Intelligent control method and system for linear heater

Abstract

The invention discloses an intelligent control method and system for a linear heater, and relates to the technical field of heaters. According to the method, the linear heater is divided into a plurality of equidistant control sections in the length direction of a bathroom floor heating floor; each control section is provided with a collector integrated with a temperature collection module, a bathtub thermal inertia detection module and a heating response rate recording module, and thermal load data such as the real-time temperature T, the heating rate delta T in unit time and the humidity Wc of the control section can be synchronously obtained. In combination with bathroom floor dry density Ps and specific heat capacity Cs parameters, a derivation method of a bathroom floor thermal inertia coefficient K is constructed based on a thermal inertia theory, and thermal response hysteresis characteristics of different bathroom floor areas can be quantitatively represented. Meanwhile, the central control server carries out data cleaning, exception elimination and interpolation complementation processing and normalization and unit standardization processing based on a set time window, a standardized data set is constructed, and an accurate and universal data basis is provided for subsequent thermal load grade evaluation.

Description

Technical Field

[0001] This invention relates to the field of heater technology, specifically to an intelligent control method and system for a linear heater. Background Technology

[0002] In existing home bathroom environments, widely used electric towel racks, wall-mounted heaters, or whole-house hot water circulation heating systems can alleviate the coldness of showering in winter to some extent. However, their temperature response is slow and their ability to regulate temperature in different areas is poor. Especially on damp floors, they suffer from delayed heat diffusion, localized overheating, or slow temperature rise over extended periods. These systems often use a fixed-power continuous heating mode and cannot dynamically sense the heat load of different floor areas. As a result, they often lead to high heating energy consumption and uneven comfort, especially in shower areas where the temperature of the floor tiles is slow to change or in areas near the door where cold air can interfere with the heating. Users often experience the discomfort of "cold water, even colder feet."

[0003] Currently, some high-end bathrooms use embedded electric underfloor heating as their heating solution. While these systems offer some timed heating and overall temperature control, they primarily employ a uniform thermal control logic without independent response modeling or differentiated adjustments for different areas. In actual use, due to variations in heat capacity distribution caused by the floor tile structure, recessed design, and drainage slope, some areas tend to overheat while others heat up slowly. This is especially true in humid environments where thermal inertia intensifies, significantly reducing floor heating efficiency. Existing systems lack the ability to feedforward control to address this heat diffusion lag, thus failing to achieve truly personalized, zoned, and precise temperature control.

[0004] The root cause of the poor heating uniformity mentioned above lies in the fact that bathroom heating equipment in most homes or smart home systems typically lacks the ability to adaptively adjust parameters during operation. Traditional heating systems mostly rely on manual presets or feedback from a single temperature sensor, failing to dynamically adjust parameters in real time based on the temperature distribution, heating response rate, or humidity changes in different areas of the floor. The lack of quantitative evaluation methods for temperature control effectiveness, such as temperature uniformity scoring and heat load level feedback mechanisms, makes it difficult to perform intelligent optimization and energy-saving control during long-term system operation. As users' demands for bathroom comfort increase, developing a new type of intelligent bathroom floor heating system capable of "heating on demand, dynamic compensation, fine-tuning, and continuous self-adaptation" is particularly necessary. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an intelligent control method and system for a linear heater, solving the problems mentioned in the background section.

[0006] To achieve the above objectives, the present invention provides the following technical solution, comprising the following steps: S1. Divide the linear heater into multiple control segments, set up a data collector at each control segment location to acquire the heat load data of each control segment, and transmit the heat load data to the central control server for preprocessing to obtain a standardized dataset. S2. Construct a heat load calculation model based on a standardized dataset, calculate the heat load level value E for each control segment, and execute multi-level heating strategy control according to the heat load level value E. S3. Based on the control results of the multi-level heating strategy, a triggering heat diffusion lag feedforward regulation mechanism is implemented to calculate the pre-compensation heating extension time Δt, and the heating strategy is adjusted by time extension to complete the heating control update. S4. After each control cycle ends, calculate the temperature discrete score Qs for all control segments and compare it with the set equilibrium threshold range. Based on the comparison results, optimize and reconstruct the parameters in the heat load calculation model.

[0007] Preferably, S1 includes S11; S11. The linear heater is evenly divided into several equidistant control segments along the length of the bathroom floor heating system. Each control segment is set as an independent logical heating area. At the same time, a data collector is set in each control segment. The data collector of each control segment is set on the bottom and side edge structure of the bathroom floor below the center area of ​​the corresponding control segment to collect heat load data in real time. The data acquisition unit includes a temperature acquisition module, a bathtub thermal inertia detection module, and a heating response rate recording module; The heat load data includes the real-time temperature T of the nth control segment region. n The unit time temperature rise rate ΔT of the nth control segment region n and the humidity Wc of the nth control segment area n .

[0008] Preferably, S1 further includes S12; S12. The heat load data collected in real time by the collector is sent to the central control server through the wireless communication module. In the central control server, the heat load data is preprocessed. The preprocessing is based on a set time window and performs data cleaning, outlier removal and interpolation completion operations. For each segment of humidity Wc in the pre-processed heat load data, combined with the preset bathroom floor dry density Ps and specific heat capacity Cs, the thermal inertia coefficient K, which is used to evaluate the degree of hysteresis of the bathroom floor thermal response in all segments, is obtained using the thermal inertia calculation formula. And the real-time temperature T of the nth control segment region in the heat load data. n and the unit time temperature rise rate ΔT of the nth control segment region nBy combining the thermal inertia coefficient K of each control segment, dimensional normalization and unit standardization are performed to form a standardized dataset with a unified format.

[0009] Preferably, S2 includes S21; S21. Construct a heat load calculation model based on a standardized dataset, calculate the heat load level value E of each control segment, and measure the current heat load status of each control segment. The heat load level value E is calculated using the following heat load calculation model; ; In the formula, E n This represents the heat load level value of the nth control segment, Ttarget represents the preset target temperature, which is the initially set dimensionless value, K n This represents the thermal inertia coefficient of the nth control segment. This represents the heating rate response factor. Indicates the thermal inertia compensation factor. This represents a tiny constant to prevent division by zero, with a value of 0.01.

