Multi-layer temperature coordinated and balanced heating power determination and control system for rice steaming box

Abstract

The application discloses a multi-layer temperature coordination and balance heating power determination and control system of a rice steaming box, relates to the technical field of temperature control of food steaming equipment, and comprises a temperature collection module, a temperature field modeling module, a temperature difference analysis module and a power decision module. The temperature collection module synchronously collects initial temperature distribution data of each layer through an array of temperature sensors at the top, middle and bottom of the inner cavity. The temperature field modeling module generates an initial three-dimensional temperature field model through spatial interpolation operation. The temperature difference analysis module calculates an initial temperature difference matrix and identifies high and low temperature regions. The power decision module generates a preliminary power distribution scheme for different partition units in each heating layer in combination with the thermal physical property parameters of rice varieties and a dynamic power distribution algorithm. The system can improve the problem of uneven heating temperature of the multi-layer rice steaming box and realize fine partition distribution of heating power.

Description

Technical Field

[0001] This invention belongs to the field of marine equipment, specifically a multi-layer temperature coordinated and balanced heating power determination and control system for rice steamers in marine galleys. Background Technology

[0002] Shipboard rice steamers are responsible for preparing staple foods for all personnel on board. They require large-capacity supply and short-term concentrated steaming, making balanced heating and consistent, controllable rice quality a long-standing and often overlooked challenge. Current rice steamer heating control technologies mostly rely on localized temperature detection, covering only a portion of the steamer's interior. They fail to utilize temperature sensor arrays deployed at the top, middle, and bottom of the interior to simultaneously collect initial temperature distribution data for each heating layer, thus lacking complete initial temperature information for the multi-layered structure. Furthermore, current technologies do not perform spatial interpolation on the temperature data, lacking the technical means to construct a three-dimensional temperature field model of the steamer's interior. Relying solely on localized, discrete temperature data for heating control fails to quantitatively characterize the overall temperature distribution within the interior.

[0003] Existing rice steamers use a unified overall power distribution method for heating power control, failing to calculate the temperature difference matrix between each heating layer based on a temperature field model. This makes it impossible to accurately identify low-temperature and high-temperature regions within the internal temperature field. Furthermore, the power distribution logic does not incorporate the thermal properties of rice varieties, and it does not employ a dynamic power distribution algorithm to generate power allocation schemes for individual zoned units within each layer. The existing control method cannot adapt to the spatial heating characteristics of multi-layer rice steamers. The heating power of each heating layer and its different zones cannot match the actual temperature distribution, resulting in insufficient temperature coordination across multiple layers, poor uniformity of the internal temperature field, and a mismatch between power configuration and actual steaming requirements. Summary of the Invention

[0004] This invention aims to overcome the technical problems existing in the prior art and provides a multi-layer temperature-coordinated and balanced heating power determination and control system for rice steamers. The system of this invention requires the comprehensive acquisition of multi-layer temperatures in the rice steamer, the construction of a three-dimensional temperature field, and the refined power allocation to different zones.

[0005] To achieve the above-mentioned objectives, the technical solution provided by this invention patent is as follows: A multi-layer temperature-coordinated and balanced heating power determination and control system for a rice steamer, comprising: The temperature acquisition module is used to start the heating process of the rice steamer. It synchronously collects the initial temperature distribution data of each layer during the initial heating stage through an array of temperature sensors distributed at the top, middle and bottom of the rice steamer cavity. The temperature field modeling module is used to perform spatial interpolation on the initial temperature distribution data to generate an initial three-dimensional temperature field model of the inner cavity of the rice steamer. The temperature difference analysis module is used to calculate the initial temperature difference matrix between each heating layer based on the initial three-dimensional temperature field model, and to identify the initial low temperature region and the initial high temperature region in the temperature field. The power decision module is used to generate a preliminary power allocation scheme for different partition units within each heating layer by calling a dynamic power allocation algorithm based on the initial temperature difference matrix and the identified low and high temperature regions, combined with the pre-stored thermal property parameters of rice varieties.

[0006] Further, spatial interpolation is performed on the initial temperature distribution data to generate an initial three-dimensional temperature field model of the inner cavity of the rice steamer, including: Read the initial temperature distribution data collected by all temperature sensor arrays and map each data point to its corresponding three-dimensional spatial coordinates; The radial basis function interpolation method is used to estimate temperature values ​​at spatial locations where no temperature sensors are deployed. A regular three-dimensional voxel mesh is established within the inner cavity of the rice steamer; The interpolated temperature values ​​are assigned to the center point of each voxel in the three-dimensional voxel grid to form the initial three-dimensional temperature field model composed of discrete temperature voxels.

[0007] Furthermore, based on the initial three-dimensional temperature field model, the initial temperature difference matrix between each heating layer is calculated, and the initial low-temperature region and the initial high-temperature region in the temperature field are identified, including: In the initial three-dimensional temperature field model, the voxel temperature sets located in the corresponding spaces of the top heating layer, middle heating layer and bottom heating layer are extracted respectively; Calculate the average value of the top voxel temperature set, the average value of the middle voxel temperature set, and the average value of the bottom voxel temperature set; The initial temperature difference matrix is ​​constructed by arranging the average temperature values ​​of the top, middle, and bottom in spatial order. Search for all voxels below a preset temperature threshold in the initial three-dimensional temperature field model, and define their spatial coordinate set as the initial low-temperature region; Search for all voxels in the initial three-dimensional temperature field model that are above a preset temperature threshold, and define their spatial coordinate set as the initial high-temperature region.

[0008] Furthermore, based on the initial temperature difference matrix and the identified low-temperature and high-temperature regions, combined with pre-stored thermal property parameters of rice varieties, a dynamic power allocation algorithm is invoked to generate a preliminary power allocation scheme for different partition units within each heating layer, including: The specific heat capacity, water absorption rate, and target cooking temperature of the rice are read from the pre-stored thermophysical property parameters of the rice type. The initial temperature difference matrix is ​​combined with the specific heat capacity and water absorption rate parameters to calculate the basic heat compensation value required to balance the temperature difference of each layer. Using the set of spatial coordinates of the initial low-temperature region as the target, additional compensation power is allocated to the heating unit covering the initial low-temperature region, and the allocation weight is determined based on the difference between the voxel temperature and the target ripening temperature within the initial low-temperature region. Using the set of spatial coordinates of the initial high-temperature region as a reference, a power upper limit constraint is set for the heating unit covering the initial high-temperature region; By combining the basic heat compensation value, the additional compensation power, and the power upper limit constraint, a preliminary power allocation scheme containing the initial power values ​​of each heating layer partition unit is generated.

