Electromagnetic heating power module control method based on space heat distribution

Abstract

The invention relates to the technical field of electromagnetic heating, and particularly discloses an electromagnetic heating power module control method based on spatial heat distribution, which comprises the following steps: extracting a temperature gradient vector of an edge area and a temperature gradient vector of a central area of a heating surface of equipment according to a temperature distribution diagram, and determining a gradient difference value between the areas; if the preset uniformity threshold value is exceeded, the average temperature change rate of the heating surface in the edge area and the center area is obtained, and the temperature change rate difference value between the areas is calculated; fusing the humidity value, the inter-region gradient difference value and the temperature change rate difference value to obtain an adjustment index value; calculating current adjustment values of the electromagnetic coils in the edge area and the center area according to the adjustment index values; and generating a corresponding energy output adjustment instruction according to the current adjustment value, and issuing the energy output adjustment instruction to the corresponding electromagnetic heating power module. The electromagnetic heating power module control method based on space heat distribution solves the problem that the temperature distribution of the heating surface of the electromagnetic heating equipment is not uniform.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic heating technology, and more specifically, to a method for controlling electromagnetic heating power modules based on spatial heat distribution. Background Technology

[0002] In the fields of modern industrial and household appliances, electromagnetic heating technology has become an indispensable core technology due to its high efficiency, environmental friendliness and strong controllability. It is widely used in heating equipment, cooking utensils and industrial processing, and its importance is self-evident.

[0003] However, many electromagnetic heating solutions have limitations in power distribution and temperature control, especially when handling large-area or multi-zone heating, making it difficult to accurately capture temperature differences within the heating space. The existence of temperature differences means an imbalance in heat distribution between heating areas, and this imbalance can further exacerbate localized temperature anomalies, such as some parts overheating while others fail to reach the expected temperature. This limitation makes it difficult for the equipment to flexibly adjust energy output according to actual needs, thus affecting the stability of the heating effect and potentially leading to resource waste or equipment damage. Summary of the Invention

[0004] In order to overcome the shortcomings of the existing technology, the present invention provides an electromagnetic heating power module control method based on spatial heat distribution, which aims to solve the problems in the above-mentioned existing technology.

[0005] The technical solution adopted by this invention to solve its technical problem is: an electromagnetic heating power module control method based on spatial heat distribution, comprising the following steps: S1: Obtain the temperature distribution map of the heating surface of the electromagnetic heating device; S2: Extract the temperature gradient vector of the edge region and the temperature gradient vector of the center region of the heating surface of the electromagnetic heating device according to the temperature distribution map, and determine the gradient difference value between the regions; S3: If the gradient difference value between the regions exceeds the preset uniformity threshold, obtain the average temperature change rate of the heating surface of the electromagnetic heating device in the edge region and the center region, and calculate the temperature change rate difference value between the regions. S4: Obtain the humidity value of the environment where the electromagnetic heating equipment is located, and combine the humidity value, the gradient difference value between regions, and the temperature change rate difference value to obtain the adjustment index value; S5: Calculate the current adjustment values ​​of the electromagnetic coils in the edge region and the center region based on the adjustment index values; S6: Generate an energy output adjustment command corresponding to the edge region based on the current adjustment value of the edge region, and send the energy output adjustment command to the electromagnetic heating power module corresponding to the edge region; generate an energy output adjustment command corresponding to the center region based on the current adjustment value of the center region, and send the energy output adjustment command to the electromagnetic heating power module corresponding to the center region.

[0006] It is worth noting that in step S1, the heating surface of the electromagnetic heating device is scanned by an infrared thermal imager to obtain real-time temperature data, wherein the real-time temperature data includes the coordinates of the temperature acquisition point and the real-time temperature value corresponding to the coordinates. A temperature distribution map is constructed using the Kriging method. The Kriging method includes: dividing the heating surface of the electromagnetic heating device into N grids. The temperature of each grid is represented by the temperature of the center point of the grid. The temperature value of the center point of the grid is obtained by weighted average of the real-time temperature values ​​of X neighboring points of the center point. The neighboring points are the X nearest temperature acquisition points to the center point, and the weight is the Euclidean distance from the temperature acquisition point to the center point. A temperature distribution map is generated by arranging the grids according to their order based on the temperature values ​​of the center points of all grids.

