A smart temperature-controlled high and low voltage switch for a new energy storage system

Abstract

This invention provides an intelligent temperature-controlled high and low voltage switchgear for a new energy storage system. It includes a monitoring and early warning component that collects real-time temperature data from key components within the switchgear using a distributed temperature sensor array, and combines this with multi-dimensional environmental sensors to detect changes in the external environment. Simultaneously, it provides early warnings of anomalies based on historical temperature data and real-time dynamics, anticipating potential risks in advance. A temperature control and adjustment component automatically switches to a heat dissipation strategy based on temperature data from key components, changes in the external environment, and equipment load. These strategies include passive cooling, air cooling, liquid cooling, and semiconductor cooling. An energy recovery component dynamically adjusts the operating parameters of the heat dissipation equipment within the heat dissipation strategy. Integrated modular waste heat recovery converts the heat generated by the heat dissipation equipment during the cooling process into heating, hot water, or electricity. This invention achieves fully automated management of the entire process from monitoring and early warning to adjustment and recovery, reducing manual intervention and improving management efficiency.

Description

Technical Field

[0001] This invention relates to the field of automatic temperature control technology, and in particular to an intelligent temperature-controlled high and low voltage switchgear for a new energy storage system. Background Technology

[0002] With the rapid development of new energy technologies, energy storage systems are playing an increasingly important role in energy management. One of the core components of energy storage systems is the high- and low-voltage switchgear, whose performance directly affects the overall system's operational efficiency and safety. However, energy storage devices generate a significant amount of heat during operation, especially during high-power output or prolonged operation, making temperature control a critical technical challenge. Inadequate temperature control can lead to decreased equipment performance, shortened lifespan, and even safety accidents. Therefore, developing an intelligent temperature control system capable of real-time monitoring and precise regulation of the high- and low-voltage switchgear has become an important research direction in the field of new energy storage systems.

[0003] Currently, the main temperature control technologies for high and low voltage switchgear on the market include passive cooling, air cooling, and liquid cooling. Passive cooling relies primarily on natural convection and heat conduction, suitable for low-power equipment, but its cooling efficiency is relatively low. Air cooling improves cooling by installing fans to force convection, but it is noisy and easily affected by the environment. Liquid cooling uses coolant circulation, resulting in high cooling efficiency, but the system is complex and has high maintenance costs. In addition, some existing technologies incorporate intelligent temperature control systems with temperature sensors, but these often only provide simple temperature monitoring and alarm functions, lacking fine-grained temperature control. Passive cooling and air cooling struggle to meet the rapid cooling requirements of high-power energy storage systems, leading to increased equipment temperature and impacting performance. While existing temperature control systems can monitor temperature, they lack intelligent control capabilities and cannot dynamically adjust cooling strategies based on actual temperature changes. Air cooling systems are noisy and energy-intensive, contradicting green and energy-saving design principles. Liquid cooling systems, while offering good cooling performance, have complex structures and high maintenance costs, making them unsuitable for large-scale application.

[0004] Existing technology 1, application number: CN202010021291.8, discloses a remotely monitored high and low voltage switchgear, including a cabinet and a shock-absorbing base; the cabinet is mounted on the shock-absorbing base; a temperature sensor and a humidity sensor are installed on the inner side wall of the cabinet; an inlet fan and an outlet fan are respectively installed at diagonal positions on both sides of the cabinet, with the inlet fan located below the outlet fan; a controller and a data exchange are installed inside the cabinet, and the temperature sensor, humidity sensor, inlet fan, and outlet fan are electrically connected to the controller, which is electrically connected to the data exchange. Although this facilitates the subsequent maintenance of the high and low voltage switchgear by back-end personnel and improves its working ability in harsh environments, it only achieves temperature monitoring and heat dissipation through a temperature sensor and a simple fan, lacking diversified heat dissipation strategies and dynamic control capabilities, and cannot meet the rapid heat dissipation requirements of high-power energy storage systems; it can only perform basic temperature monitoring and cannot provide abnormal early warnings and potential risk predictions based on historical data and real-time dynamics.

[0005] Prior art two, application number: CN201810683737.6, discloses a protection device for high and low voltage switchgear, including a protection cabinet body, a door, a dehumidifier, a fire alarm, and a heat dissipation cavity. A heat dissipation cavity is located at the center of the bottom of the protection cabinet body, and a heat dissipation plate is installed at the bottom of the cavity. Heat dissipation fans are installed inside the heat dissipation cavities on both sides of the heat dissipation plate. A fireproof barrier layer is located at the bottom of the protection cabinet body, and folding screw holes are hinged to the side walls of the fireproof barrier layer. A clamping cavity is located at the center of the interior of the protection cabinet body, and a fixing buckle is provided on the inner side wall of the clamping cavity. Lifting buckles are installed at both ends of the fixing buckle. A temperature sensor is fixed at the center of the top of the clamping cavity. Although this method not only improves the safety of the protection device during use and the convenience of installing the high and low voltage switchgear, but also extends the service life of the protection device, it suffers from low heat dissipation efficiency and high energy consumption. The use of traditional heat dissipation fans and heat dissipation plates results in limited heat dissipation efficiency and high energy consumption, which does not conform to the green and energy-saving design concept. Furthermore, the system is complex and has high maintenance costs.

[0006] Prior art three, application number: CN 202210146987.2, discloses a high and low voltage switchgear with neat cable routing function and its usage method, including a cabinet, a top shell on the top of the cabinet, a limiting mechanism at the bottom of the top shell, the limiting mechanism including a discharge pipe, the top of the discharge pipe communicating with the top shell, and a bracket welded to the left side of the rear side of the top shell. This invention utilizes the cabinet, column, top shell, feed pipe, connecting plate, electric telescopic rod, unloading mechanism, bracket, fixing frame, motor, second bracket, pull block, mounting base, through hole, integrated temperature and smoke sensor, threaded rod, guide groove, guide rod, and extension mechanism in coordination. While this solves the problems of existing high and low voltage switchgear with neat cable routing function typically lacking automatic fire detection and targeted fire extinguishing capabilities, and often unable to provide backup energy through solar power in outdoor environments, potentially causing inconvenience to users; it only performs simple detection and alarm functions, failing to achieve precise temperature control; and it fails to fully utilize the waste heat generated during equipment operation, resulting in low energy efficiency.

[0007] Current technologies 1, 2, and 3 suffer from problems such as low heat dissipation efficiency, insufficient control capability, system complexity, high energy consumption, and low energy utilization efficiency. Therefore, this invention provides an intelligent temperature-controlled high and low voltage switchgear for a new energy storage system. Summary of the Invention

[0008] To address the aforementioned technical problems, this invention provides an intelligent temperature-controlled high and low voltage switchgear for a new energy storage system, comprising:

[0009] The monitoring and early warning component is responsible for collecting temperature data of key parts inside the high and low voltage switchgear in real time through a distributed temperature sensor array, and sensing changes in the external environment of the high and low voltage switchgear in combination with multi-dimensional environmental sensors; at the same time, it provides early warning of anomalies based on historical temperature data and real-time dynamics, and predicts potential risks in advance.

