Heater energy-saving control method and system based on heat transfer optimization

Abstract

The invention relates to the technical field of heaters, and discloses a heater energy-saving control method and system based on heat transfer optimization, and the control method comprises the following steps: collecting the internal and external environment parameters of a heater and the operation state data of a working unit set in real time, and constructing an environment-operation state data set, the operation state data of the working unit set comprises operation state parameters of the heating unit, the heat dissipation unit and the control unit under different operation conditions; constructing a thermal load prediction model of the heater based on the environment-operation state data set; according to the thermal load prediction model, analyzing heat energy distribution and heat transfer efficiency of a heater, and obtaining a thermal load prediction result; and based on the thermal load prediction result, the heating power and heat flow distribution of a heater are optimized, energy consumption control parameters are determined, and a heat transfer control instruction set is synchronously generated. The invention aims to solve the problems of non-uniform heat energy distribution, low heat transfer efficiency and energy waste of a traditional control method.

Description

Technical Field

[0001] This invention relates to the field of heater technology, and specifically to a heater energy-saving control method and system based on heat transfer optimization. Background Technology

[0002] Heaters are widely used in industrial production and daily life, and their energy consumption is a major concern. Current technologies primarily rely on a few fixed parameters to adjust heating power, lacking precise and dynamic optimization for heat transfer under different operating conditions. While these methods are effective under stable conditions, they become limited in complex environments as energy efficiency requirements increase. Traditional control technologies fail to consider real-time changes in internal and external environmental parameters and dynamic fluctuations in heat load within the heater cavity, hindering intelligent and precise optimization of the heat transfer process. This results in uneven heat distribution, insufficient heat transfer efficiency, and significant energy waste, making it difficult to meet the high-efficiency and energy-saving operation requirements of heaters. Summary of the Invention

[0003] To address the existing problems, this invention aims to provide a heater energy-saving control method and system based on heat transfer optimization. This method solves the technical problems of uneven heat distribution, insufficient heat transfer efficiency, and significant energy waste caused by the lack of optimization of the heat transfer process and dynamic adaptation to environmental parameters in traditional heater control methods.

[0004] To achieve the above objectives, the present invention provides the following technical solution.

[0005] In a first aspect, the present invention provides a heater energy-saving control method based on heat transfer optimization, comprising the following steps: real-time acquisition of internal and external environmental parameters of the heater and operating status data of the set of working units to construct an environment-operating status dataset, wherein the operating status data of the set of working units includes operating status parameters of heating units, heat dissipation units, and control units under different operating conditions; based on the environment-operating status dataset, constructing a heat load prediction model for the heater; analyzing the heat energy distribution and heat transfer efficiency of the heater according to the heat load prediction model to obtain heat load prediction results; based on the heat load prediction results, optimizing the heating power and heat flow distribution of the heater, determining energy consumption control parameters, and simultaneously generating a heat transfer control instruction set; dynamically refining the energy consumption control parameters according to the environment-operating status dataset and the heat load prediction results, and optimizing the operating condition adaptation rules of the heat transfer control instruction set based on the differences in internal and external environmental parameters to generate an optimized heat transfer control instruction set; executing the optimized heat transfer control instruction set to coordinately regulate the set of working units, and correcting the energy consumption control parameters in real time through a feedback system to achieve adaptive energy-saving control of the heater.

[0006] As a further improvement of the present invention, the environmental parameters of the heater's internal and external environment and the operating status data of the set of working units are collected in real time to construct an environment-operating status dataset, including the following steps: collecting internal temperature data of the heater cavity and key components in real time through temperature sensors; collecting external temperature and humidity data and external air pressure data of the heater's external environment in real time through humidity sensors and air pressure sensors; collecting operating status data of the heating unit, heat dissipation unit, and control unit; and integrating the internal temperature data, external temperature and humidity data, external air pressure data, and operating status data to construct an environment-operating status dataset.

[0007] As a further improvement of the present invention, a heat load prediction model for the heater is constructed based on the environmental-operating status dataset, comprising the following steps: extracting feature data from the environmental-operating status dataset; constructing a heat load prediction model based on the feature data using regression analysis; and training the heat load prediction model with historical data to optimize the model parameters, wherein the historical data refers to the environmental-operating status data collected by the heater during its past operation.

[0008] As a further improvement of the present invention, the energy consumption control parameters include heating power control parameters, heat flow distribution control parameters, wind speed control parameters, and temperature setting parameters.

[0009] As a further improvement of the present invention, the energy consumption control parameters are dynamically refined based on the environmental-operating status dataset and the heat load prediction results, including the following steps: calculating the current power compensation coefficient based on the environmental-operating status dataset and the heat load prediction results, wherein the heat load prediction results are used for comparison with real-time operating conditions; adjusting the heat flow distribution gradient based on the heat load prediction results to optimize the heat flow distribution in each region of the heater; and adapting the heating power and heat flow distribution of the heater according to the difference between the real-time operating conditions and the heat load prediction results.

[0010] As a further improvement of the present invention, and based on the differences in internal and external environmental parameters, the operating condition adaptation rules of the heat transfer control instruction set are optimized to generate an optimized heat transfer control instruction set, including the following steps: setting a heat flow priority allocation logic under low temperature conditions according to the differences in internal and external environmental parameters; establishing a power compensation logic under high humidity conditions based on temperature and humidity data collected by a humidity sensor; optimizing the heat flow allocation strategy under each operating condition by combining real-time environmental data in the environment-operating status dataset; and generating an optimized heat transfer control instruction set based on the operating condition adaptation rules composed of the heat flow priority allocation logic, the power compensation logic, and the heat flow allocation strategy under each operating condition.

[0011] As a further improvement of the present invention, the energy consumption control parameters are corrected in real time through a feedback system, including the following steps: generating a feedback signal based on the operating status data of the heater working unit set; comparing the feedback signal with the current energy consumption control parameters, calculating the energy consumption deviation, and correcting the energy consumption control parameters based on the deviation value; inputting the corrected energy consumption control parameters into the heater control unit through the feedback system, and updating the heat transfer control command in real time to realize the adaptive energy-saving control of the heater.

