Temperature control intelligent regulation system for electric energy metering box

Abstract

The present application relates to the technical field of intelligent thermal management of power equipment, in particular to a temperature control intelligent regulation and control system for an electric energy metering box; comprising parameter acquisition, risk assessment, implicit factor analysis, regulation and correction and execution control modules; the system determines a comprehensive thermal runaway risk index by acquiring data such as instantaneous fluctuation gradient of junction temperature, data service flow spectrum entropy, box coating thermal light property drift coefficient and local Rayleigh number; the core is to calculate adaptive regulation and control execution amount according to the index, in combination with parasitic electromagnetic resonance index and VOC thermal barrier index determined by eddy current heating power and VOC emission index; the present application realizes the change from dependence on macro temperature feedback to multi-dimensional data forward-looking quantification, and can identify and inhibit thermal runaway risk in advance.

Description

Technical Field

[0001] This invention relates to the field of intelligent thermal management technology for power equipment, specifically to an intelligent temperature control system for an electricity metering box. Background Technology

[0002] In the operating environment of an energy metering box, its internal temperature is affected by complex operating conditions. Most existing temperature control strategies are based on real-time feedback from conventional temperature sensors. However, such strategies can only respond to macroscopic temperature changes and have significant lag. They cannot quantify the risk of nonlinear thermal runaway and ignore hidden heat sources such as parasitic electromagnetic resonance, as well as the blockage of heat dissipation channels caused by the precipitation of volatile organic compounds. In particular, when a thermal barrier is formed, continuous cooling operation may trigger positive feedback thermal collapse, which seriously threatens equipment safety and service life. Therefore, how to build an adaptive control mechanism that can integrate multi-dimensional risks, proactively suppress thermal runaway, and intelligently avoid thermal barrier failure has become an urgent technical problem to be solved. Summary of the Invention

[0003] To solve the above-mentioned technical problems, the present invention provides an intelligent temperature control system for an electricity metering box. Specifically, the technical solution of the present invention includes:

[0004] The parameter acquisition module is used to acquire the instantaneous temperature fluctuation gradient of the communication chip junction of the power metering box, the spectral entropy of the data service flow, the drift coefficient of the thermo-optical properties of the box coating, the local Rayleigh number of the air inside the box, the eddy current heating power, the volatile organic compound release index, and the standard cooling power.

[0005] The risk assessment module is used to determine the comprehensive thermal runaway risk index based on the instantaneous temperature fluctuation gradient of the communication chip junction, the spectral entropy of the data service flow, the drift coefficient of the thermo-optical properties of the enclosure coating, and the local Rayleigh number of the air inside the enclosure.

[0006] The latent factor analysis module is used to determine the parasitic electromagnetic resonance index based on eddy current heating power and the VOC thermal barrier index based on volatile organic compound (VOC) emission index.

[0007] The regulation correction module is used to calculate the adaptive regulation execution amount based on the standard cooling power, comprehensive thermal runaway risk index, parasitic electromagnetic resonance index and VOC thermal barrier index.

[0008] The execution control module is used to respond to adaptive adjustment of the execution quantity and correct the temperature control operation of the power metering box.

[0009] Preferably, before determining the comprehensive thermal runaway risk index, the risk assessment module is also used for:

[0010] The maximum-minimum normalization method is used to process the instantaneous temperature fluctuation gradient of the communication chip junction, the spectral entropy of the data service flow, and the drift coefficient of the thermo-optical properties of the enclosure coating, so as to obtain the dimensionless instantaneous heat source gradient, service randomness, and coating degradation degree.

[0011] Preferably, the risk assessment module determines a comprehensive thermal runaway risk index, including:

[0012] Based on the local Rayleigh number of the air inside the chamber, the chaotic contribution function of the Rayleigh number is determined in the form of a Gaussian function.

[0013] Based on the instantaneous heat source gradient, operational randomness, coating degradation degree, and Rayleigh number chaotic contribution function, a comprehensive thermal runaway risk index is determined through a nonlinear integrated model.

[0014] Preferably, the latent factor analysis module determines the parasitic electromagnetic resonance index, including:

[0015] The parasitic electromagnetic resonance index is determined by calculating the normalized power ratio between the eddy current heating power and the preset reference stray power.

