Dynamic adjustment heating control method based on resonant frequency and related equipment

Abstract

The invention relates to a dynamic adjustment heating control method based on resonant frequency and related equipment, and the method comprises the following steps: carrying out the multi-band frequency sweeping excitation of a heated object, and obtaining a resonant frequency response data set; wherein the heated object is arranged in the heating equipment; performing multi-dimensional spectrum analysis on the resonance frequency response data set to obtain an object resonance characteristic spectrum; carrying out heating power distribution calculation on preset heating equipment based on the object resonance characteristic spectrum to obtain a power modulation control matrix; and the heating equipment is dynamically adjusted and controlled to uniformly heat the heated object based on the power modulation control matrix, so that the technical problem that the existing heating equipment generally lacks an intelligent power distribution mechanism and cannot adjust heating in real time according to the change of the internal structure of food is solved.

Description

Technical Field

[0001] The present invention relates to the field of heating technology, and in particular to a dynamic adjustment heating control method based on resonance frequency and related equipment. Background Art

[0002] In the food processing industry, the heating process is a key link in ensuring food safety and quality. Traditional heating methods often rely on fixed heating parameters and time settings, which leads to uneven heating problems, especially when dealing with ingredients of different shapes, sizes and ingredients. For example, when heating food in a microwave oven, due to the difference in the efficiency of electromagnetic wave absorption by different parts of the food, it is often the case that one part is overheated while another part is not fully heated. These problems not only affect the taste and nutritional value of the food, but may also cause food safety risks due to local overheating.

[0003] In response to the above problems, researchers began to explore more intelligent and precise heating control methods to achieve more uniform and efficient heating of food. A dynamic adjustment heating control method based on resonant frequency came into being, which aims to optimize the heating process by analyzing the physical properties of the heated object (such as food). This method can automatically adjust the heating parameters according to the specific composition and form of the food, thereby improving heating efficiency and reducing energy consumption. However, to achieve this goal, it is necessary to solve technical challenges such as how to accurately obtain and analyze the resonant frequency response data set of the heated object, which is one of the key research directions in this field.

[0004] In addition, existing heating equipment generally lacks an intelligent power allocation mechanism and is unable to adjust the heating strategy in real time according to changes in the internal structure of the food. This means that even if advanced resonant frequency detection technology is used, it is difficult to achieve the desired heating effect if the subsequent power modulation control is not precise enough. Therefore, the development of a technology that can dynamically adjust the heating power based on the object's resonant characteristic spectrum has become the key to improving existing food processing heating systems. This research not only helps to improve the overall performance of heating equipment, but also provides new ideas and methods for promoting technological innovation in the food processing industry. Summary of the invention

[0005] The main purpose of the present invention is to provide a dynamic adjustment heating control method based on resonant frequency and related equipment, which solves the technical problem that existing heating equipment generally lacks an intelligent power allocation mechanism and cannot adjust the heating in real time according to changes in the internal structure of food.

[0006] To achieve the above object, the present invention provides a dynamic adjustment heating control method based on resonant frequency, which is applied to a heating device and includes the following steps: Performing multi-band frequency sweep excitation on the heated object to obtain a resonant frequency response data set; wherein the heated object is arranged in a heating device; Performing multi-dimensional spectrum analysis on the resonant frequency response data set to obtain a resonance characteristic spectrum of the object; Based on the object resonance characteristic spectrum, a heating power distribution calculation is performed on a preset heating device to obtain a power modulation control matrix; The heating device is dynamically adjusted and controlled based on the power modulation control matrix to achieve uniform heating of the heated object.

[0007] Furthermore, the multi-band frequency sweeping excitation is performed on the heated object to obtain a resonant frequency response data set, including: A multi-band frequency sweep excitation is performed on the heated object by a multi-band exciter arranged in the heating device to obtain frequency-amplitude response characteristic data; Based on the frequency-amplitude response characteristic data, a resonant modal analysis is performed on the heated object to obtain a resonant frequency response data set; wherein the resonant frequency response data set includes a node vibration mode distribution, a modal impedance characteristic and an energy coupling coefficient on the surface of the object.

[0008] Furthermore, the multi-dimensional spectrum analysis is performed on the resonant frequency response data set to obtain a resonance characteristic spectrum of the object, including: Performing wavelet decomposition and reconstruction on the resonant frequency response data set to obtain a multi-scale spectral component sequence, and performing resonance feature extraction on the multi-scale spectral component sequence to obtain a resonant frequency feature matrix, wherein the resonant frequency feature matrix includes frequency component amplitudes, phase differences, and energy distribution ratios; The resonant field intensity of the heated object is calculated by using the resonant frequency characteristic matrix to obtain a resonant field intensity distribution tensor, and an energy flow density analysis is performed on the resonant field intensity distribution tensor to obtain an energy transfer path diagram, wherein the energy transfer path diagram includes an energy transfer direction, a field intensity gradient, and a coupling intensity distribution; Based on the energy transfer path diagram, spatial resonance characteristics of the heated object are mapped to obtain a resonance node distribution diagram, and frequency domain response analysis is performed on the resonance node distribution diagram to obtain a frequency response characteristic data set, wherein the frequency response characteristic data set includes a resonance frequency point position, an impedance characteristic, and an energy absorption coefficient; Performing resonance mode expansion on the frequency response characteristic data set to obtain a modal feature sequence, and performing spatial resonance coupling analysis on the modal feature sequence to obtain a coupling characteristic parameter set, wherein the coupling characteristic parameter set includes a modal vibration shape distribution, a coupling coefficient matrix, and an energy conversion efficiency; The coupling characteristic parameter set is subjected to multi-dimensional resonance characteristic synthesis and spectrum feature fusion to obtain an object resonance characteristic spectrum; wherein the object resonance characteristic spectrum includes a resonance frequency distribution map, an energy absorption efficiency spectrum and a spatial resonance modal response topological structure.

[0009] Furthermore, the spatial resonance characteristic mapping of the heated object based on the energy transfer path diagram to obtain a resonance node distribution diagram includes: Extracting topological features of the energy transfer path diagram to obtain a set of key energy transfer nodes, and performing spatial discrete sampling based on the set of key energy transfer nodes to obtain a resonance sensitive point array, wherein the resonance sensitive point array includes a resonance peak point, an energy convergence point, and a phase conversion point; Performing spatial interpolation calculation on the resonance sensitive point array by tensor field decomposition technology to obtain a continuous resonance field expression, and performing isosurface cutting on the continuous resonance field expression to obtain a resonance isosurface sequence; Based on the resonance isosurface sequence, a resonance field gradient analysis is performed on the heated object to obtain a resonance gradient vector field, and a divergence and curl calculation is performed on the resonance gradient vector field to obtain a field source distribution characteristic quantity, wherein the field source distribution characteristic quantity includes a resonance source intensity, a resonance field vortex, and a resonance field divergence area; Feature point extraction and cluster analysis are performed on the field source distribution feature quantity to obtain a resonance node distribution map.

[0010] Furthermore, performing isosurface cutting on the continuous resonance field expression to obtain a resonance isosurface sequence includes: Performing eigenvalue calculation on the continuous resonance field expression by Gaussian curvature analysis to obtain a field curvature distribution matrix, and performing threshold segmentation on the field curvature distribution matrix to obtain a multi-level isosurface cutting parameter set, wherein the multi-level isosurface cutting parameter set includes an amplitude cutting threshold, a phase cutting threshold, and an energy density cutting threshold; Based on the multi-level isosurface cutting parameter set, the continuous resonance field expression is subjected to multi-dimensional field decomposition to obtain an orthogonal field component set, and independent isosurface extraction is performed on the orthogonal field component set to obtain a component isosurface group, wherein the component isosurface group includes an amplitude component isosurface, a phase component isosurface, and an energy component isosurface; The structural features of the component isosurface group are identified by topological skeleton extraction to obtain an isosurface topological feature set, and the isosurface topological feature set is hierarchically organized to obtain a resonance feature hierarchy structure, wherein the resonance feature hierarchy structure includes a field center point, a field boundary line and a field separation surface; The component isosurface groups are fused and reconstructed based on the resonance feature hierarchy to obtain a resonance isosurface sequence; wherein the resonance isosurface sequence includes a resonance peak isosurface family, a phase jump boundary surface set and an energy gradient critical surface sequence.

[0011] Furthermore, the continuous resonance field expression is subjected to multi-dimensional field decomposition based on the multi-level isosurface cutting parameter set to obtain a set of orthogonal field components, including: Performing parameter space transformation on the multi-level isosurface cutting parameter set to obtain a cutting parameter transformation matrix, and performing orthogonal basis function expansion on the continuous resonance field expression based on the cutting parameter transformation matrix to obtain a field component spectrum; Extracting spectral features of the field component spectrum by singular value decomposition to obtain a field component eigenvalue set, and sorting and screening the field component eigenvalue set by amplitude to obtain a dominant field component feature set, wherein the dominant field component feature set includes a resonant main mode, an anti-resonant feature point, and an energy transfer channel; Based on the dominant field component feature set, the continuous resonance field expression is subjected to tensor decomposition and reconstruction to obtain a field component tensor group, and the field component tensor group is subjected to spatial coordinate projection to obtain an orthogonal field projection matrix group, wherein the orthogonal field projection matrix group includes a radial field projection, a toroidal field projection, and an axial field projection; The orthogonal field projection matrix group is subjected to a resonant field synthesis transformation to obtain an orthogonal field component set; wherein the orthogonal field component set includes a fundamental frequency resonant field component, a high-order harmonic field component and a frequency cross-coupling field component.