[0012] Secondly, this invention also discloses a heater energy-saving control system based on heat transfer optimization, characterized by comprising a dataset construction module for real-time acquisition of internal and external environmental parameters of the heater and operating status data of the set of working units to construct an environment-operating status dataset, wherein the operating status data of the set of working units includes operating status parameters of heating units, heat dissipation units, and control units under different operating conditions; a prediction model construction module for constructing a heater heat load prediction model based on the environment-operating status dataset; and a prediction result acquisition module for analyzing the heat energy distribution and heat transfer efficiency of the heater according to the heat load prediction model to obtain the heat load prediction result. The instruction set generation module optimizes the heating power and heat flow distribution of the heater based on the heat load prediction results, determines the energy consumption control parameters, and simultaneously generates a heat transfer control instruction set. The optimized instruction set generation module dynamically refines the energy consumption control parameters based on the environment-operation status dataset and the heat load prediction results, and optimizes the operating condition adaptation rules of the heat transfer control instruction set based on the differences in internal and external environmental parameters to generate an optimized heat transfer control instruction set. The optimized instruction set execution module executes the optimized heat transfer control instruction set, coordinates and regulates the set of working units, and corrects the energy consumption control parameters in real time through a feedback system to achieve adaptive energy-saving control of the heater.

[0013] Thirdly, the present invention also discloses a computer-readable storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform a heater energy-saving control method based on heat transfer optimization as described in any one of the first aspects.

[0014] Fourthly, the present invention also discloses a computer program product containing instructions that, when the computer program product is run on an electronic device, cause the electronic device to perform a heater energy-saving control method based on heat transfer optimization as described in any one of the first aspects.

[0015] The present invention has the following beneficial effects: This application constructs a heat load prediction model and analyzes heat energy distribution and heat transfer efficiency by real-time acquisition of internal and external environmental parameters and operating status data of the heater. It dynamically optimizes energy consumption control parameters such as heating power and heat flow distribution, refines the heat transfer control command set in combination with differences in internal and external environments, and corrects parameters in real time through a feedback system. This allows for precise control of the heater's heat transfer process, achieving efficient distribution and utilization of heat energy under different operating conditions. It solves the problems of uneven heat energy distribution, low heat transfer efficiency, and energy waste caused by traditional control methods that fail to optimize the heat transfer process and dynamically adapt to environmental parameters. This makes the energy-saving control effect of the heater system more precise and reliable, achieving the technical effects of uniform heat energy distribution, improved heat transfer efficiency, and reduced energy waste. Attached Figure Description

[0016] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of the invention in any way. Furthermore, the shapes and proportions of the components in the drawings are merely schematic to aid in understanding the invention and are not intended to specifically limit the shapes and proportions of the components. In the drawings: Figure 1 A schematic flowchart of a heater energy-saving control method based on heat transfer optimization provided in an embodiment of this application; Figure 2 A schematic diagram of a heater energy-saving control system based on heat transfer optimization is provided for an embodiment of this application; The module comprises: 1. Dataset construction module; 2. Prediction model construction module; 3. Prediction result acquisition module; 4. Instruction set generation module; 5. Optimized instruction set generation module; and 6. Optimized instruction set execution module. Detailed Implementation

[0017] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0018] It should be noted that when an element is referred to as being "set on" another element, it can be directly on the other element or there may be an intervening element. When an element is stated to be "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only embodiments.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0020] Example 1 like Figure 1 As shown, a heater energy-saving control method based on heat transfer optimization includes: Step A100: Collect real-time environmental parameters of the heater and the operating status data of the set of working units to construct an environment-operating status dataset. The operating status data of the set of working units includes the operating status parameters of the heating unit, heat dissipation unit, and control unit under different operating conditions.

[0021] In this embodiment of the application, the internal and external environmental parameters include heater cavity temperature, key component temperature, and ambient temperature, humidity, and air pressure.

[0022] Specifically, temperature, humidity, and air pressure sensors are used to collect internal and external environmental data of the heater, as well as operating status data of the heating, heat dissipation, and control units. These data are then integrated to construct an environment-operating status dataset. The specific steps are detailed in A110-A140.

[0023] Step A200: Based on the aforementioned environment-operation status dataset, construct a heat load prediction model for the heater.

[0024] In this embodiment of the application, the feature data are key features derived from parameters in the environment-operation status dataset, such as the rate of change of temperature gradient, humidity fluctuation coefficient, and power regulation frequency, which can reflect the trend of heat load change.

[0025] Optionally, feature data can be extracted from the environmental-operational status dataset, a heat load prediction model for the heater can be constructed using regression analysis, and the model parameters can be trained and optimized using historical data. The specific steps are detailed in A210-A230.

[0026] Step A300: Based on the heat load prediction model, analyze the heat energy distribution and heat transfer efficiency of the heater to obtain the heat load prediction results.

[0027] In this embodiment, thermal energy distribution refers to the distribution of heat flux density and temperature gradient in different regions inside the heater. Heat transfer efficiency refers to the ratio of the heater's input power to its effective heat load output.

[0028] In one embodiment of this application, internal temperature data (such as temperatures at different locations within the heater cavity: 120°C on the surface of the heating tube, 105°C at the center of the cavity, and 90°C near the heat sink), heating unit power consumption (current power 1.8kW), and heat dissipation unit wind speed (2.5m / s) are first extracted from the environmental-operational state dataset. Using the output of the heat load prediction model, combined with the Fourier heat conduction equation and the heat convection formula, the heat flux density distribution in each region inside the heater is calculated. For example, under the operating conditions of a heating power of 1.8kW and a heat dissipation wind speed of 2.5m / s, the model calculates that the heat flux density in the heating tube region is 800W / m². 2 The cavity edge region has a power of 350W / m. 2 This creates a visualized thermal energy distribution cloud map, which intuitively reflects the temperature gradient and the direction of heat flow.

[0029] For heat transfer efficiency analysis, the model compares the input heating power data with the predicted effective heat load output. For example, when the input power of the heating unit is 2kW, the model predicts an effective heat load output of 1.78kW based on the external ambient temperature of 22℃, humidity of 65%RH, and cavity temperature distribution, thus calculating a heat transfer efficiency of 89%. Simultaneously, the model can analyze the influencing factors of heat transfer efficiency under different operating conditions. For instance, when the external air pressure drops from 101.3kPa to 98kPa, the gas thermal conductivity decreases by approximately 3%, and the heat transfer efficiency correspondingly decreases to 86%, thereby quantifying the impact of environmental parameters on the heat transfer process.