[0009] Furthermore, it also includes: The steady-state control module is used to control the power output of each heating element according to the preliminary power distribution scheme, so as to enter the steady-state heating stage; The model update module is used to continuously collect real-time temperature distribution data during the steady-state heating stage and periodically update the three-dimensional temperature field model to obtain an updated three-dimensional temperature field model. The trend analysis module is used to calculate the real-time temperature difference matrix between each layer at the current moment based on the updated three-dimensional temperature field model, and to analyze the trend of temperature field uniformity change. The power adjustment module is used to input the real-time temperature difference matrix and the temperature field uniformity change trend into the adaptive power adjustment strategy module. The adaptive power adjustment strategy module combines the preset cooking temperature curve of rice, calculates the power correction amount, and dynamically adjusts the preliminary power allocation scheme based on the power correction amount to form the power control command at the current moment. The closed-loop execution module is used to execute the power control command and adjust the power of each heating layer zone in a closed loop until the rice steaming process is over. During the steady-state heating phase, real-time temperature distribution data is continuously collected, and the three-dimensional temperature field model is periodically updated to obtain an updated three-dimensional temperature field model, including: Acquire new measurement data from the temperature sensor array according to a fixed sampling period; The new measurement data is assimilated with the three-dimensional temperature field model of the previous cycle, and the voxel temperature values ​​at the corresponding coordinate positions in the model are replaced. For voxels without new measurement data, a short-time prediction model based on the heat conduction equation is used to extrapolate the temperature value of the current period. The replacement values ​​are integrated with the predicted values ​​to form the updated three-dimensional temperature field model corresponding to the current sampling period.

[0010] Furthermore, based on the updated three-dimensional temperature field model, the real-time temperature difference matrix between each layer at the current moment is calculated, and the trend of temperature field uniformity variation is analyzed, including: From the updated three-dimensional temperature field model, the voxel temperature sets in the corresponding spaces of the top, middle and bottom heating layers are extracted again. Calculate the current average temperature value of these three voxel temperature sets and generate the real-time temperature difference matrix by arranging them layer by layer; Compare the real-time temperature difference matrix of the current period with the temperature difference matrix of the previous period, calculate the difference between the corresponding matrix elements, and obtain the temperature difference change vector. The standard deviation of all voxel temperature values ​​in the updated three-dimensional temperature field model is calculated, and this standard deviation is compared with the standard deviation of historical periods to obtain the standard deviation change rate. The temperature difference change vector and the standard deviation change rate together constitute the uniformity change trend of the temperature field.

[0011] Furthermore, the real-time temperature difference matrix and the temperature field uniformity variation trend are input to the adaptive power adjustment strategy module. This module, combined with the preset cooking temperature curve of the rice, calculates the power correction amount, including: The adaptive power adjustment strategy module reads the desired temperature corresponding to the current cooking stage on the preset ripening temperature curve. The average temperature of each layer in the real-time temperature difference matrix is ​​compared with the desired temperature to obtain the layer temperature deviation. Analyzing the uniformity trend of the temperature field, if the temperature difference change vector shows a decrease in temperature difference and a decrease in the rate of change of standard deviation, a tendency to reduce power compensation is generated; if it shows an increase in temperature difference or an increase in the rate of change of standard deviation, a tendency to increase power compensation is generated. Using the layered temperature deviation as the main input and the adjustment tendency as the correction direction, the power correction amount of each heating layer partition unit is calculated through fuzzy decision rules.

[0012] Furthermore, based on the power correction amount, the initial power allocation scheme is dynamically adjusted to form the power control command for the current moment, including: Obtain the current set power value of each partition unit in the currently effective preliminary power allocation scheme; The current set power value of each partition unit is algebraically superimposed with the corresponding power correction amount to obtain a new power set value; Check whether the new power setting exceeds the physical power limit or the upper power limit constraint of the heating element of the partition unit; New power settings that exceed the limit are limited to bring them within the allowable power range. All the new power settings of the partitioned units that have been verified and processed are compiled into the power control instructions that can be recognized by the heating element driver.

[0013] Further, the power control command is executed to adjust the power of each heating layer zone in a closed loop until the rice steaming process is completed, including: The power control command is sent to the power drive circuit of each heating layer partition; The power drive circuit adjusts the output voltage or duty cycle according to the command, thereby changing the actual heating power of the heating element; In the next sampling period after power adjustment, temperature distribution data is collected again, and the steps of updating the three-dimensional temperature field model, analyzing temperature difference and uniformity, calculating power correction amount, and generating power control command are repeated. When the system detects that the average temperature of all heating layers remains stable within the target temperature range of the preset cooking temperature curve, and the standard deviation of the temperature field is lower than the set threshold and remains stable for more than the set time, it determines that the rice is cooked and controls the heating element to stop working, thus ending the rice cooking process.

[0014] Furthermore, the process of establishing the pre-stored thermal property parameters for different rice varieties includes: Establish a database of rice varieties, recording the variety name, recommended rice-to-water ratio, and steaming time for each type of rice. Under standard experimental conditions, calorimetric experiments were conducted on samples of different types of rice to measure and record their specific heat capacity and phase transition enthalpy. The saturated water absorption rate curves of different types of rice during the steaming process were determined by water absorption rate testing. Based on a large number of cooking experiments, internal temperature change data of different types of rice were collected when they reached the best taste, and the preset cooking temperature curve was obtained by fitting. The variety name, specific heat capacity, phase change enthalpy, saturated water absorption rate curve and preset cooking temperature curve are associated and stored to form the pre-stored thermal property parameters of the rice variety.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. The system of the present invention uses an array of temperature sensors distributed at the top, middle and bottom of the inner cavity of a rice steamer to simultaneously collect initial temperature distribution data of each layer during the initial heating stage. Spatial interpolation is performed on the initial temperature distribution data to generate an initial three-dimensional temperature field model of the inner cavity of the rice steamer. This model can comprehensively cover the spatial temperature acquisition range of the inner cavity of the rice steamer, obtaining complete initial temperature data under a multi-layered structure. Spatial interpolation can fill in the blank areas of local temperature acquisition. The three-dimensional temperature field model can intuitively present the temperature distribution state at various locations within the inner cavity, transforming the temperature distribution of the inner cavity of the rice steamer from local discrete data into a holistic spatial representation. The comprehensiveness and spatial correspondence of the temperature data are enhanced, avoiding the loss of temperature information caused by a single temperature measurement point, and truly reflecting the spatial temperature distribution state of the inner cavity of a multi-layered rice steamer in the initial stage.

[0016] 2. The system of this invention calculates the initial temperature difference matrix between each heating layer based on an initial three-dimensional temperature field model, identifies the initial low-temperature region and the initial high-temperature region in the temperature field, and calls a dynamic power allocation algorithm in combination with pre-stored thermal property parameters of rice varieties to generate a preliminary power allocation scheme for different partition units within each heating layer. It can determine the basis for power configuration based on the actual temperature difference distribution in the cavity, accurately match the heating intensity requirements of high and low temperature regions, so that power allocation is no longer limited to a unified overall control mode. Different partition units within each heating layer can obtain heating power corresponding to their own temperature state. The thermal property parameters of rice varieties can be directly integrated into the power allocation calculation logic, so that the configuration of heating power is adapted to the heating characteristics of the food itself. The temperature coordination of multi-layer heating is improved, the distribution of the internal cavity temperature field tends to be more balanced, and the precision and adaptability of power allocation are improved. Attached Figure Description

[0017] Figure 1 This is a timing diagram of a multi-layer temperature coordinated and balanced heating power determination and control system for a rice steamer according to the present invention. Figure 2 This is a flowchart of the construction and region identification of the interlayer temperature difference matrix based on voxel temperature sets; Figure 3 A thermal map of the temperature difference matrix between layers at different heating times in a rice steamer. Figure 4 A graph showing the evolution trend of the standard deviation of the temperature field during the steady-state heating phase of a rice steamer. Figure 5 This is a periodic trend chart of power and temperature uniformity index. Detailed Implementation

[0018] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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.