[0012] Furthermore, the heating power distribution calculation is performed on the preset heating device based on the object resonance characteristic spectrum to obtain a power modulation control matrix, including: Dividing the resonance characteristic spectrum of the object into discrete regions to obtain multi-level resonance power response blocks, and performing energy absorption characteristic analysis on the multi-level resonance power response blocks to obtain an object energy absorption coefficient matrix; The power output units of the heating equipment are spatially mapped using the object energy absorption coefficient matrix to obtain a regional power allocation weight mapping table, and a power load balancing calculation is performed based on the regional power allocation weight mapping table to obtain a multi-channel power modulation parameter set; Based on the multi-channel power modulation parameter set, a resonant frequency matching calculation is performed on the output power of the heating device to obtain a power resonance coupling sequence; The output power of the heating device is modulated in real time through the power resonance coupling sequence to obtain a power modulation control matrix, wherein the rows of the power modulation control matrix represent spatially discrete power output units and the columns of the power modulation control matrix represent resonance frequency modulation parameters.

[0013] The present invention also provides a heating control device for dynamically adjusting the resonant frequency, which is applied to a heating device, comprising: An excitation module, used to perform multi-band frequency sweep excitation on the heated object to obtain a resonant frequency response data set; wherein the heated object is arranged in the heating device; An analysis module, used to perform multi-dimensional spectrum analysis on the resonant frequency response data set to obtain a resonance characteristic spectrum of the object; A calculation module, used to calculate the heating power distribution of a preset heating device based on the object resonance characteristic spectrum to obtain a power modulation control matrix; The heating module is used to dynamically adjust and control the heating device based on the power modulation control matrix to achieve uniform heating of the heated object.

[0014] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0015] The present invention provides a resonant frequency-based dynamic adjustment heating control method, comprising the following steps: performing multi-band frequency sweep excitation on a heated object to obtain a resonant frequency response data set; wherein the heated object is arranged in a heating device; performing multi-dimensional spectrum analysis on the resonant frequency response data set to obtain an object resonance characteristic spectrum; performing heating power distribution calculation on a preset heating device based on the object resonance characteristic spectrum to obtain a power modulation control matrix; dynamically adjusting and controlling the heating device based on the power modulation control matrix to achieve uniform heating of the heated object, thereby solving the technical problem that existing heating devices generally lack an intelligent power distribution mechanism and cannot adjust the heating in real time according to changes in the internal structure of food. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a schematic diagram of the steps of a heating control method for dynamic adjustment based on resonant frequency in one embodiment of the present invention; Figure 2 is a structural block diagram of a heating control device for dynamic adjustment based on resonant frequency in one embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0017] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] like Figure 1 As shown, Figure 1 It is a schematic diagram of the steps of a dynamic adjustment heating control method based on resonant frequency in one embodiment of the present invention; In one embodiment of the present invention, a method for dynamically adjusting heating control based on resonance frequency is provided, which is applied to a heating device and includes the following steps: Step S1, performing multi-band frequency sweep excitation on a heated object to obtain a resonant frequency response data set; wherein the heated object is arranged in a heating device.

[0020] Specifically, in the dynamic adjustment heating control method based on resonant frequency, it is a crucial first step to perform multi-band sweep frequency excitation on the heated object to obtain a resonant frequency response data set, wherein the heated object is set in the heating device. Specifically, this step is achieved by applying a series of excitation signals such as electromagnetic waves or sound waves of different frequencies to the heated object, and these signals cover multiple preset frequency bands. In this way, different vibration modes inside the heated object can be excited, and the response at each frequency can be recorded to form a detailed resonant frequency response data set. For example, in a food processing scenario, suppose we want to heat a piece of meat containing multiple components (such as water, fat and protein). Since each component has different absorption and reflectivity at different frequencies, it is necessary to use sweep frequency excitation to fully understand the resonant characteristics of this piece of meat during the entire heating process. In this process, the heating device first applies a series of sweep frequency excitation signals from low frequency to high frequency to the meat block, and these signals will cause different responses of the internal molecular structure of the meat block at different frequency points. By collecting and analyzing these response data, a resonant characteristic spectrum of the meat block at each frequency can be obtained. This map not only reflects the overall physical properties of the meat, but also reveals detailed information about its internal structure, such as water distribution, thickness of fat layer, etc. With these detailed data, the subsequent steps can calculate the optimal heating power distribution plan based on this information, so as to ensure that the entire heating process is both efficient and uniform. In this way, not only can the quality and taste of the food be improved, but also problems such as food deterioration or nutrient loss caused by local overheating can be effectively avoided. This dynamic adjustment method based on resonant frequency provides a scientific basis and technical support for achieving precise heating. For example, during microwave heating, if the resonant frequency characteristics of the meat can be accurately identified and utilized, the ideal heating effect can be achieved in a short time while ensuring the uniformity and safety of the heating. This not only improves the user's cooking experience, but also provides a strong guarantee for food safety and quality control.

[0021] Step S2, performing multi-dimensional spectrum analysis on the resonant frequency response data set to obtain a resonance characteristic spectrum of the object.

[0022] Specifically, in the dynamic adjustment heating control method based on resonant frequency, performing multi-dimensional spectrum analysis on the resonant frequency response data set to obtain the object resonance characteristic spectrum is one of the key steps. This step first requires importing the resonant frequency response data set obtained from the heated object into a specially designed analysis software or system. The software is capable of processing complex frequency domain signals and decomposing and analyzing these signals through a variety of mathematical algorithms and models. Specifically, the analysis process involves a variety of technical means such as spectrum analysis, time-frequency analysis, and pattern recognition, so as to fully reveal the response characteristics of the heated object at different frequencies. For example, in a food processing scenario, when we heat a piece of meat containing water, fat, and protein, we can further analyze its response at each frequency point by obtaining a data set after sweeping the frequency excitation of the piece of meat. In the multi-dimensional spectrum analysis process, the system will carefully analyze the response data at each frequency point, not only considering the information of a single frequency point, but also combining the relationship between multiple frequency points to construct a complete resonance characteristic spectrum. This spectrum is actually a multi-dimensional data representation, which can show the complexity of the internal structure of the heated object and its response characteristics to different frequency excitations. For example, when analyzing the resonant characteristics of a piece of meat, we may find that the response in certain specific frequency ranges is particularly strong, which may be related to the moisture content or fat distribution in the meat. In this way, the spatial distribution of the components inside the meat and their physical characteristics can be accurately depicted, providing an important basis for the subsequent heating power allocation. To better understand this process, suppose we heat a piece of meat containing different ingredients in a microwave oven, and obtain a series of resonant frequency response data after sweep excitation. Next, we input this data into the spectrum analysis system, and the system automatically performs multi-dimensional spectrum analysis on it. The analysis results will be displayed in the form of a chart, showing the absorption and reflection characteristics of the meat at different frequencies. Based on these characteristic maps, we can clearly see which areas are more likely to absorb energy and which areas are relatively stable. In this way, we can formulate a more scientific and reasonable heating strategy based on this information to ensure that the entire heating process is uniform and efficient, avoid local overheating or insufficient heating, and ultimately achieve the purpose of improving food quality and taste. This dynamic adjustment method based on resonant frequency characteristics not only improves heating efficiency, but also provides strong technical support for food safety and quality control. .

[0023] Step S3, performing heating power distribution calculation on a preset heating device based on the object resonance characteristic spectrum to obtain a power modulation control matrix.

