and early warning method and system for over-temperature risk of an energy storage system, aiming to solve the problem that in the current electrochemical energy storage system, due to the use of a fixed value alarm strategy, early warning cannot be given in a timely and accurate manner when facing equipment with a significant temperature rise trend and an over-temperature risk, thereby affecting the stability and safety factor of the energy storage system.

[0005] In order to achieve the above object, the present invention adopts the following technical scheme: The present invention provides a method for monitoring and early warning of over-temperature risk of an energy storage system, comprising the following steps: S1. When the energy storage system is working normally, collect the temperature time series data of the pre-selected equipment in the energy storage system, extract the highest temperature of the pre-selected equipment on the day as the characteristic temperature of the pre-selected equipment, and thereby formulate the characteristic temperature time series data of the pre-selected equipment; S2, presetting a time window, performing curve fitting on the characteristic temperature time series data of the preselected equipment within the predetermined time window to calculate the temperature rise trend data of the preselected equipment; S3. Based on the current temperature and temperature rise trend data of the pre-selected equipment, the over-temperature time of the pre-selected equipment is predicted using a time series prediction model; S4. According to the current temperature and temperature rising trend of the pre-selected equipment, the pre-selected equipment is divided into multiple health states based on the over-temperature time of the pre-selected equipment, and corresponding early warning and maintenance plans are formulated based on the different health states of the pre-selected equipment.

[0006] In some embodiments, in S1, for each preselected device, it is necessary to determine whether the energy storage system is in a normal working state; read the complete temperature sequence from the database according to the set measurement point code, and determine whether the preselected device has undergone a complete charging or discharging process to screen valid data for characteristic temperature extraction.

[0007] In some embodiments, in S2, a sliding window method is used to calculate the time series data of the characteristic temperature of the preselected equipment within a predetermined time window.

[0008] In some embodiments, in S2, a linear function is used to obtain the temperature increase trend of the device in curve fitting of characteristic temperature time series data of the preselected device.

[0009] Furthermore, in S2, the linear function used is the following formula (1): (1); in For in time The predicted temperature at The slope represents the temperature rise rate, is the intercept; Using the least squares method, we can solve the problem by minimizing the sum of squared errors using the following formula (2): and Values: (2); in, It's in time The actual temperature recorded at is the number of data points in the time window, It is the error between the fitted linear function input temperature and the actual temperature.

[0010] In some embodiments, in S3, the temperature rising trend is used in combination with the current temperature to predict the over-temperature time of the pre-selected equipment through a time series prediction model. When the predicted future temperature of the pre-selected equipment exceeds the preset temperature threshold, the remaining over-temperature time of the pre-selected equipment is calculated and a warning signal is issued.

[0011] Further, in S3, the time series prediction model includes a mapping function between the temperature and time of the pre-selected equipment.

[0012] In some embodiments, in S4, temperature thresholds and temperature rise thresholds are set according to the current temperature and temperature increase trend of the preselected equipment, and the preselected equipment is divided into several health states, including a short-term no over-temperature risk state, a recommended key monitoring state, and a recommended maintenance state as soon as possible.

[0013] Furthermore, in S4, the division of several health states is specifically as follows: Setting temperature thresholds and temperature rise threshold ; Based on the current temperature of the device and slope , classifying the pre-selected devices into the following health states: exist and In the case of, the state of the pre-selected equipment is a short-term state without over-temperature risk; exist and In the case of and When the status of the pre-selected equipment is the recommended key monitoring status; exist and In this case, the status of the pre-selected equipment is that it is recommended to arrange maintenance as soon as possible.

[0014] The present invention also provides a monitoring and early warning system for over-temperature risk of an energy storage system, the system comprising a data acquisition module, a temperature trend calculation module, a failure time prediction module and a health status classification module; wherein: The data acquisition module is used to collect the temperature time series data of the pre-selected equipment in the energy storage system and extract the highest temperature of the pre-selected equipment on the day; The temperature trend calculation module is used to perform curve fitting on the characteristic temperature time series data of the pre-selected equipment within a predetermined time window, and calculate the temperature rise trend data of the pre-selected equipment; The failure time prediction module is used to predict the over-temperature time of the pre-selected equipment based on the current temperature and temperature rise trend data of the pre-selected equipment using the time series prediction model; The health status classification module is used to classify the pre-selected equipment into different health states according to the current temperature and temperature rise trend of the pre-selected equipment and based on the over-temperature time of the pre-selected equipment, and to formulate corresponding early warning and maintenance plans based on the different health states of the pre-selected equipment.

