Preparation method of coating material for radiant cooker

Abstract

The invention relates to the technical field of data processing, in particular to a preparation method of a coating material for an electric ceramic cook.The preparation method comprises the steps that temperature field space distribution data in a sintering chamber and ceramic matrix acoustic emission signals are synchronously collected, and a time and space physical field observation matrix is constructed; inputting the physical field observation matrix into a pre-trained digital twinborn body to analyze and output a thermal gradient distribution predicted value and a material crystallization stress risk map; inputting the risk map and acoustic emission spectrum features to a reinforcement learning strategy, executing space mapping to identify crystal phase change inflection point coordinate data, and outputting a partition power compensation vector data packet; applying beam data to excite a microwave non-thermal effect, promoting atom migration response data to fill the microcrack gap space coordinates, and outputting thermal stress distribution update data and microcrack state termination data. According to the method, through closed-loop control of double-source data monitoring, quantum acceleration prediction, reinforcement learning decision and microwave interference execution, the macroscopic thermal stress gradient is eliminated, and microscopic cracks are repaired at the same time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a preparation method of coating material for electric ceramic stove. BACKGROUND

[0002] As a kind of efficient heating equipment, the surface of electric ceramic stove is easy to suffer oxidation and friction loss under long-term high-temperature operation, and needs to be in direct contact with food to maintain food safety and hygiene, so the coating material must have excellent thermal stability, chemical inertness and wear resistance to meet the functional requirements. Based on the principle of material science, aluminum oxide or silicon-based ceramic is widely selected. Such coating forms a dense structure during sintering process, which can effectively isolate extreme high temperature, resist acid and alkali corrosion, and reduce micro-cracks caused by surface thermal strain. At the same time, it provides low surface energy characteristics at the molecular level to inhibit dirt adhesion and achieve easy cleaning purpose. By optimizing the microstructure, the risk of coating peeling or contaminant migration is avoided, and the overall reliability and user experience of the stove are improved.

[0003] In the preparation process of coating material such as aluminum oxide or silicon-based ceramic for electric ceramic stove, especially in the sintering stage, the main technical pain points focus on the lack of real-time dynamic adjustment ability of the control system to thermal stress distribution. The reason is that the sintering process involves complex nonlinear thermodynamic behavior, and the operation platform needs to respond to the difference in material expansion coefficient and uneven furnace temperature instantaneously. However, the existing fixed program algorithm is difficult to model and predict the transient thermal flow coupling effect, and lacks adaptive mechanism to compensate for environmental disturbances logically. For example, the local temperature deviation in the sintering chamber on the electric ceramic stove production line is not corrected quickly, which leads to micro-regional stress concentration and micro-crack propagation on the surface of the coating. Specifically, local peeling is aggravated after multiple thermal shocks, which increases the risk of contaminant penetration, thereby reducing the overall service life of the stove and food safety. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a preparation method of coating material for electric ceramic stove, which solves the technical problem of coating micro-crack propagation induced by thermal stress gradient caused by adaptive control failure of dynamic thermal distribution in sintering process.

[0005] To solve the above technical problems, the specific technical solutions of the present application are as follows: The present application provides a preparation method of coating material for electric ceramic stove, which comprises: Step 1, synchronously collecting temperature field spatial distribution data and ceramic matrix acoustic emission signals in the sintering chamber, and using the collected data to construct a time and space physical field observation matrix; Step 2, inputting the physical field observation matrix constructed in step 1 into a pre-trained digital twin, analyzing the physical field observation matrix through the digital twin, and parallelly outputting thermal gradient distribution prediction value and material crystallization stress risk map; Step 3, input the risk map and acoustic emission spectrum features of the output of step 2 into the reinforcement learning strategy, perform spatial mapping to identify the coordinates of the crystallization phase transition inflection point data, and output the partition power compensation vector data package, which includes nanosecond-level time delay compensation factors; Step 4, receive the partition power compensation vector data package output by step 3, parse the data package to extract the magnetron array driving parameter and diffusion threshold matching condition, and drive the tunable magnetron array to generate interference heating beam geometry data; Step 5, receive the interference heating beam energy distribution data output by step 4, apply it to the coating precursor powder atomic structure, fill the micro-crack gap space coordinate data with atomic migration response data, and output the thermal stress distribution update data and the micro-crack state termination data.