[0030] Furthermore, the model can also incorporate historical operating data of the heater, including examples of heat distribution and heat transfer efficiency, to verify the accuracy of the current analysis results. For instance, historical operating data shows that under the same heating power (1.5kW) and external humidity (70%RH) conditions, the deviation between the cavity temperature distribution and the current model prediction is less than 5%, and the calculated heat transfer efficiency matches the actual test value by 92%, ensuring the reliability of the analysis results.

[0031] By comprehensively calculating multi-dimensional temperature data, power parameters, and environmental variables through a heat load prediction model, the visualization of heater heat energy distribution and the accurate quantification of heat transfer efficiency are achieved. Compared with traditional single-point detection and simplified calculation methods, this significantly improves the comprehensiveness of heat energy distribution analysis and the accuracy of heat transfer efficiency assessment. It provides a scientific data analysis basis for energy-saving control strategies such as dynamically adjusting heating power and optimizing heat flow distribution, and effectively solves the problem of insufficient energy efficiency optimization caused by crude analysis methods in existing technologies.

[0032] Step A400: Based on the heat load prediction results, optimize the heating power and heat flow distribution of the heater, determine the energy consumption control parameters, and simultaneously generate a heat transfer control command set.

[0033] In this embodiment, heating power is a parameter used to regulate the output energy of the heater. Heat flow distribution is a strategy for regulating the proportion of heat energy distribution in different regions inside the heater, and the distribution ratio is optimized based on the results of heat energy distribution analysis.

[0034] Specifically, based on the heat load prediction results output by the heat load prediction model, assuming the predicted heat load for the next 30 minutes is 12.5kW, the heating power control parameters are first optimized. When the predicted heat load is higher than the current actual output, such as a current power of 1.8kW corresponding to a heat load of 10kW, and the predicted value is 12.5kW, the power factor that needs to be increased is calculated using a power compensation algorithm: When the predicted heat load for the next 30 minutes is 12.5kW, and the current power of 1.8kW corresponds to an actual heat load of 10kW, the heat load deviation rate is first calculated as (12.5-10) / 10×100%=25%. Based on the preset power adjustment threshold (e.g., triggering compensation when the deviation exceeds 15%), the power compensation algorithm is determined. The algorithm constructs a compensation model based on the deviation ratio and the heat transfer efficiency (currently 89%). First, the basic compensation coefficient is calculated linearly as 1+25%×0.8=1.2 (where 0.8 is the efficiency correction coefficient to avoid overcompensation due to fluctuations in heat transfer efficiency), resulting in an initial adjustment power of 1.8×1.2=2.16kW. Then, the heat transfer efficiency is used for back-calculation verification. If the input power is 2.16kW, the effective heat load calculated at 89% efficiency is 2.16×89%≈1.92kW, corresponding to a heat load of approximately 10kW. The load output is 1.92 × 5.2 (power-heat load conversion coefficient, based on historical data fitting) ≈ 10.0 kW, which still differs from the predicted value of 12.5 kW. Therefore, a secondary compensation factor is introduced: based on the ambient temperature and humidity (e.g., external temperature 22℃, humidity 65%RH), the compensation table is consulted to obtain a comprehensive temperature and humidity compensation coefficient of 1.1. The final corrected power compensation coefficient is 1.2 × 1.1 = 1.32, and the heating power is adjusted to 1.8 × 1.32 = 2.376 kW. At this point, the effective heat load is calculated at 89% efficiency as 2.376 × 89% ≈ 2.12 kW, corresponding to a heat load output of 2.12 × 5.2 ≈ 11.0 kW. Then, a 15% dynamic adjustment margin is reserved through the feedback system, so that the final power output can be finely adjusted in real time based on 2.376 kW to ensure matching the predicted heat load demand and avoid overheating.

[0035] Regarding heat flux distribution optimization, based on the heat energy distribution cloud map, such as a heat flux density of 800 W / m² in the heating pipe area... 2 350W / m at the edge of the cavity 2 The heat flux ratio in each region is adjusted using a heat flux distribution gradient algorithm. For example, when an increase in predicted heat load is observed, the heat flux density in the heating pipe region is increased to 900 W / m². 2 The edge area is increased to 450W / m 2This optimizes the heat flow distribution gradient from 1.5:1 to 2:1, ensuring that the temperature uniformity error in each area is controlled within ±5℃. Simultaneously, based on the operating status of the heat dissipation unit (e.g., wind speed of 2.5 m / s), the wind speed control parameters are adjusted accordingly. After the heat flow distribution is optimized, the wind speed is increased to 2.8 m / s to enhance heat convection efficiency.

[0036] The optimization of temperature setting parameters is based on the coupling relationship between predicted heat load and environmental parameters. For example, when the external temperature drops from 22℃ to 10℃, the predicted heat load increases by 20%. At this time, the target temperature of the cavity is adjusted from 100℃ to 105℃, and the temperature setting parameters are generated by the PID control algorithm with a proportional coefficient Kp=1.2 and an integral time Ti=30s to ensure the temperature control accuracy is within ±2℃.

[0037] Next, the synchronously generated heat transfer control instruction set includes multi-condition adaptation rules: in low-temperature environments (external temperature < 15℃), the instruction set prioritizes heat flow to the cavity edge area, increasing the heat flow allocation ratio by 30%; in high-humidity environments (humidity > 70%RH), power compensation logic is automatically triggered, increasing the heating power by 15% to offset the impact of humidity on heat transfer efficiency (approximately a 5% reduction). The instruction set is written in a structured language, such as "when the external temperature is < 15℃ and the predicted heat load is > 10kW, the heating power is set to 2.2kW, the heat flow edge area allocation ratio is 40%, and the wind speed is 3.0m / s," ensuring the executability of the control instructions.