[0019] See Figure 1 This invention provides a multi-layer temperature-coordinated and balanced heating power determination and control system for rice steamers, specifically including: The temperature acquisition module, during the heating process of the rice steamer, synchronously collects temperature data from each layer during the initial heating stage using a pre-distributed array of temperature sensors installed at the top, middle, and bottom of the steamer's interior cavity, obtaining initial temperature distribution data. The temperature field modeling module performs spatial interpolation on this initial temperature distribution data to construct an initial three-dimensional temperature field model that reflects the spatial distribution of temperature throughout the entire cavity. Based on this model, the temperature difference analysis module calculates the initial temperature difference matrix between each heating layer and identifies the initial low-temperature and high-temperature regions within the entire temperature field. The power decision module, based on the calculated initial temperature difference matrix and the identified low-temperature and high-temperature region information, and combined with pre-stored thermophysical parameters for different rice types, calls a dynamic power allocation algorithm to generate a preliminary power allocation scheme for the different heating units within each heating layer, thus providing a direct basis for subsequent precise and balanced heating control.

[0020] In one embodiment of the invention, the temperature field modeling module reads the initial temperature distribution data collected by all temperature sensor arrays and maps each temperature data point to its corresponding coordinate position in the three-dimensional space of the rice steamer's inner cavity. For spatial locations where no sensors are arranged, the module uses a radial basis function interpolation method for temperature estimation. A regular three-dimensional voxel mesh is established throughout the entire space of the rice steamer's inner cavity, and the temperature value obtained through interpolation is assigned to the center point of each voxel, thereby forming an initial three-dimensional temperature field model composed of discrete temperature voxels. (See also...) Figure 2 In the initial three-dimensional temperature field model, the temperature difference analysis module extracts all voxels located within the spatial ranges corresponding to the top, middle, and bottom heating layers, forming top voxel temperature sets, middle voxel temperature sets, and bottom voxel temperature sets. The average temperature value of each of these three sets is calculated, and the average temperature values ​​of the top, middle, and bottom sets are arranged in spatial order to form the initial temperature difference matrix. Simultaneously, the module searches for all voxels with temperatures below a preset temperature threshold in the initial three-dimensional temperature field model, defining the spatial coordinate sets of these voxels as the initial low-temperature region; and searches for all voxels with temperatures above the preset temperature threshold, defining their spatial coordinate sets as the initial high-temperature region.

[0021] In specific implementation, the inner cavity of the rice steamer is defined with three heating zones along its height: a top heating layer, a middle heating layer, and a bottom heating layer. A distributed array of temperature sensors is installed, and the temperature acquisition module synchronously collects temperature data from each layer during the initial heating phase after the rice steamer's heating process is initiated. In some embodiments, four temperature sensors are arranged in the top heating zone, with coordinates (0.1, 0.1, 0.8), (0.9, 0.1, 0.8), (0.1, 0.9, 0.8), and (0.9, 0.9, 0.8), and temperature readings of 95℃, 98℃, 94℃, and 97℃, respectively. Four temperature sensors are arranged in the middle heating zone, with coordinates (0.1, 0.1, 0.5), (0.9, 0.1, 0.5), (0.1, 0.9, 0.5), and (0.9, 0.9, 0.5), and temperature readings of 92℃, 93℃, 91℃, and 94℃, respectively. Four temperature sensors are arranged in the bottom heating area, with coordinates (0.1, 0.1, 0.2), (0.9, 0.1, 0.2), (0.1, 0.9, 0.2), and (0.9, 0.9, 0.2), respectively, and temperature readings of 99℃, 101℃, 98℃, and 100℃. The temperature field modeling module reads the initial temperature distribution data collected by all temperature sensor arrays and maps each data point to its corresponding three-dimensional spatial coordinates. For spatial locations within the rice steamer cavity where no temperature sensors are deployed, the temperature field modeling module uses the radial basis function interpolation method to estimate the temperature values. A regular three-dimensional voxel mesh is established within the rice steamer cavity space, and the interpolated temperature values ​​are assigned to the center point of each voxel in the three-dimensional voxel mesh, forming an initial three-dimensional temperature field model composed of discrete temperature voxels.

[0022] It is understandable that radial basis function interpolation requires the selection of specific function forms and shape parameters. In this specific implementation, the system uses a Gaussian function as the radial basis function, and the form of the radial basis function interpolation method is as follows: ,in It is the Euclidean distance between the point to be interpolated and the known data points. These are shape parameters that control the smoothness of the function. The value of is determined through cross-validation to ensure that the interpolation model has the minimum error at the reserved data points. For the voxel center point with coordinates (0.5, 0.5, 0.5), the system calculates the distance between the point (0.5, 0.5, 0.5) and the coordinates of all known temperature sensors. Based on these distances and the corresponding Gaussian function values, a system of linear equations is solved to obtain the weighting coefficients. Finally, a weighted sum is obtained to obtain the estimated temperature value of the point (0.5, 0.5, 0.5).

[0023] The temperature difference analysis module begins operation after the initial 3D temperature field model is generated. Within the initial 3D temperature field model, the module extracts voxels located in the spaces corresponding to the top, middle, and bottom heating layers. For the top heating layer, all voxels with a Z-coordinate greater than 0.7 meters are extracted, and their temperature values ​​form the top voxel temperature set. For the middle heating layer, all voxels with a Z-coordinate between 0.3 and 0.7 meters are extracted, forming the middle voxel temperature set. For the bottom heating layer, all voxels with a Z-coordinate less than 0.3 meters are extracted, forming the bottom voxel temperature set. The module calculates the average values ​​of the top, middle, and bottom voxel temperature sets. Using the average temperatures of the top, middle, and bottom layers as matrix elements, the module arranges them in spatial order from top to bottom to form the initial temperature difference matrix. The temperature difference analysis module searches for all voxels with temperatures below a preset temperature threshold of 92℃ in the initial three-dimensional temperature field model, defining the set of spatial coordinates of all voxels below 92℃ as the initial low-temperature region. The module also searches for all voxels with temperatures above a preset temperature threshold of 100℃ in the initial three-dimensional temperature field model, defining the set of spatial coordinates of all voxels above 100℃ as the initial high-temperature region.

[0024] In practical implementation, the initial temperature difference matrix directly reflects the average temperature difference between each layer. The average temperature set of the top voxels calculated by the temperature difference analysis module is 96℃, the average temperature set of the middle voxels is 93℃, and the average temperature set of the bottom voxels is 99℃. Therefore, the initial temperature difference matrix is ​​represented as [96, 93, 99]. This initial temperature difference matrix visually shows the temperature distribution where the average temperature of the middle heating layer is the lowest and the average temperature of the bottom heating layer is the highest. The temperature difference analysis module identifies the initial low-temperature region in the initial three-dimensional temperature field model. The initial low-temperature region contains all voxels with temperatures below 92℃, and these voxels are mainly distributed in the middle area and corners of the inner cavity of the rice steamer. The temperature difference analysis module also identifies the initial high-temperature region in the initial three-dimensional temperature field model. The initial high-temperature region contains all voxels with temperatures above 100℃, and these voxels are mainly distributed in the bottom central area of ​​the inner cavity of the rice steamer. The identification of the initial low-temperature region and the initial high-temperature region provides a spatial positioning basis for subsequent power compensation.