[0015] Compared with the prior art, the monitoring and early warning method and system of over-temperature risk of energy storage system of the present invention has the following beneficial effects: The present invention provides a monitoring and early warning method for over-temperature risk of an energy storage system, comprising the following steps: S1, when the energy storage system is working normally, collecting temperature time series data of pre-selected equipment in the energy storage system, extracting the highest temperature of the pre-selected equipment on the day as the characteristic temperature of the pre-selected equipment, thereby formulating characteristic temperature time series data of the pre-selected equipment; S2, presetting a time window, performing curve fitting on the characteristic temperature time series data of the pre-selected equipment within the predetermined time window, so as to calculate the temperature increase trend data of the pre-selected equipment; S3, based on the current temperature and temperature increase trend data of the pre-selected equipment, using a time series prediction model to predict the over-temperature time of the pre-selected equipment; S4, according to the current temperature and temperature increase trend of the pre-selected equipment, dividing the pre-selected equipment into multiple health states based on the over-temperature time of the pre-selected equipment, and formulating corresponding early warning and maintenance plans based on the different health states of the pre-selected equipment. The present invention collects temperature time series data of pre-selected equipment when the energy storage system is working normally, extracts the highest temperature of the day, and then constructs characteristic temperature time series data; and performs curve fitting on the characteristic temperature time series data within a preset time window to calculate the temperature rise trend of the equipment; uses a time series prediction model to predict the over-temperature time of the equipment; then divides the equipment into different health states, and formulates corresponding early warning and maintenance plans. By predicting the over-temperature time of the equipment, the present invention can issue an early warning signal in advance to avoid damage to the equipment or safety accidents caused by over-temperature, formulate targeted early warning and maintenance plans according to the health status of the equipment, improve the maintenance efficiency and reliability of the equipment, use time series prediction for data analysis, and realize automatic and precise control and operation and maintenance of the energy storage system, which has better applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings in the specification are used to provide further understanding of the present invention and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0017] Figure 1 A method for monitoring and early warning of over-temperature risk of an energy storage system and a flow chart of the method in the system according to the present invention; Figure 2 A schematic diagram of the temperature trend of pre-selected equipment of an energy storage system in an embodiment of a monitoring and early warning method and system for over-temperature risk of an energy storage system of the present invention; Figure 3 It is a schematic diagram of temperature status assessment of pre-selected equipment of an energy storage system in an embodiment of a method and system for monitoring and early warning of over-temperature risk of an energy storage system of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0021] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0022] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0023] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0024] How to avoid equipment damage or safety accidents caused by overheating, formulate targeted early warning and maintenance plans based on the health status of the equipment, and thus improve the maintenance efficiency and reliability of the equipment.

[0025] like Figure 1 As shown, the present invention provides a monitoring and early warning method for over-temperature risk of an energy storage system, comprising the following steps: S1. When the energy storage system is working normally, collect the temperature time series data of the pre-selected equipment in the energy storage system, extract the highest temperature of the pre-selected equipment on the day as the characteristic temperature of the pre-selected equipment, and thereby formulate the characteristic temperature time series data of the pre-selected equipment; S2, presetting a time window, performing curve fitting on the characteristic temperature time series data of the preselected equipment within the predetermined time window to calculate the temperature rise trend data of the preselected equipment; S3. Based on the current temperature and temperature rise trend data of the pre-selected equipment, the over-temperature time of the pre-selected equipment is predicted using a time series prediction model; S4. According to the current temperature and temperature rising trend of the pre-selected equipment, the pre-selected equipment is divided into multiple health states based on the over-temperature time of the pre-selected equipment, and corresponding early warning and maintenance plans are formulated based on the different health states of the pre-selected equipment.

[0026] The present invention obtains the highest temperature time series data under the equipment operation state, calculates the equipment temperature rise trend, and calculates and warns the over-temperature time under the current trend. At the same time, by dividing the equipment health status according to the two characteristic dimensions of temperature and temperature rise trend, all pre-selected key equipment in the power station energy storage system can be inspected and maintained according to plan, thereby improving the reliability of the energy storage system.

