A method for automatic temperature correction in intelligent electric heaters

Abstract

This invention relates to the field of intelligent control technology for household appliances, and particularly to an automatic temperature correction method for intelligent electric heaters. The method comprises five core steps: building a dual-sensor testing platform, multi-modal automated calibration and data acquisition, constructing a segmented temperature correction model, mass production and solidification of the correction model, and precise temperature control in mass production. During the research and development phase, this method simultaneously collects the temperature of the heater casing and the actual indoor ambient temperature using dual sensors. Steady-state valid data is obtained through full-condition calibration and thermal balance determination. A segmented temperature correction model is then constructed according to the heating power range and solidified into the control unit of the mass production equipment. This invention enables mass production equipment to achieve accurate conversion and closed-loop temperature control of the ambient temperature using only the existing built-in sensors. Without increasing the cost of mass production hardware, it solves the problem of temperature detection deviation caused by heater casing heating, effectively improving the temperature control accuracy and adaptability of the electric heater.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for household appliances, and in particular to an automatic temperature correction method for an intelligent electric heater. Background Technology

[0002] Smart electric heaters are mainstream terminal devices in the residential indoor heating sector. Their core function is to achieve stable temperature regulation based on real-time detection of indoor ambient temperature and through closed-loop temperature control logic. Accurate detection of indoor ambient temperature is the core foundation for ensuring the heater's temperature control performance and meeting users' heating needs. However, due to limitations in mass production assembly structures and hardware layout design, existing smart electric heaters generally have their temperature sensors fixedly installed inside the casing. During operation, the sensor is continuously affected by the heat radiation and heat conduction from the heater's own heating element, resulting in an inherent deviation between the collected temperature data and the actual indoor ambient temperature. This directly leads to inaccurate input benchmarks for the heater's temperature control logic, preventing precise control of the indoor temperature.

[0003] Existing solutions to the aforementioned temperature detection deviation problem fall into two categories. One is to add an external ambient temperature sensor to the electric heater to directly collect the actual ambient temperature, which is unaffected by the heater's own heating. While this solution can improve detection accuracy, it significantly increases hardware material costs and assembly processes during mass production, making it difficult to balance temperature control accuracy with mass production cost control. The other solution uses a fixed temperature offset to calibrate the detection value of the built-in sensor. However, this solution cannot adapt to the differences in the heater's heating characteristics under different heating conditions, and the stability and adaptability of the calibration effect are insufficient. It cannot fundamentally solve the core problem of insufficient temperature detection and control accuracy caused by the heater's own heating. Summary of the Invention

[0004] To overcome the above shortcomings, this invention provides an automatic temperature correction method for intelligent electric heaters, aiming to improve the existing technology of intelligent electric heaters that cannot solve the problem of insufficient temperature detection and control accuracy caused by the heat generated by the built-in temperature sensor while controlling the cost of mass production hardware.

[0005] This invention provides the following technical solution: an automatic temperature correction method for an intelligent electric heater, comprising the following steps: S1: Construct a dual-sensor testing platform. During the R&D phase, reserve an interface for an external temperature sensor and connect it to the external temperature sensor. Retain the original built-in temperature sensor in the body to simultaneously collect the shell temperature and the ambient temperature. S2: Perform multimodal automated calibration and data acquisition, control the electric heater to traverse preset heating modes, synchronously monitor the temperature sequence collected by dual sensors, and after thermal balance determination, associate and record the first temperature value, the second temperature value and the current heating power parameter to form a calibration data set until the traversal is completed and a complete calibration dataset is obtained. S3: Construct a segmented temperature correction model. Divide the calibration dataset into at least two data subsets according to the heating power parameters. Establish the correspondence between the first temperature value and the second temperature value in each subset and associate it with the corresponding heating power range. Encapsulate the results to form a segmented temperature correction model. S4: Solidify the calibration model to the mass production equipment. Write the segmented temperature calibration model into the control unit of the electric heater in the mass production stage. Remove the external temperature sensor and its signal extension line from the electric heater in the mass production stage, and only retain the built-in temperature sensor. S5: Performs mass production temperature control. During the mass production stage, it acquires the current shell temperature value and current heating power status collected by the built-in sensor in real time, calls the segmented temperature correction model, selects the corresponding relationship according to the current heating power status, substitutes the current shell temperature value to calculate the equivalent ambient temperature value, and controls the working status of the heating element based on the comparison result between the equivalent ambient temperature value and the user-set target temperature.

[0006] The above-mentioned technical solution is typically adopted: During the R&D phase, a dual-sensor testing platform is built, and steady-state effective calibration data is obtained through multi-modal automated calibration and multi-dimensional thermal balance determination. A segmented temperature correction model is constructed based on the heating power range, enabling mass-produced equipment to accurately calculate the real ambient temperature using only the existing built-in sensors. This fundamentally solves the core problem of insufficient temperature detection and control accuracy caused by body heating. During mass production, no new hardware structure is required; only the existing built-in sensors and the control unit of the solidified calibration model are retained, ensuring effective temperature correction while effectively controlling mass production hardware costs. Simultaneously, the segmented calibration model can adapt to the body heating characteristics of electric heaters under different heating conditions, and the automated calibration process ensures the steady-state effectiveness of the calibration data, improving the adaptability and operational stability of the temperature correction solution.

[0007] Preferably, in step S1, the external temperature sensor is connected to a reserved interface via a signal extension cable, the length of which is sufficient to allow the external temperature sensor to be placed in an area unaffected by the heat generated by the electric heater body.

[0008] Preferably, in step S2, the synchronous monitoring of the temperature sequence collected by the dual sensors specifically involves: synchronously monitoring a first temperature sequence collected by a built-in temperature sensor and a second temperature sequence collected by an external temperature sensor; the preset heating mode includes different user-configurable target temperature levels or different electric heater heating power levels.