[0038] By linking and optimizing the heat load prediction results with heating power, heat flow distribution, wind speed and temperature parameters, a data-driven dynamic control mechanism is formed. Compared with the traditional fixed parameter control method, it achieves precise matching of heating power, improved uniformity of heat flow distribution and improved heat transfer efficiency. Furthermore, the standardized heat transfer control command set ensures the consistency of control strategies under various operating conditions, effectively solving the problems of serious energy waste and uneven heat distribution in existing technologies. This provides a systematic parameter optimization scheme for the efficient and energy-saving operation of heater systems.

[0039] Step A500: Based on the environment-operation status dataset and the heat load prediction results, the energy consumption control parameters are dynamically refined, and the operating condition adaptation rules of the heat transfer control instruction set are optimized based on the differences between the internal and external environmental parameters to generate an optimized heat transfer control instruction set.

[0040] In this embodiment of the application, the operating condition adaptation rules include heat flow priority allocation logic in low temperature environments and power compensation logic in high humidity environments.

[0041] Specifically, based on the environmental-operational status dataset and heat load prediction results, the power compensation coefficient is calculated, the heat flow distribution gradient is adjusted, and the heating power and heat flow distribution are adapted according to the difference between the real-time operating conditions and the prediction results, so as to dynamically refine the energy consumption control parameters. The specific steps are explained in detail in A510-A530.

[0042] Based on the differences in internal and external environmental parameters, a heat flow priority allocation logic is set for low-temperature environments, a power compensation logic is established for high-humidity environments, and the heat flow allocation strategy for each operating condition is optimized by combining real-time environmental data to generate an optimized heat transfer control instruction set. The specific steps are explained in detail in A540-A570.

[0043] Step A600: Execute the optimized heat transfer control instruction set, coordinate and regulate each working unit, and correct the energy consumption control parameters in real time through the feedback system to achieve adaptive energy-saving control of the heater.

[0044] Specifically, when executing the optimized heat transfer control instruction set, the control unit first parses the structured rules in the instruction set, such as "when the external temperature is <10℃ and the predicted heat load is >12kW, the heating power is set to 2.6kW, the heat flux edge distribution ratio is 45%, and the wind speed is 3.2m / s," and simultaneously sends control signals to the heating unit and the heat dissipation unit. Taking low-temperature conditions as an example, after receiving the power adjustment instruction, the heating unit increases the power from the current 2kW to 2.6kW (power compensation coefficient 1.3) through PWM modulation. At the same time, the number of energized heating tube arrays increases from 8 to 10, increasing the heat flux density from 800W / m³. 2 Increased to 950W / m 2 The heat dissipation unit increases the airflow speed from 2.8 m / s to 3.2 m / s according to the command, enhancing the heat convection efficiency and increasing the convective heat transfer coefficient from 28 W / (m²). 2 ·K) increased to 35W / (m 2 •K), to ensure uniform heat distribution after the heating power is increased.

[0045] During the coordinated control process, the control unit collects real-time operating status data of each working unit, such as the actual power of the heating unit (2.58kW) and the air velocity of the heat dissipation unit (3.1m / s), and compares this data with the command set parameters to generate a feedback signal (deviation ≤3%). If a heat flow distribution deviation is detected, such as the temperature in the edge area still being 8℃ lower than that in the center area, the control unit automatically triggers a secondary adjustment: temporarily increasing the heat flow distribution ratio at the edge from 45% to 50%, and adjusting the heating element energizing mode to reduce the temperature difference between areas to ±3℃. This closed-loop control mechanism ensures that the command set adapts to changes in equipment operating status and environment in real time during execution.

[0046] Next, the operating status data of each working unit is collected to generate feedback signals. The deviation is calculated by comparing the data with the current energy consumption control parameters, and the parameters are corrected. The corrected parameters are then input into the control unit to update the heat transfer control commands in real time, thereby realizing the adaptive energy-saving control of the heater. The specific steps are explained in detail in A610-A630.

[0047] By optimizing the structured execution of the heat transfer control command set, adjusting the parameters of multiple working units in a coordinated manner, and correcting the parameters in real time, a closed-loop control system of prediction-control-verification is formed. This system enables the heater to operate adaptively and energy-savingly under complex conditions, ensuring that each working unit always maintains the best energy efficiency matching state under the coordinated control of the command set.

[0048] Furthermore, step A100 in the method provided in this application embodiment includes: A110: Real-time acquisition of internal temperature data of the heater cavity and key components via temperature sensors.

[0049] A120: Real-time acquisition of external temperature and humidity data and external air pressure data of the heater's external environment through humidity and air pressure sensors.

[0050] A130: Collects operating status data of the heating unit, heat dissipation unit, and control unit.

[0051] A140: Integrate the internal temperature data, external temperature and humidity data, external air pressure data, and operating status data to construct the environment-operating status dataset.

[0052] Specifically, firstly, temperature sensors are deployed in key components such as the heater cavity, heating tube, and heat sink to collect internal temperature data in real time, such as the surface temperature of the heating tube reaching 120°C and the temperature of the heat sink maintained at 85°C, thereby comprehensively capturing the internal thermal state.

[0053] For the acquisition of external environmental parameters, humidity sensors and air pressure sensors are used to simultaneously collect temperature and humidity data (e.g., ambient temperature 25℃, humidity 60%RH) and air pressure data (e.g., standard atmospheric pressure 101.3kPa) of the external environment. This reduces the detection deviation caused by the influence of air pressure on heat transfer efficiency, such as the change in gas thermal conductivity when air pressure decreases.

[0054] In acquiring operational status data for each working unit, multi-dimensional parameters are collected, including power consumption data of the heating unit, airflow and flow rate of the heat dissipation unit, and operating mode of the control unit (such as PID control mode). This data can more accurately reflect the actual operating efficiency of each unit. For example, the airflow rate of the heat dissipation unit directly affects the heat convection efficiency; a low airflow rate may lead to insufficient heat dissipation.

[0055] By integrating the aforementioned internal temperature data, external temperature, humidity, and air pressure data, and working unit operating status data, an environmental-operating status dataset is formed, containing multi-dimensional information such as temperature field distribution, environmental climate parameters, and equipment operating status. This dataset can provide more comprehensive input variables for subsequent heat load prediction models. For example, by analyzing the correlation between data such as heating power, cavity temperature, and external humidity, the trend of heat load changes can be predicted more accurately.