[0006] Further, the preparation method of the coating material for the electric ceramic stove provided by the present application comprises the following steps: The thermal sensor array collects and outputs the temperature field spatial distribution data; The piezoelectric transducer network collects and outputs the acoustic emission signal; Perform fusion operation to integrate the temperature field spatial distribution data and the acoustic emission signal, and the fusion operation outputs the physical field observation matrix.

[0007] Further, the preparation method of the coating material for the electric ceramic stove provided by the present application comprises the following steps: Input the physical field observation matrix into the quantum variational algorithm; The quantum variational algorithm performs thermal gradient distribution prediction calculation path optimization, and outputs the optimized calculation path parameters to the digital twin; The digital twin uses the optimized calculation path parameters to process the physical field observation matrix, and outputs the thermal gradient distribution prediction value and the material crystallization stress risk map.

[0008] Further, the preparation method of the coating material for the electric ceramic stove provided by the present application comprises the following steps: Input the acoustic emission spectrum features and the thermal expansion coefficient into the reinforcement learning strategy; The reinforcement learning strategy performs fusion calculation on the acoustic emission spectrum features and the thermal expansion coefficient, and calculates the partition power compensation vector; Implant nanosecond-level time delay compensation factors into the partition power compensation vector to generate the compensation vector data package.

[0009] Further, the preparation method of the coating material for the electric ceramic stove provided by the present application comprises the following steps: Input the response delay history data into the time delay compensation calculation; The time delay compensation calculation outputs the nanosecond-level time delay compensation factors; The compensation factors are implanted into the partition power compensation vector to update the compensation vector data package. Further, the coating material preparation method for the electric ceramic stove, step 4 comprises: receiving the compensation vector data packet output by step 3; The data packet is parsed to extract the magnetron array driving parameters; The driving parameters control the tunable magnetron array output to match the interference heating beam geometry data of the coating diffusion threshold. Further, the coating material preparation method for the electric ceramic stove, step 5 comprises: receiving the interference heating beam geometry data output by step 4; The beam data is applied to the coating precursor powder to generate microwave non-thermal effects; The microwave non-thermal effects stimulate atomic migration response data to fill the microcrack gap space coordinate data, and output thermal stress distribution update data.

[0010] Further, the coating material preparation method for the electric ceramic stove, step 2 further comprises: Receiving the risk atlas output by the digital twin; Performing a thermal coupling cloud map rendering operation, and the rendering operation outputs a spatial thermal stress distribution visual signal. Further, the coating material preparation method for the electric ceramic stove, step 1 further comprises: inputting the acoustic emission signals collected by the piezoelectric transducer network into Kalman filtering processing; The Kalman filtering processing outputs a noise-reduced acoustic emission signal; The noise-reduced acoustic emission signal is input into a fusion operation to generate a physical field observation matrix. Further, the coating material preparation method for the electric ceramic stove, step 3 further comprises: performing a state space definition operation, and the state space is a crystallization phase transition inflection point coordinate data set; Performing an action space definition operation, and the action space is a heating coil number dimension partition compensation vector set; The state space data set is input into a reinforcement learning strategy, and the strategy outputs an action space partition compensation vector.