[0016] Preferably, the latent factor analysis module determines the VOC thermal barrier index, including:

[0017] The VOC thermal barrier index is determined by using a chemical kinetic model based on the volatile organic compound (VOC) emission index and combined with a temperature-dependent function.

[0018] Preferably, the adjustment and correction module calculates the adaptive adjustment execution amount, including:

[0019] A forward-looking amplification term is constructed, which is used to amplify and correct the standard cooling power based on the comprehensive thermal runaway risk index and the parasitic electromagnetic resonance index, so as to suppress chaotic phase transitions in advance.

[0020] Preferably, the adjustment and correction module for calculating the adaptive adjustment execution amount further includes:

[0021] A safety suppression term is constructed, which is used to suppress and correct the standard cooling power based on the VOC thermal barrier index.

[0022] Among them, the suppression correction is used to reduce the standard cooling power when the VOC thermal barrier index indicates that the cooling channel is blocked, so as to avoid positive feedback thermal collapse.

[0023] Preferably, the regulation correction module performs a multiplicative gain correction on the standard cooling power by combining a forward-looking amplification term and a safety suppression term to generate an adaptive regulation execution quantity.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1. This system collects multi-dimensional data such as instantaneous junction temperature fluctuation gradient, data service flow spectrum entropy, and local Rayleigh number, and uses a nonlinear model for evaluation to achieve forward-looking quantification of thermal runaway risk. This overcomes the lag of traditional strategies that rely solely on macroscopic temperature feedback, and can identify and suppress potential thermodynamic instability risks in advance.

[0026] 2. By monitoring the eddy current heating power and converting it into the parasitic electromagnetic resonance index, this system successfully reveals and quantifies the hidden heat source neglected by traditional models. The control correction module incorporates this factor into the calculation, ensuring that the temperature control operation can effectively cope with the additional heat load caused by electromagnetic resonance and improving the accuracy of control.

[0027] 3. This system dynamically determines the thermal barrier index of volatile organic compounds by monitoring the volatile organic compound (VOC) release index and combining it with a chemical kinetic model and a temperature-dependent function. This enables a quantitative assessment of the risk of heat dissipation channels being blocked due to material aging and release, providing a key basis for subsequent intelligent control.

[0028] 4. This system incorporates a safety suppression measure. When the volatile organic compound thermal barrier index indicates that the refrigeration channel is blocked, this suppression measure will intelligently suppress and correct the standard refrigeration power, i.e., actively reduce the refrigeration capacity. This design can effectively break the vicious cycle of the refrigerator heating the cabinet in reverse due to heat dissipation failure, avoid positive feedback thermal collapse, and greatly improve the safety of equipment operation. Attached Figure Description

[0029] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0030] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0032] Example 1:

[0033] Please see Figure 1 An intelligent temperature control system for an electricity metering box, comprising:

[0034] The parameter acquisition module is used to acquire the instantaneous temperature fluctuation gradient of the communication chip junction of the power metering box, the spectral entropy of the data service flow, the drift coefficient of the thermo-optical properties of the box coating, the local Rayleigh number of the air inside the box, the eddy current heating power, the volatile organic compound release index, and the standard cooling power.

[0035] The risk assessment module is used to determine the comprehensive thermal runaway risk index based on the instantaneous temperature fluctuation gradient of the communication chip junction, the spectral entropy of the data service flow, the drift coefficient of the thermo-optical properties of the enclosure coating, and the local Rayleigh number of the air inside the enclosure.

[0036] The latent factor analysis module is used to determine the parasitic electromagnetic resonance index based on eddy current heating power and the VOC thermal barrier index based on volatile organic compound (VOC) emission index.

[0037] The regulation correction module is used to calculate the adaptive regulation execution amount based on the standard cooling power, comprehensive thermal runaway risk index, parasitic electromagnetic resonance index and VOC thermal barrier index.

[0038] The execution control module is used to respond to adaptive adjustment of the execution quantity and correct the temperature control operation of the power metering box.