[0025] In one embodiment of the present invention, the power decision module reads the specific heat capacity, water absorption rate, and target cooking temperature of the rice to be cooked from pre-stored thermal property parameters of rice varieties. This module combines the initial temperature difference matrix with the read specific heat capacity and water absorption rate parameters to calculate the basic heat compensation value required to balance the temperature difference between layers. For identified initial low-temperature regions, the module uses the set of spatial coordinates of that region as the target and allocates additional compensation power to the heating units covering these coordinates. The allocation weight is based on the difference between the voxel temperature and the target cooking temperature within the initial low-temperature region. For initial high-temperature regions, the module uses their spatial coordinate set as a reference and sets a power upper limit constraint for the heating units covering that region. Combining the aforementioned calculated basic heat compensation value, the additional compensation power for low-temperature regions, and the power upper limit constraint for high-temperature regions, the power decision module generates a preliminary power allocation scheme containing the initial power value of each partition unit within each heating layer. The establishment of the pre-stored thermal property parameters of rice varieties requires first establishing a rice variety database, recording the variety name, recommended rice-to-water ratio, and steaming time for each type of rice. Under standard experimental conditions, calorimetric experiments were conducted on samples of different types of rice, and their specific heat capacity and phase transition enthalpy were measured and recorded. Water absorption rate tests were used to determine the saturated water absorption rate curves for different types of rice during steaming. Based on extensive cooking experiments, internal temperature change data for different types of rice at their optimal taste were collected, and a preset cooking temperature curve was obtained through fitting. Finally, the variety name, specific heat capacity, phase transition enthalpy, saturated water absorption rate curve, and preset cooking temperature curve were associated and stored to form a complete set of pre-stored thermophysical parameters for different rice varieties.

[0026] In practical implementation, the power decision module needs to generate a preliminary power allocation scheme. The power decision module first reads the specific heat capacity, water absorption rate, and target cooking temperature of the rice being cooked from pre-stored thermal property parameters for different rice types. In some embodiments, the rice being cooked is "Japonica rice," and the power decision module queries the pre-stored thermal property parameters for different rice types, reading that the specific heat capacity of "Japonica rice" is... Water absorption rate Target ripening temperature The power decision module combines the initial temperature difference matrix with the read specific heat capacity and water absorption rate parameters to calculate the basic heat compensation value required to balance the temperature difference between each layer. It can be understood that the calculation of the basic heat compensation value needs to consider the mass and temperature difference of each layer of rice. Based on the initial temperature difference matrix [96,93,99], assuming the mass of each layer of rice... To raise the temperature of the middle layer to the same level as the top and bottom layers, the power decision module calculates the base heat compensation value. Its formula is:

[0027] in: It is specific heat capacity. It's about the quality of the rice. It is a reference average temperature (such as the average of the top and bottom layers). This refers to the average temperature of the middle heating layer. The power decision module targets the set of spatial coordinates of the identified initial low-temperature region and allocates additional compensation power to the heating units covering the initial low-temperature region. In some embodiments, the initial low-temperature region includes coordinate points (0.5, 0.5, 0.5) and (0.2, 0.8, 0.4), and the heating units covering these coordinate points are the third partition units of the middle layer. The power decision module allocates additional compensation power to this heating unit, and the allocation weight is determined based on the difference between the voxel temperature and the target ripening temperature within the initial low-temperature region. For example, the voxel temperature at coordinates (0.5, 0.5, 0.5) is 91°C, with a difference of 9°C from the target ripening temperature of 100°C; the voxel temperature at coordinates (0.2, 0.8, 0.4) is 90°C, with a difference of 10°C. Based on these differences, the power decision module calculates weights and allocates a higher additional compensation power value to the third partition unit of the middle layer, for example, increasing the base power by 150 watts.

[0028] The power decision module uses the spatial coordinate set of the identified initial high-temperature region as a reference to set a power upper limit constraint for the heating units covering the initial high-temperature region. In specific implementation, the initial high-temperature region includes the coordinate point (0.5, 0.5, 0.2), and the heating unit covering this coordinate is the bottom center partition unit. The power decision module sets a power upper limit constraint for this heating unit, for example, the maximum allowable power of this unit must not exceed 800 watts, to prevent the temperature in this region from rising further. The power decision module comprehensively calculates the basic heat compensation value, the additional compensation power for the low-temperature region, and the power upper limit constraint for the high-temperature region to generate a preliminary power allocation scheme containing the initial power values ​​of each heating layer partition unit. Optionally, the preliminary power allocation scheme can be a power mapping table, which explicitly lists the power of the first partition unit of the top heating layer as 750 watts, the power of the second partition unit as 760 watts, the power of the first partition unit of the middle heating layer as 780 watts, the power of the second partition unit as 770 watts, the power of the third partition unit as 900 watts, and the power limit of the center partition unit of the bottom heating layer as 800 watts, and the power of other partition units as 820 watts.

[0029] It is understandable that establishing pre-stored thermal property parameters for different rice varieties is a preliminary data preparation process. In practice, establishing these parameters first requires creating a rice variety database, recording the variety name, recommended rice-to-water ratio, and steaming time for each type. For example, the database records the variety name of "Japonica rice," the recommended rice-to-water ratio as 1:1.2, and the standard steaming time as 25 minutes; and the variety name of "Indica rice," the recommended rice-to-water ratio as 1:1.1, and the standard steaming time as 22 minutes. Under standard experimental conditions, calorimetric experiments are conducted on samples of different rice varieties, measuring and recording their specific heat capacity and phase transition enthalpy. Water absorption rate tests are used to determine the saturated water absorption rate curves for different rice varieties during steaming. Based on numerous cooking experiments, data on the internal temperature changes of different rice varieties when they reach their optimal taste are collected, and a preset ripening temperature curve is obtained through fitting. The rice variety name, specific heat capacity, phase transition enthalpy, saturated water absorption rate curve, and preset cooking temperature curve are associated and stored to form pre-stored thermophysical parameters for the rice variety. Optionally, the saturated water absorption rate curve is stored as a time-water absorption rate percentage graph or data table, and the preset cooking temperature curve is stored as a time-core temperature relationship formula or a set of data points.

[0030] In one embodiment of the present invention, the system, based on a preliminary power allocation scheme, controls the heating elements of each layer to output power according to a set power through a steady-state control module, entering a steady-state heating stage. During the steady-state heating stage, the model update module continuously collects real-time temperature distribution data from the temperature sensor array and periodically updates the three-dimensional temperature field model. The model update module acquires new measurement data from the temperature sensor array according to a fixed sampling period. This new data is assimilated with the three-dimensional temperature field model of the previous period, directly replacing the voxel temperature values ​​at the corresponding coordinate positions in the model. For voxels not covered by new measurement data, the module uses a short-time prediction model based on the heat conduction equation to extrapolate their temperature values ​​in the current sampling period. Finally, the replaced measured temperature values ​​are integrated with the predicted temperature values ​​to form an updated three-dimensional temperature field model corresponding to the current sampling period. The trend analysis module calculates the real-time temperature difference matrix between each layer at the current moment based on the updated three-dimensional temperature field model and analyzes the trend of temperature field uniformity changes. The power adjustment module inputs the real-time temperature difference matrix and temperature field uniformity trend to the adaptive power adjustment strategy module. This module, combined with the preset cooking temperature curve of the rice, calculates the power correction amount and dynamically adjusts the initial power allocation scheme based on this correction amount, forming the power control command for the current moment. The closed-loop execution module executes this power control command, adjusting the power of each heating layer zone in a closed loop.