[0011] Advantages of the present application; The application breaks through the limitation of single sensor monitoring, improves the sensing accuracy of thermal stress state by constructing a physical field observation matrix through temperature field spatial distribution data and acoustic emission signal double source collection; solves the existing algorithm thermal simulation lag problem by quantum computing to accelerate the real-time output of thermal gradient distribution prediction value and material crystallization stress risk map of digital twin; dynamically compensates the execution response delay by strengthening the learning strategy to fuse the acoustic emission spectrum features and the thermal expansion coefficient, spatial mapping to identify the coordinates of the crystallization phase change inflection point and implanting the nanosecond level delay compensation factor; realizes the dynamic adaptation of energy field and material characteristics by driving the tunable magnetron array to generate interference heating beam geometry data through partition power compensation vector data packets, and strictly matching the beam energy gradient with the coating diffusion threshold; reduces the atomic migration barrier by microwave non-thermal effect, fills the microcrack gap space coordinates in a directional manner, synchronously outputs the thermal stress distribution update data and the microcrack state termination data, and forms a synergistic mechanism of macro stress resolution and micro crack repair. The technical scheme establishes a monitoring, decision-making, execution and verification closed loop to eliminate the risk of coating microcrack expansion caused by out-of-control thermal distribution in the sintering process. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed in the embodiments. Obviously, for those skilled in the art, other drawings can also be obtained without creative labor based on the drawings.

[0013] Figure 1 A flowchart of a coating material preparation method for an electric ceramic stove is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical scheme and advantages of the present application clearer, the following will combine the specific embodiments of the present application and the corresponding drawings to clearly and completely describe the technical scheme of the present application. Obviously, the described embodiments are only one module of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application. The technical scheme provided by each embodiment of the present application will be described in detail below in combination with the drawings. In order to better understand the purpose of the present application, the present application will be further described in detail below.

[0015] Please refer to Figure 1 The present application provides a coating material preparation method for an electric ceramic stove, which comprises: Step 1, synchronously collecting temperature field spatial distribution data and ceramic matrix acoustic emission signal in the sintering chamber, and constructing a time and space physical field observation matrix by using the collected data; Step 2: Input the physical field observation matrix constructed in Step 1 into the pre-trained digital twin, and output the thermal gradient distribution prediction value and the material crystallization stress risk map in parallel through the digital twin. Step 3: Input the risk map and acoustic emission spectrum features output in Step 2 into the reinforcement learning strategy, perform spatial mapping to identify the crystallization phase transition inflection point coordinate data, and output the partition power compensation vector data package, which includes nanosecond-level delay compensation factors. Step 4: Receive the partition power compensation vector data package output in Step 3, analyze the data package to extract the magnetron array driving parameters and diffusion threshold matching conditions, and drive the tunable magnetron array to generate interference heating beam geometry data. Step 5: Receive the interference heating beam energy distribution data output in Step 4, apply it to the coating precursor powder atomic structure, fill the micro-crack gap space coordinate data with atomic migration response data, and output the thermal stress distribution update data and micro-crack state termination data.

[0016] Step 1: Synchronously collect the temperature field spatial distribution data and ceramic substrate acoustic emission signals in the sintering chamber, and use the collected data to construct a time and space physical field observation matrix.

[0017] The thermal sensor array is distributed on the side wall of the sintering chamber with a preset spatial resolution, which can capture the dynamic changes of the three-dimensional temperature field in real time. The piezoelectric transducer network is installed on the back of the ceramic substrate to monitor the acoustic emission signals in a specific frequency band. After Kalman filter noise reduction, the temperature field spatial distribution data and acoustic emission signals are fused to generate a physical field observation matrix through a space-time correlation algorithm. The matrix row vector maps the spatial coordinate position, and the column vector records the coupling relationship between the temperature and acoustic emission amplitude at the time sequence, providing a data basis for thermodynamic behavior analysis.

[0018] Step 2: Input the physical field observation matrix constructed in Step 1 into the pre-trained digital twin, and output the thermal gradient distribution prediction value and the material crystallization stress risk map in parallel through the digital twin.

[0019] The digital twin adopts a quantum computing acceleration architecture: the physical field observation matrix is encoded as a quantum bit amplitude, the thermal conduction equation unitary transformation is performed by parameterized quantum gates, and the quantum state measurement outputs the thermal gradient distribution prediction value. In parallel channels, a deep convolutional neural network extracts acoustic emission wavelet envelope features, inputs a phase field theory model to calculate von Mises stress distribution, and generates a material crystallization stress risk map. The risk map identifies stress threshold coordinates in a thermodynamic coupling cloud chart, which reflects the sintering process thermodynamic state in real time.