[0009] Preferably, in step S2, the process of determining the thermal balance specifically includes: The first temperature change characteristic is calculated based on the first temperature sequence obtained from continuous synchronous monitoring, and the second temperature change characteristic is calculated based on the second temperature sequence. When the first temperature change characteristic is detected to be lower than the preset first change characteristic threshold, the second temperature change characteristic is lower than the preset second change characteristic threshold, and the difference between the first temperature change characteristic and the second temperature change characteristic is lower than the preset difference threshold, and all three conditions are met continuously within a preset continuous time window, it is determined that the thermal equilibrium state has been entered.

[0010] Preferably, in step S3, the process of dividing the calibration dataset into at least two data subsets according to the heating power parameter specifically includes: The first data subset is defined as all calibration data groups whose heating power parameters belong to the high power range in the complete calibration dataset. The second data subset is formed by dividing all calibration data groups in the complete calibration dataset whose heating power parameters belong to the low power range.

[0011] Preferably, in step S3, the process of establishing the correspondence between the first temperature value and the second temperature value within each subset specifically includes: For the first subset of data, fit a first relationship information that represents the correspondence between the first temperature value and the second temperature value; For the second subset of data, a second relationship information is fitted to represent the correspondence between the first temperature value and the second temperature value.

[0012] Preferably, in step S3, the segmented temperature correction model is defined as follows: when the current heating power parameter of the electric heater belongs to the high power range, the equivalent ambient temperature value is calculated using the first relationship information; when the current heating power parameter of the electric heater belongs to the low power range, the equivalent ambient temperature value is calculated using the second relationship information.

[0013] Preferably, in step S4, the control unit of the electric heater in the mass production stage is a microcontroller.

[0014] Preferably, in step S5, the process of calling the segmented temperature correction model and calculating the equivalent ambient temperature value specifically includes: the control unit of the electric heater in the mass production stage reads the current heating power status; determines the heating power parameter range to which the current heating power status belongs; calls the corresponding relationship from the segmented temperature correction model that corresponds to the heating power parameter range; substitutes the current shell temperature value obtained by the built-in temperature sensor as input into the called relationship, performs calculations based on the relationship, and determines the output value as the equivalent ambient temperature value.

[0015] Preferably, in step S5, the process of controlling the operating state of the heating element based on the comparison result between the equivalent ambient temperature value and the user-set target temperature specifically includes: When the equivalent ambient temperature is determined to be lower than the user-set target temperature, a start command or a heating sustain command is output to the heating element. When the equivalent ambient temperature value is determined to be at or above the user-set target temperature, a stop heating command is output to the heating element.

[0016] The present invention has the following beneficial effects: 1. In this invention, a dual-sensor testing platform is built to achieve synchronous acquisition of the temperature of the outer casing and the actual indoor ambient temperature. Steady-state effective calibration data is obtained through multimodal automated calibration and multi-dimensional thermal balance determination. An appropriate temperature correction model is constructed, enabling mass-produced equipment to complete the accurate conversion of the actual ambient temperature using only the original built-in sensors, effectively improving the accuracy of temperature detection and control.

[0017] 2. In this invention, during the research and development stage, an external temperature sensor is used to collect the real ambient temperature as a calibration benchmark. After constructing a segmented temperature correction model, the model is solidified into the mass production equipment. During the mass production stage, the external temperature sensor and its signal extension line are removed, and only the built-in temperature sensor is retained to complete the accurate temperature correction. While ensuring the temperature control accuracy, the material cost and manufacturing cost of a single product are significantly reduced, achieving a balance between cost control and performance improvement.

[0018] 3. In this invention, the electric heater is controlled by an automatic test program to traverse all preset heating modes, and thermal balance is determined based on the temperature change characteristics and differences of the first temperature sequence and the second temperature sequence within a continuous time window. After thermal balance is determined, data acquisition and recording are automatically triggered until a complete calibration dataset covering all working modes is obtained. The entire calibration process is executed automatically, reducing the risk of manual intervention and test omissions, and ensuring the consistency and reliability of calibration data.

[0019] 4. In this invention, the electric heater is controlled by an automatic test program to go through all preset heating modes. Based on the temperature sequence change characteristics collected by dual sensors, the thermal equilibrium state is automatically determined. The full-condition calibration data is automatically collected and integrated to ensure the temporal consistency and steady-state validity of the calibration data and reduce calibration errors caused by manual operation. Attached Figure Description

[0020] Figure 1 This is a flowchart of an automatic temperature correction method for an intelligent electric heater proposed in this invention. Detailed Implementation

[0021] The technical solutions in 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.

[0022] In an embodiment of the present invention, the present invention provides a method for automatic temperature correction of an intelligent electric heater, such as... Figure 1 As shown, it includes the following steps: S1: Construct a dual-sensor testing platform. During the R&D phase, reserve an interface for an external temperature sensor and connect it to the external temperature sensor. Retain the original built-in temperature sensor in the body to simultaneously collect the shell temperature and the ambient temperature. Furthermore, the external temperature sensor is connected to the reserved interface via a signal extension cable, the length of which is sufficient to allow the external temperature sensor to be placed in an area unaffected by the heat generated by the heater body.

[0023] Specifically, in the R&D phase of smart electric heater products, the first step is to build a dual-sensor testing platform capable of simultaneously acquiring the temperature of the heater's outer casing and the actual indoor ambient temperature. This provides a reliable hardware foundation for subsequent automated calibration and correction model construction. The specific implementation process is as follows: An external temperature sensor interface is pre-designed and reserved on the internal control board of the electric heater. This interface adopts a common digital or analog signal interface standard, such as a single-bus interface or I / O. 2 The external temperature sensor uses a USB-C interface for compatibility with various models of external temperature sensors. A signal extension cable connects the external temperature sensor to this interface. The length of the extension cable is determined by the maximum heat radiation range of the heater model under test, typically between 1.5 and 2.0 meters. This ensures the external temperature sensor is positioned away from the heater's heat-generating airflow and heat radiation. During testing, this external temperature sensor serves as a standard reference sensor, responsible for collecting the actual ambient temperature of the indoor air.