[0010] Preferably, S2 further includes S22; S22. By fitting and regressing different temperature gradients, thermal response rates and final thermal equilibrium times in historical heating cycles, a heating load threshold model is constructed, and the first judgment threshold F1, the second judgment threshold F2 and the third judgment threshold F3 are automatically generated. The heat load level value E of each control segment is divided into heat load levels according to the first judgment threshold F1, the second judgment threshold F2, and the third judgment threshold F3, and a multi-level heating strategy control is executed simultaneously; the specific division is as follows: When the heat load level value E of the nth control segment n When the value is greater than or equal to the first judgment threshold F1, it is determined to be heating level 3, and a heating strategy of 90% time percentage within the set control cycle is executed. When the second judgment threshold F2 ≤ the heat load level value E of the nth control segment n When the first judgment threshold F1 is reached, it is determined to be heating level 2, and a heating strategy of 60% time percentage within the set control cycle is executed. When the third judgment threshold F ≤ the heat load level value E of the nth control segment n When the second judgment threshold F2 is reached, it is determined to be heating level 1, and a heating strategy with a time percentage of 30% within the set control cycle is executed. When the heat load level value E of the nth control segment n When the value is less than the third judgment threshold F, the heating level is determined to be 0, and heating is not performed in the current cycle; When the heating level remains constant for two consecutive heating control cycles, and the unit time temperature rise rate ΔT in the nth control segment region... n If the value is less than 0.2, it will be automatically identified as thermal response hysteresis.

[0011] Preferably, S3 includes S31; S31. When thermal response hysteresis is identified, a thermal diffusion hysteresis feedforward control mechanism is triggered. The thermal diffusion hysteresis feedforward control mechanism uses the unit time temperature rise rate ΔT of the nth control segment region based on the current control segment standard dataset. n Combined with the thermal inertia coefficient K of the nth control segment region n The pre-compensation heating extension time Δt is calculated and output, and heating compensation is implemented for the control section with thermal response lag. The pre-compensation heating extension time Δt is calculated and output using the following algorithm formula; ; In the formula, △t n This indicates the pre-compensation heating extension time for the nth control segment. This represents the minimum heating rate threshold, and its value is dimensionless. Kavg represents the average thermal inertia value across all control terminals. This represents the feedforward response adjustment factor.

[0012] Preferably, S3 further includes S32; S32. The pre-compensation heating extension time Δt of the nth control segment calculated based on the response lag segment. n The heating strategy is adjusted by extending the heating time percentage within the current control cycle according to the following strategy, thus completing the heating control update: If the current heat load level of the control segment is Level 1, then based on the original heating time percentage of 30%, the heating time will be extended by 30% + the pre-compensation heating extension time Δt of the nth control segment. n / Complete heating control cycle time Tp; If the current heat load level of the control segment is Level 2, the heating time will be extended by 60% + the pre-compensation heating extension time Δt of the nth control segment, based on the original heating time percentage of 60%. n / Complete heating control cycle time Tp; If the current control section's heat load level is Level 3, then maintain the heating time percentage at 90% to prevent the floor from overheating and burning your feet.

[0013] Preferably, S4 includes S41; S41. After the heating control update is completed, the updated real-time temperature T' of each control segment is reacquired. The normalized difference between the updated real-time temperature T' of all control segments and the preset target temperature Ttarget is calculated. Based on the total number of control segments N, the temperature discrete score Qs is calculated and output. The deviation of all control segments is summarized and evaluated. The temperature discrete score Qs is calculated and output using the following algorithm formula; ;

[0014] In the formula, N represents the total number of control segments. This represents the updated real-time temperature of the nth control segment.

[0015] Preferably, S4 further includes S42; S42. Based on actual bathroom floor temperature control, a balance threshold range is set at ±5% of the preset target temperature Ttarget. This balance threshold range is compared with the real-time temperature discrete score Qs to determine the balance of the temperature control effect. Based on the comparison results, the parameters in the heat load calculation model are optimized and reconstructed. The specific comparison content is as follows: When the temperature dispersion score Qs is within the balance threshold range, it indicates that the temperature control operation is qualified and the current parameters should be maintained. When the temperature discrete score Qs is not within the equilibrium threshold range, it indicates that the temperature control operation is not qualified. At this time, the parameter adaptive adjustment process of the heat load calculation model is executed, and the heating control is updated again after adjustment. The adaptive parameter adjustment process of the heat load calculation model constructs an objective function based on a historical standardized dataset of M control cycles, using the original heat load calculation model form, and employs linear regression to adjust the heating rate response factor with the goal of minimizing temperature control error. With thermal inertia compensation factor Perform parameter reestimation and output the optimal combination.

[0016] A linear heater intelligent control system includes a control section acquisition module, a heat load level classification module, a multi-level heating control module, and a temperature discrete analysis module; The control segment acquisition module divides the linear heater into multiple control segments, sets an acquisition device at the location of each control segment, acquires the heat load data of each control segment, and transmits the heat load data to the central control server for preprocessing to obtain a standardized dataset. The heat load level classification module constructs a heat load calculation model based on a standardized dataset, calculates the heat load level value E for each control segment, and executes multi-level heating strategy control based on the heat load level value E. The multi-level heating control module calculates the pre-compensation heating extension time Δt by triggering a heat diffusion lag feedforward regulation mechanism based on the control results of the multi-level heating strategy, and adjusts the heating strategy by time extension to complete the heating control update. The temperature discrete analysis module calculates the temperature discrete score Qs of all control segments after each control cycle ends, compares it with the set equilibrium threshold range, and optimizes and reconstructs the parameters in the heat load calculation model based on the comparison results.