[0010] The temperature control and regulation component is responsible for automatically switching to a heat dissipation strategy based on temperature data of key components, changes in external environment data, and equipment load. The heat dissipation strategies include passive heat dissipation, air cooling, liquid cooling, and semiconductor cooling.

[0011] The energy recovery component is responsible for dynamically adjusting the operating parameters of the heat dissipation equipment in the heat dissipation strategy; the integrated modular waste heat recovery converts the heat generated by the heat dissipation equipment during the heat dissipation process into heating, hot water or electricity.

[0012] Optional monitoring and early warning components include:

[0013] The data acquisition and fusion module is responsible for combining the temperature data of key parts inside the high and low voltage switchgear with the information on changes in the external environment to form a multi-dimensional dynamic dataset. Based on historical temperature data, it constructs a dynamic temperature baseline for each key part and identifies the normal temperature fluctuation range of the equipment in the key parts under different operating conditions.

[0014] The anomaly feature extraction module is responsible for extracting features from the multidimensional dynamic dataset, focusing on the rate of temperature change, periodic fluctuations, and correlation with environmental parameters; at the same time, it combines historical temperature data to identify anomalies that deviate from the dynamic baseline.

[0015] The risk assessment module is responsible for correlating and analyzing the extracted abnormal features with environmental data and equipment load information to form a multi-dimensional risk assessment model. Based on the risk assessment results, potential risks are classified and warned in a graded manner, with warning levels ranging from low to high: observation level, prevention level, and emergency level.

[0016] Optional, temperature control adjustment components, including:

[0017] The initial strategy setting module is responsible for selecting an appropriate heat dissipation strategy from passive heat dissipation, air cooling, liquid cooling, and semiconductor cooling based on the initial temperature data of key components, external environmental change data, and equipment load when the high and low voltage switch is in use. It also sets the upper and lower limits of the temperature data for each heat dissipation strategy to be activated based on the external environmental change data and equipment load.

[0018] The heat dissipation requirement generation module is responsible for acquiring temperature data, external environmental change data, and equipment load of key components at the first, second, and third time points, forming three sets of state parameters for the key components; and analyzing the three sets of state parameters to obtain the target heat dissipation requirement.

[0019] The demand relationship judgment module is responsible for generating a heat dissipation strategy switching instruction based on the target heat dissipation demand when the initial heat dissipation strategy cannot meet the target heat dissipation demand, and replacing the initial heat dissipation strategy with a new heat dissipation strategy; when the next time point is reached, it continues to judge the relationship between the current heat dissipation strategy and the current heat dissipation demand.

[0020] Optional, the heat dissipation requirement generation module includes:

[0021] The trend judgment submodule is responsible for obtaining temperature data of key parts from three sets of state parameters, analyzing the temperature data at three time points, obtaining the temperature data trend at the next time point, and judging whether the temperature data trend is increasing or decreasing. If it is decreasing, the temperature data analysis is stopped and the system continues to wait for the next time point to judge the temperature data trend.

[0022] The load demand submodule is responsible for obtaining the external environment change data trend when the temperature data trend increases, and obtaining the equipment load based on the temperature data trend and the external environment change data trend.

[0023] The relationship judgment submodule is responsible for determining the relationship between the target heat dissipation requirement and the initial heat dissipation strategy when the temperature data exceeds the upper limit of the current heat dissipation strategy.

[0024] Optional, the load requirement submodule includes:

[0025] The weighting unit is responsible for acquiring temperature data trends and external environment change data trends, and assigning dynamic weights to each variable of external environment change data according to the different degrees of influence of external environment change data on temperature data.

[0026] The influence quantification unit is responsible for constructing a heat flow balance model of the equipment based on the first law of thermodynamics, describing the dynamic relationship of heat generation, transfer and dissipation within the equipment; and introducing external environmental variables into the heat flow balance model to quantify their impact on heat transfer.

[0027] The dynamic overlay unit is responsible for predicting the heat distribution and transfer path inside the equipment through finite element analysis; dynamically overlaying the internal temperature data change trend with the external environmental data change trend through a heat flow balance model and weight allocation; and calculating the equipment load threshold based on the current heat accumulation rate and distribution of the equipment after dynamic overlay.

[0028] Optional, the expression for the heat flow balance model: Internal heat accumulation = Heat generated by the equipment heat source - Heat lost through the heat dissipation mechanism.

[0029] Optional, dynamically stacked units, including:

[0030] The thermal analysis subunit is responsible for dynamically overlaying the internal temperature data change trend with the external environmental data change trend to form a comprehensive thermal state description; and analyzing the rate of heat accumulation and its distribution inside the equipment.

[0031] Adjust the calculation subunit, which is responsible for obtaining the dynamic heat transfer characteristics inside the equipment, and adjust the calculation model of the load threshold in combination with the potential impact of external environmental changes on the thermal state of the equipment.

[0032] The threshold determination subunit is responsible for establishing a heat accumulation model based on the equipment's structural materials and thermal characteristics, calculating the highest temperature reached by the equipment under specific operating conditions, evaluating the equipment's heat dissipation capacity under different environmental conditions, and determining the highest load threshold of the equipment within its safe operating range based on the heat accumulation model and heat dissipation capacity evaluation.

[0033] Optional, the requirement relationship judgment module includes:

[0034] The instruction generation submodule is responsible for matching heat dissipation strategies based on target heat dissipation requirements and dynamically selecting a new heat dissipation strategy; when a new heat dissipation strategy is selected, it generates the corresponding heat dissipation strategy switching instruction.

[0035] The strategy switching submodule is responsible for receiving the heat dissipation strategy switching instruction, asking the initial heat dissipation strategy whether it agrees to change when it receives the instruction. When the initial heat dissipation strategy agrees to change, it switches the initial heat dissipation strategy to the new heat dissipation strategy.

[0036] The closed-loop feedback submodule is responsible for real-time monitoring of temperature changes in key components, fluctuations in the external environment, and equipment load after the heat dissipation strategy switching command is executed; at the same time, it continues to enter the demand judgment and strategy matching stage at the next time point, forming a closed-loop feedback mechanism.

[0037] Optionally, the instructions may include specific implementation details of the new heat dissipation strategy, the timing of the switching, the adjustment of the temperature threshold, and adaptive adjustments to changes in the external environment.

[0038] Optional, the instruction generation submodule includes:

[0039] The node selection unit is responsible for designing the functional deployment and operating parameters of the heat dissipation device after the new heat dissipation strategy is determined; and for selecting a switching time node based on the target heat dissipation requirements and the real-time status of the equipment operation, as well as the temperature rise rate of key parts and the dynamic changes of the external environment.

[0040] The continuous monitoring unit is responsible for dynamically adjusting the temperature threshold based on the temperature data of key components and the equipment load, and resetting the upper and lower limits of the temperature of key components; it also continuously monitors changes in the external environment and dynamically optimizes new heat dissipation strategies.