[0039] This embodiment provides an intelligent temperature control system for an electricity metering box; the system's overall architecture includes a parameter acquisition module, a risk assessment module, a latent factor analysis module, a control and correction module, and an execution control module;

[0040] The parameter acquisition module aims to provide real-time, multi-dimensional data input for subsequent risk assessment and control. In this embodiment, the module acquires several key parameters reflecting the microscopic and macroscopic thermodynamic states inside the power metering box in real time through integrated high-precision sensors and communication interfaces. Specifically, these parameters include: the instantaneous fluctuation gradient of the junction temperature of the communication chip. Used to characterize the instantaneous rate of change of the core heat source; data service flow spectral entropy Used to quantify the dynamic randomness of business load; the drift coefficient of the thermo-optical properties of the enclosure coating. This is used to characterize the long-term aging degree of the macroscopic heat dissipation capacity of the enclosure; the local Rayleigh number of the air inside the enclosure. Used to characterize the internal fluid dynamics state; eddy current heating power Used to monitor hidden heat sources introduced by parasitic electromagnetic resonance effects; volatile organic compound release index Used to assess the potential obstruction of heat dissipation channels by aging deposits of internal materials; and standard cooling power. This value can be calculated by a conventional temperature controller based on the current measured temperature and used as a reference value for regulation;

[0041] The core purpose of the risk assessment module is to quantify the risk of the system tending towards thermodynamic instability, especially the nonlinear runaway risk that conventional temperature control models cannot capture. In this embodiment, the module receives the instantaneous fluctuation gradient of the communication chip junction temperature, the spectral entropy of the data service flow, the drift coefficient of the thermo-optical properties of the enclosure coating, and the local Rayleigh number of the air inside the enclosure from the parameter acquisition module, and determines a forward-looking comprehensive thermal runaway risk index through a nonlinear comprehensive model. ;

[0042] The latent factor analysis module aims to reveal and quantify ghost heat sources and potential thermal barriers that are neglected in traditional thermal models. These factors are key to the failure of traditional temperature control strategies. In this embodiment, the module is based on the collected eddy current heating power. Determine the parasitic electromagnetic resonance index This module quantifies the additional heat load caused by electromagnetic effects; simultaneously, it also uses volatile organic compound (VOC) emission indices as a basis. Determine the VOC thermal barrier index To mitigate the risk of quantifying chemical precipitates that could block heat dissipation channels and cause cooling failure;

[0043] The regulation and correction module, as the core of the system's intelligent decision-making, aims to integrate the aforementioned risk assessment and implicit factor analysis results to dynamically and non-linearly correct conventional temperature control strategies. In this embodiment, this module is based on standard cooling power. Comprehensive thermal runaway risk index Parasitic electromagnetic resonance index and VOC thermal barrier index An adaptive control output is calculated using a custom multiplicative gain correction control law. ;

[0044] The execution control module is designed to translate the decision instructions from the regulation and correction module into physical operations; in this embodiment, the module responds to the received adaptive regulation execution amount. The execution quantity, such as cooling power or fan speed, is sent to the temperature control hardware of the energy meter box to correct the temperature control operation of the energy meter box.

[0045] The system disclosed in this embodiment improves the temperature control of the power metering box by constructing a multi-module collaborative intelligent control closed loop. It no longer simply responds to the current temperature, but instead acquires multi-dimensional data through a parameter acquisition module and uses a risk assessment module to proactively quantify the risk of nonlinear thermal runaway. Furthermore, the latent factor analysis module revealed hidden heat sources neglected by traditional models. and heat barrier The regulation and correction module and the execution control module integrate this information to realize a predictive and adaptive temperature control strategy. This strategy can suppress chaotic phase transitions in advance and intelligently avoid positive feedback thermal collapse caused by blocked heat dissipation channels, thereby greatly improving the operational reliability, safety and service life of the power metering box under complex working conditions.

[0046] Example 2:

[0047] Before determining the comprehensive thermal runaway risk index, the risk assessment module is also used for:

[0048] The maximum-minimum normalization method is used to process the instantaneous temperature fluctuation gradient of the communication chip junction, the spectral entropy of the data service flow, and the drift coefficient of the thermo-optical properties of the enclosure coating, so as to obtain the dimensionless instantaneous heat source gradient, service randomness, and coating degradation degree.

[0049] Based on Example 1, this implementation method specifies a particular preprocessing step of the risk assessment module; before determining the comprehensive thermal runaway risk index, the module needs to standardize the input parameters from different physical processes and with different dimensions.