[0056] Through the above-mentioned multi-dimensional data collection and integration steps, this content has achieved an upgrade from single parameter detection to full-scene data fusion, providing accurate and comprehensive data support for heater heat load prediction and energy-saving control strategy formulation. It effectively solves the problem of control strategy deviation caused by incomplete data collection in the existing technology and improves the energy efficiency optimization accuracy of the heater system.

[0057] Furthermore, step A200 in the method provided in this application embodiment includes: A210: Extract feature data from the environment-operation status dataset.

[0058] A220: Based on the aforementioned feature data, a heat load prediction model is constructed using regression analysis.

[0059] A230: Train the heat load prediction model using historical data and optimize the model parameters.

[0060] In this embodiment of the application, historical data refers to environmental-operating status data collected by the heater during its past operation.

[0061] Optionally, feature data is first extracted from the environment-operation status dataset, including: heater cavity and key component temperatures, such as heating tube surface temperature 115℃ and heat sink temperature 80℃; external temperature and humidity, ambient temperature 22℃ and humidity 65%RH; air pressure, 101.2kPa; heating unit power consumption, current power 1.8kW; heat dissipation unit wind speed, 2.3m / s, and other dynamic parameters. Through feature engineering, key features such as temperature gradient change rate (5℃ increase per hour), humidity fluctuation coefficient (daily variation ±10%), and power adjustment frequency are extracted to form a multi-dimensional feature vector.

[0062] Based on the aforementioned multidimensional eigenvector data, a heat load prediction model is constructed using regression analysis methods such as multiple linear regression or ridge regression. Taking multiple linear regression as an example, the functional relationship between heat load Q and the characteristic variables is established: Q=α1·T 内 +α2·T 外 +α3·H+α4·P+α5·P 功率 +ε, where α is the regression coefficient, T 内 T外 Here, H represents the internal and external temperatures, H represents the humidity, and P represents the air pressure. 功率 The value represents the heating power. The coefficients are solved using the least squares method to minimize the mean square error between the model's predicted value and the actual heat load. This can be obtained by those skilled in the art based on the actual operating environment parameters of the heater.

[0063] The model was trained and optimized using historical operational data. Data from the past six months (containing 2000 valid samples) was divided into training and test sets in a 7:3 ratio. During training, parameters were iteratively adjusted using gradient descent. Training stopped when the mean squared error of the predictions on the test set fell below 3%. For example, in a low-temperature environment (external temperature 10℃), the trained model could predict a heat load of 12.5kW based on parameters such as a cavity temperature of 100℃, a heating power of 2kW, and a humidity of 70%RH. The deviation from the actual value of 12.3kW was only 1.6%, significantly better than traditional methods.

[0064] Through multi-dimensional feature extraction, regression analysis modeling, and training with historical data, a high-precision heat load prediction model was constructed, realizing the upgrade from empirical prediction to data-driven prediction. This provides accurate heat load prediction basis for subsequent dynamic energy-saving control strategies, enabling the heat load prediction error of the heater under different operating conditions to be controlled within 5%, effectively improving the accuracy of heat transfer optimization and energy utilization efficiency.

[0065] Furthermore, step A400 in the method provided in this application embodiment includes: A410: The energy consumption control parameters include heating power control parameters, heat flow distribution control parameters, wind speed control parameters, and temperature setting parameters.

[0066] Specifically, the heating power control parameter is used to dynamically adjust the heater's output power to match the predicted heat load and actual operating conditions. Its core function is to use power compensation algorithms and adjustment thresholds to increase or decrease power in real time when the heat load fluctuates (e.g., adjusting from 1.8kW to 2.16kW), avoiding energy waste caused by fixed power operation. This is achieved by adjusting power in low-temperature environments (due to insufficient power) or in high-temperature environments (due to overheating). Combined with heat transfer efficiency analysis, the power value is corrected to ensure a precise match between output power and heat demand, thereby improving energy efficiency.

[0067] Heat flux distribution control parameters are used to optimize the heat flux density distribution in different areas inside the heater, solving the problem of heat energy concentrating near the heating tubes and insufficient temperature in the edge areas in traditional control. By setting the heat flux distribution ratio, such as increasing the distribution ratio in the edge areas from 30% to 45%, and adjusting the working status of the heating unit, such as the number of heating tubes energized, the heat flux density gradient is rationalized, such as optimizing it from 2.5:1 to 1.8:1. This ensures that the temperature uniformity error in different areas of the cavity is controlled within ±5℃, thereby improving the overall heat transfer efficiency and avoiding energy loss caused by local overheating or underheating.

[0068] Fan speed control parameters are used to adjust the operating efficiency of fans or cooling devices, influencing the heat transfer process by controlling the intensity of thermal convection. When heat flow distribution is optimized, adjusting the fan speed synchronously, such as increasing it from 2.5 m / s to 2.8 m / s, can enhance heat exchange efficiency: accelerating heat diffusion during the heating phase to avoid localized high temperatures; and speeding up heat dissipation during the cooling phase to maintain temperature stability. This parameter works synergistically with heat flow distribution and heating power to ensure a dynamic balance in the heat transfer process, preventing heat dissipation lag due to insufficient fan speed or additional energy consumption due to excessive fan speed.

[0069] The temperature setting parameter is used to set the target operating temperature of the heater. It dynamically adjusts the threshold based on heat load prediction and environmental parameters (such as external temperature, humidity, and air pressure), for example, adjusting it from 100℃ to 105℃. Through a PID control algorithm or temperature and humidity compensation coefficient, the deviation between the actual temperature and the target temperature is controlled within ±2℃, avoiding repeated heating energy consumption caused by temperature overshoot or fluctuations. This parameter directly affects the energy efficiency ratio of the heating process. For example, in low-temperature, high-humidity environments, temperature compensation logic can increase the target temperature and match the power output, ensuring heating effect while reducing energy loss.

[0070] Furthermore, step A500 in the method provided in this application embodiment includes: A510: Calculate the current power compensation coefficient based on the environmental-operational status dataset and the heat load prediction results.

[0071] A520: Based on the heat load prediction results, adjust the heat flow distribution gradient to optimize the heat flow distribution in each region of the heater.

[0072] A530: Adjust the heating power and heat flow distribution of the heater according to the difference between real-time operating conditions and heat load prediction results.