[0017] This invention provides an intelligent control method and system for a linear heater. It has the following beneficial effects: (1) This method divides the linear heater along the length of the bathroom floor heating system into several equidistant control segments. Each control segment is equipped with a data acquisition module integrating a temperature acquisition module, a bathtub thermal inertia detection module, and a heating response rate recording module. This allows for the simultaneous acquisition of real-time temperature T, heating rate ΔT per unit time, and humidity Wc, among other heat load data, for the control segment. Combined with the bathroom floor dry density Ps and specific heat capacity Cs parameters, and based on the thermal inertia theory, a method for deriving the bathroom floor thermal inertia coefficient K can quantify the thermal response hysteresis characteristics of different bathroom floor areas. Simultaneously, the central control server performs data cleaning, anomaly removal, and interpolation completion based on a set time window. It also performs unified dimensional normalization and unit standardization on the multidimensional data to construct a standardized dataset, providing an accurate and universal data foundation for subsequent heat load level assessment. Compared with traditional single-parameter thermal control systems, this scheme significantly improves adaptability and data accuracy in complex agricultural environments.

[0018] (2) This method constructs a calculation model for the three-factor coupled heat load level value E by integrating the current temperature difference, the heating rate per unit time ΔT, and the thermal inertia coefficient K; combined with the automatically generated heat load level boundary threshold, the heat load level value E is classified into levels, matching three pulse control strategies: level 1, 30% heating ratio; level 2, 60% heating ratio; and level 3, 90% heating ratio, to achieve flexible adjustment; when the level remains unchanged for two consecutive heating cycles and ΔT is detected, the method can be used to calculate the heat load level value E. n When the value is less than or equal to 0.2, it is automatically identified as "thermal response hysteresis", which further triggers the "thermal diffusion hysteresis feedforward control mechanism" constructed based on the heating rate and thermal inertia, and outputs the pre-compensated heating extension time Δt. n It is applied to the current heating ratio in a "level gain" manner to improve the gentleness of the response and the stability of the system, avoid overheating and vibration problems, and is particularly suitable for greenhouse environments with differences in bathroom floor humidity gradient and structural thermal inertia.

[0019] (3) This method calculates the temperature discrete score Qs by setting the updated real-time temperature T' at the end of the control cycle and combining it with the preset target temperature Ttarget. This score serves as a quantitative indicator to measure the overall temperature control balance of the system. The method specifies that the temperature discrete score Qs falls within the acceptable range, otherwise it falls within the unacceptable range. When the score falls within the unacceptable range, the response factor of the heating rate in the heat load calculation model is automatically triggered. With thermal inertia compensation factor The parameter re-estimation process is as follows: Based on a standardized historical dataset spanning M control cycles, a linear regression path is constructed with the minimum temperature control error as the objective function. The optimal parameter combination is extracted to achieve adaptive model updates. This mechanism can dynamically address the temperature control mismatch caused by actual bathroom floor conditions, thermal disturbances, and environmental changes. It constructs a data-driven feedback optimization loop, significantly enhancing the overall temperature control robustness and self-learning capability of the system. Attached Figure Description

[0020] Figure 1 This is a schematic diagram illustrating the steps of an intelligent control method for a linear heater according to the present invention.

[0021] Figure 2 This is a block diagram of a linear heater intelligent control system module according to the present invention.

[0022] Figure 3 Schematic diagram of segmented data acquisition structure of a linear heater. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example

[0024] Please see Figure 1 and Figure 3 This invention provides an intelligent control method for a linear heater. To achieve the above objectives, this invention employs the following technical solution, comprising the following steps: S1. Divide the linear heater into multiple control segments, set up a data collector at each control segment location to acquire the heat load data of each control segment, and transmit the heat load data to the central control server for preprocessing to obtain a standardized dataset. S2. Construct a heat load calculation model based on a standardized dataset, calculate the heat load level value E for each control segment, and execute multi-level heating strategy control according to the heat load level value E. S3. Based on the control results of the multi-level heating strategy, a triggering heat diffusion lag feedforward regulation mechanism is implemented to calculate the pre-compensation heating extension time Δt, and the heating strategy is adjusted by time extension to complete the heating control update. S4. After each control cycle ends, calculate the temperature discrete score Qs for all control segments and compare it with the set equilibrium threshold range. Based on the comparison results, optimize and reconstruct the parameters in the heat load calculation model.

[0025] In this embodiment, the method significantly improves the response efficiency and temperature uniformity of the heating system by constructing an intelligent control process that integrates data acquisition, heat load modeling, feedforward compensation control, and closed-loop adaptive optimization. First, in step S1, the linear heater is divided into multiple control segments, and an integrated data acquisition unit is set in each segment to accurately acquire multimodal heat load data such as real-time temperature T, unit time heating rate ΔT, and humidity Wc, avoiding miscontrol problems caused by single temperature judgments. This method is particularly suitable for complex environments with structural heat dissipation differences or bathroom floor humidity gradients. Then, in step S2, a heat load calculation model based on a standardized dataset is constructed. Taking into account indicators such as the thermal inertia coefficient K and response rate ΔT, the heat load level value E is calculated, and multi-level heating strategy control is executed accordingly to achieve differentiated heating for different control segments, effectively improving the overall energy efficiency. Furthermore, in step S3, to address the response lag problem caused by heat diffusion hysteresis, a heat diffusion hysteresis feedforward control mechanism triggered by insufficient heating rate ΔT per unit time is introduced. A custom empirical compensation algorithm calculates the pre-compensation heating extension time Δt, and this pre-compensation heating extension time Δt is proportionally applied to the original heating ratio, achieving continuous gain adjustment within the heating level. This method avoids drastic temperature fluctuations caused by abrupt changes in heating level, improving system operational stability. Finally, in step S4, the overall level of temperature deviation in each control segment is evaluated using a temperature dispersion score Qs, and a balance threshold range is set to determine the control effect. If the target is not met, the parameters of the heat load model are automatically adaptively reconstructed, improving the model's long-term adaptability and preventing control performance degradation due to environmental changes. Through the above implementation process, this invention not only improves the system's ability to perceive differences in heat load in different regions but also enhances its predictive compensation capability for heating hysteresis problems, constructing a complete closed-loop temperature control link from "detection—control—evaluation—optimization," achieving high balance, high stability, and low energy consumption heating control objectives under different crops, greenhouse structures, and climatic conditions.