[0024] Meanwhile, the built-in temperature sensor, originally located in a pre-set position on the heater's outer casing, is retained. This sensor is used to sense the body temperature in mass-produced products and to collect the outer casing temperature in the testing platform. The built-in temperature sensor is usually fixedly installed on the inside of the casing near the heater's air inlet or outlet. Its reading will be significantly higher than the actual ambient temperature due to the heat conduction of the heating elements inside the unit and the heat storage effect of the casing.

[0025] With the aforementioned hardware configuration, the test platform establishes a dual-sensor collaborative architecture of "built-in sensor + external sensor." The built-in sensor continuously outputs a first temperature value reflecting the thermal state of the casing, while the external sensor synchronously outputs a second temperature value reflecting the actual indoor ambient temperature. The temperature data collected by both sensors is synchronously sampled and timestamped by the microcontroller on the electronic control board, ensuring a strict correspondence between each set of first and second temperature values ​​over time. This provides accurate raw data support for establishing the mapping relationship between the casing temperature and the ambient temperature in subsequent steps.

[0026] It should be noted that the signal extension cable should be positioned to avoid direct contact with the hot surface of the heater body to prevent cable aging or signal transmission interference. In practice, the external temperature sensor can be fixed at least 1.5 meters horizontally from the heater, at a height comparable to the human activity area, to simulate the ambient temperature perception conditions in actual user scenarios. This arrangement ensures that the temperature value collected by the external sensor represents the true room temperature of the user's area, rather than a localized high temperature affected by the heater body's heat source.

[0027] By establishing the dual-sensor test platform in step S1, researchers obtained two temperature data streams, representing the fuselage thermal state and the actual environmental state respectively, within the same time coordinate system. This provided the necessary hardware prerequisites for multimodal automated calibration and thermal balance data acquisition in step S2. The core function of this step is to construct a benchmark test environment during the R&D phase that can accurately measure the correspondence between "fuselage temperature and ambient temperature" at extremely low hardware modification costs, thereby enabling precise temperature calibration to be achieved in the mass production phase without additional hardware.

[0028] S2: Perform multimodal automated calibration and data acquisition, control the electric heater to traverse preset heating modes, synchronously monitor the temperature sequence collected by dual sensors, and after thermal balance determination, associate and record the first temperature value, the second temperature value and the current heating power parameter to form a calibration data set until the traversal is completed and a complete calibration dataset is obtained. Furthermore, the simultaneous monitoring of the temperature sequence collected by the dual sensors specifically involves: simultaneously monitoring a first temperature sequence collected by the built-in temperature sensor and a second temperature sequence collected by the external temperature sensor; preset heating modes include different user-set target temperature levels or different electric heater heating power levels; Furthermore, the process of determining thermal equilibrium specifically includes: The first temperature change characteristic is calculated based on the first temperature sequence obtained from continuous synchronous monitoring, and the second temperature change characteristic is calculated based on the second temperature sequence. When the first temperature change characteristic is detected to be lower than the preset first change characteristic threshold, the second temperature change characteristic is lower than the preset second change characteristic threshold, and the difference between the first temperature change characteristic and the second temperature change characteristic is lower than the preset difference threshold, and all three conditions are met continuously within a preset continuous time window, it is determined that the thermal equilibrium state has been entered.

[0029] Specifically, after completing the dual-sensor test platform setup in step S1, the automated calibration and data acquisition phase begins. The core task of this step is to control the electric heater to sequentially run through all preset heating modes using an automatic test program pre-installed in the heater's control board. In each heating mode, the thermal equilibrium state is accurately determined based on the temperature sequence collected by the dual sensors. When the system determines that thermal equilibrium has been reached, data acquisition is automatically triggered, recording the first temperature value, the second temperature value, and the current heating power parameters under that steady-state condition, forming a set of calibration data. After traversing all preset heating modes, all calibration data sets are collected to form a complete calibration dataset, providing the initial data foundation for constructing the segmented temperature correction model in step S3.

[0030] In practice, the automatic testing program pre-stores all the preset heating modes supported by the heater under test. These preset heating modes include different user-settable target temperature levels, such as temperature increments of 1℃ from 16℃ to 30℃; they also include different heater power levels, such as half-power and full-power operation modes. After the automatic testing program starts, it sequentially switches the heater to each preset heating mode in a preset order, maintaining stable operation in that mode until the thermal balance determination and data acquisition for that mode are completed, before moving on to the next preset heating mode.

[0031] For each preset heating mode, the automatic test program performs tests at fixed sampling time intervals Δt. s Simultaneously read temperature data from both the built-in and external temperature sensors. Where Δt s This represents the time interval between two consecutive samples, typically set to a fixed value between 1 and 5 seconds. Let T1(t) be the temperature value collected by the built-in temperature sensor at a given sampling time t. These temperature values, arranged in chronological order, constitute the first temperature sequence. Similarly, let T2(t) be the temperature value collected by the external temperature sensor at the same sampling time t. These temperature values, arranged in chronological order, constitute the second temperature sequence. The first temperature sequence characterizes the dynamic change of the fuselage temperature over time, while the second temperature sequence characterizes the dynamic change of the indoor ambient temperature over time.

[0032] To accurately determine whether the electric heater has reached thermal equilibrium, the automatic testing program calculates the change characteristics of two temperature sequences in each sampling period. Specifically, at sampling time t, the first temperature change rate, denoted as ΔT1(t), is calculated based on the first temperature sequence. It is defined as the absolute value of the change in the first temperature value per unit time, calculated as follows: ; The symbols in the above formulas have the following meanings: T1(t) represents the first temperature value collected by the built-in temperature sensor at the current sampling time t; T1(t-Δt) represents the first temperature value collected by the built-in temperature sensor at the current sampling time t. s ) represents the first temperature value collected by the built-in temperature sensor at the previous sampling time; Δt s The vertical bar indicates the sampling time interval; the vertical bar symbol indicates the absolute value operation. The value of the first temperature change rate ΔT1(t) reflects the degree of temperature change of the fuselage shell per unit time. The larger the value, the faster the heating or cooling rate, and the smaller the value, the more stable the temperature.