[0007] Preferably, in step S2, the heating surface of the electromagnetic heating device is divided into an edge region located at the edge of the heating surface and a central region located at the center of the heating surface. Obtain the set of temperature points in the edge region from the temperature distribution map, process the set of temperature points in the edge region using OpenCV's Sobel operator, and obtain the edge temperature gradient vector; Obtain the set of temperature points in the central region from the temperature distribution map, process the set of temperature points in the central region using the Sobel operator, and obtain the central temperature gradient vector.

[0008] Optionally, in step S2, the edge temperature gradient vector and the center temperature gradient vector are fused together, and an overall temperature gradient matrix is ​​constructed by splicing vectors along the row axis. Based on the overall temperature gradient matrix, NumPy is used to calculate the gradient difference between regions, where the gradient difference between regions D = mean(|M_edge-M_center|), M_edge represents the element of the edge submatrix belonging to the edge region in the overall temperature gradient matrix, M_center represents the element of the center submatrix belonging to the center region in the overall temperature gradient matrix, |M_edge-M_center| represents the absolute value of the difference between any element in the edge submatrix and any element in the center submatrix, and mean(|M_edge-M_center|) represents the average of the absolute values ​​of the differences between all elements in the edge submatrix and all elements in the center submatrix.

[0009] Specifically, in step S3, if the gradient difference value between regions exceeds the preset uniformity threshold, the average temperature data of the edge region and the temperature data of the center region of the heating surface of the electromagnetic heating device at each time point from the start-up to the current time are obtained. The average temperature data of the edge region is obtained by calculating the average value of the edge region temperature data corresponding to all edge regions. Linear regression was used to calculate the average temperature change rate of the peripheral region and the average temperature change rate of the central region, respectively. The absolute difference between the average temperature change rate of the peripheral region and the average temperature change rate of the central region was calculated to obtain the temperature change rate difference value.

[0010] Preferably, in step S4, the humidity value of the electromagnetic heating device in the current environment is obtained by a sensor; For the humidity value, the gradient difference value between regions, and the temperature change rate difference value between regions, a weighted average calculation based on a preset weighting coefficient is used to calculate and fuse the results to obtain the adjustment index value. The fusion result is equal to the humidity value multiplied by the first preset weighting coefficient, plus the gradient difference value between regions multiplied by the second preset weighting coefficient, plus the temperature change rate difference value multiplied by the third preset weighting coefficient, and then divided by the sum of the weighting coefficients. The first preset weighting coefficient represents the weight of the humidity value, the second preset weighting coefficient represents the weight of the gradient difference value between regions, and the third preset weighting coefficient represents the weight of the temperature change rate difference value.

[0011] It is worth noting that in step S5, the electromagnetic coil distribution density in the edge region and the electromagnetic coil distribution density in the center region are obtained from the electromagnetic coil layout of the electromagnetic heating device. The electromagnetic coil distribution density was processed by the finite element analysis software ANSYS, and electromagnetic field simulation was run to obtain the equilibrium parameters of the edge region and the equilibrium parameters of the center region. By merging the adjustment index value and the corresponding balance parameter through weighted averaging, the optimized temperature value of the edge region and the optimized temperature value of the center region are calculated respectively. By using the corresponding temperature optimization values, the electromagnetic coil current adjustment values ​​for the edge region and the electromagnetic coil current adjustment values ​​for the center region are obtained through a lookup table.

[0012] Preferably, in step S6, a proportional-integral-derivative (PID) controller is used to calculate the corresponding pulse width modulation (PWM) signal for the current adjustment value. The proportional term is calculated by multiplying the current adjustment value by the proportional gain Kp, where Kp is a preset proportional gain. The integral term is calculated by integrating the current adjustment value by multiplying it by the integral gain Ki, where Ki is a preset integral gain. The duty cycle of the PWM signal is the sum of the proportional and integral terms. The pulse width modulation signal corresponding to the edge region is used as the energy output adjustment command corresponding to the edge region and sent to the electromagnetic heating power module corresponding to the edge region to drive the electromagnetic heating power module to operate. The pulse width modulation signal corresponding to the central region is used as the energy output adjustment command for the central region and sent to the electromagnetic heating power module corresponding to the central region to drive the electromagnetic heating power module to operate.