[0031] In practical implementation, the system controls each heating element to enter the working state according to the preliminary power allocation scheme. The steady-state control module reads and executes the preliminary power allocation scheme, controls the power output of each heating element in the rice steamer, and the system enters the steady-state heating stage. In some embodiments, the preliminary power allocation scheme exists in the form of a power command table. The steady-state control module sends control signals to the power drive circuit of each partition unit according to the table. Refer to Table 1 for the set power values ​​of each partition unit during the steady-state heating stage.

[0032] Table 1: Preliminary Power Allocation during Steady-State Heating Stage

[0033] The model update module begins its periodic operation during the steady-state heating phase. Following a fixed sampling period of 5 seconds, the module acquires new measurement data from the temperature sensor array. It then assimilates the new measurement data with the previous period's 3D temperature field model, directly replacing the voxel temperature value at the corresponding coordinate position in the model. Data assimilation is one of the core operations of the model update. In practice, the temperature sensor at coordinates (0.1, 0.1, 0.8) had a voxel temperature of 95.0℃ in the previous period's model. The new measurement data read in the current sampling period is 95.7℃, and the model update module replaces the original voxel temperature value of 95.0℃ with 95.7℃. For voxels in the 3D temperature field model not directly covered by new measurement data, the module uses a short-time prediction model based on the heat conduction equation to deduce their temperature value for the current period. The short-time prediction model can be expressed as:

[0034] in: It is the predicted temperature at position (x, y, z) at the current time t. Is this position at the previous moment? Temperature value, It is the equivalent thermal diffusivity of the rice-steam mixture. It is the second derivative (i.e., temperature distribution curvature) of the Laplace operator used to calculate the temperature field in space. This refers to the sampling period duration. The model update module integrates the replaced measured temperature values ​​with the predicted temperature values ​​obtained through the short-time prediction model to form an updated three-dimensional temperature field model corresponding to the current sampling period.

[0035] The trend analysis module performs calculations based on the updated 3D temperature field model output by the model update module. The trend analysis module calculates the real-time temperature difference matrix between each layer at the current moment and analyzes the uniformity trend of the temperature field. The power adjustment module inputs the real-time temperature difference matrix and the temperature field uniformity trend into the adaptive power adjustment strategy module. The adaptive power adjustment strategy module, combined with the preset cooking temperature curve of the rice, calculates the power correction amount. Based on the power correction amount, the adaptive power adjustment strategy module dynamically adjusts the initial power allocation scheme to form the power control command for the current moment. The closed-loop execution module executes the power control command to achieve closed-loop adjustment of the power of each heating layer. In some embodiments, the adaptive power adjustment strategy module reads the preset cooking temperature curve and obtains the desired temperature of 85℃ at the 120th second of the current cooking stage. The adaptive power adjustment strategy module compares the average temperature of each layer in the real-time temperature difference matrix [94,96,98] with the desired temperature of 85℃, obtaining the layer temperature deviation as [+9℃,+11℃,+13℃]. The adaptive power adjustment strategy module analyzes the trend of temperature field uniformity changes. If the analysis results show that the temperature difference is widening, it generates an adjustment tendency to increase power compensation. The adaptive power adjustment strategy module takes the layer temperature deviation as the main input and increases power compensation as the adjustment direction. It calculates the power correction amount for each heating layer partition unit through internally set fuzzy decision rules. For example, the power correction amount calculated for the middle heating layer partition D is +30 watts.

[0036] See Figure 3 The graph presents the temperature difference distribution characteristics between the top, middle, and bottom heating layers of a rice steamer at five key heating times: 30s, 60s, 90s, 120s, and 150s. The graph uses color depth to represent the magnitude of the temperature deviation; darker colors indicate higher deviations. The values ​​show a significant gradient along the vertical axis (heating time) and the horizontal axis (heating layer distribution). Numerically, at 30s, the temperature deviations at the top, middle, and bottom are 9℃, 11℃, and 13℃ respectively, representing the peak values. As heating time gradually increases, the temperature deviations of each layer decrease systematically, with deviations at 150s being only 1℃, 3℃, and 5℃. Spatially, at the same heating time, the temperature deviation of the bottom heating layer is consistently higher than that of the middle and top heating layers, and the middle heating layer is higher than the top heating layer, exhibiting a "bottom > middle > top" temperature difference distribution characteristic. This distribution pattern is consistent with the physical mechanism of heat transfer during the heating process of the rice steamer and the results of voxel temperature extraction and average temperature calculation from the three-dimensional temperature field model. It also provides core quantitative basis for the construction of the temperature difference matrix, the identification of low and high temperature regions, and the formulation of subsequent dynamic power allocation schemes. It intuitively reflects the spatiotemporal evolution characteristics of the temperature deviation between layers in the initial heating stage of the rice steamer.

[0037] In one embodiment of the present invention, the trend analysis module extracts voxels from the updated three-dimensional temperature field model, respectively, within the spaces corresponding to the top, middle, and bottom heating layers, forming the current voxel temperature sets. The module calculates the current average temperature value of these three voxel temperature sets and generates a real-time temperature difference matrix by arranging them layer by layer. This module compares the real-time temperature difference matrix of the current sampling period with the temperature difference matrix of the previous period, calculating the difference between corresponding matrix elements to obtain a temperature difference change vector. Simultaneously, it calculates the standard deviation of all voxel temperature values ​​in the updated three-dimensional temperature field model and compares this standard deviation with the standard deviation of historical periods to obtain the standard deviation change rate. The temperature difference change vector and the standard deviation change rate together constitute the uniformity trend of the temperature field. The adaptive power adjustment strategy module in the power adjustment module reads the desired temperature corresponding to the preset ripening temperature curve at the current cooking stage. This module compares the average temperature of each layer in the real-time temperature difference matrix with this desired temperature to obtain the layer temperature deviation. This module analyzes the uniformity trend of the temperature field. If the temperature difference change vector shows that the temperature difference between each layer is decreasing and the rate of change of the standard deviation is decreasing, it generates an adjustment tendency to reduce power compensation; if the trend shows that the temperature difference is increasing or the rate of change of the standard deviation is increasing, it generates an adjustment tendency to increase power compensation. This module uses the layer temperature deviation as the main input and the above adjustment tendency as the correction direction, and calculates the power correction amount for each heating layer partition unit through the built-in fuzzy decision rules.

[0038] In specific implementation, the trend analysis module calculates based on the updated three-dimensional temperature field model provided by the model update module. The updated three-dimensional temperature field model contains the temperature values ​​of all voxels in the inner cavity of the rice steamer during the current sampling period. From the updated three-dimensional temperature field model, the trend analysis module extracts voxels located in the corresponding spaces of the top heating layer, middle heating layer, and bottom heating layer, respectively. These voxels constitute the current top voxel temperature set, middle voxel temperature set, and bottom voxel temperature set. The trend analysis module calculates the current average temperature value of these three voxel temperature sets and generates a real-time temperature difference matrix by arranging them layer by layer. In some embodiments, the top voxel temperature set extracted from the updated three-dimensional temperature field model contains 1024 voxels with an average temperature of 97.2℃, the middle voxel temperature set contains 1024 voxels with an average temperature of 95.8℃, and the bottom voxel temperature set contains 1024 voxels with an average temperature of 98.5℃. The generated real-time temperature difference matrix is ​​[97.2, 95.8, 98.5].