[0033] Similarly, at sampling time t, the rate of change of the second temperature is calculated based on the second temperature sequence, denoted as ΔT2(t), which is defined as the absolute value of the change in the second temperature value per unit time. The calculation method is as follows: ; The symbols in the above formulas have the following meanings: T2(t) represents the second temperature value collected by the external temperature sensor at the current sampling time t; T2(t-Δt) represents the second temperature value collected by the external temperature sensor at the current sampling time t. s ) represents the second temperature value collected by the external temperature sensor at the previous sampling time; Δt s This indicates the sampling time interval. The magnitude of the second temperature change rate ΔT2(t) reflects the degree of drastic change in indoor ambient temperature per unit time; the smaller the value, the more stable the indoor ambient temperature tends to be.

[0034] Based on the temperature change rate obtained above, the automatic testing program performs parallel judgments on the following three conditions in each sampling period: Condition 1: The first temperature change rate ΔT1(t) is lower than the preset first change rate threshold ε1, that is, ΔT1(t) < ε1; Condition 2: The second temperature change rate ΔT2(t) is lower than the preset second change rate threshold ε2, that is, ΔT2(t) < ε2; Condition 3: The absolute value of the difference between the first temperature change rate and the second temperature change rate is lower than the preset difference threshold εdiff, that is, |ΔT1(t)-ΔT2(t)|<εdiff.

[0035] The automatic testing program determines that the electric heater has entered a thermal equilibrium state only when all three conditions mentioned above are simultaneously met in every sampling period within a preset continuous time window W. The time window W represents the shortest duration required to continuously meet the determination conditions, and its function is to avoid misjudgments caused by instantaneous disturbances. This determination criterion comprehensively considers the stability of the heater's own temperature changes, the stability of the ambient temperature changes, and the dynamic balance of the heat transfer process between the two. Only when the heater's heat accumulation tends to saturate, the indoor air temperature tends to stabilize, and the temperature difference between the two tends to level off, does the collected temperature correspondence have calibration value.

[0036] Taking a heating mode with a target temperature set at 24℃ as an example, in the initial stage of heating, T1(t) in the first temperature sequence rises rapidly, resulting in a large calculated value of ΔT1(t); T2(t) in the second temperature sequence rises slowly, resulting in a relatively small calculated value of ΔT2(t). The difference in their change characteristics is significant, and at this time, the value of |ΔT1(t)-ΔT2(t)| is large, failing to meet the thermal equilibrium judgment condition. As heating continues, the heat storage in the unit gradually saturates, and ΔT1(t) gradually decreases and approaches zero; the indoor air temperature tends to stabilize, and ΔT2(t) also gradually decreases and approaches zero; simultaneously, the heat transfer process between the unit and the indoor air tends to reach dynamic equilibrium, and the value of |ΔT1(t)-ΔT2(t)| gradually decreases to within the difference threshold εdiff. When all three conditions are met in each sampling period within the continuous time window W, the automatic test program determines that the electric heater has entered a thermal equilibrium state.

[0037] Upon determining that thermal equilibrium has been reached, the automatic test program immediately triggers a data acquisition event. The program associates and records the first temperature value T1(t) acquired by the built-in temperature sensor, the second temperature value T2(t) acquired by the external temperature sensor, and the current heating power parameter P of the heater at that moment, forming a calibration data set. This calibration data set is then stored in the non-volatile memory of the control board. The heating power parameter P is used to identify the power range of the current heating mode; for example, a value of 1 represents the low power range, and a value of 2 represents the high power range. After completing the data acquisition for the current preset heating mode, the automatic test program controls the heater to switch to the next preset heating mode, repeating the above temperature monitoring, thermal equilibrium determination, and data acquisition process until all preset heating modes have been traversed.

[0038] Once all preset heating modes have been tested, the automatic testing program will aggregate all stored calibration data sets to form a complete calibration dataset. Each data set in this complete calibration dataset corresponds to a specific heating mode and was collected under thermal equilibrium conditions within that heating mode, thus ensuring the reliability and representativeness of the data.

[0039] Through multimodal automated calibration and thermal balance data acquisition in step S2, researchers obtained data on the relationship between the heater's body temperature and ambient temperature under steady-state conditions, covering all operating modes of the electric heater. The core function of this step is twofold: First, by automating the process through all preset heating modes, the tedious operation and risk of omissions associated with manual testing are eliminated, improving calibration efficiency and data integrity. Second, by introducing a thermal balance determination method based on the temperature change rate and its difference using dual sensors, it is ensured that each set of calibration data is collected when the system reaches a true thermal steady state, avoiding interference from unsteady-state data on model accuracy. This provides a high-quality data foundation for constructing a high-precision segmented temperature correction model in step S3.

[0040] S3: Construct a segmented temperature correction model. Divide the calibration dataset into at least two data subsets according to the heating power parameters. Establish the correspondence between the first temperature value and the second temperature value in each subset and associate it with the corresponding heating power range. Encapsulate the results to form a segmented temperature correction model. Furthermore, the process of dividing the calibration dataset into at least two subsets based on heating power parameters specifically includes: The first data subset is defined as all calibration data groups whose heating power parameters belong to the high power range in the complete calibration dataset. The second data subset is formed by dividing all calibration data groups in the complete calibration dataset whose heating power parameters belong to the low power range. Furthermore, the process of establishing the correspondence between the first temperature value and the second temperature value within each subset specifically includes: For the first subset of data, fit a first relationship information that represents the correspondence between the first temperature value and the second temperature value; For the second subset of data, fit a second relationship information that represents the correspondence between the first temperature value and the second temperature value; Furthermore, the segmented temperature correction model is defined as follows: when the current heating power parameter of the electric heater belongs to the high power range, the equivalent ambient temperature value is calculated using the first relationship information; when the current heating power parameter of the electric heater belongs to the low power range, the equivalent ambient temperature value is calculated using the second relationship information.