[0013] The beneficial effects of this invention are as follows: In the electromagnetic heating power module control method based on spatial heat distribution, by acquiring the temperature distribution map of the heating surface, extracting the temperature gradient vector between the edge and center regions, calculating the gradient difference value between regions, and further analyzing the temperature change rate difference value when the difference value exceeds the standard, while integrating the ambient humidity value and the gradient difference value between regions, an adjustment index value is calculated, and finally, the current adjustment value for the electromagnetic coils in the edge and center regions is generated, and the corresponding energy output adjustment command is generated and sent to the corresponding electromagnetic heating power module to achieve precise driving. This solution solves the problem of excessive temperature difference and uneven temperature distribution between the edge and center regions of the heating surface through multi-dimensional data fusion and dynamic adjustment, effectively improving the heating uniformity and stability of the electromagnetic heating equipment. Attached Figure Description

[0014] Figure 1 This is a flowchart of an electromagnetic heating power module control method based on spatial heat distribution.

[0015] Figure 2 This is a temperature distribution diagram of the heating surface of an electromagnetic heating device constructed based on the Kriging method.

[0016] Figure 3 This is a comparison of the temperature change rates between the edge and center regions after linear regression analysis.

[0017] Figure 4 A schematic diagram illustrating the calculation of the pulse width modulation signal for a proportional-integral controller. Detailed Implementation

[0018] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. It should be noted that these descriptions are for the purpose of aiding understanding the present invention, but do not constitute a limitation thereof. Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0019] Combination Figure 1 value Figure 4 The method for controlling an electromagnetic heating power module based on spatial heat distribution, as shown, includes the following steps: S1: Obtain the temperature distribution map of the heating surface of the electromagnetic heating device; S2: Extract the temperature gradient vector of the edge region and the temperature gradient vector of the center region of the heating surface of the electromagnetic heating device according to the temperature distribution map, and determine the gradient difference value between the regions; S3: If the gradient difference value between the regions exceeds the preset uniformity threshold, obtain the average temperature change rate of the heating surface of the electromagnetic heating device in the edge region and the center region, and calculate the temperature change rate difference value between the regions. S4: Obtain the humidity value of the environment where the electromagnetic heating equipment is located, and combine the humidity value, the gradient difference value between regions, and the temperature change rate difference value to obtain the adjustment index value; S5: Calculate the current adjustment values ​​of the electromagnetic coils in the edge region and the center region based on the adjustment index values; S6: Generate an energy output adjustment command corresponding to the edge region based on the current adjustment value of the edge region, and send the energy output adjustment command to the electromagnetic heating power module corresponding to the edge region; generate an energy output adjustment command corresponding to the center region based on the current adjustment value of the center region, and send the energy output adjustment command to the electromagnetic heating power module corresponding to the center region.

[0020] In the electromagnetic heating power module control method based on spatial heat distribution, the temperature distribution map of the heating surface is acquired, the temperature gradient vector between the edge and center regions is extracted, the gradient difference value between regions is calculated, and when the difference value exceeds the standard, the temperature change rate difference value is further analyzed. Simultaneously, the ambient humidity value and the gradient difference value between regions are integrated to calculate the adjustment index value, ultimately generating the current adjustment value for the electromagnetic coils in the edge and center regions. Corresponding energy output adjustment commands are then generated and sent to the corresponding electromagnetic heating power modules to achieve precise driving. This solution, through multi-dimensional data fusion and dynamic adjustment, solves the problems of excessive temperature difference and uneven temperature distribution between the edge and center regions of the heating surface, effectively improving the heating uniformity and stability of the electromagnetic heating equipment.

[0021] Preferably, in step S1, the heating surface of the electromagnetic heating device is scanned by an infrared thermal imager to obtain real-time temperature data, wherein the real-time temperature data includes the coordinates of the temperature acquisition point and the real-time temperature value corresponding to the coordinates. A temperature distribution map is constructed using the Kriging method. The Kriging method includes: dividing the heating surface of the electromagnetic heating device into N grids. The temperature of each grid is represented by the temperature of the center point of the grid. The temperature value of the center point of the grid is obtained by weighted average of the real-time temperature values ​​of X neighboring points of the center point. The neighboring points are the X nearest temperature acquisition points to the center point, and the weight is the Euclidean distance from the temperature acquisition point to the center point. A temperature distribution map is generated by arranging the grids according to their order based on the temperature values ​​of the center points of all grids.