[0039] The trend analysis module compares the real-time temperature difference matrix of the current period with that of the previous period, calculates the difference between corresponding matrix elements, and obtains the temperature difference change vector. In specific implementation, the temperature difference matrix of the previous period is [96.8, 95.5, 98.1], and the real-time temperature difference matrix of the current period is [97.2, 95.8, 98.5]. The trend analysis module calculates the average temperature difference change at the top as +0.4℃, the average temperature difference change in the middle as +0.3℃, and the average temperature difference change at the bottom as +0.4℃, resulting in the temperature difference change vector [+0.4, +0.3, +0.4]. The trend analysis module calculates the standard deviation of all voxel temperature values ​​in the updated three-dimensional temperature field model and compares this standard deviation with the standard deviation of historical periods to obtain the standard deviation change rate. It can be understood that the standard deviation is a key indicator for measuring the uniformity of the temperature field. The formula for calculating the standard deviation is:

[0040] in: It is the standard deviation of the temperature field during the current sampling period. It is the total number of voxels in the updated three-dimensional temperature field model. It is the first Temperature value of individual elements This is the average temperature of all voxels. The trend analysis module obtains the standard deviation of the temperature field for historical periods (such as the previous sampling period). Then calculate the rate of change of standard deviation. The temperature difference change vector and the standard deviation change rate together constitute the uniformity trend of the temperature field. See Table 2 for the analysis results of the uniformity trend.

[0041] Table 2: Example Table of Temperature Field Uniformity Variation Trend Analysis

[0042] The power adjustment module inputs the real-time temperature difference matrix and the temperature field uniformity trend to the adaptive power adjustment strategy module. The adaptive power adjustment strategy module reads the desired temperature corresponding to the current cooking stage on the preset ripening temperature curve. In some embodiments, when cooking reaches the 180th second, the adaptive power adjustment strategy module queries the preset ripening temperature curve to obtain the desired temperature corresponding to the 180th second. The adaptive power adjustment strategy module compares the average temperature of each layer in the real-time temperature difference matrix with the desired temperature to obtain the layer temperature deviation. For the real-time temperature difference matrix [97.2, 95.8, 98.5] and the desired temperature of 90℃, the layer temperature deviation is [+7.2℃, +5.8℃, +8.5℃].

[0043] The adaptive power adjustment strategy module analyzes the uniformity trend of the temperature field. If the temperature difference change vector shows that the temperature difference between each layer is decreasing and the rate of change of the standard deviation is decreasing, the adaptive power adjustment strategy module generates an adjustment tendency to reduce power compensation; if the temperature difference change vector shows that the temperature difference is increasing or the rate of change of the standard deviation is increasing, the adaptive power adjustment strategy module generates an adjustment tendency to increase power compensation. Optionally, the adjustment tendency can be represented by a sign and intensity level. Based on the example in Table 2, the temperature difference change vector shows that the average temperature of each layer is rising, but the interlayer temperature difference (such as the temperature difference between the top and bottom) changes from 1.3℃ to 1.3℃ (98.5-97.2 vs. 98.1-96.8), which is basically flat, while the rate of change of the standard deviation Δσ is negative (-0.087), indicating that the uniformity is improving. The adaptive power adjustment strategy module may generate an adjustment tendency of "slightly reducing compensation" or "maintaining". The adaptive power adjustment strategy module uses the layer temperature deviation as the main input and the adjustment tendency as the correction direction, and calculates the power correction amount of each heating layer partition unit through fuzzy decision rules. It is understandable that the inputs to the fuzzy decision rule include the magnitude and sign of the stratified temperature deviation, as well as the type and intensity of the adjustment tendency. Power correction amount. The calculation can be described as follows:

[0044] in: It is a power correction amount. Represents fuzzy decision-making rules. It is a temperature deviation in layers. It is a regulation tendency. For the top heating layer, its layer temperature deviation is +7.2℃, the regulation tendency is "hold", and the fuzzy decision rule may output a small positive power correction.

[0045] See Figure 4In the steady-state optimization stage of multi-layer temperature-coordinated and balanced heating power control in a rice steamer, the evolution characteristics of temperature field uniformity are quantitatively characterized by the coupling relationship between standard deviation and its rate of change. Specifically, the figure uses "standard deviation of the previous period," "standard deviation of the current period," and "rate of change of standard deviation" as three key characterization nodes to construct a time-series evolution map of the temperature field dispersion during steady-state heating. Data shows that the standard deviation of the temperature field in the previous period is at a high benchmark, characterizing the temperature heterogeneity within the cavity under the initial steady state. With the intervention of the adaptive power adjustment strategy, the standard deviation of the current period shows a slight decline, indicating the initial convergence of the inter-layer and spatial temperature gradients. The rate of change of standard deviation dropping to near zero reveals that the temperature field uniformity under closed-loop control has reached a critical state of significant optimization, and the dispersion of temperature of each voxel in space approaches a minimum. The trend of this curve confirms the engineering effectiveness of the dynamic power compensation mechanism in eliminating hot and cold dead zones and driving the temperature field towards global uniformity. Its numerical variation amplitude and trend provide core quantitative basis for the steady-state accuracy assessment of the rice steamer thermal control system.

[0046] In one embodiment of the present invention, the power adjustment module obtains the current set power value of each partition unit in the currently effective preliminary power allocation scheme. The current set power value of each partition unit is algebraically superimposed with the corresponding power correction amount calculated by the adaptive power adjustment strategy module to obtain a new power set value for that partition unit. The system checks whether each new power set value exceeds the physical power limit of the heating element of that partition unit or violates the previously set power upper limit constraint. Any new power set value exceeding the allowable range is limited to within the allowable power range. All new power set values ​​of partition units that have been verified and limited are compiled into a power control command that can be recognized and executed by the heating element driver. The closed-loop execution module sends this power control command to the power drive circuit of each heating layer partition. The power drive circuit adjusts its output voltage or pulse duty cycle according to the received command, thereby changing the actual heating power of the heating element. In the next sampling cycle after the completion of this power adjustment operation, the system again collects temperature distribution data and repeats the steps of updating the three-dimensional temperature field model, analyzing the trend of temperature difference and uniformity changes, calculating the power correction amount, and generating the power control command. When the system detects that the average temperature of all heating layers remains stable within the target temperature range specified by the preset cooking temperature curve, and the standard deviation of the entire temperature field is lower than the set threshold for a period of time exceeding the preset stability duration, it determines that the rice is cooked and then controls all heating elements to stop working, thus ending the rice cooking process.

[0047] In practical implementation, the power adjustment module adjusts the initial power allocation scheme based on the power correction calculated by the adaptive power adjustment strategy module. The power adjustment module obtains the current set power value for each zone unit in the currently effective initial power allocation scheme. The current set power value originates from the power command being executed by the steady-state control module. In some embodiments, for "Top Heating Layer - Zone A", the current set power value read by the power adjustment module is 750 watts; for "Middle Heating Layer - Zone D", the current set power value reads is 900 watts. The power adjustment module algebraically superimposes the current set power value of each zone unit with the corresponding power correction to obtain a new power set value. The formula for calculating the new power set value is:

[0048] in: Representing the The new power setting value is calculated by each partition unit. Representing the The current set power value for each partition unit. The adaptive power adjustment strategy module is the first one. The power correction amount is calculated for each partition unit. Based on this formula, if the power correction amount for "Top Heating Layer - Partition A" is... If it is +5 watts, then its new power setting value If the power correction amount of "Central Heating Layer - Zone D" If it is +30 watts, then its new power setting value watt.