[0026] Example 2 Please see Figure 1 and Figure 3 Specifically: S1 includes S11; S11. The linear heater is evenly divided into several equidistant control segments along the length of the bathroom floor heating system. Each control segment is set as an independent logical heating area. At the same time, a data collector is set in each control segment. The data collector of each control segment is set on the bottom and side edge structure of the bathroom floor below the center area of ​​the corresponding control segment to collect heat load data in real time. The data acquisition unit includes a temperature acquisition module, a bathtub thermal inertia detection module, and a heating response rate recording module; Heat load data includes the real-time temperature T of the nth control segment region. n The unit time temperature rise rate ΔT of the nth control segment region n and the humidity Wc of the nth control segment area n ; The temperature acquisition module includes a thermistor, which is used to periodically collect the thermal changes between the surface and bottom of the bathroom floor and to obtain the real-time temperature T of each control segment. The bathtub thermal inertia detection module is based on the humidity Wc of the nth control segment region. n ; The heating response rate recording module is used to record the temperature rise amplitude and time difference of the control segment in the previous heating cycle, and to calculate the unit time heating rate ΔT of each control segment.

[0027] S1 also includes S12; S12. The heat load data collected in real time by the collector is sent to the central control server through the wireless communication module. In the central control server, the heat load data is preprocessed. The preprocessing is based on a set time window and performs data cleaning, outlier removal and interpolation completion operations. For each segment of humidity Wc in the pre-processed heat load data, combined with the preset bathroom floor dry density Ps and specific heat capacity Cs, the thermal inertia coefficient K, which is used to evaluate the degree of hysteresis of the bathroom floor thermal response in all segments, is obtained using the thermal inertia calculation formula. The main reason for replacing thermal conductivity in the thermal inertia calculation formula with moisture Wc is that there is a positive correlation between the thermal conductivity of bathroom floor and moisture Wc, mainly due to the high thermal conductivity of water and the fact that water fills the pores of bathroom floor. And the real-time temperature T of the nth control segment region in the heat load data. n and the unit time temperature rise rate ΔT of the nth control segment region n By combining the thermal inertia coefficient K of each control segment, dimensional normalization and unit standardization are performed to form a standardized dataset with a unified format.

[0028] In this embodiment, the method divides the linear heater along the length of the bathroom floor heating system into multiple equidistant control segments. Each control segment is equipped with a temperature acquisition module, a bathtub thermal inertia detection module, and a heating response rate recording module. This enables precise monitoring of thermal behavior within each logical area, avoiding the problems of regional overheating or insufficient heating caused by traditional systems that rely solely on single-point temperature feedback. For example, if a certain area is near a greenhouse vent, its heat loss is significant. Relying solely on edge probe sampling will underestimate the overall heat load. By setting up a multi-point acquisition structure at the center and edges, this problem can be effectively avoided, enhancing the representativeness of the heat load assessment. After data acquisition, real-time data is uploaded to the central control server using a wireless communication module. Data cleaning and interpolation are performed based on a sliding time window to ensure data continuity and validity, effectively eliminating interference from acquisition jitter and transmission packet loss on the calculation model. Furthermore, by incorporating moisture content Wc, bathroom floor dry density Ps, and specific heat capacity Cs into the thermal inertia formula to calculate the thermal inertia coefficient K, this method can replace traditional thermal conductivity detection hardware, reducing system complexity. Moreover, due to the high thermal conductivity and strong pore-filling effect of water, bathroom floor thermal conductivity is positively correlated with moisture content Wc, providing a sound physical basis for substitution. Finally, the real-time temperature T, heating rate ΔT per unit time, and thermal inertia coefficient K of the control segment are normalized and standardized to unify the data scale and dimensions, forming a standardized dataset. This facilitates subsequent heat load modeling and control decisions, improves algorithm adaptability and stability, and ensures universality and scalability across different environments and crop types.

[0029] Example 3 Please see Figure 1 Specifically: S2 includes S21; S21. Construct a heat load calculation model based on a standardized dataset, calculate the heat load level value E of each control segment, and measure the current heat load status of each control segment. The heat load level value E is calculated using the following heat load calculation model; ; In the formula, E n This represents the heat load level value of the nth control segment, Ttarget represents the preset target temperature, which is the initially set dimensionless value, K n This represents the thermal inertia coefficient of the nth control segment. This represents the heating rate response factor. Indicates the thermal inertia compensation factor. This represents a tiny constant to prevent division by zero, with a value of 0.01.

[0030] The construction of the heat load calculation model includes setting the heating rate response factor. With thermal inertia compensation factor Heating rate response factor The weight used to adjust the heating rate ΔT, thermal inertia compensation factor The weights used to adjust the thermal inertia coefficient K, and the heating rate response factor. The value ranges from 0.1 to 1.0; thermal inertia compensation factor. The value ranges from 0.05 to 0.6 and can be adaptively set through historical data fitting, structural thermal resistance measurement, or feedback from the heating system. The heat load calculation model is constructed based on the basic theory of heat conduction and the method for estimating the dynamic response characteristics of heat transfer; in: The temperature difference term represents the deviation of the current temperature from the target temperature, and is the most intuitive indicator of heating demand. This represents the response rate correction term, and its reciprocal represents the time required for a unit temperature rise. A larger reciprocal indicates that the current segment is difficult to heat up and has poor heat transfer capacity. This is to prevent the unit time temperature rise rate ΔT in the nth control segment from being reduced. n A value of zero can lead to a division-by-zero error, so a small constant ε, such as 0.01, is introduced for numerical protection. This represents the thermal inertia term, characterizing the degree of response delay of the control segment to temperature changes. A larger value indicates stronger heating inertia, requiring higher energy consumption and a longer time to maintain the temperature rise. This is further addressed by the thermal inertia compensation factor. Adjust the weight of the thermal inertia coefficient Kn of the nth control segment in the evaluation of the heat load level value E; In the dimensional consistency analysis, the three factors in the standardized dataset are all after dimensional normalization and unit normalization. The preset target temperature Ttarget is the initial input and is a dimensionless value that adapts to the heat load calculation model. Therefore, the heat load level value E is a dimensionless numerical index used for heat load level classification. It has relativity and stepness, so it belongs to the numerical control factor in the control algorithm and is not a physical output quantity.