[0073] In this embodiment, the power compensation coefficient is a parameter used to adjust the heating power of the heater. The heating power is dynamically corrected by this coefficient to match the predicted heat load demand and optimize energy efficiency.

[0074] Specifically, firstly, based on the environmental-operational status dataset and the heat load prediction results, the power compensation coefficient is calculated. For example, when the current power of 1.8kW corresponds to an actual heat load of 10kW, the heat load deviation rate is 25%. The power compensation coefficient of 1.2 is calculated through the linear compensation model (1 + deviation rate × efficiency correction coefficient 0.8), and the power is adjusted to 2.16kW, so that the matching error between the output and the predicted heat load is controlled within ±5%.

[0075] Secondly, the heat flux distribution gradient is adjusted based on the heat load prediction results. For example, in the original heat flux distribution, the heat flux density in the heating pipe region is 800 W / m. 2 350W / m at the edge of the cavity 2 Temperature uniformity error ±10℃, heat flux density optimized to 900W / m³ using gradient adjustment algorithm. 2 and 450W / m 2 This optimized the gradient from 2.3:1 to 2:1, reduced the regional temperature difference to ±3℃, and improved the heat transfer efficiency to 89%.

[0076] Finally, the parameters are dynamically adjusted based on the difference between real-time operating conditions and predicted results. When the real-time detection shows that the fan speed of the heat dissipation unit drops from 2.5m / s to 2.0m / s (affecting heat convection efficiency), and the predicted heat load is 15% higher than the current output, the heating power is simultaneously fine-tuned from 2.16kW to 2.3kW, and the heat flow edge distribution ratio is increased from 35% to 40%, ensuring that the heat output error is ≤3% under fluctuating fan speed conditions.

[0077] By combining environmental data with heat load prediction, dynamically optimizing heat flow distribution gradients, and adapting to real-time operating conditions, the energy consumption control parameters have been upgraded from static setting to dynamic refinement, ensuring that the heater can achieve the best energy-saving effect through precise parameter control under different operating conditions.

[0078] Furthermore, step A500 in the method provided in this application embodiment includes: A540: Based on the differences in the internal and external environmental parameters, set the heat flow priority allocation logic under low temperature environment.

[0079] A550: Based on the temperature and humidity data collected by the humidity sensor, establish power compensation logic for high humidity environments.

[0080] A560: Combines real-time environmental data from the environment-operation status dataset to optimize heat flow distribution strategies under various operating conditions.

[0081] A570: Based on the operating condition adaptation rules composed of heat flow priority allocation logic, power compensation logic, and heat flow allocation strategies under various operating conditions, it generates an optimized heat transfer control instruction set.

[0082] Specifically, firstly, a heat flow priority distribution logic for low-temperature environments is set based on the differences in internal and external environmental parameters. When the external temperature is <15℃, the heat flow priority distribution mechanism for the edge region is triggered. For example, the heat flow distribution ratio is adjusted from the conventional 60% for the center region and 40% for the edge region to 50% for the center and 50% for the edge region. Furthermore, the number of heating tubes energized in the edge region is increased, for example, from 4 sets to 6 sets, so that the heat flow density in the edge region increases from 350W / m². 2 Increased to 500W / m 2 The temperature uniformity error was reduced from ±10℃ to ±3℃.

[0083] Secondly, a power compensation logic for high humidity environments is established based on temperature and humidity data collected by a humidity sensor. When the humidity is greater than 75%RH, the power compensation algorithm is activated: if the current heating power is 2kW, based on the rule of 5% power compensation for every 10%RH increase in humidity, the compensated power is calculated to be 2.3kW, in order to offset the decrease in heat transfer efficiency caused by humidity. The efficiency decreases by about 3% for every 10%RH, ensuring that the actual heating time is consistent with the normal temperature operating conditions.

[0084] Then, by combining real-time environmental data from the environment-operation status dataset, such as temperature 22℃, humidity 60%RH, and air pressure 101kPa, the heat flow distribution strategy under various operating conditions is dynamically optimized. For example, during the equipment startup phase, when the heat load demand surges, the heat flow is preferentially distributed to the heating pipe area at a ratio of 70% to quickly increase the cavity temperature; during the constant temperature phase, the distribution is adjusted to uniform distribution at a ratio of 55%:45% to maintain temperature stability.

[0085] Finally, based on the aforementioned heat flow priority allocation logic, power compensation logic, and dynamic heat flow allocation strategy, a structured optimized heat transfer control instruction set is generated. The instruction set is written in a condition-action format, such as "when the external temperature is <10℃ and the humidity is >70%RH, the heating power is set to 2.5kW, the edge heat flow allocation ratio is 55%, and the forced convection mode (wind speed 3.0m / s) is activated," ensuring the accurate execution of the control strategy under various operating conditions.

[0086] Through analysis of differences in internal and external environmental parameters, construction of multi-condition adaptation logic, and optimization of strategies driven by real-time data, a heat transfer control instruction set covering complex scenarios such as low temperature and high humidity has been formed, enabling the heater to operate efficiently and energy-savingly under all conditions.

[0087] Furthermore, step A600 in the method provided in this application embodiment includes: A610: Generates feedback signals based on the operating status data of each working unit of the heater.

[0088] A620: Compare the feedback signal with the current energy consumption control parameters, calculate the energy consumption deviation, and correct the energy consumption control parameters based on the deviation value.

[0089] A630: The corrected energy consumption control parameters are input into the heater control unit through the feedback system, and the heat transfer control command is updated in real time to realize the adaptive energy-saving control of the heater.

[0090] In one embodiment, firstly, sensors deployed in each working unit (such as power sensors in the heating unit and wind speed sensors in the heat dissipation unit) are used to collect real-time operating status data. Parameters such as the actual output value of heating power (e.g., 2.16kW) and heat dissipation wind speed are converted into electrical signals to generate feedback signals. Taking the heating unit as an example, when an 8% deviation is detected between the actual power and the set value (2.3kW), the feedback signal will be transmitted to the control unit in real time.