[0022] Specifically, assuming the heating surface of the electromagnetic heating device is 400mm × 300mm, the infrared thermal imager scans at a speed of 30 frames per second, and the coordinates of the acquisition point can be represented as (x, y), where x and y correspond to the length and width directions of the heating surface of the electromagnetic heating device, respectively. The temperature value of each acquisition point is directly measured by the infrared sensor of the infrared thermal imager. These data can be transmitted to the control system in real time to form a two-dimensional temperature matrix, thus providing a basis for subsequent analysis.

[0023] like Figure 2 As shown, when using the Kriging method to convert discrete point data into a continuous temperature field image, for example, after dividing the heating surface of an electromagnetic heating device into N grids, for the center point (100, 150) of a grid, the preset number of neighboring points is 5. Then, using the real-time temperature values ​​of these 5 temperature acquisition points closest to the center point, combined with the Euclidean distance from the temperature acquisition point to the center point as a weight, the weighted average of the fused temperature values ​​of these 5 neighboring points is calculated as the temperature value of the center point. Finally, the temperature distribution map is generated by arranging the temperature values ​​of the center points of all grids according to the grid arrangement order.

[0024] Optionally, in step S2, the heating surface of the electromagnetic heating device is divided into an edge region located at the edge of the heating surface and a central region located at the center of the heating surface. Obtain the set of temperature points in the edge region from the temperature distribution map, process the set of temperature points in the edge region using OpenCV's Sobel operator, and obtain the edge temperature gradient vector; Obtain the set of temperature points in the central region from the temperature distribution map, process the set of temperature points in the central region using the Sobel operator, and obtain the central temperature gradient vector.

[0025] In one embodiment, when dividing the heating surface of the electromagnetic heating device into an edge region located at the edge of the heating surface and a central region located at the center of the heating surface, the partitioning can be based on the geometry of the heating surface of the electromagnetic heating device. For example, for a rectangular heating surface of an electromagnetic heating device, the edge region is defined as a strip-shaped region within 100mm of the boundary, while the central region is the remaining inner rectangular portion. Similarly, for a circular heating surface of an electromagnetic heating device, the edge region is defined as an annular region within 100mm of the boundary, while the central region is the remaining inner circular portion.

[0026] Specifically, obtaining the set of temperature points in the edge region from the temperature distribution map involves extracting temperature data at corresponding coordinates. The set of points in the edge region may include hundreds of grid points near the boundary, each with a temperature value. Next, the set of temperature points in the edge region is processed using OpenCV's Sobel operator to obtain the edge temperature gradient vector. The Sobel operator is an edge detection tool in image processing that quantifies the steepness of temperature changes by calculating the difference between the temperature field in the horizontal and vertical directions. For example, during processing, the set of temperature points is treated as a grayscale image, where temperature values ​​are mapped to pixel intensities. After the operator is applied, it outputs a vector field representing the direction and magnitude of the temperature gradient at each point. The set of temperature points in the center region is processed in the same way to obtain the center temperature gradient vector.

[0027] It is worth noting that in step S2, the edge temperature gradient vector and the center temperature gradient vector are fused together, and the overall temperature gradient matrix is ​​constructed by splicing vectors along the row axis. Based on the overall temperature gradient matrix, NumPy is used to calculate the gradient difference between regions, where the gradient difference between regions D = mean(|M_edge-M_center|), M_edge represents the element of the edge submatrix belonging to the edge region in the overall temperature gradient matrix, M_center represents the element of the center submatrix belonging to the center region in the overall temperature gradient matrix, |M_edge-M_center| represents the absolute value of the difference between any element in the edge submatrix and any element in the center submatrix, and mean(|M_edge-M_center|) represents the average of the absolute values ​​of the differences between all elements in the edge submatrix and all elements in the center submatrix.

[0028] The process of merging the edge temperature gradient vector and the center temperature gradient vector to construct the overall temperature gradient matrix by splicing vectors along the row axis can be understood as connecting two vector arrays in the row direction to form a complete matrix. For example, the edge temperature gradient vector is the upper half and the center temperature gradient vector is the lower half. This splicing ensures that the matrix covers the entire heating surface of the electromagnetic heating device.

[0029] Specifically, based on the overall temperature gradient matrix, NumPy is used to calculate the gradient difference value between regions. The temperature gradient difference value D is obtained by averaging the absolute values ​​of the differences between the elements of the edge submatrix and the center submatrix. As a numerical computing library, NumPy can efficiently handle matrix operations. For example, during the calculation, the submatrix is ​​extracted first, and then the absolute values ​​are subtracted element by element and the average value is calculated. In business terms, this can quantify thermal non-uniformity.