[0041] The content embedding unit is responsible for feeding the specific implementation method, switching time node, temperature threshold adjustment, and external environment adaptive adjustment into the heat dissipation strategy switching instruction to generate a complete heat dissipation strategy switching instruction.

[0042] The monitoring and early warning component of this invention collects real-time temperature data from key components inside the high and low voltage switchgear using a distributed temperature sensor array, and combines this with multi-dimensional environmental sensors to detect changes in the external environment. Simultaneously, it provides early warnings of anomalies based on historical temperature data and real-time dynamics, anticipating potential risks in advance. The temperature control and regulation component automatically switches to a heat dissipation strategy based on temperature data from key components, external environmental changes, and equipment load. These strategies include passive cooling, air cooling, liquid cooling, and semiconductor cooling. The energy recovery component dynamically adjusts the operating parameters of the heat dissipation equipment within the heat dissipation strategy. Integrated modular waste heat recovery converts the heat generated by the heat dissipation equipment during the cooling process into heating, hot water, or electricity. This monitoring and early warning component, through real-time temperature data collection and multi-dimensional environmental sensing, can promptly detect anomalies and anticipate risks, effectively preventing equipment failures or safety hazards caused by excessively high temperatures or environmental changes. The temperature control and regulation component automatically switches to the most suitable heat dissipation strategy based on equipment load and internal / external environmental data, ensuring that the temperature of key components remains within the ideal range, thus improving heat dissipation efficiency and reducing energy consumption. Energy recovery components convert waste heat generated during the heat dissipation process into heating, hot water, or electricity, realizing the cascade utilization of energy, reducing energy waste, and conforming to the concept of green and sustainable development.

[0043] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0044] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0045] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0046] Figure 1 This is a block diagram of the intelligent temperature control high and low voltage cabinet of the new energy storage system in Embodiment 1 of the present invention;

[0047] Figure 2 This is a schematic diagram of the intelligent temperature control high and low voltage switchgear of the new energy storage system in Embodiment 1 of the present invention;

[0048] Figure 3 This is a block diagram of the monitoring and early warning component in Embodiment 2 of the present invention;

[0049] Figure 4 This is a block diagram of the temperature control adjustment component in Embodiment 3 of the present invention;

[0050] Figure 5This is a block diagram of the energy recovery component in Embodiment 9 of the present invention. Detailed Implementation

[0051] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0052] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the embodiments of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0053] In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application. In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0054] Example 1: As Figure 1 As shown, this embodiment of the invention provides an intelligent temperature-controlled high and low voltage switchgear for a new energy storage system, comprising:

[0055] The monitoring and early warning component is responsible for collecting temperature data of key parts inside the high and low voltage switchgear in real time through a distributed temperature sensor array, and sensing changes in the external environment of the high and low voltage switchgear in combination with multi-dimensional environmental sensors; at the same time, it provides early warning of anomalies based on historical temperature data and real-time dynamics, and predicts potential risks in advance.

[0056] The temperature control and regulation component is responsible for automatically switching to a heat dissipation strategy based on temperature data of key components, changes in external environment data, and equipment load. The heat dissipation strategies include passive heat dissipation, air cooling, liquid cooling, and semiconductor cooling.

[0057] The energy recovery component is responsible for dynamically adjusting the operating parameters of the heat dissipation equipment in the heat dissipation strategy; the integrated modular waste heat recovery converts the heat generated by the heat dissipation equipment during the heat dissipation process into heating, hot water or electricity.

[0058] The working principle and beneficial effects of the above technical solution are as follows: The monitoring and early warning component of this embodiment collects temperature data of key parts inside the high and low voltage switchgear in real time through a distributed temperature sensor array, and combines it with multi-dimensional environmental sensors to perceive changes in the external environment of the high and low voltage switchgear; at the same time, it provides abnormal early warning based on historical temperature data and real-time dynamics, and predicts potential risks in advance; the temperature control and adjustment component automatically switches to a heat dissipation strategy based on the temperature data of key parts, changes in the external environment data, and equipment load. The heat dissipation strategy includes passive heat dissipation, air cooling, liquid cooling, and semiconductor cooling; the energy recovery component dynamically adjusts the operating parameters of the heat dissipation equipment in the heat dissipation strategy; and the integrated modular waste heat recovery converts the heat generated by the heat dissipation equipment during the heat dissipation process into heating, hot water, or electricity (see appendix for specific principles). Figure 2 The aforementioned monitoring and early warning components, through real-time temperature data collection and multi-dimensional environmental sensing, can promptly detect anomalies and predict risks in advance, effectively preventing equipment failures or safety hazards caused by excessively high temperatures or environmental changes. The temperature control and regulation components automatically switch to the most suitable heat dissipation strategy based on equipment load and internal / external environmental data, ensuring that the temperature of critical components remains within the ideal range, thus improving heat dissipation efficiency and reducing energy consumption. The energy recovery components convert waste heat generated during the heat dissipation process into heating, hot water, or electricity, achieving cascaded energy utilization, reducing energy waste, and aligning with the concept of green and sustainable development.

[0059] In summary, the intelligent linkage of each module in this embodiment enables the equipment to maintain stable operation under different working conditions, extending the equipment's service life and reducing maintenance costs. It achieves fully automated management of the entire process from monitoring and early warning to adjustment and recycling, reducing manual intervention, improving management efficiency, and providing users with a more convenient user experience. It not only ensures the safety and reliability of the equipment, but also creates higher economic value and environmental benefits for users through energy conservation, environmental protection, and intelligent management.

[0060] Example 2: As Figure 3 As shown, based on Embodiment 1, the monitoring and early warning component provided in this embodiment of the invention includes:

[0061] The data acquisition and fusion module is responsible for combining the temperature data of key parts inside the high and low voltage switchgear with the information on changes in the external environment to form a multi-dimensional dynamic dataset. Based on historical temperature data, it constructs a dynamic temperature baseline for each key part and identifies the normal temperature fluctuation range of the equipment in the key parts under different operating conditions.

[0062] The anomaly feature extraction module is responsible for extracting features from the multidimensional dynamic dataset, focusing on the rate of temperature change, periodic fluctuations, and correlation with environmental parameters; at the same time, it combines historical temperature data to identify anomalies that deviate from the dynamic baseline.

[0063] The risk assessment module is responsible for correlating and analyzing the extracted abnormal features with environmental data and equipment load information to form a multi-dimensional risk assessment model. Based on the risk assessment results, potential risks are classified and warned in a graded manner, with warning levels ranging from low to high: observation level, prevention level, and emergency level.