[0024] Specifically, the calculation of the heating power distribution for the preset heating device based on the object resonance characteristic map to obtain the power modulation control matrix is ​​the core step in the entire dynamic regulation heating control method. This step first requires the use of the object resonance characteristic map obtained in the previous step, which describes in detail the response characteristics of the heated object at different frequencies. By analyzing these characteristics, the system can identify which areas or components are more sensitive to specific frequencies, thereby determining the most effective heating strategy. For example, in a food processing scenario, when we heat a piece of meat containing water, fat and protein, according to its resonance characteristic map, we can know that the part with a higher moisture content has a stronger absorption of certain frequencies, while the fat layer may be more sensitive to other frequencies. Next, the system will combine these resonance characteristics with the performance parameters of the heating device to calculate the optimal heating power distribution plan. Specifically, the system will formulate a detailed power modulation plan based on the data in the resonance characteristic map, combined with the capabilities and constraints of the heating device, such as maximum power output, frequency range, etc. This process involves complex mathematical models and algorithms to optimize the distribution of power in different frequency bands to ensure the optimization of the heating effect. For example, in the process of heating that piece of meat, the system may decide to increase the power output to the area with higher moisture content in a certain period of time, while reducing the heating intensity of the fat layer to avoid local overheating. To achieve this, the system generates a power modulation control matrix, which specifies in detail the specific power values ​​that should be applied to each frequency band at each time point. This matrix not only takes into account the physical properties of the heated object, but also combines the actual operating capabilities of the heating equipment, making the heating process both efficient and safe. For example, in the application scenario of heating meat in a microwave oven, assuming that the system finds that a certain part is particularly easy to absorb energy based on the resonance characteristic spectrum, it will set a higher power value in the power modulation control matrix, while reducing the power in other parts that absorb less energy. In this way, not only can the entire heating process be ensured to be uniform and efficient, but also the quality and taste of the food can be significantly improved, and food spoilage or nutrient loss caused by local overheating can be avoided. This dynamic adjustment method based on the resonant frequency characteristics provides a scientific basis and technical support for the precise control of the heating process, greatly improving the heating efficiency and food safety. In addition, this method can also be applied to other fields, such as industrial material processing and medical equipment heating, showing broad application prospects and potential.

[0025] Step S4, dynamically adjusting and controlling the heating device based on the power modulation control matrix to achieve uniform heating of the heated object.

[0026] Specifically, dynamically adjusting and controlling the heating device to achieve uniform heating of the heated object based on the power modulation control matrix is ​​the final execution step of the entire method. This process ensures that the heating task is completed accurately and efficiently based on the data and calculation results obtained in the previous steps. First, the system loads the previously generated power modulation control matrix into the control system of the heating device. This matrix specifies in detail the specific power values ​​that should be applied to each frequency band at each time point, thereby providing a detailed instruction set for the heating device. For example, in a food processing scenario, when we heat a piece of meat containing water, fat and protein, the power modulation control matrix instructs the heating device to increase the power output in certain frequency bands during a specific time period and reduce the power output in other time periods to adapt to the resonant characteristics of different components inside the meat. Then, the heating device makes real-time adjustments based on these instructions. The control system dynamically adjusts the working state of the heater according to the data in the power modulation control matrix, including adjusting the frequency and intensity of the electromagnetic wave or sound wave to achieve precise heating of different areas. For example, in the application scenario of heating meat in a microwave oven, assume that the power modulation control matrix shows that a certain part requires higher power to accelerate the evaporation of water, while another part requires lower power to prevent overheating of fat. The heating equipment will achieve this goal by changing the working parameters of the microwave generator, such as frequency and power level, according to these instructions. In this way, not only can local overheating be avoided, but also the whole piece of meat can be heated evenly, improving the quality and taste of the food. Throughout the process, the control system of the heating equipment will continuously monitor the actual heating effect and compare it with the preset target. If deviations are found, the system will automatically make fine adjustments to ensure that the desired heating effect is finally achieved. For example, when it is detected that the temperature rises too fast in a certain area, the system will immediately reduce the power input in that area, and may increase the power in other areas to balance the overall heating situation. This real-time monitoring and feedback mechanism makes the heating process more intelligent and controllable, greatly improving the heating efficiency and uniformity. This method can not only optimize the heating process in food processing, but also be applied to other fields, such as industrial material processing and medical equipment heating, showing a wide range of application prospects and technical advantages. This dynamic adjustment method based on the resonant frequency characteristics not only improves the heating accuracy and efficiency, but also provides a strong technical guarantee for food safety and quality control.

[0027] In a specific embodiment, performing multi-band frequency sweep excitation on the heated object to obtain a resonant frequency response data set includes: A multi-band frequency sweep excitation is performed on the heated object by a multi-band exciter arranged in the heating device to obtain frequency-amplitude response characteristic data; Based on the frequency-amplitude response characteristic data, a resonant modal analysis is performed on the heated object to obtain a resonant frequency response data set; wherein the resonant frequency response data set includes a node vibration mode distribution, a modal impedance characteristic and an energy coupling coefficient on the surface of the object.

[0028] Specifically, in the dynamic adjustment heating control method based on resonant frequency, the process of performing multi-band sweep frequency excitation on the heated object to obtain a resonant frequency response data set includes two main steps: first, the heated object is subjected to multi-band sweep frequency excitation by a multi-band exciter set in the heating device, thereby obtaining frequency-amplitude response characteristic data; then, the heated object is subjected to resonant modal analysis based on these frequency-amplitude response characteristic data, and finally a resonant frequency response data set is obtained. This process not only provides key data support for subsequent power allocation and modulation, but also ensures the accuracy and uniformity of heating. Specifically, in the first step, the multi-band exciter inside the heating device applies a series of excitation signals such as electromagnetic waves or sound waves of different frequencies to the heated object. These excitation signals cover multiple preset frequency band ranges, aiming to excite different vibration modes inside the heated object and record the response at each frequency. For example, in a food processing scenario, suppose we want to heat a piece of meat containing water, fat and protein. Since each component has different absorption and reflectivity at different frequencies, it is necessary to use sweep frequency excitation to fully understand the resonant characteristics of this piece of meat during the entire heating process. During this process, the multi-band exciter applies a series of sweep frequency excitation signals from low frequency to high frequency to the meat block, which will cause different responses of the internal molecular structure of the meat block at different frequency points. By collecting and analyzing these response data, the frequency-amplitude response characteristic data of the meat block at various frequencies can be obtained. Next, based on these frequency-amplitude response characteristic data, the system will perform resonant modal analysis on the heated object. This step involves complex mathematical models and algorithms for identifying and analyzing the vibration modes of the heated object at different frequencies. Through resonant modal analysis, not only can the node vibration mode distribution on the surface of the object be determined, but also important information such as modal impedance characteristics and energy coupling coefficients can be obtained. For example, when analyzing that piece of meat, the system may find that the response in certain specific frequency ranges is particularly strong, which may be related to the moisture content or fat distribution in the meat block. In this way, the spatial distribution of each component inside the meat block and its physical properties can be accurately depicted. To better understand this process, suppose we heat a piece of meat containing different components in a microwave oven, and obtain a series of frequency-amplitude response characteristic data after sweep frequency excitation. Next, we input this data into the resonant modal analysis system, which automatically analyzes it in detail. The results are displayed in the form of graphs, showing the absorption and reflection characteristics of the meat at different frequencies. Based on these characteristic graphs, we can clearly see which areas are more prone to energy absorption and which areas are relatively stable. For example, the system may find that one part is particularly prone to energy absorption, which may be due to the high moisture content of this part, while another part may absorb less energy due to the presence of a fat layer.Through this detailed resonant modal analysis, the system can generate a complete resonant frequency response data set, including the node vibration mode distribution, modal impedance characteristics and energy coupling coefficients on the surface of the object. These data are essential for developing the optimal heating strategy. For example, in the process of heating that piece of meat, the system can determine which areas require more energy input based on the node vibration mode distribution, and use the modal impedance characteristics and energy coupling coefficients to optimize the overall energy transmission efficiency. In this way, not only can the entire heating process be ensured to be uniform and efficient, but also the quality and taste of the food can be significantly improved, and food spoilage or nutrient loss caused by local overheating can be avoided. In summary, the multi-band exciter set in the heating device is used to perform multi-band sweep frequency excitation on the heated object, and the resonant modal analysis is performed based on the frequency-amplitude response characteristic data to finally obtain the resonant frequency response data set, which is a key step in achieving precise heating. This method not only improves the heating efficiency and uniformity, but also provides strong technical support for food safety and quality control, and has broad application prospects and development potential. Whether in food processing or other industrial fields, this dynamic adjustment method based on resonant frequency characteristics has demonstrated its unique technical advantages and application value.

[0029] In a specific embodiment, performing multi-dimensional spectrum analysis on the resonant frequency response data set to obtain an object resonance characteristic spectrum includes: Performing wavelet decomposition and reconstruction on the resonant frequency response data set to obtain a multi-scale spectral component sequence, and performing resonance feature extraction on the multi-scale spectral component sequence to obtain a resonant frequency feature matrix, wherein the resonant frequency feature matrix includes frequency component amplitudes, phase differences, and energy distribution ratios; The resonant field intensity of the heated object is calculated by using the resonant frequency characteristic matrix to obtain a resonant field intensity distribution tensor, and an energy flow density analysis is performed on the resonant field intensity distribution tensor to obtain an energy transfer path diagram, wherein the energy transfer path diagram includes an energy transfer direction, a field intensity gradient, and a coupling intensity distribution; Based on the energy transfer path diagram, spatial resonance characteristics of the heated object are mapped to obtain a resonance node distribution diagram, and frequency domain response analysis is performed on the resonance node distribution diagram to obtain a frequency response characteristic data set, wherein the frequency response characteristic data set includes a resonance frequency point position, an impedance characteristic, and an energy absorption coefficient; Performing resonance mode expansion on the frequency response characteristic data set to obtain a modal feature sequence, and performing spatial resonance coupling analysis on the modal feature sequence to obtain a coupling characteristic parameter set, wherein the coupling characteristic parameter set includes a modal vibration shape distribution, a coupling coefficient matrix, and an energy conversion efficiency; The coupling characteristic parameter set is subjected to multi-dimensional resonance characteristic synthesis and spectrum feature fusion to obtain an object resonance characteristic spectrum; wherein the object resonance characteristic spectrum includes a resonance frequency distribution map, an energy absorption efficiency spectrum and a spatial resonance modal response topological structure.