[0050] In this embodiment, the risk assessment module employs a max-min normalization method to analyze the instantaneous temperature fluctuation gradient of the communication chip junction. Data service flow spectrum entropy and the thermo-optical property drift coefficient of the enclosure coating Process it;

[0051] Instantaneous heat source gradient The calculation method is as follows: ;

[0052] in, The instantaneous fluctuation gradient of the junction temperature of the communication chip is collected in real time by the parameter acquisition module; and They are respectively The expected maximum and minimum values ​​are obtained through multi-condition experimental calibration of a specific model of metering box or through historical data statistics.

[0053] Business randomness The calculation method is as follows: ;

[0054] in, The data service stream spectrum entropy is collected and calculated in real time by the parameter acquisition module; and They are respectively The expected maximum and minimum values ​​are also obtained through experimental calibration or historical data statistics.

[0055] Coating degradation The calculation method is as follows: ;

[0056] in, The drift coefficient of the thermo-optical properties of the enclosure coating is monitored in real time by the parameter acquisition module; and They are respectively The expected maximum and minimum values ​​are obtained through experimental calibration or historical data statistics.

[0057] This implementation introduces a max-min normalization method, which will... , and Original parameters with different physical dimensions and numerical ranges are uniformly converted into dimensionless instantaneous heat source gradients in the interval [0, 1]. Business randomness and coating degradation This ensures the subsequent construction of a comprehensive thermal runaway risk index. At this time, all input factors are mathematically comparable and weighted, avoiding the problem of model distortion or unreasonable amplification of the weight of specific factors due to inconsistent dimensions. This is the mathematical foundation for the correct establishment and operation of subsequent nonlinear integrated models.

[0058] Example 3:

[0059] The risk assessment module determines the comprehensive thermal runaway risk index, including:

[0060] Based on the local Rayleigh number of the air inside the chamber, the chaotic contribution function of the Rayleigh number is determined in the form of a Gaussian function.

[0061] Based on the instantaneous heat source gradient, operational randomness, coating degradation degree, and Rayleigh number chaotic contribution function, a comprehensive thermal runaway risk index is determined through a nonlinear integrated model.

[0062] This implementation method, based on Example 2, further specifies in detail the calculation of the comprehensive thermal runaway risk index by the risk assessment module. Specific technical implementation;

[0063] The risk assessment module is based on the local Rayleigh number of the air inside the chamber. The Rayleigh number chaos contribution function is determined using the Gaussian function form. This Rayleigh number is a chaotic contribution function. This refers to a characterization of when When the number is in a specific critical region, the nonlinear amplification factor of the heat transfer mode in the chamber exhibits intermittent chaotic abrupt behavior; its role is to simulate the risk contribution that is significantly amplified only when the fluid dynamic state enters the critical transition region;

[0064] The calculation method is as follows: ;

[0065] in, The local Rayleigh number of the air inside the chamber, acquired or calculated by the parameter acquisition module; The critical Rayleigh number center value; This is the critical section width factor; These are the chaos amplification factors; these three parameters , and All measurements were determined based on the specific internal structure of the metering chamber through computational fluid dynamics simulations or particle image velocimetry experiments. To further clarify, a Gaussian function form was used, utilizing its value at the center point... Achieving a peak value and rapidly decaying to both sides allows for accurate simulation of physical phenomena: only when Falling into When the critical transition region centered on the center is reached. Only when it is significantly greater than 1, leading to It is nonlinearly amplified; while in stable laminar or sufficiently turbulent regions, All approach 1, for No significant effect;

[0066] Based on instantaneous heat source gradient Business randomness Coating deterioration and the Rayleigh number chaotic contribution function calculated in the above steps. This embodiment uses a nonlinear comprehensive model to determine the comprehensive thermal runaway risk index. This nonlinear synthesis model refers to a custom heuristic risk quantification model, designed to incorporate microscopic heat sources. Business Dynamics Macro-aging with mesoscopic fluids The nonlinear effects;

[0067] The calculation method is as follows: ;

[0068] in, , , and All of these are dimensionless parameters obtained from the preceding steps; , , These are the corresponding weighting coefficients; to ensure model effectiveness, these coefficients are not preset fixed values, but rather obtained by conducting multi-condition stress tests on a digital twin model of a specific model of metering box and calibrating them using multiple regression analysis. The goal is to ensure... The changing trend is strongly correlated with the probability of thermal runaway occurring in the simulation;