[0064] The expression in the multi-dimensional risk assessment model is as follows:

[0065]

[0066]

[0067] In the formula, R(i,t) represents the risk assessment value of the i-th critical part at time t; F(i,t) represents the abnormal characteristic value of the i-th critical part at time t; E(t) represents the environmental parameters at time t; L(t) represents the equipment load information at time t; λ1, λ2, λ3, λ4, λ5 represent weighting coefficients used to balance the contributions of different dimensions. represents the nonlinear risk amplification term, used to capture the interaction effect between anomalous features and environmental parameters; cos(ωt+φ) represents the periodic fluctuation adjustment term; ΔT(i,t) represents the absolute value of the rate of temperature change of the i-th critical component at time t; α represents the periodic fluctuation characteristic weight; sin(2πft) represents the periodic fluctuation function, where f is the frequency; β represents the temperature deviation characteristic weight; ΔT(i,t) represents the temperature deviation characteristic weight. 2 The value represents the deviation of the temperature of the i-th critical location from the dynamic baseline at time t; γ represents the environmental change characteristic weight; E(t) represents the rate of change of the environmental parameter at time t; δ represents the time logarithmic characteristic weight; T base (i,t) represents the dynamic temperature baseline of the i-th critical component at time t; T(i,j,t) represents the temperature value of the i-th critical component at time t in the j-th historical data set; N represents the total number of historical data sets; σ T(i,j,t) denoted by , w1 and w2 represent the standard deviation of temperature at time t in the j-th historical data set; w1 and w2 represent weighting coefficients used to adjust the contribution ratio of the mean and standard deviation; κ represents the environmental factor weight, used to introduce the influence of the external environmental variable E(t); η represents the periodic fluctuation adjustment coefficient; ω represents the angular frequency of the periodic fluctuation; φ represents the phase offset, used to adjust the initial state of the periodic fluctuation; W(i,t) represents the warning level of the i-th critical part at time t; R(i,t) represents the risk assessment value of the i-th critical part at time t; and θ1 and θ2 represent the warning thresholds, used to distinguish different levels of risk. The above formulas, through dynamic temperature baseline construction, anomaly feature extraction, multi-dimensional risk assessment, and graded warning, achieve comprehensive monitoring and early warning of potential equipment risks. Each formula incorporates key factors from real-world application scenarios, ensuring the model's accuracy and practicality.

[0068] The working principle and beneficial effects of the above technical solution are as follows: The data acquisition and fusion module of this invention combines temperature data from key internal components of the high and low voltage switchgear with external environmental change information to form a multidimensional dynamic dataset; based on historical temperature data, a dynamic temperature baseline is constructed for each key component to identify the normal temperature fluctuation range of the equipment under different operating conditions; the abnormal feature extraction module extracts features from the multidimensional dynamic dataset, focusing on the rate of temperature change, periodic fluctuations, and correlation with environmental parameters. Simultaneously, combined with historical temperature data, abnormal features deviating from the dynamic baseline are identified; the risk assessment module performs correlation analysis between the extracted abnormal features and environmental data and equipment load information to form a multidimensional risk assessment model; based on the risk assessment results, potential risks are graded and warned, with warning levels ranging from low to high: observation level, prevention level, and emergency level. Through the data acquisition and fusion module, the system combines internal temperature data with external environmental information to construct a multidimensional dynamic dataset, enabling real-time perception of equipment operating status and environmental changes, providing a comprehensive and accurate data foundation; the construction of the dynamic temperature baseline ensures accurate identification of the normal temperature range of the equipment under different operating conditions. The anomaly feature extraction module, through in-depth analysis of multidimensional data, focuses on the rate of temperature change, periodic fluctuations, and their correlation with environmental parameters, enabling rapid identification of anomalies deviating from the dynamic baseline. This intelligent anomaly identification method overcomes the limitations of traditional single-threshold monitoring, significantly improving the ability to detect potential risks. The risk assessment module combines anomaly features with environmental data and equipment load information to form a multidimensional risk assessment model. It considers not only temperature itself but also environmental factors and equipment operating status, making risk assessment more comprehensive and scientific. The tiered early warning mechanism provides different levels of warnings based on the severity of the risk, avoiding overreaction while ensuring timely handling of emergencies.

[0069] In summary, this embodiment achieves real-time monitoring, anomaly identification, and risk assessment of the operating status of high and low voltage switchgear, providing users with an efficient and accurate early warning mechanism. Through the collaborative work of multiple modules, it can detect and warn of risks in their early stages, effectively preventing equipment failures or safety accidents. Simultaneously, the tiered early warning mechanism ensures efficient resource allocation, reducing operating costs while improving the reliability and safety of equipment operation.

[0070] Example 3: As Figure 4 As shown, based on Embodiment 1, the temperature control and adjustment component provided in this embodiment of the invention includes:

[0071] The initial strategy setting module is responsible for selecting an appropriate heat dissipation strategy from passive heat dissipation, air cooling, liquid cooling, and semiconductor cooling based on the initial temperature data of key components, external environmental change data, and equipment load when the high and low voltage switch is in use. It also sets the upper and lower limits of the temperature data for each heat dissipation strategy to be activated based on the external environmental change data and equipment load.

[0072] The heat dissipation requirement generation module is responsible for acquiring temperature data, external environmental change data, and equipment load of key components at the first, second, and third time points, forming three sets of state parameters for the key components; and analyzing the three sets of state parameters to obtain the target heat dissipation requirement.

[0073] The demand relationship judgment module is responsible for generating a heat dissipation strategy switching instruction based on the target heat dissipation demand when the initial heat dissipation strategy cannot meet the target heat dissipation demand, and replacing the initial heat dissipation strategy with a new heat dissipation strategy; when the next time point is reached, it continues to judge the relationship between the current heat dissipation strategy and the current heat dissipation demand.

[0074] The working principle and beneficial effects of the above technical solution are as follows: The initial strategy setting module of this embodiment selects an appropriate heat dissipation strategy from passive heat dissipation, air cooling, liquid cooling, and semiconductor cooling based on the initial temperature data of key parts, external environmental change data, and equipment load when the high and low voltage switch is in use. Simultaneously, it sets the upper and lower limits of the temperature data for each heat dissipation strategy to be activated based on the external environmental change data and equipment load. The heat dissipation demand generation module acquires the temperature data of key parts, external environmental change data, and equipment load at the first, second, and third time points, forming three sets of state parameters for the key parts. The three sets of state parameters are analyzed to obtain the target heat dissipation demand. When the initial heat dissipation strategy cannot meet the target heat dissipation demand, the demand relationship judgment module generates a heat dissipation strategy switching instruction based on the target heat dissipation demand, replacing the initial heat dissipation strategy with a new one. When the next time point is reached, the relationship between the current heat dissipation strategy and the current heat dissipation demand is further determined. The initial strategy setting module of the above scheme analyzes temperature data of key components, external environmental change data, and equipment load to select the most suitable initial heat dissipation strategy and sets the upper and lower temperature limits for each strategy, providing a basic framework for temperature control and ensuring that the equipment dissipates heat in the optimal way during startup. The heat dissipation demand generation module collects temperature data of key components, external environmental change data, and equipment load at the first and second time points, forming three sets of state parameters and analyzing them to derive the target heat dissipation demand; it also monitors the equipment's operating status in real time, providing data support for adjusting the heat dissipation strategy. The demand relationship judgment module generates a heat dissipation strategy switching command when the initial heat dissipation strategy cannot meet the target heat dissipation demand, replacing the initial strategy with a new one, and continues to judge the relationship between the current strategy and the demand at the next time point; it dynamically adjusts the heat dissipation strategy to ensure that the equipment is always in the best heat dissipation state under different operating conditions.