[0091] Next, the system compares the feedback signal with the current energy consumption control parameters (such as a power compensation coefficient of 1.2 and a heat flow distribution ratio of 45%), and calculates the energy consumption deviation value using a preset deviation calculation model (such as an absolute error formula). If the deviation exceeds a preset threshold (such as 5%), a parameter correction mechanism is activated: for example, if the temperature in the edge area is still 7°C lower than that in the center area after heat flow distribution (the deviation exceeds the ±5°C threshold), the system automatically increases the heat flow edge distribution ratio from 40% to 45% and recalculates the power compensation coefficient to 1.25.

[0092] Finally, the feedback system inputs the corrected parameters (such as heating power 2.3kW, heat flow distribution ratio 45%:55%) into the control unit via the RS485 communication protocol, and synchronously updates the heat transfer control command set. In a low-temperature environment (external temperature 10℃), the corrected command set will trigger a 10% increase in the heating unit power and activate the strong wind mode of the heat dissipation unit (wind speed 3.2m / s), so that the cavity temperature uniformity error is controlled within ±3℃.

[0093] By using a closed-loop control process of real-time data acquisition, deviation calculation and parameter correction, the energy consumption parameter correction cycle is shortened, heat transfer efficiency is improved, and energy consumption deviation is controlled within 3%. This enables the heater to operate adaptively and save energy under all operating conditions, and solves the problem of low energy efficiency caused by parameter correction lag in existing technologies.

[0094] In summary, the embodiments of this application have the following technical effects: This invention constructs a heat load prediction model by comprehensively collecting internal and external environmental parameters and operating status data of the heater and its working unit. Based on this model, a series of optimized control operations are performed to accurately grasp the heat load of the heater, dynamically optimize heating power and heat flow distribution, adapt to different operating conditions, and achieve adaptive energy-saving control of the heater. This effectively solves the problems of uneven heat distribution, low heat transfer efficiency, and energy waste caused by traditional methods that do not consider real-time changes and dynamic fluctuations, thereby improving the energy efficiency of the heater.

[0095] Example 2 like Figure 2 As shown in the figure, this application provides a heater energy-saving control system based on heat transfer optimization, the system comprising: Dataset construction module 1 is used to collect the internal and external environmental parameters of the heater and the operating status data of the working unit set in real time, and construct the environment-operating status dataset. The working unit set includes heating unit, heat dissipation unit and control unit.

[0096] Prediction model building module 2, which builds a heater heat load prediction model based on the environment-operation status dataset.

[0097] The prediction result acquisition module 3 is used to analyze the heat energy distribution and heat transfer efficiency of the heater according to the heat load prediction model, and obtain the heat load prediction result.

[0098] The instruction set generation module 4 optimizes the heating power and heat flow distribution of the heater based on the heat load prediction results, determines the energy consumption control parameters, and simultaneously generates a heat transfer control instruction set.

[0099] The optimized instruction set generation module 5 is used to dynamically refine the energy consumption control parameters based on the environment-operation status dataset and the heat load prediction results, and optimize the operating condition adaptation rules of the heat transfer control instruction set based on the differences between the internal and external environmental parameters, thereby generating an optimized heat transfer control instruction set.

[0100] The optimized instruction set execution module 6 is used to execute the optimized heat transfer control instruction set, coordinate and regulate each working unit, and correct the energy consumption control parameters in real time through the feedback system to achieve adaptive energy-saving control of the heater.

[0101] Furthermore, the dataset construction module 1 is used to perform the following steps: The internal temperature data of the heater cavity and key components are collected in real time by temperature sensors; the external temperature and humidity data and external air pressure data of the heater's external environment are collected in real time by humidity sensors and air pressure sensors; the operating status data of the heating unit, heat dissipation unit, and control unit are collected; the internal temperature data, external temperature and humidity data, external air pressure data and operating status data are integrated to construct the environment-operating status dataset.

[0102] Furthermore, the prediction model building module 2 is used to perform the following steps: Extract feature data from the environment-operation status dataset; construct a heat load prediction model based on the feature data using regression analysis; train the heat load prediction model using historical data and optimize the model parameters.

[0103] Furthermore, the instruction set generation module 4 is used to perform the following steps: The energy consumption control parameters include heating power control parameters, heat flow distribution control parameters, wind speed control parameters, and temperature setting parameters.

[0104] Furthermore, the optimized instruction set generation module 5 is used to perform the following steps: Based on the environmental-operational status dataset and the heat load prediction results, the current power compensation coefficient is calculated; based on the heat load prediction results, the heat flow distribution gradient is adjusted to optimize the heat flow distribution in each area of ​​the heater; and the heating power and heat flow distribution of the heater are adapted according to the difference between the real-time operating conditions and the heat load prediction results.

[0105] Furthermore, the optimized instruction set generation module 5 is used to perform the following steps: Based on the differences in internal and external environmental parameters, a heat flow priority allocation logic is set for low-temperature environments; a power compensation logic is established for high-humidity environments based on temperature and humidity data collected by humidity sensors; a heat flow allocation strategy for each operating condition is optimized by combining real-time environmental data from the environment-operation status dataset; and an optimized heat transfer control instruction set is generated based on the operating condition adaptation rules composed of the heat flow priority allocation logic, power compensation logic, and heat flow allocation strategies for each operating condition.

[0106] Furthermore, the optimized instruction set execution module 6 is used to perform the following steps: Based on the operating status data of each working unit of the heater, a feedback signal is generated; the feedback signal is compared with the current energy consumption control parameters, the energy consumption deviation is calculated, and the energy consumption control parameters are corrected according to the deviation value; the corrected energy consumption control parameters are input into the heater control unit through the feedback system, and the heat transfer control command is updated in real time to realize the adaptive energy-saving control of the heater.

[0107] Example 3 This application provides a computer-readable storage medium storing instructions. The instructions, when executed on an electronic device, cause the electronic device to perform a heater energy-saving control method based on heat transfer optimization as described in Embodiment 1. The computer-readable storage medium includes, but is not limited to, USB flash drives, hard drives, portable hard drives, cloud storage under cloud technology, and even web pages (here, a web page specifically refers to a web page capable of containing the aforementioned computer program).

[0108] Example 4 This application provides a computer program product containing instructions, characterized in that, when the computer program product is run on an electronic device, the electronic device executes a heater energy-saving control method based on heat transfer optimization as described in Embodiment 1.