[0027] The present invention first extracts the temperature time series data of the preselected equipment on a certain day. When the calculation conditions are met, the highest temperature of the day is extracted as the characteristic temperature of the equipment, and the characteristic temperature time series data of the preselected equipment is constructed. Secondly, a time window is defined, the temperature rise trend within the time window is calculated, and a time series prediction model is constructed to predict the over-temperature time of the equipment; finally, according to the current temperature and temperature rise trend of the preselected equipment, the temperature status of the preselected equipment is evaluated, and an operation and maintenance strategy is formulated. The method of the present invention abandons the existing conventional fixed value alarm strategy, extracts the characteristic temperature of the equipment, and performs temperature rise trend evaluation and time series prediction to evaluate the current status and predict the over-temperature time. The temperature early warning of key equipment in the energy storage system has certain applicability for the safe and reliable operation of the energy storage system power station.

[0028] The monitoring and early warning method of the present invention sets the conditions for data screening in the data collection stage: judging whether the energy storage system is in a normal working state, reading the complete temperature sequence from the database according to the set measuring point code, judging whether the pre-selected equipment has undergone a complete charging or discharging process, and screening valid data to extract characteristic temperature. The present invention ensures the accuracy of characteristic temperature extraction by screening valid data, improves the reliability of subsequent analysis and prediction, avoids early warning in abnormal working conditions or when data is incomplete, and reduces the false alarm rate.

[0029] In the trend calculation stage, the monitoring and early warning method of the present invention uses a sliding window method to calculate the time series data of the characteristic temperature within a predetermined time window. The sliding window method can dynamically reflect the changing trend of the equipment temperature, improve the accuracy of the analysis, and enable the method of the present invention to adapt to the data analysis requirements of different time scales. Furthermore, the present invention uses a linear function to curve fit the characteristic temperature time series data to obtain the temperature rise trend of the equipment. The linear function can quickly obtain the temperature rise trend of the equipment, which is convenient for operation and maintenance personnel to grasp the temperature change of the equipment.

[0030] Specifically, the linear function used in the present invention is the following formula (1): (1); in For in time The predicted temperature at The slope represents the temperature rise rate, is the intercept; Using the least squares method, we can solve the problem by minimizing the sum of squared errors using the following formula (2): and Values: (2); in, It's in time The actual temperature recorded at is the number of data points in the time window, It is the error between the fitted linear function input temperature and the actual temperature.

[0031] The least square method used in the present invention can ensure that the error between the fitted linear function and the actual data is minimized, improve the accuracy of the fitting, and facilitate the realization of automated calculation and data analysis.

[0032] The monitoring and early warning method of the present invention, in the over-temperature time prediction stage, uses the temperature rise trend and the current temperature to predict the over-temperature time of the equipment through a time series prediction model, and sends out an early warning signal when the predicted future temperature exceeds a preset temperature threshold. By predicting the over-temperature time and setting an early warning signal, the present invention can take measures in advance to avoid over-temperature of the equipment, allowing users at the operating end to set temperature thresholds according to actual needs, thereby improving the flexibility and accuracy of early warning.

[0033] In the monitoring and early warning method of the present invention, the time series prediction model includes a mapping function between the temperature and time of the equipment. The form of the mapping function provides a basis for the optimization of the model, and the model parameters can be adjusted according to actual data to improve the prediction accuracy.

[0034] The present invention also provides a monitoring and early warning system for over-temperature risk of an energy storage system, the system comprising a data acquisition module, a temperature trend calculation module, a failure time prediction module and a health status classification module; wherein: The data acquisition module is used to collect the temperature time series data of the pre-selected equipment in the energy storage system and extract the highest temperature of the pre-selected equipment on the day; The temperature trend calculation module is used to perform curve fitting on the characteristic temperature time series data of the pre-selected equipment within a predetermined time window, and calculate the temperature rise trend data of the pre-selected equipment; The failure time prediction module is used to predict the over-temperature time of the pre-selected equipment based on the current temperature and temperature rise trend data of the pre-selected equipment using the time series prediction model; The health status classification module is used to classify the pre-selected equipment into different health states according to the current temperature and temperature rise trend of the pre-selected equipment and based on the over-temperature time of the pre-selected equipment, and to formulate corresponding early warning and maintenance plans based on the different health states of the pre-selected equipment.

[0035] The present invention integrates the module into the system to realize automatic monitoring and early warning of over-temperature risk of the energy storage system. Through automatic monitoring and early warning, manual intervention is reduced, the efficiency of inspection and maintenance is improved, and failures are avoided to adapt to energy storage systems of different sizes and types.