[0030] Specifically, in the dynamic adjustment heating control method based on resonant frequency, it is a complex and multi-level process to perform multi-dimensional spectrum analysis on the resonant frequency response data set to obtain the resonant characteristic spectrum of the object. This process, through a series of sophisticated data processing and analysis steps, finally generates a spectrum that fully describes the resonant characteristics of the heated object, providing a key basis for the subsequent heating power allocation. First, the system will perform wavelet decomposition and reconstruction on the resonant frequency response data set to obtain a multi-scale spectral component sequence, and extract the resonant features of these sequences to form a resonant frequency feature matrix. Specifically, wavelet decomposition and reconstruction is a powerful signal processing technology that can decompose the original resonant frequency response data set into multiple spectral component sequences of different scales. Each component sequence represents the response characteristics of the heated object in different frequency ranges. For example, in a food processing scenario, when we heat a piece of meat containing water, fat and protein, wavelet decomposition can reveal the vibration modes of different components at different frequencies. By extracting the resonant features of these multi-scale spectral component sequences, we can obtain a resonant frequency feature matrix, which includes information such as frequency component amplitude, phase difference and energy distribution ratio. This information describes in detail the vibration characteristics and energy distribution of each component inside the meat at different frequencies. Next, the system uses the resonant frequency characteristic matrix to calculate the resonant field intensity of the heated object and obtain the resonant field intensity distribution tensor. This step involves complex mathematical models and algorithms to quantify the resonant field intensity of different regions of the heated object. For example, when analyzing the piece of meat, the system may find that certain specific areas show stronger resonant field intensity due to higher moisture content. Then, the system performs energy flow density analysis on the resonant field intensity distribution tensor to obtain an energy transfer path diagram. The energy transfer path diagram not only shows the direction of energy transfer inside the heated object, but also reveals the field intensity gradient and coupling intensity distribution. For example, during the heating process of the meat, the system can identify which areas are more likely to absorb energy and which areas are relatively stable, thereby optimizing the overall energy transfer path. Based on the energy transfer path diagram, the system further maps the spatial resonance characteristics of the heated object to obtain a resonant node distribution diagram. This process involves converting the resonant field intensity distribution into a specific physical location distribution diagram, showing the specific location of each node and its resonant characteristics. Next, the system performs frequency domain response analysis on the resonant node distribution diagram to obtain a frequency response characteristic data set. The frequency response characteristic data set includes information such as the location of the resonant frequency point, impedance characteristics, and energy absorption coefficient, which describe in detail the response characteristics of the heated object at different frequencies. For example, in the process of heating that piece of meat, the system can determine which frequency range is most suitable for heating through the frequency response characteristic data set, thereby avoiding local overheating or insufficient heating. Subsequently, the system performs resonant mode expansion on the frequency response characteristic data set to obtain a modal feature sequence.The modal feature sequence describes in detail the vibration modes of the heated object at different frequencies and their interrelationships. For example, when analyzing the piece of meat, the system may find that the modal features in certain specific frequency ranges are particularly strong, which may be related to the moisture content or fat distribution in the meat. Then, the system performs spatial resonant coupling analysis on the modal feature sequence to obtain a coupling characteristic parameter set. The coupling characteristic parameter set includes information such as modal vibration shape distribution, coupling coefficient matrix, and energy conversion efficiency, which describe in detail the interaction between the internal parts of the heated object and the energy transfer efficiency. Finally, the system performs multi-dimensional resonant characteristic synthesis and spectrum feature fusion on the coupling characteristic parameter set to obtain the object resonance characteristic spectrum. This process integrates the results of all previous steps to form a spectrum that comprehensively describes the resonant characteristics of the heated object. The object resonance characteristic spectrum includes information such as resonant frequency distribution mapping, energy absorption efficiency spectrum, and spatial resonant modal response topology. For example, in the application scenario of heating the piece of meat, the object resonance characteristic spectrum not only shows the resonant characteristics of the meat at different frequencies, but also reveals the absorption efficiency and spatial distribution of energy inside the meat. With this detailed resonant characteristic map, the system can accurately formulate the optimal heating strategy to ensure that the entire heating process is uniform and efficient. To better understand this process, suppose we heat a piece of meat containing different ingredients in a microwave oven, and obtain a series of resonant frequency response data after sweeping frequency excitation. First, the system will perform wavelet decomposition and reconstruction on these data to obtain a multi-scale spectral component sequence, and extract the resonant frequency characteristic matrix from it. For example, the system may find that the amplitude of the frequency component in a certain frequency range is particularly high, which may be related to the moisture content in the meat. Then, the system uses the resonant frequency characteristic matrix to calculate the resonant field intensity, obtain the resonant field intensity distribution tensor, and perform energy flow density analysis on it to obtain an energy transfer path diagram. For example, the system may find that a certain part is particularly easy to absorb energy, which may be due to the high moisture content in this part, while another part absorbs less energy due to the presence of a fat layer. Then, based on the energy transfer path diagram, the system performs spatial resonant characteristic mapping of the meat block to obtain a resonant node distribution map, and performs frequency domain response analysis on it to obtain a frequency response characteristic data set. For example, the system may find that the resonant frequency points in a certain frequency range are particularly dense, which may be related to the fat layer in the meat block. Next, the system performs resonant modal expansion on the frequency response characteristic data set to obtain a modal feature sequence, and performs spatial resonant coupling analysis on it to obtain a coupling characteristic parameter set. For example, the system may find that the modal vibration shape distribution within certain specific frequency ranges is particularly complex, which may be related to the distribution of components inside the meat. Finally, the system performs multi-dimensional resonant characteristic synthesis and spectral feature fusion on the coupling characteristic parameter set to obtain the object resonance characteristic spectrum. For example, the system may find that the energy absorption efficiency inside the meat is particularly high in certain frequency ranges, but lower in other frequency ranges.Through this detailed resonance characteristic map, the system can accurately formulate the optimal heating strategy to ensure that the entire heating process is uniform and efficient. This dynamic adjustment method based on the resonance frequency characteristics not only improves the heating efficiency and uniformity, but also provides strong technical support for food safety and quality control, and has broad application prospects and development potential. Whether in food processing or other industrial fields, this method has demonstrated its unique technical advantages and application value.

[0031] In a specific embodiment, the spatial resonance characteristic mapping of the heated object based on the energy transfer path diagram to obtain a resonance node distribution diagram includes: Extracting topological features of the energy transfer path diagram to obtain a set of key energy transfer nodes, and performing spatial discrete sampling based on the set of key energy transfer nodes to obtain a resonance sensitive point array, wherein the resonance sensitive point array includes a resonance peak point, an energy convergence point, and a phase conversion point; Performing spatial interpolation calculation on the resonance sensitive point array by tensor field decomposition technology to obtain a continuous resonance field expression, and performing isosurface cutting on the continuous resonance field expression to obtain a resonance isosurface sequence; Based on the resonance isosurface sequence, a resonance field gradient analysis is performed on the heated object to obtain a resonance gradient vector field, and a divergence and curl calculation is performed on the resonance gradient vector field to obtain a field source distribution characteristic quantity, wherein the field source distribution characteristic quantity includes a resonance source intensity, a resonance field vortex, and a resonance field divergence area; Feature point extraction and cluster analysis are performed on the field source distribution feature quantity to obtain a resonance node distribution map.