[0030] Specifically, in step S3, if the gradient difference value between regions exceeds the preset uniformity threshold, the average temperature data of the edge region and the temperature data of the center region of the heating surface of the electromagnetic heating device at each time point from the start-up to the current time are obtained. The average temperature data of the edge region is obtained by calculating the average value of the edge region temperature data corresponding to all edge regions. Linear regression was used to calculate the average temperature change rate of the peripheral region and the average temperature change rate of the central region, respectively. The absolute difference between the average temperature change rate of the peripheral region and the average temperature change rate of the central region was calculated to obtain the temperature change rate difference value.

[0031] When the gradient difference between regions exceeds a preset uniformity threshold, it indicates a significant uneven heat distribution on the heating surface of the electromagnetic heating equipment, requiring further analysis to avoid affecting its performance. Specifically, the uniformity threshold is typically set based on historical production data and expert recommendations; once the gradient difference between regions exceeds this limit, subsequent steps are triggered.

[0032] For example, in actual use, assuming that an electromagnetic heating device is being used, when the calculated gradient difference between regions reaches 7℃ / mm and exceeds the threshold of 4℃ / mm, the system will automatically extract the average temperature data of the edge region and the average temperature data of the center region at each time point from the time the electromagnetic heating device is turned on to the current time from the database. These data are obtained from real-time monitoring by sensors and are collected once every rated number of seconds, covering the average temperature of multiple time points in the edge region and the average temperature of multiple time points in the center region.

[0033] like Figure 3As shown, in one embodiment, when calculating the average temperature change rate of the edge region using linear regression, the average temperature data of the edge region is first arranged by time to obtain a time series of the edge region. In this time series, time is used as the independent variable, and the average temperature data of the edge region is used as the dependent variable. The slope of the straight line fitted using the least squares method is taken as the average temperature change rate of the edge region. For example, the data points corresponding to the collected edge region show that the temperature increases linearly with time, with a change rate of 0.5℃ / min. Similarly, the same method is used to process the average temperature data of the central region, arranged by time to obtain a time series of the central region, thus obtaining the average temperature change rate of the central region. This quantifies the heating rate in business applications.

[0034] When calculating the absolute difference between the average temperature change rate of the peripheral region and the average temperature change rate of the central region, assuming that the average temperature change rate of the central region is 0.3℃ / min and the average temperature change rate of the peripheral region is 0.4℃ / min, the difference in temperature change rate is 0.1℃ / min, which quantifies the inconsistency of temperature change between regions.

[0035] Preferably, in step S4, the humidity value of the electromagnetic heating device in the current environment is obtained by a sensor; For the humidity value, the gradient difference value between regions, and the temperature change rate difference value between regions, a weighted average calculation based on a preset weighting coefficient is used to calculate and fuse the results to obtain the adjustment index value. The fusion result is equal to the humidity value multiplied by the first preset weighting coefficient, plus the gradient difference value between regions multiplied by the second preset weighting coefficient, plus the temperature change rate difference value multiplied by the third preset weighting coefficient, and then divided by the sum of the weighting coefficients. The first preset weighting coefficient represents the weight of the humidity value, the second preset weighting coefficient represents the weight of the gradient difference value between regions, and the third preset weighting coefficient represents the weight of the temperature change rate difference value.

[0036] In one embodiment, when obtaining the current ambient humidity value during the operation of the electromagnetic heating device through a sensor, the humidity sensor is typically deployed to monitor changes in ambient humidity in real time, and then the reading of the humidity sensor at the current moment is read as the current ambient humidity value.

[0037] When calculating the fusion result using a weighted average based on preset weighting coefficients for the humidity value, inter-regional gradient difference value, and inter-regional moisture evaporation rate difference value, it is necessary to first define the basis for setting the weighting coefficients. These coefficients are usually derived from historical production experience and expert evaluation. For example, the first preset weighting coefficient for humidity value is set to 0.4, the second preset weighting coefficient for temperature gradient difference value is set to 0.3, and the third preset weighting coefficient for temperature change rate difference value is set to 0.3, with a total of 1. Specifically, the weighted average calculation process involves multiplying each parameter by its weight, summing the results, and then dividing by the total weighted average. This adjustment index value reflects the combined influence of multiple factors.