[0020] Step 3: Input the risk map and acoustic emission spectrum features output in Step 2 into the reinforcement learning strategy, perform spatial mapping to identify the crystallization phase transition inflection point coordinate data, and output the partition power compensation vector data package.

[0021] The reinforcement learning strategy defines the state space as the crystallization phase transition inflection point coordinate set in the risk map (the second derivative of the inflection point temperature is zero), and the action space is the power compensation vector of the number of heating coils dimension; The acoustic emission spectrum characteristics and the material thermal expansion coefficient are fused, and the acoustic emission characteristic frequency and the risk map coordinates are associated through a space mapping algorithm; The output partition power compensation vector data packet is implanted with a nanosecond-level delay compensation factor dynamically calculated according to the actuator response delay historical data, so as to avoid power overshoot and cause thermal oscillation.

[0022] Step 4: receiving the partition power compensation vector data packet output in step 3, analyzing the data packet to extract the magnetron array driving parameter and the diffusion threshold matching condition, and driving the parameter to control the tunable magnetron array to generate the interference heating beam geometry data.

[0023] The compensation vector data packet is analyzed to obtain the magnetron driving parameter and the coating precursor diffusion threshold constraint; The tunable magnetron array generates a fundamental wave and a harmonic wave according to the driving parameter, and forms a spatial energy distribution through waveguide interference; The interference heating beam geometry data strictly matches the coating activation energy requirement (such as the diffusion threshold of aluminum oxide), and realizes the precise coverage of the microwave energy gradient to the high temperature stress concentration area.

[0024] Step 5: receiving the interference heating beam geometry data output in step 4, and applying it to the coating precursor powder atomic structure to output the thermal stress distribution update data and the microcrack state termination data.

[0025] The interference heating beam geometry data acts on the coating precursor powder, excites the microwave non-thermal effect to reduce the atomic migration potential barrier; The atomic migration response data fills the microcrack gap space coordinates in a directional manner, and simultaneously realizes the thermal stress gradient resolution and the microcrack repair; The thermal stress distribution update data verifies the macroscopic stress elimination effect, and the microcrack state termination data confirms the completion of the microscopic defect repair.

[0026] Specifically, the coating material preparation method for the electric ceramic stove provided by the application comprises the following steps: The thermosensitive sensor array collects and outputs the temperature field spatial distribution data; The piezoelectric transducer network collects and outputs the acoustic emission signal; The fusion operation is performed to integrate the temperature field spatial distribution data and the acoustic emission signal, and the fusion operation outputs the physical field observation matrix.

[0027] A thermal sensor array is embedded in the sidewall of the sintering chamber in a gridded layout to collect three-dimensional temperature field spatial distribution data. A piezoelectric transducer network is arranged on the back of the ceramic substrate to capture acoustic emission signals in the 20-200kHz frequency band. A spatiotemporal registration algorithm is used to perform the fusion operation, aligning the timestamps and spatial coordinates of the temperature field spatial distribution data with the acoustic emission signals, and outputting a physical field observation matrix. The row vectors of the matrix correspond to the spatial grid numbers, and the column vectors record the coupling values ​​of the temperature gradient and acoustic emission amplitude under the time series, thus constructing the basis for joint thermo-acoustic monitoring.

[0028] Specifically, in the method for preparing the coating material for electric ceramic stoves according to the present invention, step 2 includes: Input the physical field observation matrix into the quantum variational algorithm; The quantum variational algorithm performs thermal gradient distribution prediction computation path optimization and outputs the optimized computation path parameters to the digital twin. The digital twin uses optimized computational path parameters to process the physical field observation matrix and outputs predicted values ​​of thermal gradient distribution and material crystallization stress risk maps.