[0032] Specifically, in the dynamic adjustment heating control method based on resonant frequency, it is a complex and multi-level process to map the spatial resonance characteristics of the heated object based on the energy transfer path diagram to obtain a resonance node distribution diagram. This process, through a series of sophisticated data processing and analysis steps, finally generates a node distribution diagram that fully describes the resonance characteristics of the heated object, providing a key basis for the subsequent heating power allocation. First, the system extracts the topological features of the energy transfer path diagram to obtain a set of key energy transfer nodes. These key nodes represent the main transmission paths and intersections of energy inside the heated object. For example, in a food processing scenario, when we heat a piece of meat containing water, fat and protein, the energy transfer path diagram will show which areas are more likely to absorb energy and which areas are relatively stable. By extracting the topological features of these path diagrams, the key nodes of energy transfer can be identified, which may be energy convergence points or phase transition points due to composition differences or structural characteristics. Then, based on these energy transfer key node sets, spatial discrete sampling is performed to obtain a resonance sensitive point array. This array includes information such as resonance peak points, energy convergence points and phase transition points, and describes in detail the energy transfer characteristics and vibration modes of different regions inside the heated object. Next, the system uses tensor field decomposition technology to perform spatial interpolation calculations on the resonant sensitive point array to obtain a continuous resonant field expression. Tensor field decomposition is a powerful mathematical tool that can convert discrete data points into continuous spatial field expressions. For example, when analyzing the piece of meat, the system can expand the information of each resonant sensitive point to the entire space through tensor field decomposition to form a continuous resonant field expression. Then, the system performs isosurface cutting on the continuous resonant field expression to obtain a resonant isosurface sequence. These isosurface sequences reveal the resonant characteristics and energy distribution of different regions inside the meat. Based on these resonant isosurface sequences, the system further performs resonant field gradient analysis on the heated object to obtain a resonant gradient vector field. Resonant field gradient analysis aims to quantify the rate of change of the resonant field in space and reveal the direction and intensity of energy flow. For example, when analyzing the piece of meat, the system can determine which areas have faster energy flow and which areas have slower energy flow through resonant field gradient analysis. Then, the system calculates the divergence and curl of the resonant gradient vector field to obtain the characteristic quantity of field source distribution. Divergence and curl are important parameters for describing vector fields, which reflect the divergence and rotation of the field source respectively. For example, the system may find that some areas have strong resonance source intensities, while other areas may have resonance field vortices or divergence zones. These source distribution characteristics describe in detail the interactions between the internal parts of the heated object and the energy transfer efficiency. Finally, the system performs feature point extraction and cluster analysis on the source distribution characteristics to obtain a resonance node distribution map. Feature point extraction technology is used to identify key points in the source distribution characteristics, which may correspond to important locations such as energy convergence points, resonance peak points, or phase transition points.Then, these feature points are classified through cluster analysis to form a complete resonant node distribution map. For example, in the application scenario of heating the piece of meat, the resonant node distribution map not only shows the energy distribution of each area inside the meat, but also reveals its resonant characteristics and energy transfer path. With this detailed resonant node distribution map, the system can accurately formulate the optimal heating strategy to ensure that the entire heating process is uniform and efficient. To better understand this process, suppose we heat a piece of meat with different ingredients in a microwave oven, and after sweeping excitation, we get a series of energy transfer path maps. First, the system extracts the topological features of these path maps to obtain a set of key energy transfer nodes, and then performs spatial discrete sampling based on these node sets to obtain a resonant sensitive point array. For example, the system may find that certain specific areas show stronger energy convergence points or phase transition points due to high moisture content. Then, the system performs spatial interpolation calculations on the resonant sensitive point array through tensor field decomposition technology to obtain a continuous resonant field expression, and performs isosurface cutting on it to obtain a resonant isosurface sequence. These resonant isosurface sequence surfaces reveal the resonant characteristics and energy distribution of different areas inside the meat. Then, the system performs a resonance field gradient analysis on the meat block based on the resonance isosurface sequence to obtain the resonance gradient vector field, and calculates its divergence and curl to obtain the field source distribution feature. For example, the system may find that some areas have strong resonance source intensity, while other areas may have resonance field vortices or divergence areas. Then, the system performs feature point extraction and cluster analysis on the field source distribution feature to obtain a resonance node distribution map. For example, the system may find that the resonance nodes in certain specific frequency ranges are particularly dense, which may be related to the fat layer in the meat block. With this detailed resonance node distribution map, the system can accurately formulate the optimal heating strategy to ensure that the entire heating process is uniform and efficient. For example, in the process of heating that piece of meat, the system can determine which areas require more energy input based on the resonance node distribution map, and use the field source distribution feature to optimize the overall energy transmission path. In this way, not only can local overheating be avoided, but also the entire meat block can be heated evenly, improving the quality and taste of the food. In addition, this method can also be applied to other fields, such as industrial material processing and medical equipment heating, showing a wide range of application prospects and technical advantages. This dynamic adjustment method based on the resonant frequency characteristics not only improves the heating accuracy and efficiency, but also provides strong technical support for food safety and quality control. Whether in food processing or other industrial fields, this method has demonstrated its unique technical advantages and application value.

[0033] In a specific embodiment, performing isosurface cutting on the continuous resonance field expression to obtain a resonance isosurface sequence includes: Performing eigenvalue calculation on the continuous resonance field expression by Gaussian curvature analysis to obtain a field curvature distribution matrix, and performing threshold segmentation on the field curvature distribution matrix to obtain a multi-level isosurface cutting parameter set, wherein the multi-level isosurface cutting parameter set includes an amplitude cutting threshold, a phase cutting threshold, and an energy density cutting threshold; Based on the multi-level isosurface cutting parameter set, the continuous resonance field expression is subjected to multi-dimensional field decomposition to obtain an orthogonal field component set, and independent isosurface extraction is performed on the orthogonal field component set to obtain a component isosurface group, wherein the component isosurface group includes an amplitude component isosurface, a phase component isosurface, and an energy component isosurface; The structural features of the component isosurface group are identified by topological skeleton extraction to obtain an isosurface topological feature set, and the isosurface topological feature set is hierarchically organized to obtain a resonance feature hierarchy structure, wherein the resonance feature hierarchy structure includes a field center point, a field boundary line and a field separation surface; The component isosurface groups are fused and reconstructed based on the resonance feature hierarchy to obtain a resonance isosurface sequence; wherein the resonance isosurface sequence includes a resonance peak isosurface family, a phase jump boundary surface set and an energy gradient critical surface sequence.

[0034] Specifically, in the dynamic adjustment heating control method based on resonant frequency, it is a complex and multi-level process to perform isosurface cutting on the continuous resonant field expression to obtain a resonant isosurface sequence. This process, through a series of sophisticated data processing and analysis steps, finally generates an isosurface sequence that fully describes the resonant characteristics of the heated object, providing a key basis for the subsequent heating power distribution. First, the system calculates the eigenvalues ​​by performing Gaussian curvature analysis on the continuous resonant field expression to obtain the field curvature distribution matrix. Gaussian curvature analysis is a mathematical tool used to describe the local geometric characteristics of a spatial surface, which can reveal the curvature changes of the resonant field at different positions. For example, in a food processing scenario, when we heat a piece of meat containing water, fat and protein, Gaussian curvature analysis can help identify which areas inside the meat have higher curvature changes, which may be energy convergence points or phase transition points due to composition differences or structural characteristics. Then, the system performs threshold segmentation on the field curvature distribution matrix to obtain a multi-level isosurface cutting parameter set. The threshold segmentation technique is used to extract information within a specific range from continuous data. The multi-level isosurface cutting parameter set includes information such as amplitude cutting threshold, phase cutting threshold, and energy density cutting threshold. These thresholds define different isosurface cutting standards so that subsequent steps can perform accurate isosurface extraction according to these standards. Based on the multi-level isosurface cutting parameter set, the system performs multi-dimensional field decomposition on the continuous resonance field expression to obtain a set of orthogonal field components. The multi-dimensional field decomposition technique decomposes the complex resonance field into multiple independent orthogonal field components, each of which represents the characteristics of the resonance field in a specific direction or frequency. For example, when analyzing that piece of meat, the system can decompose the resonance field into different orthogonal field components such as amplitude component, phase component, and energy component through multi-dimensional field decomposition. Then, the system performs independent isosurface extraction on these orthogonal field component sets to obtain component isosurface groups. The independent isosurface extraction technique is used to extract isosurfaces of specific amplitude, phase, or energy density from each orthogonal field component. These isosurfaces describe in detail the resonance characteristics and energy distribution of different regions inside the meat. For example, the system may generate different isosurface groups such as amplitude component isosurfaces, phase component isosurfaces, and energy component isosurfaces. Next, the system identifies the structural features of the component isosurface groups through topological skeleton extraction to obtain an isosurface topological feature set. Topological skeleton extraction is a technology used to identify key structural features in complex geometric shapes. It can reveal the connection relationship and topological structure between isosurfaces. For example, when analyzing that piece of meat, the system can identify which areas are energy convergence points and which areas are phase transition points through topological skeleton extraction, and determine the connection relationship between these areas. Then, the system organizes the isosurface topological feature set hierarchically to obtain a resonant feature hierarchy. Hierarchical organization technology is used to classify and sort complex topological feature sets in a certain logical order to form a clear hierarchical structure.For example, the system may identify different hierarchical structures such as field center points, field boundaries, and field separation surfaces, which describe in detail the interactions and energy transfer paths between the various parts of the meat. Finally, based on the resonance feature hierarchy, the system fuses and reconstructs the component isosurface groups to obtain a resonance isosurface sequence. The fusion reconstruction technique recombines the individual component isosurfaces together to form a complete resonance isosurface sequence. For example, in the application scenario of heating the piece of meat, the system can fuse the individual component isosurfaces according to the resonance feature hierarchy to generate different resonance isosurface sequences such as resonance peak isosurface families, phase jump boundary surface sets, and energy gradient critical surface sequences. These isosurface sequences not only show the energy distribution of each region inside the meat, but also reveal its resonance characteristics and energy transfer paths. With this detailed resonance isosurface sequence, the system can accurately formulate the optimal heating strategy to ensure that the entire heating process is uniform and efficient. To better understand this process, suppose we heat a piece of meat containing different ingredients in a microwave oven, and the field curvature distribution matrix is ​​obtained after calculating the continuous resonance field expression. First, the system performs threshold segmentation on the matrix to obtain a multi-level isosurface cutting parameter set. For example, the system may set different amplitude cutting thresholds, phase cutting thresholds, and energy density cutting thresholds so that the subsequent steps can perform accurate isosurface extraction based on these thresholds. Then, the system performs multi-dimensional field decomposition on the continuous resonance field expression based on these cutting parameter sets to obtain a set of orthogonal field components. For example, the system may decompose the resonance field into different orthogonal field components such as amplitude component, phase component, and energy component. Then, the system performs independent isosurface extraction on these orthogonal field component sets to obtain component isosurface groups. For example, the system may generate different isosurface groups such as amplitude component isosurface, phase component isosurface, and energy component isosurface. Then, the system performs structural feature recognition on these component isosurface groups through topological skeleton extraction to obtain isosurface topological feature sets. For example, the system may recognize that certain specific areas are energy convergence points or phase transition points, and determine the connection relationship between these areas. Then, the system organizes the isosurface topological feature set hierarchically to obtain a resonance feature hierarchy. For example, the system may identify different hierarchical structures such as field center points, field boundaries, and field separation surfaces, which describe in detail the interactions and energy transfer paths between the various parts inside the meat. Finally, based on the resonance feature hierarchy, the system fuses and reconstructs the component isosurface groups to obtain a resonance isosurface sequence. For example, the system can fuse the component isosurfaces according to the resonance feature hierarchy to generate different resonance isosurface sequences such as a resonance peak isosurface family, a phase jump boundary surface set, and an energy gradient critical surface sequence. These isosurface sequences not only show the energy distribution of each region inside the meat, but also reveal its resonance characteristics and energy transfer paths.Through this detailed resonant isosurface sequence, the system can accurately formulate the optimal heating strategy to ensure that the entire heating process is uniform and efficient. This method not only improves heating accuracy and efficiency, but also provides strong technical support for food safety and quality control, and has broad application prospects and development potential. Whether in food processing or other industrial fields, this method has demonstrated its unique technical advantages and application value.