[0041] Specifically, after completing the multimodal automated calibration and data acquisition in step S2, a complete calibration dataset containing thermal equilibrium state calibration data for all preset heating modes is obtained. The core task of this step is to send this complete calibration dataset to the data processing terminal, which classifies the calibration data according to the heating power parameters in the calibration data set, establishes a correspondence between the first temperature value and the second temperature value for different power ranges, and finally associates these correspondences with their respective heating power ranges, encapsulating them into a segmented temperature correction model, providing an algorithmic foundation for precise temperature control in the subsequent mass production stage.

[0042] In implementation, the complete calibration dataset stored in the non-volatile memory in step S2 is first sent to the external data processing terminal via the serial communication interface on the internal control board of the electric heater. Each set of calibration data in the complete calibration dataset contains three associated parameters: a first temperature value collected by the built-in temperature sensor, a second temperature value collected by the external temperature sensor, and the heating power parameter of the electric heater at the time of data acquisition. The heating power parameter is used to identify the power range category to which the current heating mode belongs. For example, during the data acquisition process in step S2, the heating power parameter has been recorded as a value representing the high power range or a value representing the low power range according to the actual operating state of the electric heater.

[0043] After receiving the complete calibration dataset, the data processing terminal first divides the dataset into at least two subsets based on the heating power parameters within the calibration data sets. Specifically, the division process is as follows: Each calibration data set in the complete dataset is traversed, and its heating power parameter value is read. All calibration data sets whose heating power parameters belong to the high-power range are extracted and assigned to the first subset; all calibration data sets whose heating power parameters belong to the low-power range are extracted and assigned to the second subset. The calibration data sets in the first subset reflect the relationship between the heater's casing temperature and the indoor ambient temperature when the heater is operating at high power, while the calibration data sets in the second subset reflect the relationship between the heater's casing temperature and the indoor ambient temperature when the heater is operating at low power.

[0044] It's important to note that segmenting the calibration data according to heating power parameters is necessary because electric heaters exhibit varying heat output from their internal heating elements, different temperature rise characteristics of the casing, and varying intensity of thermal radiation and convection heat transfer to the surrounding environment at different heating power levels. Specifically, in high-power operation mode, the heating elements operate at full power, resulting in a large temperature rise in the casing and a significant difference between the casing temperature and the ambient temperature. In low-power operation mode, the heating elements operate at partial power, leading to a relatively smaller temperature rise in the casing and a smaller difference between the casing temperature and the ambient temperature. Mixing the calibration data from both power modes for unified fitting would fail to accurately reflect the differences in the relationship between casing temperature and ambient temperature under different power conditions, resulting in decreased conversion accuracy of the calibration model in actual mass production. Therefore, segmenting the data according to heating power parameters is a crucial step in ensuring the accuracy of the calibration model.

[0045] After dividing the data into subsets, a correspondence between the first and second temperature values ​​is established for each subset. For the first data subset, the first temperature value of each set of calibration data in the subset is used as the input variable and the second temperature value as the output variable. A least squares method is used for linear fitting to obtain the first relationship information characterizing the correspondence between the first and second temperature values ​​within the high-power range. The first relationship information is in the form T2 = a1·T1 + b1, where T1 represents the first temperature value collected by the built-in temperature sensor, T2 represents the corresponding equivalent ambient temperature value, and a1 and b1 are the coefficient of the first-order term and the constant term obtained from fitting the first data subset, respectively. The first relationship information describes the quantitative relationship of the change in indoor ambient temperature when the fuselage temperature changes by one unit under high-power operating conditions.

[0046] For the second data subset, the first temperature value of each set of calibration data in this subset is used as the input variable and the second temperature value as the output variable. A least squares method is used for linear fitting to obtain second relationship information characterizing the correspondence between the first and second temperature values ​​within the low-power range. The second relationship information is in the form T2 = a2·T1 + b2, where T1 represents the first temperature value collected by the built-in temperature sensor, T2 represents the corresponding equivalent ambient temperature value, and a2 and b2 are the coefficient of the first-order term and the constant term obtained from fitting the second data subset, respectively. The second relationship information describes the quantitative relationship between the change in indoor ambient temperature for every unit change in the fuselage temperature under low-power operating conditions.

[0047] Taking the actual calibration data of a certain model of electric heater as an example, assuming the first data subset contains multiple sets of steady-state data collected in high-power mode, the first relationship information obtained through fitting is T2 = 0.78·T1 + 1.2; the second data subset contains multiple sets of steady-state data collected in low-power mode, and the second relationship information obtained through fitting is T2 = 0.85·T1 + 0.8. The difference in coefficients between the two sets of relationship information shows that the mapping relationship between the heater body temperature and the ambient temperature differs significantly across different power ranges. If a single relationship information is used for correction, the calculated equivalent ambient temperature will deviate when the electric heater switches between high and low power, thus affecting the temperature control accuracy.

[0048] After obtaining the relational information corresponding to each data subset, the data processing terminal associates the first relational information with the high-power range and the second relational information with the low-power range, and encapsulates these associations to form a segmented temperature correction model. The segmented temperature correction model is defined as follows: when the current heating power parameter of the electric heater belongs to the high-power range, the first relational information is used to calculate the equivalent ambient temperature value; when the current heating power parameter of the electric heater belongs to the low-power range, the second relational information is used to calculate the equivalent ambient temperature value. The encapsulated segmented temperature correction model is saved in the form of a data table or function library, containing the power range judgment logic and the corresponding relational information calculation formulas, and can be directly embedded into the electric heater control software for use.

[0049] After the segmented temperature calibration model is completed, the data processing terminal transmits the model back to the electric heater prototype in the R&D stage through the data interface or saves it directly as a software code module for use in step S4 when the calibration model is solidified into the mass production equipment.

[0050] Through the segmented temperature correction model construction in step S3, the R&D personnel transformed the raw calibration data collected in step S2 into a temperature correction algorithm model that can be directly used by electric heaters in the mass production stage. The core function of this step is reflected in two aspects: First, by segmenting the calibration data according to the heating power parameters, the differences in the thermal characteristics of the electric heater under different power states are fully considered, so that the constructed correction model can more accurately reflect the true mapping relationship between the body temperature and the ambient temperature; Second, by associating and encapsulating each relationship information with the corresponding power range, a segmented correction model with a clear structure and convenient access is formed, providing a technical basis for electric heaters in the mass production stage to dynamically select the correction relationship according to the real-time heating power state, thereby achieving accurate conversion of indoor ambient temperature under low-cost hardware conditions that only retain the built-in temperature sensor.