[0038] It is worth noting that in step S5, the electromagnetic coil distribution density in the edge region and the electromagnetic coil distribution density in the center region are obtained from the electromagnetic coil layout of the electromagnetic heating device. The electromagnetic coil distribution density was processed by the finite element analysis software ANSYS, and electromagnetic field simulation was run to obtain the equilibrium parameters of the edge region and the equilibrium parameters of the center region. By merging the adjustment index value and the corresponding balance parameter through weighted averaging, the optimized temperature value of the edge region and the optimized temperature value of the center region are calculated respectively. By using the corresponding temperature optimization values, the electromagnetic coil current adjustment values ​​for the edge region and the electromagnetic coil current adjustment values ​​for the center region are obtained through a lookup table.

[0039] In one embodiment, when obtaining the electromagnetic coil distribution density of the edge region and the electromagnetic coil distribution density of the central region from a preset electromagnetic coil layout, the coil arrangement scheme of the electromagnetic heating equipment needs to be considered first. This coil arrangement scheme is set at the factory and varies depending on the model. Specifically, this layout is often designed based on the requirement for uniform heating. The coil distribution density in the edge region might be 15 coils per square meter to compensate for heat loss, while the central region might have 10 coils per square meter to avoid overheating. These density values ​​can be obtained as the coil arrangement scheme by scanning the layout drawings or extracting data using CAD software. For example, in actual production, the edge density is calculated by dividing the number of coils by the area. Assuming the edge region has an area of ​​2 square meters and 30 coils, the density is 15 coils per square meter, which helps with subsequent simulation preparation.

[0040] After obtaining these density values, the electromagnetic coil distribution density is processed using the finite element analysis software ANSYS. Electromagnetic field simulations are then run to obtain the equilibrium parameters for the edge and central regions. This process requires first constructing an electromagnetic coil model, treating the coil as a current source, and inputting material properties such as the permeability of the heating surface of the electromagnetic heating device into ANSYS. For example, if the heating surface is an iron pot, the permeability of the corresponding iron material is input. ANSYS is a tool for simulating physical fields. It discretizes complex structures through mesh generation and then solves the electromagnetic field equations. In this embodiment, separate electromagnetic field simulations are used to handle the edge and central regions.

[0041] Specifically, when running the simulation, first import the electromagnetic coil distribution density of the edge region or the electromagnetic coil distribution density of the central region, set boundary conditions such as an ambient temperature of 25℃, and then perform steady-state electromagnetic analysis to calculate the magnetic field strength distribution. Finally, output the corresponding equilibrium parameters. For example, the equilibrium parameter of the edge region is 0.8, while the equilibrium parameter of the central region is 0.92. These parameters reflect the uniformity of the magnetic field within the region, avoiding errors from manual calculation.

[0042] In one embodiment, the adjustment index value is assigned a weight of 0.6, the balance parameter is assigned a weight of 0.4, and then the temperature optimization value of the edge region is obtained by combining the adjustment index value and the balance parameter of the edge region with the corresponding weights. The temperature optimization value of the center region is obtained by combining the adjustment index value and the balance parameter of the center region with the corresponding weights. This value is used to guide temperature adjustment.

[0043] Finally, by using a mapping table, the range of the temperature optimization value in the edge region is mapped to the current adjustment value in the edge region, and the range of the temperature optimization value in the center region is mapped to the current adjustment value in the center region, thereby obtaining the current adjustment values ​​for the edge region and the center region. Then, the current adjustment value of the edge region is used as a reference to drive the electromagnetic coil in the edge region, and the current adjustment value of the center region is used as a reference to drive the electromagnetic coil in the center region, so as to balance the temperature of the edge region and the center region.

[0044] Preferably, in step S6, a proportional-integral controller is used to calculate the corresponding pulse width modulation signal for the current adjustment value. The proportional term is calculated by multiplying the current adjustment value by the proportional gain Kp, where Kp is a preset proportional gain. The integral term is calculated by integrating the current adjustment value by multiplying it by the integral gain Ki, where Ki is a preset integral gain. The duty cycle of the pulse width modulation signal is the sum of the proportional term and the integral term. The pulse width modulation signal corresponding to the edge region is used as the energy output adjustment command corresponding to the edge region and sent to the electromagnetic heating power module corresponding to the edge region to drive the electromagnetic heating power module to operate. The pulse width modulation signal corresponding to the central region is used as the energy output adjustment command for the central region and sent to the electromagnetic heating power module corresponding to the central region to drive the electromagnetic heating power module to operate.