[0075] In summary, this embodiment can intelligently select and dynamically adjust the heat dissipation strategy according to the equipment's operating status and environmental changes, thereby effectively avoiding the problems of overheating or insufficient heat dissipation, improving the stability and safety of equipment operation, and optimizing heat dissipation efficiency to reduce energy consumption.

[0076] Example 4: Based on Example 3, the heat dissipation requirement generation module provided in this embodiment of the invention includes:

[0077] The trend judgment submodule is responsible for obtaining temperature data of key parts from three sets of state parameters, analyzing the temperature data at three time points, obtaining the temperature data trend at the next time point, and judging whether the temperature data trend is increasing or decreasing. If it is decreasing, the temperature data analysis is stopped and the system continues to wait for the next time point to judge the temperature data trend.

[0078] The load demand submodule is responsible for obtaining the external environment change data trend when the temperature data trend increases, and obtaining the equipment load based on the temperature data trend and the external environment change data trend.

[0079] The relationship judgment submodule is responsible for determining the relationship between the target heat dissipation requirement and the initial heat dissipation strategy when the temperature data exceeds the upper limit of the current heat dissipation strategy.

[0080] The working principle and beneficial effects of the above technical solution are as follows: The trend judgment submodule in this embodiment obtains temperature data for key components from three sets of state parameters, analyzes the temperature data at three time points to obtain the temperature data trend for the next time point, and determines whether the temperature data trend is increasing or decreasing. If it is decreasing, the temperature data analysis stops, and the module continues to wait for the next time point to judge the temperature data trend. The load demand submodule, when the temperature data trend increases, obtains the external environment change data trend and determines the equipment load based on the temperature data trend and the external environment change data trend. The relationship judgment submodule, when the temperature data exceeds the upper limit of the current heat dissipation strategy, uses the current temperature data as the target heat dissipation demand and judges the relationship between the target heat dissipation demand and the initial heat dissipation strategy. The trend judgment submodule in the above solution predicts the temperature change trend for the next time point by analyzing the temperature data at three time points. If a decreasing temperature trend is detected, the module temporarily stops analysis and waits for the data update at the next time point, avoiding unnecessary resource consumption while ensuring real-time monitoring of temperature changes. The load demand submodule takes action when the trend judgment submodule detects a temperature increase. By combining external environmental trends with a comprehensive analysis of equipment load demands, and flexibly adjusting equipment operating states based on changes in internal and external factors, the system ensures that temperatures do not spiral out of control. If the relationship judgment submodule detects that temperature data exceeds the upper limit of the current heat dissipation strategy, it will use the current temperature data as the target heat dissipation requirement and compare it with the initial heat dissipation strategy.

[0081] In summary, this embodiment constructs a dynamic heat dissipation demand generation system. It not only monitors equipment temperature changes in real time but also intelligently adjusts equipment load and heat dissipation strategies based on trends and changes in the external environment. This ensures efficient equipment operation while effectively preventing performance degradation or equipment damage due to overheating, providing a strong guarantee for equipment stability and lifespan.

[0082] Example 5: Based on Example 4, the load demand submodule provided in this embodiment of the invention includes:

[0083] The weighting unit is responsible for acquiring temperature data trends and external environment change data trends, and assigning dynamic weights to each variable of external environment change data according to the different degrees of influence of external environment change data on temperature data.

[0084] The influence quantification unit is responsible for constructing a heat flow balance model of the equipment based on the first law of thermodynamics, describing the dynamic relationship of heat generation, transfer and dissipation within the equipment; and introducing external environmental variables into the heat flow balance model to quantify their impact on heat transfer.

[0085] The expression for the heat flow balance model is: Internal heat accumulation = Heat generated by the equipment heat source - Heat lost through the heat dissipation mechanism;

[0086] The dynamic overlay unit is responsible for predicting the heat distribution and transfer path inside the equipment through finite element analysis; dynamically overlaying the internal temperature data change trend with the external environmental data change trend through a heat flow balance model and weight allocation; and calculating the equipment load threshold based on the current heat accumulation rate and distribution of the equipment after dynamic overlay.

[0087] The working principle and beneficial effects of the above technical solution are as follows: The weight allocation unit in this embodiment is responsible for acquiring temperature data trends and external environmental change data trends. Based on the varying degrees of influence of external environmental change data on temperature data, dynamic weights are assigned to each variable in the external environmental change data. The influence quantification unit, based on the first law of thermodynamics, constructs a heat flow balance model for the equipment, describing the dynamic relationships in the generation, transfer, and dissipation of heat within the equipment. External environmental variables are introduced into the heat flow balance model to quantify their impact on heat transfer. The expression of the heat flow balance model is: Internal heat accumulation = Heat generated by the equipment's heat source - Heat dissipated through the heat dissipation mechanism. The dynamic superposition unit predicts the heat distribution and transfer path within the equipment through finite element analysis. Through the heat flow balance model and weight allocation, the internal temperature data change trend is dynamically superimposed with the external environmental change data trend. Based on the current heat accumulation rate and distribution of the equipment after dynamic superposition, the equipment load threshold is calculated. The weight allocation unit in this solution dynamically allocates weights according to the degree of influence of external environmental change data on temperature data, flexibly adjusting the analysis model based on the importance of different variables, thus improving the adaptability to complex environmental changes. The quantification unit constructs a heat flow balance model based on the first law of thermodynamics, introducing external environmental variables into the model and quantifying their impact to ensure an accurate description of the heat transfer process. This quantitative analysis provides a scientific basis for subsequent temperature prediction and load calculation. The dynamic overlay unit predicts heat distribution and transfer paths through finite element analysis and, combined with the heat flow balance model and dynamic weight allocation, dynamically overlays internal temperature change trends with external environmental change trends. This more accurately reflects the current thermal state of the equipment. Based on the dynamically overlaid heat accumulation rate and distribution, the system can calculate the equipment's load threshold in real time, providing a scientific basis for load adjustment in the equipment's thermal management, thereby preventing overheating or overload and ensuring its safe operation.

[0088] In summary, this embodiment achieves a complete closed loop from data acquisition to trend analysis and load threshold calculation, significantly improving the thermal management efficiency and overall operational reliability of the equipment. The combined use of each unit enables precise monitoring, dynamic analysis, and scientific management of the equipment's thermal status, ensuring the safe and stable operation of the equipment under different environmental conditions.

[0089] Example 6: Based on Example 5, the dynamic overlay unit provided in this embodiment of the invention includes:

[0090] The thermal analysis subunit is responsible for dynamically overlaying the internal temperature data change trend with the external environmental data change trend to form a comprehensive thermal state description; and analyzing the rate of heat accumulation and its distribution inside the equipment.

[0091] Adjust the calculation subunit, which is responsible for obtaining the dynamic heat transfer characteristics inside the equipment, and adjust the calculation model of the load threshold in combination with the potential impact of external environmental changes on the thermal state of the equipment.