[0031] S2 also includes S22; S22. By fitting and regressing different temperature gradients, thermal response rates, and final thermal equilibrium times in historical heating cycles, a heating load threshold model is constructed, automatically generating a first judgment threshold F1, a second judgment threshold F2, and a third judgment threshold F3, which correspond to the boundaries of the control segment reaching high, medium, and low heat loads, respectively. The heating load threshold model can be constructed using multinomial regression or logistic regression, and its regression output corresponds to the comprehensive heat load level response of the control segment. Different level boundary values ​​are extracted through clustering or quantile analysis methods. The heat load level value E of each control segment is divided into heat load levels according to the first judgment threshold F1, the second judgment threshold F2, and the third judgment threshold F3, and a multi-level heating strategy control is executed simultaneously; the specific division is as follows: When the heat load level value E of the nth control segment n When the value is greater than or equal to the first judgment threshold F1, it is determined to be heating level 3, and a heating strategy of 90% time percentage within the set control cycle is executed. When the second judgment threshold F2 ≤ the heat load level value E of the nth control segment n When the first judgment threshold F1 is reached, it is determined to be heating level 2, and a heating strategy of 60% time percentage within the set control cycle is executed. When the third judgment threshold F ≤ the heat load level value E of the nth control segment n When the second judgment threshold F2 is reached, it is determined to be heating level 1, and a heating strategy with a time percentage of 30% within the set control cycle is executed. When the heat load level value E of the nth control segment n When the value is less than the third judgment threshold F, the heating level is determined to be 0, and heating is not performed in the current cycle; When the heating level remains constant for two consecutive heating control cycles, and the unit time temperature rise rate ΔT in the nth control segment region... n If the value is less than 0.2, it will be automatically identified as thermal response hysteresis; Here, time-proportion heating control refers to pulse control of the heater in the control segment within a fixed heating control cycle, such as 60 seconds, based on time. For example, the heating strategy of level 3 corresponds to the heater being powered on for no less than 90% of the cycle, i.e., more than 54 seconds, in order to achieve a higher heat input. This method achieves dynamic response of segmented temperature control by controlling the heater on / off ratio.

[0032] In this embodiment, a heat load calculation model based on a standardized dataset outputs a heat load level value E for each control segment. This allows heating decisions to simultaneously consider multiple factors such as real-time temperature difference, heating rate ΔT per unit time, and thermal inertia coefficient K. In particular, using thermal inertia as a modeling factor effectively identifies regions with slow heat transfer, avoiding misjudgments based solely on current temperature levels and improving the robustness of thermal response identification. The heat load calculation model incorporates a heating rate response factor and a thermal inertia compensation factor as adjustment weights, which can be flexibly and adaptively set according to historical operating conditions, achieving dynamic modeling capabilities. For example, under conditions of a sudden change in the moisture content of the bathroom floor after showering, thermal inertia increases dramatically. If the thermal inertia coefficient K parameter is not considered, the slow heating of the control segment may be misjudged as a "sufficient state" requiring no heating. The thermal inertia term effectively avoids this risk. After the heat load level value E is calculated, section S22 uses fitted regression to construct a multi-level heating threshold model, automatically dividing the control segment state into four levels and executing precise control using a "heating time percentage" method. This avoids the energy waste or temperature overshoot problems caused by traditional jump-level switching. Taking a 60-second control cycle as an example, the power-on time for level 3 is limited to 54 seconds (90% upper limit) to ensure temperature control strength, while reserving 10% of non-power-on time for observing key parameters such as the unit time heating rate ΔT, maintaining the system control window. In particular, setting "no change for two consecutive cycles + unit time heating rate ΔT < 0.2" as the criterion for thermal response lag helps identify temperature rise stagnation segments and trigger compensation mechanisms in a timely manner, preventing control inertia. Through the implementation of this strategy, the overall intelligence, judgment accuracy, and energy efficiency of heating control are improved, adapting to dynamic heat load changes in complex bathroom floor environments, and enhancing the system's control precision and temperature control stability.

[0033] Example 4 Please see Figure 1 Specifically: S3 includes S31; S31. When thermal response hysteresis is identified, the thermal diffusion hysteresis feedforward control mechanism is triggered. The thermal diffusion hysteresis feedforward control mechanism controls the temperature rise rate ΔT per unit time in the nth control segment region based on the current control segment standard dataset. n Combined with the thermal inertia coefficient K of the nth control segment region n The pre-compensation heating extension time Δt is calculated and output, and heating compensation is implemented for the control section with thermal response lag. The pre-compensation heating extension time Δt is calculated and output using the following algorithm formula; ; In the formula, △t n This indicates the pre-compensation heating extension time for the nth control segment. This represents the minimum heating rate threshold, and its value is dimensionless. Kavg represents the average thermal inertia value across all control terminals. represents the feedforward response adjustment factor, used to compensate for the preheating delay required for insufficient heating rate response delay. In the control system, it is used to quantify the influence of the current heating hysteresis on the compensation time. A value of 10 seconds represents the compensation time under the maximum hysteresis state. v represents the structural thermal inertia adjustment factor, used to adjust the weight of the structural response delay influence of the thermal inertia coefficient K. A value of 5 seconds represents the compensation time added when the thermal inertia coefficient K is equal to the average thermal inertia value Kavg at the control end. In the control system, it is used to quantify the ability of different bathroom floor or environmental thermal inertia to adjust the heating advance amount. This formula is not directly derived from a classical physics formula, but rather is an empirical control formula constructed by combining the control compensation requirements of thermal diffusion hysteresis phenomenon and performing feedforward compensation heating regulation on the response hysteresis control segment of the control system. It has the following derivation basis:

[0034] First item The response rate compensation term is based on the heating rate ΔT and the minimum heating rate threshold. The response difference factor is constructed from the ratio between them; it represents the degree of deviation between the actual heating rate and the minimum reasonable heating threshold in this segment; if ΔT n If the value approaches 0, there is no significant temperature increase; this term approaches 0. The extension time is the longest; if ΔT n ≥ If the value approaches 0, then no compensation is needed. Second item The thermal diffusion hysteresis term, derived from thermal inertia theory, is used to measure the delay characteristics of thermal response in different control segments; the thermal inertia coefficient K of the nth control segment region. n The larger the value, the slower the response of that segment to heat conduction, and the longer the compensation time required; the average thermal inertia value Kavg of all control segments is used to normalize the characteristics of each segment and prevent absolute values ​​from affecting the stability of regulation. Dimensional consistency explanation: Δt n : The unit is seconds (s); ΔT n , : is dimensionless; K n Kavg: thermal inert, unitless; V: The unit is seconds, used to ensure the overall Δt n The output dimensions are consistent; Therefore, both weighting terms are dimensionless coefficients multiplied by unit factors, resulting in seconds, and the final result is Δt. n The physical dimensions are consistent, and the unit is uniformly "second," which makes logical sense.

[0035] S3 also includes S32; S32. The pre-compensation heating extension time Δt of the nth control segment calculated based on the response lag segment. n The heating strategy is adjusted by extending the heating time percentage within the current control cycle according to the following strategy, thus completing the heating control update: If the current heat load level of the control segment is Level 1, then based on the original heating time percentage of 30%, the heating time will be extended by 30% + the pre-compensation heating extension time Δt of the nth control segment. n / Complete heating control cycle time Tp; If the current heat load level of the control segment is Level 2, the heating time will be extended by 60% + the pre-compensation heating extension time Δt of the nth control segment, based on the original heating time percentage of 60%. n / Complete heating control cycle time Tp; If the current control section's heat load level is level 3, then the heating time percentage should remain unchanged at 90% to prevent the ground from overheating and burning your feet. Among them, the pre-compensation heating extension time Δt of the nth control segment n The complete heating control cycle time Tp represents the relative time extension ratio, which is directly applied to the current heating percentage and limits the final heating percentage to no more than 100%. For example: the complete heating control cycle time Tp is 60s, the original heating level 1 is 30%, and the pre-compensation heating extension time Δt of the nth control segment... n If the heating time is 6 seconds, then the heating rate becomes (30% + 6 / 60) = 40%. Instead of making large adjustments through "level jumps", it is better to use a "continuous gain" method, such as the pre-compensation heating extension time Δtn of the nth control segment / the complete heating control cycle time Tp, to achieve a gentle transition; It can significantly reduce the risk of system oscillation and improve stability, and is particularly suitable for greenhouse scenarios with slow thermal response; Level 3 is set at a 90% upper limit for two reasons: to prevent overheating risks, avoiding burns or damage to the heating element caused by excessively high temperatures; and to ensure remaining control flexibility, allowing for non-heating time to facilitate the system's detection of the temperature rise rate ΔT. n If parameters such as 100% power-on are set, the observation window will be lost.

[0036] In this embodiment, the overheat diffusion lag feedforward control mechanism activates the feedforward control strategy in real time. Its core lies in predicting the required heating compensation amount in advance based on the physical characteristics of insufficient thermal response. Especially considering the significant impact of the bathroom floor's thermal inertia coefficient Kn on heat conduction delay, without compensation, a "false steady-state" misjudgment is highly likely, meaning the temperature remains at the critical point for a long time without further power increase. By introducing two parameters, ΔTn and Kn, as the basis for feedforward calculation, the system can immediately make time-proportion compensation within the existing insufficient response period, improving the adjustment sensitivity. In S32, the pre-compensation heating extension time Δt of the nth control segment is further used. n To compensate for the performance indicators, the time percentage of the current heating level is precisely extended, rather than using a crude "level + 1" adjustment. For example, if the original heating time for level 1 is 30%, it is adjusted to 40% when Δtn=6s and Tp=60s. This maintains level stability while smoothly increasing output, avoiding frequent jumps that could cause system oscillations. Furthermore, level 3 is capped at 90% of the energizing time to reserve a 10% observation window for subsequent judgment of key parameters such as ΔTn and Kn, preventing blind control. This mechanism is particularly suitable for adaptive heating temperature scenarios in bathroom underfloor heating. In situations with high structural thermal resistance, large heat capacity, and slow response, it can significantly reduce control lag time, improving the continuity and energy efficiency of the entire temperature control process.

[0037] Example 6 Please see Figure 1 Specifically: S4 includes S41; S41. After the heating control update is completed, the updated real-time temperature T' of each control segment is reacquired. The normalized difference between the updated real-time temperature T' of all control segments and the preset target temperature Ttarget is calculated. Based on the total number of control segments N, the temperature discrete score Qs is calculated and output. The deviation of all control segments is summarized and evaluated. The temperature discrete score Qs is calculated and output using the following algorithm formula; ; In the formula, N represents the total number of control segments. This represents the updated real-time temperature of the nth control segment.