[0036] The following is a detailed description of a monitoring and early warning method and system for over-temperature risk of an energy storage system according to the present invention through specific embodiments.

[0037] like Figure 1 and Figure 2 As shown, the dataset is collected; The main equipment of the energy storage system includes batteries, converters and transformers. For batteries, the main monitored temperatures include battery internal temperature, battery surface temperature, battery connector temperature, etc. For converters, the main monitored temperatures include IGBT temperature, etc. For transformers, the main monitored temperatures include transformer three-phase temperature, transformer core temperature, etc.

[0038] First, according to the corresponding measurement point code, read yesterday's (recorded as ) Complete temperature series; Determine whether the battery compartment corresponding to the transformer has undergone at least one complete charging or discharging process yesterday. If it has not undergone a complete charging or discharging process, the transformer skips this calculation; if the battery compartment has undergone at least one complete charging or discharging process, calculate the highest temperature of the three-phase winding of the transformer yesterday [TA n , TB n , TC n ] as the characteristic temperature.

[0039] Define a time window, set to n days. Then calculate the time series data of the three-phase winding of the transformer in the most recent time window. TA=[TA1, TA2, ..., TA n ], TB=[TB1, TB2,..., TB n ], TC=[TC1, TC2,…,TC n ].

[0040] like Figure 2 As shown, temperature trend calculation; 1) Taking the temperature of phase A of the transformer as an example, calculate the time x=[1, 2, ..., n] and TA n =[TA1,TA2,……,TA n ]’s mapping relationship TA= f (x). f (·) Using a linear function, i.e., TA = kx + b, the least squares principle is used to calculate the values ​​of the parameters k and b: ; 2) Repeat the steps in 1) to obtain the mapping functions of the transformer A phase temperature, B phase temperature, C phase temperature and time respectively. f (x).

[0041] Failure time prediction.

[0042] 1) Obtain the mapping function of transformer temperature and time by fitting, and calculate the fault time. Taking the temperature of transformer A phase as an example, if the preset temperature value of the equipment is TAmax, the mapping function is used f (x) Temperature prediction, when f(x)>TAmax, the temperature setting is triggered. There are xn days left before the temperature fault alarm of transformer phase A. If within the predicted time window, when f(x)≤TAmax, there is no short-term over-temperature risk of transformer phase A. Where x=n+1, n+2, …, m. m is the preset prediction time window, such as m=30.

[0043] 2) If f (x) is a linear function. When k>0, the remaining time to the transformer A phase temperature fault alarm is (TAmax-TAn) / k days. When k≤0, there is no short-term over-temperature risk for transformer A phase.

[0044] 3) Repeat step 1) or 2) to calculate the remaining time of the temperature fault of phase A, phase B, and phase C of the transformer in turn.

[0045] like Figure 3 As shown, the health status is divided; 1) Calculate the current temperatures of phase A, phase B, and phase C of all transformers in the energy storage power station in turn.

[0046] 2) Calculate the temperature rise trend of phase A, phase B, and phase C of all transformers in the energy storage power station in sequence. f (x) When a linear function is used, the temperature rise trend is the slope k of the straight line.

[0047] 3) Divide the temperature threshold and temperature rise threshold into and .

[0048] 4) Classify the health status, taking transformer phase A as an example: ; 5) Develop a detection strategy. Based on the above classification criteria, transformer windings in good condition do not need to be repaired or maintained in the short term; transformer windings in good condition are recommended to be monitored to prevent failures; transformer windings in poor condition are recommended to be repaired as soon as possible. This will enable monitoring and operation of the energy storage system and improve its stability.

[0049] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any ordinary technician in the industry can smoothly implement the present invention as shown in the specification and described above. However, any equivalent changes, modifications and evolutions made by technicians familiar with the profession without departing from the scope of the technical solution of the present invention using the technical content disclosed above are all equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the technical solution of the present invention.