[0069] This implementation provides a complete realization of calculating the comprehensive thermal runaway risk index through the above steps; it introduces the Rayleigh number chaotic contribution function. This invention accurately captures the risk amplification effect caused by abrupt changes in fluid dynamics, which is completely ignored by conventional linear thermal models. Through a constructed nonlinear comprehensive model, it nonlinearly fuses risk factors at four different scales: microscopic heat sources, operational dynamics, macroscopic aging, and mesoscopic fluids. The resulting comprehensive thermal runaway risk index... Therefore, it possesses extremely high sensitivity and accuracy, and can truly quantify the forward-looking risks of the system tending towards thermodynamic instability, providing a reliable basis for subsequent adaptive control.

[0070] Example 4:

[0071] The latent factor analysis module determines the parasitic electromagnetic resonance index, including:

[0072] The parasitic electromagnetic resonance index is determined by calculating the normalized power ratio between the eddy current heating power and the preset reference stray power.

[0073] This implementation specifies the determination of the parasitic electromagnetic resonance index in the latent factor analysis module. Specific methods;

[0074] The latent factor analysis module analyzes the collected eddy current heating power. With a preset reference stray power The parasitic electromagnetic resonance index is determined by calculating the normalized power ratio. This parasitic electromagnetic resonance index It refers to a dimensionless parameter used to quantify the relative intensity of eddy current heating caused by parasitic electromagnetic resonance, i.e., ghost heat sources; its function is to evaluate additional heat loads beyond conventional thermal models.

[0075] The calculation method is as follows: ;

[0076] in, The dimensionless parasitic electromagnetic resonance index; The eddy current heating power is monitored in real time by the parameter acquisition module through a high-frequency power sensor; The reference stray power is a reference value measured by the device in an anechoic chamber and obtained through experimental calibration. The calibration coefficients are derived from experiments, such as correlations between different... Calibrate with the actual local temperature rise to ensure It can accurately reflect the thermal effect;

[0077] This embodiment provides a standardized method for quantifying ghost heat sources; by measuring the eddy current heating power... With a reproducible reference spurious power By normalizing, the system can assess the relative severity of hidden heat sources caused by electromagnetic compatibility issues; this allows for the assessment of the parasitic electromagnetic resonance index. This becomes a meaningful input parameter that can be used for subsequent control logic, ensuring that the system can respond to this heat source that is ignored by traditional models.

[0078] Example 5:

[0079] The latent factor analysis module determines the VOC thermal barrier index, including:

[0080] The VOC thermal barrier index is determined by using a chemical kinetic model based on the volatile organic compound (VOC) emission index and combined with a temperature-dependent function.

[0081] This implementation specifies the determination of the VOC thermal barrier index in the latent factor analysis module. Specific methods;

[0082] The latent factor analysis module, based on volatile organic compound (VOC) emission indices and combined with a temperature-dependent function, determines the VOC thermal barrier index through a chemical kinetic model. This VOC thermal barrier index It refers to a dimensionless parameter used to quantify the risk of volatile organic compounds released at high temperatures from internal cables and other materials forming thermal barriers in heat dissipation channels, leading to refrigeration failure.

[0083] In this embodiment, the chemical kinetic model is an empirical degenerate model based on the Arrhenius equation, and its calculation method is as follows: ;

[0084] in, The VOC thermal barrier index is dimensionless.

[0085] The normalized VOC precipitate index is obtained from the chemical sensor data processing of the parameter acquisition module; for example, it can be obtained by dividing the real-time monitored VOC concentration or cumulative thickness by a preset thermal barrier to form a baseline value. Obtain the benchmark value Calibration was achieved through accelerated aging tests on the materials.

[0086] This is a temperature-dependent function used to describe the effect of precipitation rate and thermal barrier formation process on the ambient temperature inside the chamber. The dependency relationship can be calculated using the formula... ,in The activation energy for material precipitation. It is the ideal gas constant;

[0087] These are dimensionless calibration coefficients; to adhere to the principle of independence of cyclic variables, these coefficients are obtained through independent calibration experiments; specifically, a set of calibration data points can be obtained experimentally. ,in The precipitate index applied in the experiment, The experimental ambient temperature, This represents the measured decrease in cooling efficiency under these conditions; based on this calibration dataset. The results were obtained through fitting using mathematical methods such as regression analysis. ;

[0088] This embodiment provides a method for predicting and quantifying the risk of refrigeration channel obstruction; by combining a chemical kinetic model and the VOC thermal barrier index... It is no longer a simple concentration measurement, but rather incorporates the cumulative effect of precipitates. and temperature acceleration effect Dynamic risk assessment enables the system to predict when the refrigeration system may fail due to thermal barriers, providing a key basis for subsequent safety suppression control.