[0092] The threshold determination subunit is responsible for establishing a heat accumulation model based on the equipment's structural materials and thermal characteristics, calculating the highest temperature reached by the equipment under specific operating conditions, evaluating the equipment's heat dissipation capacity under different environmental conditions, and determining the highest load threshold of the equipment within its safe operating range based on the heat accumulation model and heat dissipation capacity evaluation.

[0093] in,

[0094] In the formula, Q 综合 (t) represents the overall thermal state at time t; T 内部 (s) represents the internal temperature data at time s; E 外部 (s) represents the external environmental data (such as temperature, humidity, etc.) at time s; α1 represents the weighting coefficient of internal temperature, reflecting its contribution to the overall heat; α2 represents the weighting coefficient of external environment, reflecting its contribution to the overall heat; β represents the dynamic superposition adjustment coefficient, used to enhance the interaction between internal and external changes; t0 represents the initial time point; s represents the time variable, representing the time point in the integration process.

[0095] The working principle and beneficial effects of the above technical solution are as follows: The heat analysis subunit of this embodiment dynamically superimposes the internal temperature data change trend with the external environmental change data trend to form a comprehensive heat state description; it analyzes the heat accumulation rate and distribution inside the equipment; the calculation subunit adjusts the calculation model of the load threshold based on the dynamic heat transfer characteristics inside the equipment and the potential impact of external environmental changes on the equipment's thermal state; the threshold determination subunit establishes a heat accumulation model based on the equipment's structural materials and thermal characteristics, and calculates the highest temperature reached by the equipment under specific operating conditions; it evaluates the equipment's heat dissipation capacity under different environmental conditions; and based on the heat accumulation model and heat dissipation capacity evaluation, it determines the highest load threshold of the equipment within its safe operating range. Through the heat analysis subunit, the above solution can dynamically superimpose the internal temperature change trend with the external environmental change trend to form a comprehensive heat state description; the heat analysis subunit can analyze in detail the heat accumulation rate and distribution inside the equipment, revealing the heat accumulation characteristics and transfer paths within the equipment, and helping to identify potential hotspot areas. The adjustment calculation subunit, by acquiring the dynamic heat transfer characteristics within the equipment and considering the potential impact of external environmental changes on the thermal state, adjusts the load threshold calculation model to more closely reflect actual conditions and improve the accuracy of the calculation results. The threshold determination subunit, by establishing a heat accumulation model and evaluating heat dissipation capacity, comprehensively analyzes the temperature limits and heat dissipation efficiency of the equipment under specific operating conditions, providing a scientific basis for determining the safe load range. Based on the above analysis, the threshold determination subunit can accurately calculate the highest load threshold of the equipment within its safe operating range and dynamically optimize it using real-time data, ensuring that the equipment operates within a safe and efficient range. This achieves multi-dimensional monitoring, analysis, and optimization of the equipment's thermal state, providing reliable technical support for stable equipment operation, extended service life, and improved energy efficiency.

[0096] Example 7: Based on Example 3, the demand relationship judgment module provided in this embodiment of the invention includes:

[0097] The instruction generation submodule is responsible for matching heat dissipation strategies according to the target heat dissipation requirements and dynamically selecting a new heat dissipation strategy. When a new heat dissipation strategy is selected, a corresponding heat dissipation strategy switching instruction is generated. The instruction includes the specific implementation method of the new heat dissipation strategy, the switching time node, the adjustment of the temperature threshold, and the adaptive adjustment to changes in the external environment.

[0098] The strategy switching submodule is responsible for receiving the heat dissipation strategy switching instruction, asking the initial heat dissipation strategy whether it agrees to change when it receives the instruction. When the initial heat dissipation strategy agrees to change, it switches the initial heat dissipation strategy to the new heat dissipation strategy.

[0099] The closed-loop feedback submodule is responsible for real-time monitoring of temperature changes in key components, fluctuations in the external environment, and equipment load after the heat dissipation strategy switching command is executed; at the same time, it continues to enter the demand judgment and strategy matching stage at the next time point, forming a closed-loop feedback mechanism.

[0100] The working principle and beneficial effects of the above technical solution are as follows: The instruction generation submodule of this embodiment matches a heat dissipation strategy based on the target heat dissipation requirements and dynamically selects a new heat dissipation strategy. When a new heat dissipation strategy is selected, a corresponding heat dissipation strategy switching instruction is generated. This instruction includes the specific implementation method of the new heat dissipation strategy, the switching time point, the adjustment of the temperature threshold, and adaptive adjustments to changes in the external environment. When the strategy switching submodule receives the heat dissipation strategy switching instruction, it acts as the switching input terminal to inquire whether the initial heat dissipation strategy agrees to be changed. If the initial heat dissipation strategy agrees to be changed, it acts as the switching input terminal to switch the initial heat dissipation strategy to the new one. After the heat dissipation strategy switching instruction is executed, the closed-loop feedback submodule monitors the temperature changes of key components, fluctuations in the external environment, and the equipment load status in real time. Simultaneously, it continues to enter the next time point's requirement judgment and strategy matching stage, forming a closed-loop feedback mechanism. The instruction generation submodule of the above solution dynamically selects the most suitable heat dissipation strategy based on the target heat dissipation requirements and generates detailed switching instructions. This ensures that the heat dissipation strategy can accurately match the specific operating state of the equipment and changes in the external environment, avoiding low heat dissipation efficiency or energy waste caused by strategy mismatch. Meanwhile, the switching time points, temperature threshold adjustments, and adaptive adjustment mechanisms included in the instructions further enhance the flexibility and adaptability of strategy switching. Upon receiving the switching instruction, the strategy switching submodule interacts with the initial cooling strategy to ensure the stability and safety of the switching process; this respects the initial strategy and avoids system fluctuations that may result from sudden strategy changes. Simultaneously, this submodule, as the input terminal for switching, simplifies and standardizes the execution process of strategy switching. After the strategy switching is completed, the closed-loop feedback submodule monitors the temperature of key components, external environmental fluctuations, and equipment load in real time, and feeds this information back to the system's demand judgment and strategy matching stages. This closed-loop mechanism allows the system to continuously optimize the cooling strategy based on actual operating data, ensuring that the equipment is always in optimal cooling condition. Furthermore, this feedback mechanism improves the responsiveness to dynamic changes, enabling it to quickly adapt to new operating conditions.

[0101] In summary, the demand relationship judgment module in this embodiment, through the collaborative work of its internal sub-modules, achieves precise matching, safe switching, and continuous optimization of heat dissipation strategies. This not only improves the heat dissipation efficiency and operational stability of the equipment but also reduces energy consumption and operational risks, providing strong support for the efficient operation of the equipment in complex and ever-changing environments.

[0102] Example 8: Based on Example 7, the instruction generation submodule provided in this embodiment of the invention includes:

[0103] The node selection unit is responsible for designing the functional deployment and operating parameters of the heat dissipation device after the new heat dissipation strategy is determined; and for selecting a switching time node based on the target heat dissipation requirements and the real-time status of the equipment operation, as well as the temperature rise rate of key parts and the dynamic changes of the external environment.