[0038] S4 also includes S42; S42. Based on actual bathroom floor temperature control, a balance threshold range is set at ±5% of the preset target temperature Ttarget. This balance threshold range is compared with the real-time temperature discrete score Qs to determine the balance of the temperature control effect. Based on the comparison results, the parameters in the heat load calculation model are optimized and reconstructed. The specific comparison content is as follows: When the temperature dispersion score Qs is within the balance threshold range, it indicates that the temperature control operation is qualified and the current parameters should be maintained. When the temperature discrete score Qs is not within the equilibrium threshold range, it indicates that the temperature control operation is not qualified. At this time, the parameter adaptive adjustment process of the heat load calculation model is executed, and the heating control is updated again after adjustment. The parameter adaptive adjustment process of the heat load calculation model constructs an objective function based on a historical standardized dataset of M control cycles, using the original heat load calculation model, and employs linear regression to adjust the heating rate response factor with the goal of minimizing temperature control error. With thermal inertia compensation factor Parameters are re-evaluated, and the optimal combination is output to achieve adaptive optimization of the calculation logic for heat load levels, thereby improving the overall balance and response efficiency of the system temperature control.

[0039] In this embodiment, the method calculates the normalized deviation of the temperature of each control segment relative to the target temperature Ttarget using the temperature discrete score Qs, thus evaluating the overall thermal equilibrium performance of the system. This approach can intuitively reflect the consistency of the current heating strategy in each region, avoiding the masking of overall problems due to lag or overheating in a certain region. For example, if a control segment far from the heat source is consistently more than 5°C below the target temperature, but other regions have normal temperature rises, traditional mean analysis would ignore this anomaly, while the temperature discrete score Qs can truly reflect the distribution of the overall control error. S42 sets an equilibrium threshold range based on ±5% of the target temperature as a criterion for judging temperature control accuracy, avoiding excessive convergence that could lead to control fluctuations. When the temperature discrete score Qs fails to meet the standard, the re-estimation logic of the heating rate response factor and thermal inertia compensation factor in the heat load calculation model is triggered. Combined with the historical M-period standardized dataset, with minimizing the system error as the objective function, a better parameter combination is output through linear regression, thereby dynamically optimizing the system's response capability. This adaptive mechanism is particularly suitable for situations involving seasonal changes or sudden changes in bathroom floor moisture content, and can significantly improve the environmental adaptability and robustness of the temperature control model.

[0040] Example 6 Please see Figure 1 and Figure 2 A linear heater intelligent control system includes a control section acquisition module, a heat load level classification module, a multi-level heating control module, and a temperature discrete analysis module. The control section acquisition module divides the linear heater into multiple control sections, sets up an acquisition device at the location of each control section, acquires the heat load data of each control section, and transmits the heat load data to the central control server for preprocessing to obtain a standardized dataset. The heat load level classification module constructs a heat load calculation model based on a standardized dataset, calculates the heat load level value E for each control segment, and executes multi-level heating strategy control based on the heat load level value E. The multi-level heating control module calculates the pre-compensation heating extension time Δt by triggering a heat diffusion lag feedforward regulation mechanism based on the control results of the multi-level heating strategy, and adjusts the heating strategy by time extension to complete the heating control update. The temperature discrete analysis module calculates the temperature discrete score Qs of all control segments after each control cycle ends, compares it with the set equilibrium threshold range, and optimizes and reconstructs the parameters in the heat load calculation model based on the comparison results.

[0041] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A method for intelligent control of a linear heater, characterized in that: Includes the following steps: S1. Divide the linear heater into multiple control segments, set up a data collector at each control segment location to acquire the heat load data of each control segment, and transmit the heat load data to the central control server for preprocessing to obtain a standardized dataset. S2. Construct a heat load calculation model based on a standardized dataset, calculate the heat load level value E for each control segment, and execute multi-level heating strategy control according to the heat load level value E. S3. Based on the control results of the multi-level heating strategy, a triggering heat diffusion lag feedforward regulation mechanism is implemented to calculate the pre-compensation heating extension time Δt, and the heating strategy is adjusted by time extension to complete the heating control update. S4. After each control cycle ends, calculate the temperature discrete score Qs of all control segments and compare it with the set equilibrium threshold range. Based on the comparison results, optimize and reconstruct the parameters in the heat load calculation model.

2. The intelligent control method for a linear heater according to claim 1, characterized in that: S1 includes S11; S11. The linear heater is evenly divided into several equidistant control segments along the length of the bathroom floor heating system. Each control segment is set as an independent logical heating area. At the same time, a data collector is set in each control segment. The data collector of each control segment is set on the bottom and side edge structure of the bathroom floor below the center area of ​​the corresponding control segment to collect heat load data in real time. The data acquisition device includes a temperature acquisition module, a humidity recognition module, and a heating response rate recording module; The heat load data includes the real-time temperature T of the nth control segment region. n The unit time temperature rise rate ΔT of the nth control segment region n and the humidity Wc of the nth control segment area n .

3. The intelligent control method for a linear heater according to claim 2, characterized in that: S1 further includes S12; S12. The heat load data collected in real time by the collector is sent to the central control server through the wireless communication module. In the central control server, the heat load data is preprocessed. The preprocessing is based on a set time window and performs data cleaning, outlier removal and interpolation completion operations. For each segment of humidity Wc in the pre-processed heat load data, combined with the preset bathroom floor dry density Ps and specific heat capacity Cs, the thermal inertia coefficient K, which is used to evaluate the degree of hysteresis of the bathroom floor thermal response in all segments, is obtained using the thermal inertia calculation formula. And the real-time temperature T of the nth control segment region in the heat load data. n and the unit time temperature rise rate ΔT of the nth control segment region n By combining the thermal inertia coefficient K of each control segment, dimensional normalization and unit standardization are performed to form a standardized dataset with a unified format.

4. The intelligent control method for a linear heater according to claim 3, characterized in that: S2 includes S21; S21. Construct a heat load calculation model based on a standardized dataset, calculate the heat load level value E of each control segment, and measure the current heat load status of each control segment. The heat load level value E is calculated using the following heat load calculation model; ； In the formula, E n This represents the heat load level value of the nth control segment, Ttarget represents the preset target temperature, which is the initially set dimensionless value, K n This represents the thermal inertia coefficient of the nth control segment. This represents the heating rate response factor. Indicates the thermal inertia compensation factor. This represents a tiny constant to prevent division by zero, with a value of 0.