[0049] The power regulation module checks whether the new power setting exceeds the physical power limit of the heating element in the zone unit or the upper power constraint defined in the preliminary power allocation scheme. The physical power limit of the heating element is determined by its specifications; for example, the maximum physical power capacity of a heating unit may be 1200 watts. The upper power constraint is set by the power decision module when generating the preliminary power allocation scheme; for example, the upper power constraint for "Central Heating Layer - Zone D" might be 1000 watts. The power regulation module limits new power settings that exceed the limits, bringing them within the allowable power range. In practice, if the calculated new power setting for a zone unit is 1050 watts, but its upper power constraint is 1000 watts, the power regulation module modifies this value to 1000 watts through limiting. The power regulation module compiles all verified and limited new power settings for zone units into power control instructions that can be recognized by the heating element driver.

[0050] The closed-loop execution module executes power control commands to perform closed-loop regulation of the rice steaming process. The module sends power control commands to the power drive circuits corresponding to each heating layer zone. The power drive circuits adjust their output voltage or the duty cycle of their pulse signals according to the received power control commands, thereby changing the actual heating power of the corresponding heating elements. In some embodiments, for a command targeting 755 watts, the power drive circuit adjusts its output voltage to the voltage level corresponding to 755 watts, or adjusts the duty cycle of the PWM signal to the ratio corresponding to 755 watts. In the next sampling cycle after the power adjustment operation is completed, the system again collects temperature distribution data through the temperature sensor array and repeats the steps of updating the three-dimensional temperature field model, analyzing temperature difference and uniformity, calculating power correction, and generating power control commands. This can be understood as a continuous feedback control loop. In practice, the temperature sensor collects new data in the next sampling cycle, the model update module generates an updated three-dimensional temperature field model based on the new data, the trend analysis module calculates the new real-time temperature difference matrix and uniformity change trend, the adaptive power adjustment strategy module outputs a new power correction amount, the power adjustment module generates a new power control command, and the closed-loop execution module drives the power drive circuit to execute the new command.

[0051] When the system detects that the average temperature of all heating layers remains stable within the target temperature range of the preset cooking temperature curve, and the standard deviation of the temperature field is lower than the set threshold and remains stable for more than the set time, the closed-loop execution module determines that the rice is cooked and controls the heating element to stop working, thus ending the rice cooking process.

[0052] See Figure 5In the steady-state closed-loop adjustment stage of the multi-layer temperature collaborative and balanced heating control system of the rice steamer, the heating power and temperature uniformity index of the top-A zone exhibit a typical negative correlation dynamic adjustment trend, intuitively reflecting the closed-loop control effect of the adaptive power adjustment strategy. The power of the top-A zone represents the real-time output power of the heating element in the top heating layer A zone, which is a direct reflection of the system's execution of power control commands. The temperature uniformity index is derived from the standard deviation of the temperature of all voxels in the three-dimensional temperature field model. The higher the index, the worse the uniformity of the temperature distribution inside the rice steamer cavity; the lower the index, the more uniform the temperature field. Initial adjustment stage (cycles 1-4): In order to quickly level out the initial temperature difference, the system increases the power of the top-A zone from 750W to a peak of 755W and maintains it for 2 cycles, compensating for the initial temperature disadvantage of the top area through additional power compensation. During this stage, the temperature uniformity index gradually increases from 3.2 to 4.2, corresponding to the temporary change in uniformity caused by local temperature fluctuations during the process of the system actively intervening in the temperature field and eliminating the initial temperature difference. Dynamic Equilibrium Phase (Cycles 4-7): The power of the top-A zone is gradually reduced from 753W to a base power of 750W. After completing heat compensation for the top region, the system enters the power reduction and steady-state maintenance phase. The temperature uniformity index continuously rises from 4.2 to 6.0, corresponding to the temperature field gradually converging towards global equilibrium under dynamic power adjustment. The uniformity index synchronously reflects the temperature field evolution during the control process. Steady-State Optimization Phase (Cycles 7-10): The power of the top-A zone fluctuates slightly within the range of 752W-750W, eventually stabilizing at a base power of 750W, achieving refined closed-loop power control. The temperature uniformity index continuously climbs from 6.0 to 10.0, corresponding to the system achieving global uniformity of the temperature field inside the rice cooker cavity through multiple rounds of power correction, with the uniformity index reaching the optimal control state. The trend shown in the figure perfectly matches the adaptive power adjustment strategy of this invention: the system takes the real-time temperature difference matrix and the temperature uniformity change trend as input, and calculates the power correction amount in combination with the preset rice cooking temperature curve. By dynamically adjusting the power of each heating layer zone, such as the top-A zone, a closed-loop control of "power compensation-temperature difference convergence-uniformity optimization" is achieved, which ultimately ensures that the rice is cooked in a uniform temperature field throughout the cavity, solving the technical problems of uneven temperature and inconsistent cooking effects in traditional rice steamers.

[0053] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A multi-layer temperature coordinated and balanced heating power determination and control system for a rice steaming box, characterized in that, include: The temperature acquisition module is used to start the heating process of the rice steamer. It synchronously collects the initial temperature distribution data of each layer during the initial heating stage through an array of temperature sensors distributed at the top, middle and bottom of the rice steamer cavity. The temperature field modeling module is used to perform spatial interpolation on the initial temperature distribution data to generate an initial three-dimensional temperature field model of the inner cavity of the rice steamer. The temperature difference analysis module is used to calculate the initial temperature difference matrix between each heating layer based on the initial three-dimensional temperature field model, and to identify the initial low temperature region and the initial high temperature region in the temperature field. The power decision module is used to generate a preliminary power allocation scheme for different partition units within each heating layer by calling a dynamic power allocation algorithm based on the initial temperature difference matrix and the identified low and high temperature regions, combined with the pre-stored thermal property parameters of rice varieties. The trend analysis module is used to calculate the real-time temperature difference matrix between each layer based on the updated three-dimensional temperature field model, and to analyze the trend of temperature field uniformity changes, including: From the updated three-dimensional temperature field model, the voxel temperature sets in the corresponding spaces of the top, middle and bottom heating layers are extracted again. Calculate the current average temperature value of these three voxel temperature sets and generate the real-time temperature difference matrix by arranging them layer by layer; Compare the real-time temperature difference matrix of the current period with the temperature difference matrix of the previous period, calculate the difference between the corresponding matrix elements, and obtain the temperature difference change vector. The standard deviation of all voxel temperature values ​​in the updated three-dimensional temperature field model is calculated, and this standard deviation is compared with the standard deviation of historical periods to obtain the standard deviation change rate. The temperature difference change vector and the standard deviation change rate together constitute the uniformity change trend of the temperature field.

2. The multi-layer temperature synergic equalization heating power determination and control system of rice steaming box according to claim 1, characterized in that, Spatial interpolation is performed on the initial temperature distribution data to generate an initial three-dimensional temperature field model of the inner cavity of the rice steamer, including: Read the initial temperature distribution data collected by all temperature sensor arrays and map each data point to its corresponding three-dimensional spatial coordinates; The radial basis function interpolation method is used to estimate temperature values ​​at spatial locations where no temperature sensors are deployed. A regular three-dimensional voxel mesh is established within the inner cavity of the rice steamer; The interpolated temperature values ​​are assigned to the center point of each voxel in the three-dimensional voxel grid to form the initial three-dimensional temperature field model composed of discrete temperature voxels.