[0104] The continuous monitoring unit is responsible for dynamically adjusting the temperature threshold based on the temperature data of key components and the equipment load, and resetting the upper and lower limits of the temperature of key components; it also continuously monitors changes in the external environment and dynamically optimizes new heat dissipation strategies.

[0105] The content embedding unit is responsible for feeding the specific implementation method, switching time node, temperature threshold adjustment, and external environment adaptive adjustment into the heat dissipation strategy switching instruction to generate a complete heat dissipation strategy switching instruction.

[0106] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, after a new heat dissipation strategy is determined, the node selection unit designs and limits the functional deployment and operating parameters of the heat dissipation device; based on the target heat dissipation requirements and the real-time operating status of the equipment, it selects a switching time node according to the temperature rise rate of key components and the dynamic changes in the external environment; the continuous monitoring unit dynamically adjusts the temperature threshold and resets the upper and lower temperature limits of key components based on the temperature data of key components and the equipment load; it continuously monitors changes in the external environment and dynamically optimizes the new heat dissipation strategy; the content embedding unit inputs the specific implementation method, switching time node, temperature threshold adjustment, and adaptive adjustment of the external environment into the heat dissipation strategy switching instruction, generating a complete heat dissipation strategy switching instruction. The node selection unit in the above solution, by combining the target heat dissipation requirements, equipment operating status, temperature rise rate of key components, and dynamic changes in the external environment, can trigger strategy switching at the most appropriate time, avoiding insufficient heat dissipation or resource waste due to switching too early or too late. The continuous monitoring unit, by continuously monitoring the temperature data of key components, equipment load, and changes in the external environment, can adjust the temperature threshold in a timely manner, reset the upper and lower temperature limits of key components, and optimize the heat dissipation strategy according to environmental changes. The content embedding unit embeds key information such as specific implementation methods, switching time nodes, temperature threshold adjustments, and adaptive adjustments to the external environment into the heat dissipation strategy switching instruction, generating a complete and executable instruction.

[0107] Example 9: As Figure 5 As shown, based on Embodiment 1, the energy recovery component provided in this embodiment of the invention includes:

[0108] The heat guiding module is responsible for capturing the heat released by the heat dissipation equipment during operation through a heat energy collection device and guiding it to a heat exchange unit.

[0109] The thermal energy conversion module is responsible for converting the thermal energy of the heat exchange unit into secondary energy, using the temperature difference to generate electricity; at the same time, the thermal energy enters the thermal energy storage device.

[0110] The heat distribution module is responsible for equipping a multi-level heat distribution network. When heating or hot water is needed, heat energy is distributed to the heat utilization unit through intelligent regulation. The heat utilization unit adopts a zoned design to precisely control the heat output according to different needs.

[0111] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, the heat guiding module captures the heat released by the heat dissipation equipment during operation through a heat energy acquisition device; guides it to a heat exchange unit; the heat energy conversion module converts the heat energy of the heat exchange unit into secondary energy, generating electricity through temperature difference; simultaneously, the heat energy enters the heat energy storage device; the heat energy distribution module is equipped with a multi-level heat energy distribution network; when heating or hot water is needed, the heat energy is distributed to the heat utilization unit through intelligent control. The heat utilization unit adopts a zoned design, precisely controlling the heat energy output according to different needs. The heat guiding module of the above solution ensures efficient heat energy capture and transmission by accurately capturing and guiding the heat released by the heat dissipation equipment; the heat energy conversion module converts the collected heat energy into secondary energy, generating electricity through temperature difference, and simultaneously stores excess heat energy in the heat energy storage device, realizing multi-level utilization of energy and improving energy utilization efficiency. The heat energy distribution module, through a multi-level heat energy distribution network and intelligent control system, accurately distributes heat energy to heat utilization units such as heating and hot water, meeting the heat energy needs in different scenarios and further expanding the application scope of heat energy. This not only effectively improves the energy utilization efficiency of new energy storage systems, but also achieves energy recycling and optimized allocation through the multi-functional conversion and distribution of thermal energy, providing strong support for the sustainable operation of the system. While ensuring efficient energy recovery, it also provides users with a flexible and intelligent energy management experience, demonstrating significant practical value and environmental significance.

[0112] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of equivalents of this invention, this invention is also intended to include these modifications and variations.

Claims

1. An intelligent temperature-controlled high and low voltage switchgear for a new energy storage system, characterized in that, Include: The monitoring and early warning component is responsible for collecting temperature data of key parts inside the high and low voltage switchgear in real time through a distributed temperature sensor array, and sensing changes in the external environment of the high and low voltage switchgear in combination with multi-dimensional environmental sensors; at the same time, it provides early warning of anomalies based on historical temperature data and real-time dynamics, and predicts potential risks in advance. The temperature control and regulation component is responsible for automatically switching to a heat dissipation strategy based on the temperature data of key components, changes in the external environment, and equipment load. The heat dissipation strategies include passive heat dissipation, air cooling, liquid cooling, and semiconductor cooling. The energy recovery component is responsible for dynamically adjusting the operating parameters of the heat dissipation equipment in the heat dissipation strategy, and converting the heat generated by the heat dissipation equipment during the heat dissipation process into heating, hot water or electricity. The monitoring and early warning component includes: The data acquisition and fusion module is responsible for combining temperature data from key components inside the high and low voltage switchgear with external environmental change data to form a multi-dimensional dynamic dataset. Based on historical temperature data, it constructs a dynamic temperature baseline for each key component and identifies the normal temperature fluctuation range of the equipment in the key components under different operating conditions. The anomaly feature extraction module is responsible for extracting features from the multidimensional dynamic dataset, focusing on the rate of temperature change, periodic fluctuations, and correlation with environmental parameters; at the same time, it identifies anomalous features that deviate from the dynamic baseline by combining historical temperature data. The risk assessment module is responsible for correlating and analyzing the extracted abnormal features with environmental data and equipment load information to form a multi-dimensional risk assessment model; based on the risk assessment results, potential risks are classified and warned in a graded manner, with warning levels ranging from low to high: observation level, prevention level, and emergency level. The expression in the multi-dimensional risk assessment model is as follows: ; ; ; ; In the formula, Indicates the first i Key parts in time t The risk assessment value; Indicates the first i Key parts in time t Abnormal characteristic values; Indicates time t Environmental parameters; Indicates time t Device load information; , , , , This represents the weighting coefficient, used to balance the contributions of different dimensions; This represents a nonlinear risk amplification term, used to capture the interaction effect between anomalous features and environmental parameters; This indicates a periodic fluctuation adjustment term; Indicates the first i Key parts in time t The absolute value of the rate of temperature change; Indicates the weight of periodic fluctuation characteristics; Represents a periodic fluctuation function. f For frequency; Indicates the weight of temperature deviation characteristics; Indicates the first i Key parts in time t The deviation of temperature from the dynamic baseline; Indicates the weight of environmental change characteristics; Indicates environmental parameters over time t The rate of change; Indicates the logarithmic feature weights over time; Indicates the first i Key parts in time t Dynamic temperature baseline; Indicates the first i The key parts are in the j Time in the next historical data t Temperature value; N Indicates the total number of times historical data is used; Indicates the first i The key parts are in the j Time in the next historical data t Temperature standard deviation; , This represents the weighting coefficient, used to adjust the contribution ratio of the mean and standard deviation; Represents the environmental factor weights, used to introduce external environmental variables. The impact; Indicates the adjustment coefficient for periodic fluctuations; Angular frequency representing periodic fluctuations; This indicates a phase shift, used to adjust the initial state of periodic fluctuations; Indicates the first i Key parts in time t The warning level; Indicates the first i Key parts in time t The risk assessment value; , This indicates the warning threshold, used to distinguish different levels of risk; The above expression achieves comprehensive monitoring and early warning of potential risks to equipment through dynamic temperature baseline construction, abnormal feature extraction, multi-dimensional risk assessment, and graded early warning.