[0051] S4: Solidify the calibration model to the mass production equipment. Write the segmented temperature calibration model into the control unit of the electric heater in the mass production stage. Remove the external temperature sensor and its signal extension line from the electric heater in the mass production stage, and only retain the built-in temperature sensor. Furthermore, in the mass production stage, the control unit of the electric heater is a microcontroller.

[0052] Specifically, after completing the segmented temperature calibration model construction in step S3, the mass production and solidification stage of the calibration model begins. The core task of this step is to write the segmented temperature calibration model generated in the R&D stage into the control unit of the smart electric heater in the mass production stage through software burning. At the same time, the external temperature sensor and its signal extension line are removed in the manufacturing process, so that the mass-produced product only retains the original built-in temperature sensor of the body, thereby effectively controlling hardware costs while achieving accurate temperature calibration.

[0053] During implementation, the segmented temperature correction model generated by the data processing terminal in step S3 is first converted into an executable code module in the electric heater control software. The segmented temperature correction model contains two core components: first, power range judgment logic, used to determine the relational information to be invoked based on the current heating power status of the electric heater; second, the temperature correspondence between each power range, namely, the first relational information and the second relational information. The above content is written as embedded program code in the form of lookup table functions or conditional branch statements and integrated with the original temperature control main program of the electric heater.

[0054] The integrated control software is programmed into the control unit of the electric heater in mass production via a programming tool on the production line and a pre-installed programming interface on the electric heater's control board. The control unit is a microcontroller, which integrates a central processing unit, random access memory, and non-volatile program memory. A segmented temperature correction model is permanently stored in the microcontroller's non-volatile program memory, ensuring that the correction model can still be fully read and normally invoked when the electric heater is powered on again after a power outage.

[0055] Taking a certain model of electric heater as an example, its control unit uses an 8-bit microcontroller with an internal program memory capacity of 8KB. The total amount of integrated control software code is approximately 6.5KB, of which the segmented temperature correction model occupies approximately 200 bytes of storage space, containing instruction code for power range judgment logic and coefficient data in the first and second relationship information. During program burning, the burning fixture writes the complete control software image file into the microcontroller's internal program memory through the microcontroller's online programming interface. After burning is completed, a verification read operation is performed to confirm the correctness of the written data.

[0056] In the hardware assembly stage of mass production, the electric heater retains only the built-in temperature sensor installed in a pre-positioned location on the outer casing. This sensor is electrically connected to the built-in temperature sensor interface on the control board via soldering or connectors. The external temperature sensor interface used for calibration testing during the R&D stage remains in the control board circuit design, but in the mass production stage, no components are soldered to this interface; it remains unused. The external temperature sensor and its signal extension cable are no longer assembled as part of the mass-produced product. Through these hardware simplification measures, each electric heater in the mass production stage can save on the cost of external temperature sensor components, signal extension cable materials, and corresponding soldering and assembly time.

[0057] It should be noted that the external temperature sensor interface reserved during the R&D phase exists only as an empty pad on the mass-produced control board, without incurring additional bill of materials costs. The existence of this unused interface does not affect the normal operation of the heater, and at the same time provides the feasibility of hardware expansion for subsequent product return calibration or after-sales maintenance. For example, if users report temperature control deviations after long-term use, after-sales service personnel can temporarily connect an external temperature sensor through this reserved interface to re-execute the calibration process and update the segmented temperature correction model without replacing the entire control board.

[0058] After the program is burned and the hardware is assembled, the mass-produced electric heater undergoes a power-on initialization phase. During this phase, the microcontroller first loads a segmented temperature correction model from the non-volatile program memory into the random access memory (RAM) for the main control program to access in real time during operation. At this point, the electric heater possesses the complete software capability to accurately calculate ambient temperature using a single built-in temperature sensor.

[0059] Through the mass production and solidification of the calibration model in step S4, the high-precision temperature correspondence obtained through dual-sensor calibration during the R&D phase is seamlessly transferred to the mass-produced product retaining only a single sensor. The core function of this step is twofold: firstly, by transforming the calibration data accumulated during the R&D phase into a calibration algorithm directly usable in the mass-produced product through software, a low-cost solution of "software compensation for hardware deficiencies" is achieved; secondly, by removing the external temperature sensor and its accessories during mass production, the material and manufacturing costs per unit are significantly reduced while ensuring temperature control accuracy, providing technical support for the product's market competitiveness. The segmented temperature calibration model solidified in the microcontroller will be dynamically invoked in step S5 based on the real-time operating status of the electric heater, ultimately achieving accurate conversion and control of the indoor ambient temperature.

[0060] S5: Performs mass production temperature control. During the mass production stage, it acquires the current shell temperature value and current heating power status collected by the built-in sensor in real time, calls the segmented temperature correction model, selects the corresponding relationship according to the current heating power status, substitutes the current shell temperature value to calculate the equivalent ambient temperature value, and controls the working status of the heating element based on the comparison result between the equivalent ambient temperature value and the user-set target temperature.

[0061] Furthermore, the process of calling the segmented temperature correction model and calculating the equivalent ambient temperature value specifically includes: the control unit of the electric heater in the mass production stage reads the current heating power status; determines the heating power parameter range to which the current heating power status belongs; calls the corresponding relationship from the segmented temperature correction model that corresponds to the heating power parameter range; substitutes the current shell temperature value obtained by the built-in temperature sensor as input into the called relationship, performs calculations based on the relationship, and determines the output value as the equivalent ambient temperature value. Furthermore, the process of controlling the operating state of the heating element based on the comparison between the equivalent ambient temperature value and the user-set target temperature specifically includes: When the equivalent ambient temperature is determined to be lower than the user-set target temperature, a start command or a heating sustain command is output to the heating element. When the equivalent ambient temperature value is determined to be at or above the user-set target temperature, a stop heating command is output to the heating element.