[0045] like Figure 4 As shown, in one embodiment, when processing the current adjustment value of the electromagnetic heating device, a proportional-integral controller is first introduced to generate a pulse width modulation signal to adapt to the energy demand of the heating area. For example, in the calculation process, the proportional term is obtained by multiplying the current adjustment value by a preset proportional gain Kp. Kp is an instantaneous correction of the current adjustment value; the larger the value of Kp, the faster the response of the electromagnetic heating device. The integral term involves integrating the current adjustment value and multiplying it by the integral gain Ki. Ki is used to accumulate the current adjustment value to eliminate steady-state error; the larger the value of Ki, the faster the steady-state error is eliminated and the shorter the response time of the electromagnetic heating device. Finally, the duty cycle of the pulse width modulation signal is the sum of the proportional and integral terms, where the duty cycle determines the conduction ratio of the signal, thereby affecting the output power.

[0046] Specifically, during the output phase, the pulse width modulation signal of the edge region is sent as an energy output adjustment command to the corresponding electromagnetic heating power module. This module typically includes a power transistor and a drive circuit. This command drives the electromagnetic heating power module to adjust its output voltage to control the operation of the electromagnetic coil in the edge region, thereby matching the heating requirements. Similarly, the pulse width modulation signal of the center region is sent as an energy output adjustment command to the corresponding electromagnetic heating power module. This command drives the electromagnetic heating power module to adjust its output voltage to control the operation of the electromagnetic coil in the center region, thereby matching the heating requirements.

[0047] It is worth noting that both the edge and central regions have corresponding independent electromagnetic heating power modules. The electromagnetic heating power module for the edge region receives the pulse width modulation signal corresponding to the edge region and drives the electromagnetic coil in the edge region to operate. The electromagnetic heating power module for the central region receives the pulse width modulation signal corresponding to the central region and drives the electromagnetic coil in the central region to operate, thereby ensuring independent control between different regions. In this way, precise control of the electromagnetic heating equipment is achieved.

[0048] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and these variations still fall within the protection scope of the present invention.

Claims

1. A control method for an electromagnetic heating power module based on spatial heat distribution, characterized in that, Includes the following steps: S1: Obtain the temperature distribution map of the heating surface of the electromagnetic heating device; S2: Extract the temperature gradient vector of the edge region and the temperature gradient vector of the center region of the heating surface of the electromagnetic heating device according to the temperature distribution map, and determine the gradient difference value between the regions; S3: If the gradient difference value between the regions exceeds the preset uniformity threshold, obtain the average temperature change rate of the heating surface of the electromagnetic heating device in the edge region and the center region, and calculate the temperature change rate difference value between the regions. S4: Obtain the humidity value of the environment where the electromagnetic heating equipment is located, and combine the humidity value, the gradient difference value between regions, and the temperature change rate difference value to obtain the adjustment index value; S5: Calculate the current adjustment values ​​of the electromagnetic coils in the edge region and the center region based on the adjustment index values; S6: Generate an energy output adjustment command corresponding to the edge region based on the current adjustment value of the edge region, and send the energy output adjustment command to the electromagnetic heating power module corresponding to the edge region; generate an energy output adjustment command corresponding to the center region based on the current adjustment value of the center region, and send the energy output adjustment command to the electromagnetic heating power module corresponding to the center region.

2. The electromagnetic heating power module control method based on spatial heat distribution according to claim 1, characterized in that: In step S1, the heating surface of the electromagnetic heating device is scanned by an infrared thermal imager to obtain real-time temperature data, wherein the real-time temperature data includes the coordinates of the temperature acquisition point and the real-time temperature value corresponding to the coordinates. A temperature distribution map is constructed using the Kriging method. The Kriging method includes: dividing the heating surface of the electromagnetic heating device into N grids. The temperature of each grid is represented by the temperature of the center point of the grid. The temperature value of the center point of the grid is obtained by weighted average of the real-time temperature values ​​of X neighboring points of the center point. The neighboring points are the X nearest temperature acquisition points to the center point, and the weight is the Euclidean distance from the temperature acquisition point to the center point. A temperature distribution map is generated by arranging the grids according to their order based on the temperature values ​​of the center points of all grids.