[0029] The quantum variational algorithm receives the physical field observation matrix and encodes the matrix elements into qubit probability amplitudes; the parameterized quantum gate performs the unitary operator transformation of the heat conduction partial differential equation and outputs optimized computation path parameters; the digital twin uses the optimized path parameters to reconstruct the numerical solution of heat conduction, and combines the acoustic emission wavelet features extracted by the deep convolutional neural network to output the predicted value of the thermal gradient distribution and the von Mises stress risk map, with the red area in the map indicating the stress over-limit coordinates.

[0030] Specifically, in the method for preparing the coating material for electric ceramic stoves according to the present invention, step 3 includes: Input acoustic emission spectral characteristics and thermal expansion coefficient into reinforcement learning strategies; The reinforcement learning strategy performs a fusion calculation of acoustic emission spectral features and thermal expansion coefficient, and calculates the output partitioned power compensation vector. A nanosecond-level delay compensation factor is implanted into the partition power compensation vector to generate a compensation vector data packet.

[0031] The reinforcement learning strategy takes acoustic emission spectrum features and material thermal expansion coefficient as inputs; the acoustic emission spectrum features are used to extract the main frequency energy distribution through fast Fourier transform, and the thermal expansion coefficient is associated with the material phase transition temperature threshold; the strategy executes feature fusion calculation and outputs a partitioned power compensation vector; the compensation vector is implanted with a nanosecond-level delay compensation factor generated based on historical delay data to generate a compensation vector data packet.

[0032] Specifically, in the method for preparing the coating material for electric ceramic stoves according to the present invention, step 3 further includes: Input historical response delay data into the delay compensation calculation; Delay compensation calculation outputs nanosecond-level delay compensation factor; The compensation factor is implanted into the partition power compensation vector, and the compensation vector data packet is updated.

[0033] The delay compensation calculation inputs historical data of actuator response delay, and uses a time series prediction model to calculate the compensation factor; the compensation factor value is matched with the dimension of the power compensation vector; an operation update compensation vector data packet is implanted, and the data packet format includes vector components, delay factor, and timestamp check code.

[0034] Specifically, in the method for preparing coating materials for electric ceramic stoves according to the present invention, step 4 includes: receiving the compensation vector data packet output in step 3; Parse data packets to extract magnetron array drive parameters; The driving parameters control the output of the tunable magnetron array to obtain the interferometric heating beam geometry data that matches the coating diffusion threshold.

[0035] The compensation vector data packet is parsed to extract the magnetron array driving parameters, including the fundamental frequency, harmonic intensity, and phase difference. The driving parameters are input to the tunable magnetron array, and the array outputs the geometric configuration data of the interferometric heating beam. The geometric configuration data includes the beam energy density gradient, and the gradient value matches the activation energy threshold of the coating precursor.

[0036] Specifically, in the method for preparing coating materials for electric ceramic stoves according to the present invention, step 5 includes: receiving the interference heating beam geometry data output in step 4; Applying beam data to coating precursor powder generates microwave non-thermal effects; Microwave non-thermal effect-induced atomic migration response data fills the spatial coordinate data of microcrack gaps, and outputs updated thermal stress distribution data.

[0037] Interferometric heating beam geometry data is applied to the coating precursor powder; microwave non-thermal effects reduce atomic diffusion activation energy, prompting alumina particles to migrate directionally along the microcrack boundary; atomic migration response data fills the spatial coordinate data in real time, updates the thermal stress distribution data, and terminates the microcrack propagation state.

[0038] Specifically, in the method for preparing the coating material for electric ceramic stoves according to the present invention, step 2 further includes: Receive risk map output from digital twin; Perform a thermally coupled cloud map rendering operation, and output the spatial thermal stress distribution visual signal.

[0039] The system receives a risk map output from a digital twin, including a mapping table of spatial coordinates and stress values; it performs a thermally coupled cloud map rendering operation, using RGB color mapping to convert stress values ​​into visual color levels; and it outputs a visual signal of spatial thermal stress distribution, with the highlighted areas in the signal corresponding to the coordinates of stress concentration.