[0109] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0110] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0111] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0112] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that an article or apparatus comprising a list of elements includes not only those elements but also other elements not expressly listed. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or apparatus that includes said element.

[0113] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A heater energy-saving control method based on heat transfer optimization, characterized in that, Includes the following steps: Real-time acquisition of internal and external environmental parameters of the heater and operating status data of the set of working units is used to construct an environment-operating status dataset. The operating status data of the set of working units includes the operating status parameters of the heating unit, heat dissipation unit, and control unit under different operating conditions. Based on the aforementioned environment-operation status dataset, a heat load prediction model for the heater is constructed. Based on the heat load prediction model, the heat energy distribution and heat transfer efficiency of the heater are analyzed to obtain the heat load prediction results. Based on the heat load prediction results, the heating power and heat flow distribution of the heater are optimized, energy consumption control parameters are determined, and a heat transfer control instruction set is generated simultaneously. Based on the environmental-operational status dataset and the heat load prediction results, the energy consumption control parameters are dynamically refined, and the operating condition adaptation rules of the heat transfer control instruction set are optimized based on the differences in internal and external environmental parameters to generate an optimized heat transfer control instruction set. The optimized heat transfer control instruction set is executed, the set of working units is coordinated and controlled, and the energy consumption control parameters are corrected in real time through the feedback system to achieve adaptive energy-saving control of the heater.

2. The heater energy-saving control method based on heat transfer optimization according to claim 1, characterized in that, Real-time acquisition of internal and external environmental parameters of the heater and operational status data of the working unit set, constructing an environment-operation status dataset, including the following steps: The internal temperature data of the heater cavity and key components are collected in real time by temperature sensors; the external temperature, humidity and air pressure data of the heater's external environment are collected in real time by humidity sensors and air pressure sensors. Collect operating status data of heating unit, heat dissipation unit, and control unit; integrate the internal temperature data, external temperature and humidity data, external air pressure data, and operating status data to construct an environment-operating status dataset.

3. The heater energy-saving control method based on heat transfer optimization according to claim 1, characterized in that, Based on the aforementioned environment-operational status dataset, a heat load prediction model for the heater is constructed, including the following steps: Extract feature data from the environment-operational status dataset; Based on the aforementioned feature data, a heat load prediction model is constructed using regression analysis. The heat load prediction model is trained and the model parameters are optimized using historical data, where historical data refers to environmental and operating status data collected during the past operation of the heater.

4. The heater energy-saving control method based on heat transfer optimization according to claim 1, characterized in that, The energy consumption control parameters include heating power control parameters, heat flow distribution control parameters, wind speed control parameters, and temperature setting parameters.

5. The heater energy-saving control method based on heat transfer optimization according to claim 1, characterized in that, Based on the environmental-operational status dataset and the heat load prediction results, the energy consumption control parameters are dynamically refined, including the following steps: Based on the environmental-operating status dataset and the heat load prediction results, the current power compensation coefficient is calculated, wherein the heat load prediction results are used for comparison with real-time operating conditions; Based on the heat load prediction results, the heat flow distribution gradient is adjusted to optimize the heat flow distribution in each region of the heater; the heating power and heat flow distribution of the heater are adapted according to the difference between the real-time operating conditions and the heat load prediction results.

6. The heater energy-saving control method based on heat transfer optimization according to claim 1, characterized in that, The optimized heat transfer control command set is generated by optimizing the operating condition adaptation rules based on the differences in internal and external environmental parameters, including the following steps: Based on the differences in the internal and external environmental parameters, a priority allocation logic for heat flow under low-temperature conditions is set. Based on the temperature and humidity data collected by the humidity sensor, a power compensation logic for high humidity environments is established; combined with real-time environmental data in the environment-operation status dataset, the heat flow distribution strategy under various operating conditions is optimized. Based on the operating condition adaptation rules consisting of heat flow priority allocation logic, power compensation logic, and heat flow allocation strategies under various operating conditions, an optimized heat transfer control instruction set is generated.

7. The heater energy-saving control method based on heat transfer optimization according to claim 1, characterized in that, The energy consumption control parameters are corrected in real time through a feedback system, including the following steps: generating a feedback signal based on the operating status data of the heater working unit set; The feedback signal is compared with the current energy consumption control parameters to calculate the energy consumption deviation, and the energy consumption control parameters are corrected based on the deviation value. The corrected energy consumption control parameters are input into the heater control unit through the feedback system, and the heat transfer control commands are updated in real time to achieve adaptive energy-saving control of the heater.

8. The control system of the heater energy-saving control method based on heat transfer optimization according to any one of claims 1-7, characterized in that, include: The dataset construction module is used to collect the internal and external environmental parameters of the heater and the operating status data of the set of working units in real time, and construct the environment-operating status dataset. The operating status data of the set of working units includes the operating status parameters of the heating unit, heat dissipation unit, and control unit under different operating conditions. The prediction model building module constructs a heat load prediction model for the heater based on the environment-operation status dataset. The prediction result acquisition module is used to analyze the heat energy distribution and heat transfer efficiency of the heater according to the heat load prediction model, and obtain the heat load prediction result. The instruction set generation module optimizes the heating power and heat flow distribution of the heater based on the heat load prediction results, determines the energy consumption control parameters, and simultaneously generates a heat transfer control instruction set. The optimized instruction set generation module is used to dynamically refine the energy consumption control parameters based on the environment-operation status dataset and the heat load prediction results, and optimize the operating condition adaptation rules of the heat transfer control instruction set based on the differences between the internal and external environmental parameters, thereby generating an optimized heat transfer control instruction set. An optimized instruction set execution module is used to execute the optimized heat transfer control instruction set, coordinate and regulate the set of working units, and correct energy consumption control parameters in real time through a feedback system to achieve adaptive energy-saving control of the heater.

9. A computer-readable storage medium storing instructions, characterized in that, When the instruction is executed on the electronic device, the electronic device performs a heater energy-saving control method based on heat transfer optimization as described in any one of claims 1-7.

10. A computer program product containing instructions, characterized in that, When the computer program product is run on an electronic device, the electronic device performs a heater energy-saving control method based on heat transfer optimization as described in any one of claims 1-7.

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