[0062] Specifically, after the calibration model in step S4 is solidified for mass production, the mass-produced smart electric heaters already possess the complete capability to achieve accurate ambient temperature conversion and closed-loop temperature control with only the built-in temperature sensor as hardware. The core task of this step is that during the daily operation of the electric heater, the control unit collects the current outer shell temperature value output by the built-in temperature sensor and the current heating power status of the electric heater in real time. It dynamically calls the pre-installed segmented temperature calibration model in the microcontroller, selects the corresponding temperature correspondence based on the current heating power status, converts the current outer shell temperature value into an equivalent ambient temperature value, compares this equivalent ambient temperature value with the user-set target temperature, and generates control commands for the heating element based on the comparison result, thereby achieving accurate closed-loop control of the indoor temperature.

[0063] In practice, after the electric heater is powered on and initialized during mass production, the control unit enters the main cycle workflow. Within each control cycle, the control unit first reads the electrical signal output from the built-in temperature sensor via an analog-to-digital converter, converts it into the current outer casing temperature value using a temperature conversion algorithm, and denoted as T. shell At the same time, the control unit reads the current heating power status, which is maintained by the status register inside the control unit and can reflect the current heating power level of the electric heater in real time, such as half power operation or full power operation.

[0064] The control unit determines the heating power parameter range to which the current heating power status belongs based on the read data. Specifically, the determination logic is as follows: if the current heating power status corresponds to full-power operation mode, it is determined to belong to the high-power range; if the current heating power status corresponds to half-power operation mode, it is determined to belong to the low-power range. Based on the determination result, the control unit retrieves the corresponding relationship for that heating power parameter range from the segmented temperature correction model. If it belongs to the high-power range, it retrieves the first relationship information, whose mathematical expression is T. env =a1·T shell +b1, where T env T represents the equivalent ambient temperature value. shell This represents the current casing temperature, where a1 and b1 are the coefficient of the first-order term and the constant term, respectively, for the high-power range. If the range is low, the second relation information is invoked, its mathematical expression being T. env =a2·T shell +b2, where a2 and b2 are the coefficient of the first term and the constant term corresponding to the low power range, respectively.

[0065] The control unit will use the current casing temperature value T obtained by the built-in temperature sensor. shell Substituting the input into the invoked correspondence, multiplication and addition operations are performed according to the mathematical expression of the correspondence to calculate the output value, which is then determined as the equivalent ambient temperature T at the current moment. env This equivalent ambient temperature value is the control unit's estimate of the current actual indoor ambient temperature, and will serve as the basis for subsequent temperature control decisions.

[0066] Taking a specific operating scenario as an example, suppose a mass-produced electric heater is currently operating in full-power heating mode, and its control unit reads the current outer shell temperature T through the built-in temperature sensor. shell The temperature is 38.5℃, and the current heating power is in the high power range. The control unit retrieves the first relationship information T from the segmented temperature correction model. env =0.78·T shell +1.2. T shell Substituting into the calculation, we obtain the equivalent ambient temperature value T. env =0.78×38.5+1.2=31.23℃. This calculation shows that although the outer casing temperature has reached 38.5℃, the actual indoor ambient temperature is approximately 31.2℃, a difference of about 7.3℃. If this temperature correction mechanism is not used, and the outer casing temperature is directly used as the temperature control basis, when the user sets the target temperature to 32℃, the control unit will mistakenly interpret it as the indoor temperature exceeding the limit and prematurely stop heating. This results in the user's actual perceived ambient temperature being far below the set value, affecting the user experience.

[0067] In obtaining the equivalent ambient temperature value T env Then, the control unit executes the closed-loop temperature control logic. The control unit will set T... env The target temperature value T set by the user via the control panel or remote control target A comparison is made. When the equivalent ambient temperature is determined to be lower than the user-set target temperature, i.e., T... env <T target When the heating element is off, the control unit outputs a start command or a maintain heating command to the heating element. If the heating element is currently off, a start command is output to initiate heating; if the heating element is currently on, a maintain heating command is output to continue heating. When the equivalent ambient temperature reaches or exceeds the user-set target temperature, i.e., T... env ≥T target When the heating element stops, the control unit outputs a stop heating command to the heating element, causing it to stop heating and preventing the indoor temperature from continuously rising above the user-set value.

[0068] Taking another operating scenario as an example, suppose the same electric heater gradually increases in indoor temperature, and the control unit reads the current outer casing temperature value T. shell The temperature is 34.2℃. The current heating power has switched to half-power operation mode due to entering the thermostatic adjustment phase, which is within the low-power range. The control unit retrieves the second relationship information T from the segmented temperature correction model. env =0.85·T shell +0.8. Substituting T_shell into the calculation, we obtain the equivalent ambient temperature value T. env =0.85×34.2+0.8=29.87℃. If the user sets the target temperature to 30℃, then T env Still slightly lower than T target The control unit continues to maintain half-power heating; when the casing temperature T shell When the temperature continues to rise slightly to 34.5℃, the equivalent ambient temperature value T env The calculated temperature is 0.85 × 34.5 + 0.8 = 30.125℃. Upon reaching the target temperature, the control unit immediately outputs a stop heating command, and the heating element shuts off. Afterward, as the indoor temperature naturally drops, T... env Lower than T again target When the temperature is high, the control unit restarts the heating, and this cycle continues to maintain the indoor temperature near the set value.

[0069] It should be noted that the control unit typically incorporates a temperature hysteresis control mechanism when making temperature control decisions to prevent the heating element from frequently starting and stopping near the critical temperature. For example, when T... env Reaching Tt arget Then stop heating and wait for T. env Falling back to below Ttarget Heating is restarted only after a preset hysteresis value is subtracted. This hysteresis control strategy is a conventional technique in this field and will not be described in detail here.