2. The intelligent temperature-controlled high and low voltage switchgear of the new energy storage system as described in claim 1, characterized in that, Temperature control and regulation components, including: The initial strategy setting module is responsible for selecting an appropriate heat dissipation strategy from passive heat dissipation, air cooling, liquid cooling, and semiconductor cooling based on the initial temperature data of key components, external environmental change data, and equipment load when the high and low voltage switch is in use. It also sets the upper and lower limits of the temperature data for each heat dissipation strategy to be activated based on the external environmental change data and equipment load. The heat dissipation requirement generation module is responsible for collecting temperature data, external environmental change data, and equipment load of key components at the first, second, and third time points to form three sets of state parameters for the key components; and analyzing the three sets of state parameters to obtain the target heat dissipation requirement. The demand relationship judgment module is responsible for generating a heat dissipation strategy switching instruction based on the target heat dissipation demand when the initial heat dissipation strategy cannot meet the target heat dissipation demand, and replacing the initial heat dissipation strategy with a new heat dissipation strategy; when the next time point is reached, it continues to judge the relationship between the current heat dissipation strategy and the current heat dissipation demand.

3. The intelligent temperature-controlled high and low voltage switchgear of the new energy storage system as described in claim 2, characterized in that, The heat dissipation requirement generation module includes: The trend judgment submodule is responsible for obtaining temperature data of key parts from three sets of state parameters, analyzing the temperature data at three time points, obtaining the temperature data trend at the next time point, and judging whether the temperature data trend is increasing or decreasing. If it is decreasing, the temperature data analysis is stopped and the system continues to wait for the next time point to judge the temperature data trend again. The load demand submodule is responsible for obtaining the external environment change data trend when the temperature data trend increases, and obtaining the equipment load based on the temperature data trend and the external environment change data trend. The relationship judgment submodule is responsible for determining the relationship between the target heat dissipation requirement and the initial heat dissipation strategy when the temperature data exceeds the upper limit of the current heat dissipation strategy.

4. The intelligent temperature-controlled high and low voltage switchgear of the new energy storage system as described in claim 3, characterized in that, The load requirement submodule includes: The weighting unit is responsible for acquiring temperature data trends and external environment change data trends, and assigning dynamic weights to each variable of external environment change data according to the different degrees of influence of external environment change data on temperature data. The influence quantification unit is responsible for constructing a heat flow balance model of the equipment based on the first law of thermodynamics, describing the dynamic relationship of heat generation, transfer and dissipation within the equipment; and introducing external environmental variables into the heat flow balance model to quantify their impact on heat transfer. The dynamic overlay unit is responsible for predicting the heat distribution and transfer path inside the equipment through finite element analysis; By using a heat flow balance model and weight allocation, the internal temperature data change trend is dynamically superimposed with the external environmental data change trend; based on the current heat accumulation rate and distribution of the equipment after dynamic superposition, the equipment load threshold is calculated.

5. The intelligent temperature-controlled high and low voltage switchgear of the new energy storage system as described in claim 4, characterized in that, The expression for the heat flow balance model is: Internal heat accumulation = Heat generated by the equipment's heat source - Heat lost through the heat dissipation mechanism.

6. The intelligent temperature-controlled high and low voltage switchgear of the new energy storage system as described in claim 4, characterized in that, Dynamic overlay unit, including: The thermal analysis subunit is responsible for dynamically overlaying the internal temperature data change trend with the external environmental data change trend to form a comprehensive thermal state description; and analyzing the rate of heat accumulation and its distribution inside the equipment. Adjust the calculation subunit, which is responsible for obtaining the dynamic heat transfer characteristics inside the equipment, and adjust the calculation model of the load threshold in combination with the potential impact of external environmental changes on the thermal state of the equipment. The threshold determination subunit is responsible for establishing a heat accumulation model based on the equipment's structural materials and thermal characteristics, calculating the highest temperature reached by the equipment under operating conditions, evaluating the equipment's heat dissipation capacity under different environmental conditions, and determining the highest load threshold of the equipment within its safe operating range based on the heat accumulation model and heat dissipation capacity evaluation.

7. The intelligent temperature-controlled high and low voltage switchgear of the new energy storage system as described in claim 2, characterized in that, The requirement relationship judgment module includes: The instruction generation submodule is responsible for matching heat dissipation strategies based on target heat dissipation requirements and dynamically selecting a new heat dissipation strategy; when a new heat dissipation strategy is selected, it generates the corresponding heat dissipation strategy switching instruction. The strategy switching submodule is responsible for receiving the heat dissipation strategy switching instruction, asking as a switching input terminal whether it agrees to change the initial heat dissipation strategy, and switching the initial heat dissipation strategy to the new heat dissipation strategy as a switching input terminal when it agrees to change the initial heat dissipation strategy. The closed-loop feedback submodule is responsible for real-time monitoring of temperature changes in key components, fluctuations in the external environment, and equipment load after the heat dissipation strategy switching command is executed; at the same time, it continues to enter the demand judgment and strategy matching stage at the next time point, forming a closed-loop feedback mechanism.

8. The intelligent temperature-controlled high and low voltage switchgear of the new energy storage system as described in claim 7, characterized in that, The heat dissipation strategy switching command includes a new heat dissipation strategy, the switching time point, the adjustment of the temperature threshold, and adaptive adjustments to changes in the external environment.

9. The intelligent temperature-controlled high and low voltage switchgear of the new energy storage system as described in claim 7, characterized in that, The instruction generation submodule contains: The node selection unit is responsible for designing the functional deployment and operating parameters of the heat dissipation device after the new heat dissipation strategy is determined; and for selecting a switching time node based on the target heat dissipation requirements and the real-time status of the equipment operation, as well as the temperature rise rate of key parts and the dynamic changes of the external environment. The continuous monitoring unit is responsible for dynamically adjusting the temperature threshold and resetting the upper and lower limits of the temperature of critical parts based on the temperature data of critical parts and the equipment load. Continuously monitor changes in the external environment and dynamically optimize new heat dissipation strategies; The content embedding unit is responsible for embedding the new heat dissipation strategy, the switching time point, the adjustment of the temperature threshold, and the adaptive adjustment to changes in the external environment into the heat dissipation strategy switching instruction, thereby generating a complete heat dissipation strategy switching instruction.

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