[0035] In a specific embodiment, the multi-dimensional field decomposition of the continuous resonance field expression based on the multi-level isosurface cutting parameter set to obtain a set of orthogonal field components includes: Performing parameter space transformation on the multi-level isosurface cutting parameter set to obtain a cutting parameter transformation matrix, and performing orthogonal basis function expansion on the continuous resonance field expression based on the cutting parameter transformation matrix to obtain a field component spectrum; Extracting spectral features of the field component spectrum by singular value decomposition to obtain a field component eigenvalue set, and sorting and screening the field component eigenvalue set by amplitude to obtain a dominant field component feature set, wherein the dominant field component feature set includes a resonant main mode, an anti-resonant feature point, and an energy transfer channel; Based on the dominant field component feature set, the continuous resonance field expression is subjected to tensor decomposition and reconstruction to obtain a field component tensor group, and the field component tensor group is subjected to spatial coordinate projection to obtain an orthogonal field projection matrix group, wherein the orthogonal field projection matrix group includes a radial field projection, a toroidal field projection, and an axial field projection; The orthogonal field projection matrix group is subjected to a resonant field synthesis transformation to obtain an orthogonal field component set; wherein the orthogonal field component set includes a fundamental frequency resonant field component, a high-order harmonic field component and a frequency cross-coupling field component.

[0036] Specifically, in the dynamic adjustment heating control method based on resonant frequency, it is a complex and multi-level process to perform multi-dimensional field decomposition of the continuous resonant field expression based on the multi-level isosurface cutting parameter set to obtain an orthogonal field component set. This process, through a series of sophisticated data processing and analysis steps, ultimately generates an orthogonal field component set that fully describes the resonant characteristics of the heated object, providing a key basis for the subsequent heating power allocation. First, the system performs parameter space transformation on the multi-level isosurface cutting parameter set to obtain a cutting parameter transformation matrix. Parameter space transformation is a technique for converting raw data from one coordinate system to another, which can reveal the intrinsic relationship between different parameters. For example, in a food processing scenario, when we heat a piece of meat containing water, fat and protein, parameter space transformation can help identify which parameter combinations can more effectively describe the energy distribution characteristics inside the meat. Then, the system performs orthogonal basis function expansion on the continuous resonant field expression based on the cutting parameter transformation matrix to obtain a field component spectrum. The orthogonal basis function expansion technology decomposes the complex resonant field expression into multiple independent basis function components, each of which represents the characteristics of the resonant field in a specific direction or frequency. For example, when analyzing that piece of meat, the system can decompose the resonant field into multiple basis function components through orthogonal basis function expansion, which describe in detail the vibration modes and energy distribution of each region inside the meat. Next, the system extracts spectral features from the field component spectrum through singular value decomposition to obtain the field component eigenvalue set. Singular value decomposition is a powerful mathematical tool that can extract the main eigenvalues ​​from complex spectral data to reveal the main vibration modes of the resonant field. For example, when analyzing that piece of meat, the system can identify which vibration modes in which frequency ranges are most significant through singular value decomposition, which may be related to the compositional differences or structural characteristics in the meat. Then, the system performs amplitude sorting and screening on the field component eigenvalue set to obtain the dominant field component feature set. The amplitude sorting and screening technology is used to select the eigenvalues ​​with the largest contribution from a large number of eigenvalues, which represent the main vibration modes of the resonant field and their energy transfer paths. For example, the system may identify different dominant field component features such as resonant main modes, anti-resonant feature points, and energy transfer channels, which describe in detail the interaction between the internal parts of the meat and the energy transfer efficiency. Based on the dominant field component feature set, the system performs tensor decomposition and reconstruction on the continuous resonant field expression to obtain a field component tensor group. The tensor decomposition and reconstruction technology further decomposes the complex resonant field expression into multiple independent tensor components, each of which represents the characteristics of the resonant field in a specific direction or frequency. For example, when analyzing that piece of meat, the system can decompose the resonant field into multiple tensor components through tensor decomposition and reconstruction. These components describe in detail the vibration modes and energy distribution of each area inside the meat. Then, the system performs spatial coordinate projection on the field component tensor group to obtain an orthogonal field projection matrix group.The spatial coordinate projection technique is used to project complex tensor components into different spatial coordinate systems in order to better understand their geometric properties. For example, the system may generate different orthogonal field projection matrices such as radial field projection, annular field projection, and axial field projection, which describe in detail the energy distribution and geometric structure of each region inside the meat. Finally, the system performs a resonant field synthesis transformation on the orthogonal field projection matrix group to obtain a set of orthogonal field components. The resonant field synthesis transformation technology recombines the individual orthogonal field projection matrices together to form a complete set of orthogonal field components. For example, in the application scenario of heating the piece of meat, the system can fuse the individual orthogonal field projection matrices according to the resonant field synthesis transformation to generate different sets of orthogonal field components such as fundamental frequency resonant field components, high-order harmonic field components, and frequency cross-coupling field components. These orthogonal field components not only show the energy distribution of each region inside the meat, but also reveal its resonant characteristics and energy transfer paths. With this detailed set of orthogonal field components, the system can accurately formulate the optimal heating strategy to ensure that the entire heating process is uniform and efficient. To better understand this process, suppose we heat a piece of meat with different ingredients in a microwave oven. After calculating the multi-level isosurface cutting parameter set, we get the cutting parameter transformation matrix. First, the system performs orthogonal basis function expansion on the continuous resonant field expression based on the matrix to obtain the field component spectrum. For example, the system may decompose the resonant field into multiple basis function components, which describe in detail the vibration modes and energy distribution of each region inside the meat. Then, the system extracts spectral features from the field component spectrum through singular value decomposition to obtain the field component eigenvalue set. For example, the system may identify which frequency ranges have the most significant vibration modes and select the dominant field component feature set from them. These dominant field component features include information such as resonant main modes, anti-resonant feature points, and energy transfer channels, which describe in detail the interactions and energy transfer paths between the various parts inside the meat. Then, the system performs tensor decomposition and reconstruction on the continuous resonant field expression based on the dominant field component feature set to obtain the field component tensor group. For example, the system may further decompose the resonant field into multiple tensor components, which describe in detail the vibration modes and energy distribution of each region inside the meat. Next, the system performs spatial coordinate projection on the field component tensor group to obtain an orthogonal field projection matrix group. For example, the system may generate different orthogonal field projection matrices such as radial field projection, annular field projection, and axial field projection. These matrices describe in detail the energy distribution and geometric structure of each area inside the meat block. Finally, the system performs a resonant field synthesis transformation on the orthogonal field projection matrix group to obtain an orthogonal field component set. For example, the system can fuse the various orthogonal field projection matrices according to the resonant field synthesis transformation to generate different orthogonal field component sets such as fundamental frequency resonant field components, high-order harmonic field components, and frequency cross-coupling field components.These orthogonal field components not only show the energy distribution in each area of ​​the meat, but also reveal its resonance characteristics and energy transfer path. With this detailed set of orthogonal field components, the system can accurately formulate the optimal heating strategy to ensure that the entire heating process is uniform and efficient. This method not only improves heating accuracy and efficiency, but also provides strong technical support for food safety and quality control, and has broad application prospects and development potential. Whether in food processing or other industrial fields, this method has demonstrated its unique technical advantages and application value.