3. The electromagnetic heating power module control method based on spatial heat distribution according to claim 1, characterized in that: In step S2, the heating surface of the electromagnetic heating device is divided into an edge region located at the edge of the heating surface and a central region located at the center of the heating surface. Obtain the set of temperature points in the edge region from the temperature distribution map, process the set of temperature points in the edge region using OpenCV's Sobel operator, and obtain the edge temperature gradient vector; Obtain the set of temperature points in the central region from the temperature distribution map, process the set of temperature points in the central region using the Sobel operator, and obtain the central temperature gradient vector.

4. The electromagnetic heating power module control method based on spatial heat distribution according to claim 3, characterized in that: In step S2, the edge temperature gradient vector and the center temperature gradient vector are fused together, and the overall temperature gradient matrix is ​​constructed by splicing vectors along the row axis. Based on the overall temperature gradient matrix, NumPy is used to calculate the gradient difference between regions, where the gradient difference between regions D = mean(|M_edge-M_center|), M_edge represents the element of the edge submatrix belonging to the edge region in the overall temperature gradient matrix, M_center represents the element of the center submatrix belonging to the center region in the overall temperature gradient matrix, |M_edge-M_center| represents the absolute value of the difference between any element in the edge submatrix and any element in the center submatrix, and mean(|M_edge-M_center|) represents the average of the absolute values ​​of the differences between all elements in the edge submatrix and all elements in the center submatrix.

5. The electromagnetic heating power module control method based on spatial heat distribution according to claim 1, characterized in that: In step S3, if the gradient difference between regions exceeds a preset uniformity threshold, the average temperature data of the edge region and the temperature data of the center region of the electromagnetic heating device at each time point from power-on to the current time are obtained. The average temperature data of the edge region is obtained by calculating the average value of the edge region temperature data corresponding to all edge regions. Linear regression was used to calculate the average temperature change rate of the peripheral region and the average temperature change rate of the central region, respectively. The absolute difference between the average temperature change rate of the peripheral region and the average temperature change rate of the central region was calculated to obtain the temperature change rate difference value.

6. The electromagnetic heating power module control method based on spatial heat distribution according to claim 1, characterized in that: In step S4, the humidity value of the electromagnetic heating device in the current environment is obtained by a sensor; For the humidity value, the gradient difference value between regions, and the temperature change rate difference value between regions, a weighted average calculation based on a preset weighting coefficient is used to calculate and fuse the results to obtain the adjustment index value. The fusion result is equal to the humidity value multiplied by the first preset weighting coefficient, plus the gradient difference value between regions multiplied by the second preset weighting coefficient, plus the temperature change rate difference value multiplied by the third preset weighting coefficient, and then divided by the sum of the weighting coefficients. The first preset weighting coefficient represents the weight of the humidity value, the second preset weighting coefficient represents the weight of the gradient difference value between regions, and the third preset weighting coefficient represents the weight of the temperature change rate difference value.

7. The electromagnetic heating power module control method based on spatial heat distribution according to claim 1, characterized in that: In step S5, the electromagnetic coil distribution density of the edge region and the electromagnetic coil distribution density of the center region are obtained from the electromagnetic coil layout of the electromagnetic heating device. The electromagnetic coil distribution density was processed by the finite element analysis software ANSYS, and electromagnetic field simulation was run to obtain the equilibrium parameters of the edge region and the equilibrium parameters of the center region. By merging the adjustment index value and the corresponding balance parameter through weighted averaging, the optimized temperature value of the edge region and the optimized temperature value of the center region are calculated respectively. By using the corresponding temperature optimization values, the electromagnetic coil current adjustment values ​​for the edge region and the electromagnetic coil current adjustment values ​​for the center region are obtained through a lookup table.

8. The electromagnetic heating power module control method based on spatial heat distribution according to claim 1, characterized in that: In step S6, a proportional-integral-derivative (PID) controller is used to calculate the corresponding pulse width modulation (PWM) signal for the current adjustment value. The proportional term is calculated by multiplying the current adjustment value by the proportional gain Kp, where Kp is a preset proportional gain. The integral term is calculated by integrating the current adjustment value by multiplying it by the integral gain Ki, where Ki is a preset integral gain. The duty cycle of the PWM signal is the sum of the proportional and integral terms. The pulse width modulation signal corresponding to the edge region is used as the energy output adjustment command corresponding to the edge region and sent to the electromagnetic heating power module corresponding to the edge region to drive the electromagnetic heating power module to operate. The pulse width modulation signal corresponding to the central region is used as the energy output adjustment command for the central region and sent to the electromagnetic heating power module corresponding to the central region to drive the electromagnetic heating power module to operate.

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