01.

5. The intelligent control method for a linear heater according to claim 4, characterized in that: S2 further includes S22; S22. By fitting and regressing different temperature gradients, thermal response rates and final thermal equilibrium times in historical heating cycles, a heating load threshold model is constructed, and the first judgment threshold F1, the second judgment threshold F2 and the third judgment threshold F3 are automatically generated. The heat load level value E of each control segment is divided into heat load levels according to the first judgment threshold F1, the second judgment threshold F2, and the third judgment threshold F3, and a multi-level heating strategy control is executed simultaneously; the specific division is as follows: When the heat load level value E of the nth control segment n When the value is greater than or equal to the first judgment threshold F1, it is determined to be heating level 3, and a heating strategy of 90% time percentage within the set control cycle is executed. When the second judgment threshold F2 ≤ the heat load level value E of the nth control segment n When the first judgment threshold F1 is reached, it is determined to be heating level 2, and a heating strategy of 60% time percentage within the set control cycle is executed. When the third judgment threshold F ≤ the heat load level value E of the nth control segment n When the second judgment threshold F2 is reached, it is determined to be heating level 1, and a heating strategy with a time percentage of 30% within the set control cycle is executed. When the heat load level value E of the nth control segment n When the value is less than the third judgment threshold F, the heating level is determined to be 0, and heating is not performed in the current cycle; When the heating level remains constant for two consecutive heating control cycles, and the unit time temperature rise rate ΔT in the nth control segment region... n If the value is less than 0.2, it will be automatically identified as thermal response hysteresis.

6. The intelligent control method for a linear heater according to claim 5, characterized in that: S3 includes S31; S31. When thermal response hysteresis is identified, a thermal diffusion hysteresis feedforward control mechanism is triggered. The thermal diffusion hysteresis feedforward control mechanism uses the unit time temperature rise rate ΔT of the nth control segment region based on the current control segment standard dataset. n Combined with the thermal inertia coefficient K of the nth control segment region n The pre-compensation heating extension time Δt is calculated and output, and heating compensation is implemented for the control section with thermal response lag. The pre-compensation heating extension time Δt is calculated and output using the following algorithm formula; ； In the formula, △t n This indicates the pre-compensation heating extension time for the nth control segment. This represents the minimum heating rate threshold, and its value is dimensionless. Kavg represents the average thermal inertia value across all control terminals. This represents the feedforward response adjustment factor.

7. The intelligent control method for a linear heater according to claim 6, characterized in that: S3 further includes S32; S32. The pre-compensation heating extension time Δt of the nth control segment calculated based on the response lag segment. n The heating strategy is adjusted by extending the heating time percentage within the current control cycle according to the following strategy, thus completing the heating control update: If the current heat load level of the control segment is Level 1, then based on the original heating time percentage of 30%, the heating time will be extended by 30% + the pre-compensation heating extension time Δt of the nth control segment. n / Complete heating control cycle time Tp; If the current heat load level of the control segment is Level 2, the heating time will be extended by 60% + the pre-compensation heating extension time Δt of the nth control segment, based on the original heating time percentage of 60%. n / Complete heating control cycle time Tp; If the current control section heat load level is level 3, then the heating time percentage remains unchanged at 90%.

8. The intelligent control method for a linear heater according to claim 6, characterized in that: S4 includes S41; S41. After the heating control update is completed, the updated real-time temperature T' of each control segment is reacquired. The normalized difference between the updated real-time temperature T' of all control segments and the preset target temperature Ttarget is calculated. Based on the total number of control segments N, the temperature discrete score Qs is calculated and output. The deviation of all control segments is summarized and evaluated. The temperature discrete score Qs is calculated and output using the following algorithm formula; ； In the formula, N represents the total number of control segments. This represents the updated real-time temperature of the nth control segment.

9. The intelligent control method for a linear heater according to claim 8, characterized in that: S4 further includes S42; S42. Based on actual bathroom floor temperature control, a balance threshold range is set at ±5% of the preset target temperature Ttarget. This balance threshold range is compared with the real-time temperature discrete score Qs to determine the balance of the temperature control effect. Based on the comparison results, the parameters in the heat load calculation model are optimized and reconstructed. The specific comparison content is as follows: When the temperature dispersion score Qs is within the balance threshold range, it indicates that the temperature control operation is qualified and the current parameters should be maintained. When the temperature discrete score Qs is not within the equilibrium threshold range, it indicates that the temperature control operation is not qualified. At this time, the parameter adaptive adjustment process of the heat load calculation model is executed, and the heating control is updated again after adjustment. The adaptive parameter adjustment process of the heat load calculation model constructs an objective function based on a historical standardized dataset of M control cycles, using the original heat load calculation model form, and employs linear regression to adjust the heating rate response factor with the goal of minimizing temperature control error. With thermal inertia compensation factor Perform parameter reestimation and output the optimal combination.

10. A linear heater intelligent control system, applied to the linear heater intelligent control method according to any one of claims 1-9, characterized in that: It includes a control section acquisition module, a heat load level classification module, a multi-level heating control module, and a temperature discrete analysis module; The control segment acquisition module divides the linear heater into multiple control segments, sets an acquisition device at the location of each control segment, acquires the heat load data of each control segment, and transmits the heat load data to the central control server for preprocessing to obtain a standardized dataset. The heat load level classification module constructs a heat load calculation model based on a standardized dataset, calculates the heat load level value E for each control segment, and executes multi-level heating strategy control based on the heat load level value E. The multi-level heating control module calculates the pre-compensation heating extension time Δt by triggering a heat diffusion lag feedforward regulation mechanism based on the control results of the multi-level heating strategy, and adjusts the heating strategy by time extension to complete the heating control update. The temperature discrete analysis module calculates the temperature discrete score Qs of all control segments after each control cycle ends, compares it with the set equilibrium threshold range, and optimizes and reconstructs the parameters in the heat load calculation model based on the comparison results.

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