[0089] Example 6:

[0090] The adjustment and correction module calculates the adaptive adjustment execution amount, including:

[0091] A forward-looking amplification term is constructed, which is used to amplify and correct the standard cooling power based on the comprehensive thermal runaway risk index and the parasitic electromagnetic resonance index, so as to suppress chaotic phase transitions in advance.

[0092] The adjustment and correction module calculates the adaptive adjustment execution amount and also includes:

[0093] A safety suppression term is constructed, which is used to suppress and correct the standard cooling power based on the VOC thermal barrier index.

[0094] Among them, the suppression correction is used to reduce the standard cooling power when the VOC thermal barrier index indicates that the cooling channel is blocked, so as to avoid positive feedback thermal collapse.

[0095] The regulation correction module performs a multiplicative gain correction on the standard cooling power by combining a forward-looking amplification term and a safety suppression term, in order to generate an adaptive regulation execution quantity.

[0096] This implementation method, based on Example 1, focuses on how the adjustment and correction module calculates the adaptive adjustment execution amount. Detailed and interrelated limitations have been defined; the embodiment defines a set of nonlinear adaptive closed-loop correction control logic, which is implemented through a unified mathematical model in this embodiment;

[0097] The core function of the adjustment and correction module is to adjust the standard cooling power by combining a forward-looking amplification term and a safety suppression term. Perform multiplication gain correction to generate adaptively controlled execution amount. ;

[0098] This adaptive control execution amount This is the core of the solution, derived from a custom-designed multiplicative gain correction control law based on risk and implicit factor feedforward; it does not replace the conventional controller, but rather modifies its output. Perform dynamic correction;

[0099] In this embodiment, the calculation method is as follows: ;

[0100] in, To adaptively control the execution quantity, its physical dimensions are the same as... Consistent parameters, such as power or speed, are ultimately sent to the execution control module; The standard cooling capacity can be calculated by a conventional controller based on the measured temperature, or provided by a parameter acquisition module. The comprehensive thermal runaway risk index is calculated by the risk assessment module. The parasitic electromagnetic resonance index is calculated by the latent factor analysis module; The VOC thermal barrier index is calculated by the latent factor analysis module. , , All of these are dimensionless tuning parameters, which are obtained through robust control design or reinforcement learning training on a digital twin system. The training objective is to ensure that the system can maintain the chip junction temperature gradient within a safe threshold even under the worst operating conditions.

[0101] The formula logically integrates the defined features;

[0102] In the formula This is the defined forward-looking amplification term; this term is used based on the comprehensive thermal runaway risk index. With parasitic electromagnetic resonance index The standard cooling power is amplified and corrected; the underlying logic is that the purpose of this design is to suppress chaotic phase transitions in advance; when Increase or When the value increases, if this value is greater than 1, the control amount will be actively amplified. This allows the system to determine the temperature before the macroscopic temperature sensor lags behind. and Intervene early to implement stronger cooling or heat dissipation;

[0103] In the formula This is the defined safety suppression term; this term is used based on the VOC thermal barrier index. This involves suppressing and correcting the standard cooling power; the underlying logic is that this is a key safety design to address counterintuitive phenomena; the suppression correction is used to address the VOC thermal barrier index. When the cooling path is blocked, the standard cooling power is reduced to avoid positive feedback thermal collapse; when When the value increases, this term is less than 1; when... As the value approaches 1, the inhibition term approaches 0, leading to... It is forced to be reduced to near zero; the motivation for this logic is: once a refrigeration failure is detected, the operation of the refrigeration actuator must be actively stopped in order to break the positive feedback thermal collapse cycle of overheating of the hot end of the refrigeration unit and reverse heating of the housing.