[0037] In a specific embodiment, the heating power distribution calculation is performed on a preset heating device based on the object resonance characteristic spectrum to obtain a power modulation control matrix, including: Dividing the resonance characteristic spectrum of the object into discrete regions to obtain multi-level resonance power response blocks, and performing energy absorption characteristic analysis on the multi-level resonance power response blocks to obtain an object energy absorption coefficient matrix; The power output units of the heating equipment are spatially mapped using the object energy absorption coefficient matrix to obtain a regional power allocation weight mapping table, and a power load balancing calculation is performed based on the regional power allocation weight mapping table to obtain a multi-channel power modulation parameter set; Based on the multi-channel power modulation parameter set, a resonant frequency matching calculation is performed on the output power of the heating device to obtain a power resonance coupling sequence; The output power of the heating device is modulated in real time through the power resonance coupling sequence to obtain a power modulation control matrix, wherein the rows of the power modulation control matrix represent spatially discrete power output units and the columns of the power modulation control matrix represent resonance frequency modulation parameters.

[0038] Specifically, in the dynamic adjustment heating control method based on resonant frequency, it is a complex and multi-level process to calculate the heating power distribution of the preset heating equipment based on the object resonance characteristic map to obtain the power modulation control matrix. This process, through a series of sophisticated data processing and analysis steps, finally generates a power modulation control matrix that fully describes the output power regulation strategy of the heating equipment, which provides a key basis for ensuring the uniformity and efficiency of the heating process. First, the system will divide the object resonance characteristic map into discrete regions to obtain multi-level resonant power response blocks. The discrete region division technology is used to decompose the complex resonance characteristic map into multiple independent regions, each of which represents the response characteristics of the heated object at a specific position or frequency. For example, in the food processing scenario, when we heat a piece of meat containing water, fat and protein, discrete region division can help identify which regions have similar energy absorption characteristics. Then, the system analyzes the energy absorption characteristics of these multi-level resonant power response blocks to obtain the object energy absorption coefficient matrix. The energy absorption characteristic analysis aims to quantify the energy absorption efficiency of each block and reveal the response of different regions to excitation signals such as electromagnetic waves or sound waves. For example, when analyzing the piece of meat, the system can determine which areas are more likely to absorb energy and which areas are relatively stable through energy absorption characteristics analysis, thereby optimizing the overall energy transmission path. Next, the system performs spatial correspondence mapping on the power output units of the heating device through the object energy absorption coefficient matrix to obtain a regional power allocation weight mapping table. The spatial correspondence mapping technology is used to correspond the data in the energy absorption coefficient matrix to the actual power output units of the heating device one by one to form a detailed power allocation weight mapping table. For example, in the application scenario of heating meat in a microwave oven, the system can determine which power output units require more energy input and which units can reduce power output based on the energy absorption coefficient matrix. Then, the system performs power load balancing calculation based on the regional power allocation weight mapping table to obtain a multi-channel power modulation parameter set. The power load balancing calculation is intended to ensure uniform energy distribution between each power output unit to avoid local overheating. For example, the system may adjust the power value of each power output unit based on the power load balancing calculation to make the entire heating process more uniform and efficient. Based on the multi-channel power modulation parameter set, the system performs a resonant frequency matching calculation on the output power of the heating device to obtain a power resonance coupling sequence. Resonant frequency matching calculation is a technology used to optimize power output, which ensures that the heating device produces the maximum energy absorption effect at a specific frequency. For example, when analyzing that piece of meat, the system can determine which frequency range is most suitable for heating through resonant frequency matching calculation, thereby avoiding local overheating or insufficient heating. The system then modulates the output power of the heating device in real time through a power resonance coupling sequence to obtain a power modulation control matrix.The power modulation control matrix describes in detail the power output strategy of the heating device at different times and frequencies to ensure that the entire heating process is both efficient and uniform. For example, in the process of heating a piece of meat in a microwave oven, the power modulation control matrix not only specifies the specific power value of each power output unit, but also specifies the corresponding resonant frequency modulation parameters to achieve accurate energy transfer. To better understand this process, suppose we heat a piece of meat containing different ingredients in a microwave oven, and after calculating the object resonance characteristic spectrum, we obtain a multi-level resonant power response block. First, the system analyzes the energy absorption characteristics of these blocks to obtain the object energy absorption coefficient matrix. For example, the system may find that certain specific areas show stronger energy absorption characteristics due to high moisture content. Then, the system uses the object energy absorption coefficient matrix to perform spatial mapping of the power output units of the heating device to obtain a regional power allocation weight mapping table. For example, the system can determine which power output units require more energy input and which units can reduce power output based on the energy absorption coefficient matrix. Then, the system performs power load balancing calculation based on the regional power allocation weight mapping table to obtain a multi-channel power modulation parameter set. For example, the system may adjust the power value of each power output unit based on the power load balancing calculation to make the entire heating process more uniform and efficient. Next, the system performs a resonant frequency matching calculation on the output power of the heating device based on the multi-channel power modulation parameter set to obtain a power resonance coupling sequence. For example, when analyzing that piece of meat, the system can determine which frequency ranges are most suitable for heating through a resonant frequency matching calculation, thereby avoiding the problem of local overheating or insufficient heating. Then, the system modulates the output power of the heating device in real time through a power resonance coupling sequence to obtain a power modulation control matrix. For example, in the process of heating a piece of meat in a microwave oven, the power modulation control matrix not only specifies the specific power value of each power output unit, but also specifies the corresponding resonant frequency modulation parameters. In this way, not only can the entire heating process be ensured to be uniform and efficient, but also the quality and taste of the food can be significantly improved, and food deterioration or nutrient loss caused by local overheating can be avoided. Specifically, in actual operation, assuming that we have a piece of meat containing different ingredients that needs to be heated, the system will first perform a sweep frequency excitation on the meat and obtain its resonant frequency response data set, thereby generating an object resonance characteristic spectrum. Then, the system divides the spectrum into discrete regions to obtain multi-level resonant power response blocks, and analyzes its energy absorption characteristics to obtain an object energy absorption coefficient matrix. For example, the system may find that a certain part is particularly prone to absorbing energy, which may be due to the high water content in that part, while another part absorbs less energy due to the presence of a fat layer. Then, the system maps the power output units of the heating equipment to spatial correspondence through the object energy absorption coefficient matrix to obtain a regional power allocation weight mapping table.For example, the system may determine which power output units require more energy input and which units can reduce power output based on the energy absorption coefficient matrix. Then, the system performs power load balancing calculation based on the power allocation weight mapping table of the area to obtain a multi-channel power modulation parameter set. For example, the system may adjust the power value of each power output unit based on the power load balancing calculation to make the entire heating process more uniform and efficient. Based on the multi-channel power modulation parameter set, the system performs a resonant frequency matching calculation on the output power of the heating device to obtain a power resonance coupling sequence. For example, when analyzing that piece of meat, the system can determine which frequency range is most suitable for heating through a resonant frequency matching calculation, thereby avoiding the problem of local overheating or insufficient heating. Finally, the system modulates the output power of the heating device in real time through a power resonance coupling sequence to obtain a power modulation control matrix. This matrix not only specifies the specific power value of each power output unit, but also specifies the corresponding resonant frequency modulation parameters to achieve precise energy transfer. Through this detailed power modulation control matrix, the system can accurately formulate the optimal heating strategy to ensure that the entire heating process is uniform and efficient. This method not only improves heating accuracy and efficiency, but also provides strong technical support for food safety and quality control, and has broad application prospects and development potential. Whether in food processing or other industrial fields, this method has demonstrated its unique technical advantages and application value.

[0039] The above describes the dynamic adjustment heating control method based on the resonant frequency in the embodiment of the present invention. The following describes the dynamic adjustment heating control device based on the resonant frequency in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, a heating control device for dynamically adjusting the resonant frequency includes: The excitation module 21 is used to perform multi-band frequency sweep excitation on the heated object to obtain a resonant frequency response data set; wherein the heated object is arranged in the heating device; An analysis module 22, configured to perform multi-dimensional spectrum analysis on the resonant frequency response data set to obtain a resonance characteristic spectrum of the object; A calculation module 23, configured to calculate heating power distribution for a preset heating device based on the object resonance characteristic spectrum to obtain a power modulation control matrix; The heating module 24 is used to dynamically adjust and control the heating device based on the power modulation control matrix to achieve uniform heating of the heated object.