[0070] Through the execution of mass production temperature control in step S5, the electric heater in the mass production stage, under the low-cost hardware condition of retaining only the built-in temperature sensor, utilizes a segmented temperature correction model embedded in the control unit to achieve accurate dynamic conversion of the shell temperature to the ambient temperature, and completes closed-loop control of the heating element based on this. The core function of this step is reflected in two aspects: First, by dynamically selecting the corresponding correction relationship according to the current heating power state, the conversion error caused by the difference in the thermal characteristics of the body under different power modes is effectively eliminated, making the ambient temperature estimation result closer to the true value; Second, by using the accurate equivalent ambient temperature value as the sole basis for temperature control decision, the problem of temperature control deviation caused by the temperature measurement point being far from the user's activity area in traditional single-sensor electric heaters is fundamentally solved, significantly improving the user's actual physical comfort and product satisfaction.

[0071] Finally, it should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for automatic temperature correction in an intelligent electric heater, characterized in that, Includes the following steps: S1: Construct a dual-sensor testing platform. During the R&D phase, reserve an interface for an external temperature sensor and connect it to the external temperature sensor. Retain the original built-in temperature sensor in the body to simultaneously collect the shell temperature and the ambient temperature. S2: Perform multimodal automated calibration and data acquisition, control the electric heater to traverse preset heating modes, synchronously monitor the temperature sequence collected by dual sensors, and after thermal balance determination, associate and record the first temperature value, the second temperature value and the current heating power parameter to form a calibration data set until the traversal is completed and a complete calibration dataset is obtained. S3: Construct a segmented temperature correction model. Divide the calibration dataset into at least two data subsets according to the heating power parameters. Establish the correspondence between the first temperature value and the second temperature value in each subset and associate it with the corresponding heating power range. Encapsulate the results to form a segmented temperature correction model. S4: Solidify the calibration model to the mass production equipment. Write the segmented temperature calibration model into the control unit of the electric heater in the mass production stage. Remove the external temperature sensor and its signal extension line from the electric heater in the mass production stage, and only retain the built-in temperature sensor. S5: Performs mass production temperature control. During the mass production stage, it acquires the current shell temperature value and current heating power status collected by the built-in sensor in real time, calls the segmented temperature correction model, selects the corresponding relationship according to the current heating power status, substitutes the current shell temperature value to calculate the equivalent ambient temperature value, and controls the working status of the heating element based on the comparison result between the equivalent ambient temperature value and the user-set target temperature.

2. The method for automatic temperature correction of an intelligent electric heater according to claim 1, characterized in that, In step S1, the external temperature sensor is connected to the reserved interface via a signal extension cable. The length of the signal extension cable is sufficient to allow the external temperature sensor to be placed in an area unaffected by the heat generated by the electric heater body.

3. The method for automatic temperature correction of an intelligent electric heater according to claim 1, characterized in that, In step S2, the synchronous monitoring of the temperature sequence collected by the dual sensors specifically involves: synchronously monitoring the first temperature sequence collected by the built-in temperature sensor and the second temperature sequence collected by the external temperature sensor; the preset heating mode includes different user-set target temperature levels or different electric heater heating power levels.

4. The method for automatic temperature correction of an intelligent electric heater according to claim 1, characterized in that, In step S2, the process of determining the thermal balance specifically includes: The first temperature change characteristic is calculated based on the first temperature sequence obtained from continuous synchronous monitoring, and the second temperature change characteristic is calculated based on the second temperature sequence. When the first temperature change characteristic is detected to be lower than the preset first change characteristic threshold, the second temperature change characteristic is lower than the preset second change characteristic threshold, and the difference between the first temperature change characteristic and the second temperature change characteristic is lower than the preset difference threshold, and all three conditions are met continuously within a preset continuous time window, it is determined that the thermal equilibrium state has been entered.

5. The method for automatic temperature correction of an intelligent electric heater according to claim 1, characterized in that, In step S3, the process of dividing the calibration dataset into at least two data subsets according to the heating power parameter specifically includes: The first data subset is defined as all calibration data groups whose heating power parameters belong to the high power range in the complete calibration dataset. The second data subset is formed by dividing all calibration data groups in the complete calibration dataset whose heating power parameters belong to the low power range.

6. The method for automatic temperature correction of an intelligent electric heater according to claim 1, characterized in that, In step S3, the process of establishing the correspondence between the first temperature value and the second temperature value within each subset specifically includes: For the first subset of data, fit a first relationship information that represents the correspondence between the first temperature value and the second temperature value; For the second subset of data, a second relationship information is fitted to represent the correspondence between the first temperature value and the second temperature value.

7. The method for automatic temperature correction of an intelligent electric heater according to claim 1, characterized in that, In step S3, the segmented temperature correction model is defined as follows: when the current heating power parameter of the electric heater belongs to the high power range, the equivalent ambient temperature value is calculated using the first relationship information; when the current heating power parameter of the electric heater belongs to the low power range, the equivalent ambient temperature value is calculated using the second relationship information.

8. The method for automatic temperature correction of an intelligent electric heater according to claim 1, characterized in that, In step S4, the control unit of the electric heater in the mass production stage is a microcontroller.

9. The method for automatic temperature correction of an intelligent electric heater according to claim 1, characterized in that, In step S5, the process of calling the segmented temperature correction model and calculating the equivalent ambient temperature value specifically includes: the control unit of the electric heater in the mass production stage reads the current heating power status; determines the heating power parameter range to which the current heating power status belongs; calls the corresponding relationship from the segmented temperature correction model that corresponds to the heating power parameter range; substitutes the current shell temperature value obtained by the built-in temperature sensor as input into the called relationship, performs calculations based on the relationship, and determines the output value as the equivalent ambient temperature value.

10. The method for automatic temperature correction of an intelligent electric heater according to claim 1, characterized in that, In step S5, the process of controlling the operating state of the heating element based on the comparison result between the equivalent ambient temperature value and the user-set target temperature specifically includes: When the equivalent ambient temperature is determined to be lower than the user-set target temperature, a start command or a heating sustain command is output to the heating element. When the equivalent ambient temperature is determined to be at or above the user-set target temperature, a stop heating command is output to the heating element.

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