[0104] This implementation provides a highly intelligent nonlinear control law; through a forward-looking amplification term, the system gains predictive capability, no longer passively waiting for the temperature to exceed the limit, but instead acting based on risk. and hidden heat sources By taking proactive measures, the system is pulled away from the edge of instability and chaos. Through a safety suppression term, the system gains reflective capabilities, enabling it to identify special operating conditions where the cooling path is blocked. At this point, it intelligently stops cooling to avoid positive feedback thermal collapse. Through a combination of multiplicative gain correction, the system dynamically and non-linearly integrates the above two capabilities, ensuring the final adaptive control execution. It can make optimal decisions under any operating conditions, achieving robust, safe, and efficient temperature control of the power metering box in extremely complex environments.

[0105] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A temperature control intelligent regulation system for an electricity metering box, characterized in that, include: The parameter acquisition module is used to acquire the instantaneous temperature fluctuation gradient of the communication chip junction of the power metering box, the spectral entropy of the data service flow, the drift coefficient of the thermo-optical properties of the box coating, the local Rayleigh number of the air inside the box, the eddy current heating power, the volatile organic compound release index, and the standard cooling power. The risk assessment module is used to determine the comprehensive thermal runaway risk index based on the instantaneous temperature fluctuation gradient of the communication chip junction, the spectral entropy of the data service flow, the drift coefficient of the thermo-optical properties of the enclosure coating, and the local Rayleigh number of the air inside the enclosure. The latent factor analysis module is used to determine the parasitic electromagnetic resonance index based on eddy current heating power and the VOC thermal barrier index based on volatile organic compound (VOC) emission index. The regulation correction module is used to calculate the adaptive regulation execution amount based on the standard cooling power, comprehensive thermal runaway risk index, parasitic electromagnetic resonance index and VOC thermal barrier index. The execution control module is used to respond to adaptive adjustment of the execution quantity and correct the temperature control operation of the power metering box; Before determining the comprehensive thermal runaway risk index, the risk assessment module is also used for: The maximum-minimum normalization method is used to process the instantaneous temperature fluctuation gradient of the communication chip junction, the spectral entropy of the data service flow, and the drift coefficient of the thermo-optical properties of the enclosure coating, so as to obtain the dimensionless instantaneous heat source gradient, service randomness, and coating degradation degree. The risk assessment module determines a comprehensive thermal runaway risk index, including: Based on the local Rayleigh number of the air inside the chamber, the chaotic contribution function of the Rayleigh number is determined in the form of a Gaussian function. Based on the instantaneous heat source gradient, operational randomness, coating degradation degree, and Rayleigh number chaotic contribution function, a comprehensive thermal runaway risk index is determined through a nonlinear integrated model. The latent factor analysis module determines the parasitic electromagnetic resonance index, including: The parasitic electromagnetic resonance index is determined by calculating the normalized power ratio between the eddy current heating power and the preset reference stray power. The latent factor analysis module determines the VOC thermal barrier index, including: Based on the volatile organic compound (VOC) emission index and combined with the temperature-dependent function, the VOC thermal barrier index is determined by a chemical kinetic model. Data service flow spectrum entropy is used to quantify the dynamic randomness of service load; The thermo-optical property drift coefficient of the body coating is used to characterize the long-term aging degree of the macroscopic heat dissipation capacity of the enclosure.

2. The intelligent temperature control system for an electricity metering box according to claim 1, characterized in that, The regulation correction module calculates the adaptive regulation execution amount, including: A forward-looking amplification term is constructed, which is used to amplify and correct the standard cooling power based on the comprehensive thermal runaway risk index and the parasitic electromagnetic resonance index, so as to suppress chaotic phase transitions in advance.

3. The intelligent temperature control system for an electricity metering box according to claim 2, characterized in that, The regulation correction module calculates the adaptive regulation execution amount, and also includes: A safety suppression term is constructed, which is used to suppress and correct the standard cooling power based on the VOC thermal barrier index. Among them, the suppression correction is used to reduce the standard cooling power when the VOC thermal barrier index indicates that the cooling channel is blocked, so as to avoid positive feedback thermal collapse.

4. The intelligent temperature control system for an electricity metering box according to claim 3, characterized in that, The regulation correction module performs a multiplicative gain correction on the standard cooling power by combining a forward-looking amplification term and a safety suppression term, in order to generate an adaptive regulation execution quantity.

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