Claims

1. A method for monitoring and early warning of over-temperature risk of an energy storage system, characterized in that: The steps include: S1. When the energy storage system is working normally, collect the temperature time series data of the pre-selected equipment in the energy storage system, extract the highest temperature of the pre-selected equipment on the day as the characteristic temperature of the pre-selected equipment, and thereby formulate the characteristic temperature time series data of the pre-selected equipment; S2, presetting a time window, performing curve fitting on the characteristic temperature time series data of the preselected equipment within the predetermined time window to calculate the temperature rise trend data of the preselected equipment; S3. Based on the current temperature and temperature rise trend data of the pre-selected equipment, the over-temperature time of the pre-selected equipment is predicted using a time series prediction model; S4. According to the current temperature and temperature rising trend of the pre-selected equipment, the pre-selected equipment is divided into multiple health states based on the over-temperature time of the pre-selected equipment, and corresponding early warning and maintenance plans are formulated based on the different health states of the pre-selected equipment.

2. The method for monitoring and early warning of over-temperature risk of an energy storage system according to claim 1, characterized in that: In S1, for each of the pre-selected devices, it is necessary to determine whether the energy storage system is in a normal working state; read the complete temperature sequence from the database according to the set measuring point code, and determine whether the pre-selected device has undergone a complete charging or discharging process, so as to screen valid data for extracting characteristic temperature.

3. The method for monitoring and early warning of over-temperature risk of an energy storage system according to claim 1, characterized in that: In S2, a sliding window method is used to calculate the time series data of the characteristic temperature of the preselected equipment within a predetermined time window.

4. The method for monitoring and early warning of over-temperature risk of an energy storage system according to claim 1, characterized in that: In S2, a linear function is used to obtain the temperature increase trend of the equipment in curve fitting of the characteristic temperature time series data of the pre-selected equipment.

5. The method for monitoring and early warning of over-temperature risk of an energy storage system according to claim 4, characterized in that: In S2, the linear function used is the following formula (1): （1）； in For in time The predicted temperature at The slope represents the temperature rise rate, is the intercept; Using the least squares method, we can solve the problem by minimizing the sum of squared errors using the following formula (2): and Values: （2）； in, It's in time The actual temperature recorded at is the number of data points in the time window, It is the error between the fitted linear function input temperature and the actual temperature.

6. The method for monitoring and early warning of over-temperature risk of energy storage system according to claim 1, characterized in that: Specifically, S3 comprises: utilizing the temperature rising trend and combining it with the current temperature to predict the over-temperature time of the pre-selected equipment through a time series prediction model; when the predicted future temperature of the pre-selected equipment exceeds a preset temperature threshold, calculating the remaining over-temperature time of the pre-selected equipment and issuing a warning signal.

7. The method for monitoring and early warning of over-temperature risk of an energy storage system according to claim 6, characterized in that: In S3, the time series prediction model includes a mapping function between the temperature and time of the pre-selected equipment.

8. The method for monitoring and early warning of over-temperature risk of an energy storage system according to claim 1, characterized in that: In S4, the temperature threshold and the temperature rise threshold are set according to the current temperature and the temperature increase trend of the preselected equipment, and the preselected equipment is divided into several health states, including a short-term no over-temperature risk state, a key monitoring recommended state, and a maintenance recommended state as soon as possible.

9. The method for monitoring and early warning of over-temperature risk of an energy storage system according to claim 8, characterized in that: In S4, the division of the several health states is specifically as follows: Setting temperature thresholds and temperature rise threshold ; Based on the current temperature of the device and slope , classifying the pre-selected devices into the following health states: exist and In the case of, the state of the pre-selected equipment is a short-term state without over-temperature risk; exist and In the case of and When the status of the pre-selected equipment is the recommended key monitoring status; exist and In this case, the status of the pre-selected equipment is that it is recommended to arrange maintenance as soon as possible.

10. A system based on the monitoring and early warning method for over-temperature risk of energy storage system according to any one of claims 1 to 9, characterized in that: The system includes a data acquisition module, a temperature trend calculation module, a failure time prediction module and a health status classification module; wherein: The data acquisition module is used to collect temperature time series data of pre-selected equipment in the energy storage system and extract the highest temperature of the pre-selected equipment on the day; The temperature trend calculation module is used to perform curve fitting on the characteristic temperature time series data of the pre-selected equipment within a predetermined time window to calculate the temperature rise trend data of the pre-selected equipment; The failure time prediction module is used to predict the over-temperature time of the pre-selected equipment based on the current temperature and temperature rise trend data of the pre-selected equipment using a time series prediction model; The health status classification module is used to classify the preselected equipment into different health states according to the current temperature and temperature increase trend of the preselected equipment and based on the over-temperature time of the preselected equipment, and formulate corresponding early warning and maintenance plans based on the different health states of the preselected equipment.

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