3. The multi-layer temperature synergic equalization heating power determination and control system of rice steaming box according to claim 2, characterized in that, Based on the initial three-dimensional temperature field model, the initial temperature difference matrix between each heating layer is calculated, and the initial low-temperature region and the initial high-temperature region in the temperature field are identified, including: In the initial three-dimensional temperature field model, the voxel temperature sets located in the corresponding spaces of the top heating layer, middle heating layer and bottom heating layer are extracted respectively; Calculate the average value of the top voxel temperature set, the average value of the middle voxel temperature set, and the average value of the bottom voxel temperature set; The initial temperature difference matrix is ​​constructed by arranging the average temperature values ​​of the top, middle, and bottom in spatial order. Search for all voxels below a preset temperature threshold in the initial three-dimensional temperature field model, and define their spatial coordinate set as the initial low-temperature region; Search for all voxels in the initial three-dimensional temperature field model that are above a preset temperature threshold, and define their spatial coordinate set as the initial high-temperature region.

4. The multi-layer temperature synergic equalization heating power determination and control system of rice steaming box according to claim 3, characterized in that, Based on the initial temperature difference matrix and the identified low and high temperature regions, combined with pre-stored thermal property parameters of rice varieties, a dynamic power allocation algorithm is invoked to generate a preliminary power allocation scheme for different partition units within each heating layer, including: The specific heat capacity, water absorption rate, and target cooking temperature of the rice are read from the pre-stored thermophysical property parameters of the rice type. The initial temperature difference matrix is ​​combined with the specific heat capacity and water absorption rate parameters to calculate the basic heat compensation value required to balance the temperature difference of each layer. Using the set of spatial coordinates of the initial low-temperature region as the target, additional compensation power is allocated to the heating unit covering the initial low-temperature region, and the allocation weight is determined based on the difference between the voxel temperature and the target ripening temperature within the initial low-temperature region. Using the set of spatial coordinates of the initial high-temperature region as a reference, a power upper limit constraint is set for the heating unit covering the initial high-temperature region; By combining the basic heat compensation value, the additional compensation power, and the power upper limit constraint, a preliminary power allocation scheme containing the initial power values ​​of each heating layer partition unit is generated.

5. The multi-layer temperature synergic equalization heating power determination and control system of rice steaming box according to claim 4, characterized in that, Also includes: The steady-state control module is used to control the power output of each heating element according to the preliminary power distribution scheme, so as to enter the steady-state heating stage; The model update module is used to continuously collect real-time temperature distribution data during the steady-state heating stage and periodically update the three-dimensional temperature field model to obtain an updated three-dimensional temperature field model. The power adjustment module is used to input the real-time temperature difference matrix and the temperature field uniformity change trend into the adaptive power adjustment strategy module. The adaptive power adjustment strategy module combines the preset cooking temperature curve of rice, calculates the power correction amount, and dynamically adjusts the preliminary power allocation scheme based on the power correction amount to form the power control command at the current moment. The closed-loop execution module is used to execute the power control command and adjust the power of each heating layer zone in a closed loop until the rice steaming process is over. During the steady-state heating phase, real-time temperature distribution data is continuously collected, and the three-dimensional temperature field model is periodically updated to obtain an updated three-dimensional temperature field model, including: Acquire new measurement data from the temperature sensor array according to a fixed sampling period; The new measurement data is assimilated with the three-dimensional temperature field model of the previous cycle, and the voxel temperature values ​​at the corresponding coordinate positions in the model are replaced. For voxels without new measurement data, a short-time prediction model based on the heat conduction equation is used to extrapolate the temperature value of the current period. The replacement values ​​are integrated with the predicted values ​​to form the updated three-dimensional temperature field model corresponding to the current sampling period.

6. The multi-layer temperature synergic equalization heating power determination and control system of rice steaming box according to claim 5, characterized in that, The real-time temperature difference matrix and the temperature field uniformity variation trend are input into the adaptive power adjustment strategy module. The adaptive power adjustment strategy module, combined with the preset cooking temperature curve of the rice, calculates the power correction amount, including: The adaptive power adjustment strategy module reads the desired temperature corresponding to the current cooking stage on the preset ripening temperature curve. The average temperature of each layer in the real-time temperature difference matrix is ​​compared with the desired temperature to obtain the layer temperature deviation. Analyzing the uniformity trend of the temperature field, if the temperature difference change vector shows a decrease in temperature difference and a decrease in the rate of change of standard deviation, a tendency to reduce power compensation is generated; if it shows an increase in temperature difference or an increase in the rate of change of standard deviation, a tendency to increase power compensation is generated. Using the layered temperature deviation as the main input and the adjustment tendency as the correction direction, the power correction amount of each heating layer partition unit is calculated through fuzzy decision rules.

7. The multi-layer temperature synergic equalization heating power determination and control system of rice steaming box according to claim 6, characterized in that, Based on the power correction amount, the initial power allocation scheme is dynamically adjusted to generate the power control command for the current moment, including: Obtain the current set power value of each partition unit in the currently effective preliminary power allocation scheme; The current set power value of each partition unit is algebraically superimposed with the corresponding power correction amount to obtain a new power set value; Check whether the new power setting exceeds the physical power limit or the upper power limit constraint of the heating element of the partition unit; New power settings that exceed the limit are limited to bring them within the allowable power range. All the new power settings of the partitioned units that have been verified and processed are compiled into the power control instructions that can be recognized by the heating element driver.

8. The multi-layer temperature synergic equalization heating power determination and control system of rice steaming box according to claim 7, characterized in that, Execute the power control command to adjust the power of each heating layer zone in a closed loop until the rice steaming process is completed, including: The power control command is sent to the power drive circuit of each heating layer partition; The power drive circuit adjusts the output voltage or duty cycle according to the command, thereby changing the actual heating power of the heating element; In the next sampling period after power adjustment, temperature distribution data is collected again, and the steps of updating the three-dimensional temperature field model, analyzing temperature difference and uniformity, calculating power correction amount, and generating power control command are repeated. When the system detects that the average temperature of all heating layers remains stable within the target temperature range of the preset cooking temperature curve, and the standard deviation of the temperature field is lower than the set threshold and remains stable for more than the set time, it determines that the rice is cooked and controls the heating element to stop working, thus ending the rice cooking process.

9. The multi-layer temperature synergic equalization heating power determination and control system of rice steaming box according to claim 8, characterized in that, The process of establishing the pre-stored thermal property parameters of rice varieties includes: Establish a database of rice varieties, recording the variety name, recommended rice-to-water ratio, and steaming time for each type of rice. Under standard experimental conditions, calorimetric experiments were conducted on samples of different types of rice to measure and record their specific heat capacity and phase transition enthalpy. The saturated water absorption rate curves of different types of rice during the steaming process were determined by water absorption rate testing. Based on a large number of cooking experiments, internal temperature change data of different types of rice were collected when they reached the best taste, and the preset cooking temperature curve was obtained by fitting. The variety name, specific heat capacity, phase change enthalpy, saturated water absorption rate curve and preset cooking temperature curve are associated and stored to form the pre-stored thermal property parameters of the rice variety.

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