[0040] Specifically, the method for preparing coating materials for electric ceramic stoves according to the present invention further includes step 1: inputting the acoustic emission signal collected by the piezoelectric transducer network to Kalman filtering processing; Kalman filtering is used to reduce the noise in the output transmitted signal. The noise-reduced transmitted signal input fusion operation generates the physical field observation matrix.

[0041] The original acoustic emission signal is input to a Kalman filter for processing. The filtering process uses state-space equations to suppress high-frequency noise. The output is a noise-reduced emission signal with a signal-to-noise ratio improved to the operating threshold. The noise-reduced emission signal is then input to a fusion operation to generate a physical field observation matrix synchronously with the temperature field data.

[0042] Specifically, in the method for preparing coating materials for electric ceramic stoves according to the present invention, step 3 further includes: performing a state space definition operation, wherein the state space is a dataset of coordinates of crystallization phase transition inflection points; Perform operations defined in the action space, which is a set of compensation vectors partitioned by the number of heating coils. The reinforcement learning policy is input into the state space dataset, and the policy outputs an action space partition compensation vector.

[0043] Perform state space definition operation: the crystallization phase transition inflection point coordinate dataset is the set of coordinates in the risk map where the second derivative of temperature is zero; perform action space definition operation: the dimension of the partition compensation vector is equal to the number of heating coils, and the vector components correspond to the coil power adjustment amount; the state space dataset is input into the reinforcement learning policy, and the policy outputs the action space partition compensation vector.

[0044] This invention addresses the microcrack problem caused by uncontrolled thermal distribution during sintering by constructing a closed-loop adaptive control system. First, it synchronously acquires spatial distribution data of the temperature field within the sintering chamber and acoustic emission signals from the ceramic matrix, fusing them to generate a physical field observation matrix. This matrix is ​​then input into a pre-trained digital twin, which analyzes and outputs predicted values ​​of the thermal gradient distribution and a material crystallization stress risk map, quantifying the thermodynamic state in real time. A reinforcement learning strategy fuses the risk map and acoustic emission spectrum features, identifying the coordinates of crystallization phase transition inflection points through spatial mapping, and outputting a partitioned power compensation vector data package. This data includes a nanosecond-level delay compensation factor to compensate for dynamic deviations in actuator response delay calculations based on historical data. The analyzed compensation vector data package drives a tunable magnetron array to generate interference heating beam geometry data, with the beam energy gradient matching the diffusion threshold of the coating precursor. The interference beam excites microwave non-thermal effects to lower the atomic migration barrier, prompting powder atoms to directionally fill the spatial coordinates of the microcrack gaps, ultimately outputting updated thermal stress distribution data and microcrack termination data. The technological logic forms a closed loop of monitoring-decision-execution-repair: dual-source monitoring of temperature and acoustic emission breaks through the limitations of single sensors; quantum-accelerated digital twins enable real-time prediction of thermodynamic behavior; reinforcement learning nanosecond delay compensation solves control lag; microwave interference matching material threshold synchronization eliminates macroscopic stress and repairs microscopic cracks.

[0045] In the preparation of coating materials for an electric ceramic furnace, this invention embeds a thermistor array on the sidewall of the sintering chamber. The array is distributed with a preset spatial resolution to collect real-time spatial distribution data of the temperature field. A piezoelectric transducer network is installed on the back of the ceramic substrate to capture acoustic emission signals in the 20-200kHz frequency band. After Kalman filtering to suppress high-frequency noise, the spatial distribution data of the temperature field and the acoustic emission signals are aligned with timestamps and spatial coordinates using a spatiotemporal registration algorithm, and then fused to generate a physical field observation matrix. The matrix's row vectors map the spatial grid coordinates of the chamber, and the column vectors record the coupling values ​​of temperature gradient and acoustic emission amplitude over time, providing a data carrier for thermodynamic behavior analysis.