[0040] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to the above method embodiment, which will not be repeated here.

[0041] Reference Figure 3The present invention also provides a computer device in an embodiment, wherein the internal structure of the computer device can be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0042] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0043] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0044] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0045] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0046] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A dynamic adjustment heating control method based on resonant frequency, applied to heating equipment, characterized in that: The following steps are involved: Performing multi-band frequency sweep excitation on the heated object to obtain a resonant frequency response data set; wherein the heated object is arranged in a heating device; Performing multi-dimensional spectrum analysis on the resonant frequency response data set to obtain a resonance characteristic spectrum of the object; Based on the object resonance characteristic spectrum, a heating power distribution calculation is performed on a preset heating device to obtain a power modulation control matrix; The heating device is dynamically adjusted and controlled based on the power modulation control matrix to achieve uniform heating of the heated object.

2. The method for dynamically adjusting heating control based on resonance frequency according to claim 1, characterized in that: The step of performing multi-band frequency sweep excitation on the heated object to obtain a resonant frequency response data set includes: A multi-band frequency sweep excitation is performed on the heated object by a multi-band exciter arranged in the heating device to obtain frequency-amplitude response characteristic data; Based on the frequency-amplitude response characteristic data, a resonant modal analysis is performed on the heated object to obtain a resonant frequency response data set; wherein the resonant frequency response data set includes a node vibration mode distribution, a modal impedance characteristic and an energy coupling coefficient on the surface of the object.

3. The method for dynamically adjusting heating control based on resonance frequency according to claim 1, characterized in that: The performing multi-dimensional spectrum analysis on the resonant frequency response data set to obtain a resonance characteristic spectrum of the object includes: Performing wavelet decomposition and reconstruction on the resonant frequency response data set to obtain a multi-scale spectral component sequence, and performing resonance feature extraction on the multi-scale spectral component sequence to obtain a resonant frequency feature matrix, wherein the resonant frequency feature matrix includes frequency component amplitudes, phase differences, and energy distribution ratios; The resonant field intensity of the heated object is calculated by using the resonant frequency characteristic matrix to obtain a resonant field intensity distribution tensor, and an energy flow density analysis is performed on the resonant field intensity distribution tensor to obtain an energy transfer path diagram, wherein the energy transfer path diagram includes an energy transfer direction, a field intensity gradient, and a coupling intensity distribution; Based on the energy transfer path diagram, spatial resonance characteristics of the heated object are mapped to obtain a resonance node distribution diagram, and frequency domain response analysis is performed on the resonance node distribution diagram to obtain a frequency response characteristic data set, wherein the frequency response characteristic data set includes a resonance frequency point position, an impedance characteristic, and an energy absorption coefficient; Performing resonance mode expansion on the frequency response characteristic data set to obtain a modal feature sequence, and performing spatial resonance coupling analysis on the modal feature sequence to obtain a coupling characteristic parameter set, wherein the coupling characteristic parameter set includes a modal vibration shape distribution, a coupling coefficient matrix, and an energy conversion efficiency; The coupling characteristic parameter set is subjected to multi-dimensional resonance characteristic synthesis and spectrum feature fusion to obtain an object resonance characteristic spectrum; wherein the object resonance characteristic spectrum includes a resonance frequency distribution map, an energy absorption efficiency spectrum and a spatial resonance modal response topological structure.

4. The method for dynamically adjusting heating control based on resonance frequency according to claim 3 is characterized in that: The step of mapping the spatial resonance characteristics of the heated object based on the energy transfer path diagram to obtain a resonance node distribution diagram includes: Extracting topological features of the energy transfer path diagram to obtain a set of key energy transfer nodes, and performing spatial discrete sampling based on the set of key energy transfer nodes to obtain a resonance sensitive point array, wherein the resonance sensitive point array includes a resonance peak point, an energy convergence point, and a phase conversion point; Performing spatial interpolation calculation on the resonance sensitive point array by tensor field decomposition technology to obtain a continuous resonance field expression, and performing isosurface cutting on the continuous resonance field expression to obtain a resonance isosurface sequence; Based on the resonance isosurface sequence, a resonance field gradient analysis is performed on the heated object to obtain a resonance gradient vector field, and a divergence and curl calculation is performed on the resonance gradient vector field to obtain a field source distribution characteristic quantity, wherein the field source distribution characteristic quantity includes a resonance source intensity, a resonance field vortex, and a resonance field divergence area; Feature point extraction and cluster analysis are performed on the field source distribution feature quantity to obtain a resonance node distribution map.

5. The method for dynamically adjusting heating control based on resonance frequency according to claim 4, characterized in that: The step of performing isosurface cutting on the continuous resonance field expression to obtain a resonance isosurface sequence includes: Performing eigenvalue calculation on the continuous resonance field expression by Gaussian curvature analysis to obtain a field curvature distribution matrix, and performing threshold segmentation on the field curvature distribution matrix to obtain a multi-level isosurface cutting parameter set, wherein the multi-level isosurface cutting parameter set includes an amplitude cutting threshold, a phase cutting threshold, and an energy density cutting threshold; Based on the multi-level isosurface cutting parameter set, the continuous resonance field expression is subjected to multi-dimensional field decomposition to obtain an orthogonal field component set, and independent isosurface extraction is performed on the orthogonal field component set to obtain a component isosurface group, wherein the component isosurface group includes an amplitude component isosurface, a phase component isosurface, and an energy component isosurface; The structural features of the component isosurface group are identified by topological skeleton extraction to obtain an isosurface topological feature set, and the isosurface topological feature set is hierarchically organized to obtain a resonance feature hierarchy structure, wherein the resonance feature hierarchy structure includes a field center point, a field boundary line and a field separation surface; The component isosurface groups are fused and reconstructed based on the resonance feature hierarchy to obtain a resonance isosurface sequence; wherein the resonance isosurface sequence includes a resonance peak isosurface family, a phase jump boundary surface set and an energy gradient critical surface sequence.

6. The method for dynamically adjusting heating control based on resonance frequency according to claim 5, characterized in that: The method of performing multi-dimensional field decomposition on the continuous resonance field expression based on the multi-level isosurface cutting parameter set to obtain a set of orthogonal field components includes: Performing parameter space transformation on the multi-level isosurface cutting parameter set to obtain a cutting parameter transformation matrix, and performing orthogonal basis function expansion on the continuous resonance field expression based on the cutting parameter transformation matrix to obtain a field component spectrum; Extracting spectral features of the field component spectrum by singular value decomposition to obtain a field component eigenvalue set, and sorting and screening the field component eigenvalue set by amplitude to obtain a dominant field component feature set, wherein the dominant field component feature set includes a resonant main mode, an anti-resonant feature point, and an energy transfer channel; Based on the dominant field component feature set, the continuous resonance field expression is subjected to tensor decomposition and reconstruction to obtain a field component tensor group, and the field component tensor group is subjected to spatial coordinate projection to obtain an orthogonal field projection matrix group, wherein the orthogonal field projection matrix group includes a radial field projection, a toroidal field projection, and an axial field projection; The orthogonal field projection matrix group is subjected to a resonant field synthesis transformation to obtain an orthogonal field component set; wherein the orthogonal field component set includes a fundamental frequency resonant field component, a high-order harmonic field component and a frequency cross-coupling field component.

7. The method for dynamically adjusting heating control based on resonance frequency according to claim 1, characterized in that: The heating power distribution calculation is performed on the preset heating device based on the object resonance characteristic spectrum to obtain a power modulation control matrix, including: Dividing the resonance characteristic spectrum of the object into discrete regions to obtain multi-level resonance power response blocks, and performing energy absorption characteristic analysis on the multi-level resonance power response blocks to obtain an object energy absorption coefficient matrix; The power output units of the heating equipment are spatially mapped using the object energy absorption coefficient matrix to obtain a regional power allocation weight mapping table, and a power load balancing calculation is performed based on the regional power allocation weight mapping table to obtain a multi-channel power modulation parameter set; Based on the multi-channel power modulation parameter set, a resonant frequency matching calculation is performed on the output power of the heating device to obtain a power resonance coupling sequence; The output power of the heating device is modulated in real time through the power resonance coupling sequence to obtain a power modulation control matrix, wherein the rows of the power modulation control matrix represent spatially discrete power output units and the columns of the power modulation control matrix represent resonance frequency modulation parameters.

8. A dynamic adjustment heating control device based on resonant frequency, characterized in that: Applied to heating equipment, including: An excitation module, used to perform multi-band frequency sweep excitation on the heated object to obtain a resonant frequency response data set; wherein the heated object is arranged in the heating device; An analysis module, used to perform multi-dimensional spectrum analysis on the resonant frequency response data set to obtain a resonance characteristic spectrum of the object; A calculation module, used to calculate the heating power distribution of a preset heating device based on the object resonance characteristic spectrum to obtain a power modulation control matrix; The heating module is used to dynamically adjust and control the heating device based on the power modulation control matrix to achieve uniform heating of the heated object.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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