[0046] The physical field observation matrix is ​​input into the pre-trained digital twin. The digital twin employs a quantum computing-accelerated architecture: matrix elements are encoded as qubit probability amplitudes, parameterized quantum gates perform unitary operator transformations on the partial differential equations of heat conduction, and output predicted values ​​of the thermal gradient distribution; in parallel channels, a deep convolutional neural network extracts wavelet envelope features of the acoustic emission signal, inputs them into the phase-field theory model to calculate the von Mises stress distribution, and generates a stress risk map for material crystallization. The map is visualized as a thermo-coupling cloud map, with red areas marking coordinates where the stress exceeds the material's yield threshold.

[0047] The reinforcement learning strategy receives risk maps and acoustic emission spectrum features. The acoustic emission spectrum features are extracted using a Fast Fourier Transform (FFT) to obtain the dominant frequency energy distribution, which is then correlated with the material's thermal expansion coefficient and phase transition temperature threshold. The strategy performs a spatial mapping operation to identify the coordinates of the crystallization phase transition inflection point in the risk map (where the second derivative of temperature is zero), and outputs a partitioned power compensation vector. Historical response delay data is input into the time series prediction model, dynamically calculating nanosecond-level delay compensation factors and embedding them into the vector to generate a compensation vector data packet. The data packet format includes vector components, delay factors, and a timestamp checksum.

[0048] The compensation vector data packet is parsed to extract the magnetron array driving parameters, including the fundamental frequency, harmonic intensity, and phase difference. These driving parameters control the tunable magnetron array, generating interferometric heating beam geometry data. The beam energy density gradient is matched to the activation energy threshold of the coating precursor (e.g., the alumina diffusion threshold), and waveguide interference is used to precisely cover high-temperature stress concentration areas by forming a spatial energy distribution.

[0049] Interferometric heating beam geometry data is applied to the coating precursor powder. Microwave non-thermal effects reduce the atomic diffusion activation energy, prompting alumina particles to migrate directionally along the microcrack boundaries. Atomic migration response data fills the microcrack gap space coordinates, simultaneously outputting updated thermal stress distribution data and microcrack termination data. Updated thermal stress distribution data verifies the elimination of macroscopic stress gradients, and microcrack termination data confirms defect repair through electron microscopy.

Claims

1. A method for preparing a coating material for an electric ceramic hob, characterized in that, The method comprises the following steps: Step 1, synchronously collect the temperature field spatial distribution data in the sintering chamber and the acoustic emission signal of the ceramic matrix, and use the collected data to construct a time and space physical field observation matrix; Step 2, input the physical field observation matrix constructed in step 1 into the pre-trained digital twin, analyze the physical field observation matrix through the digital twin, and output the thermal gradient distribution prediction value and the material crystallization stress risk map in parallel; Step 3, input the risk map and acoustic emission spectrum feature output in step 2 into the reinforcement learning strategy, execute spatial mapping to identify the crystallization phase change inflection point coordinate data, and output the partition power compensation vector data package, which includes nanosecond-level delay compensation factor; Step 4, receive the partition power compensation vector data package output in step 3, analyze the data package to extract the magnetron array driving parameter and diffusion threshold matching condition, and drive the tunable magnetron array to generate the interference heating beam geometric configuration data; Step 5, receive the interference heating beam energy distribution data output in step 4, apply it to the coating precursor powder atomic structure, fill the microcrack gap space coordinate data with atomic migration response data, and output the thermal stress distribution update data and the microcrack state termination data.

2. The method for preparing a coating material for an electric ceramic stove according to claim 1, characterized in that, Step 1 comprises: The thermosensitive sensor array collects and outputs the temperature field spatial distribution data; The piezoelectric transducer network collects and outputs the acoustic emission signal; Perform fusion operation to integrate the temperature field spatial distribution data and the acoustic emission signal, and the fusion operation outputs the physical field observation matrix.

3. The method for preparing a coating material for an electric ceramic stove according to claim 2, characterized in that, Step 2 comprises: Input the physical field observation matrix into the quantum variational algorithm; The quantum variational algorithm executes the thermal gradient distribution prediction calculation path optimization, and outputs the optimized calculation path parameters to the digital twin; The digital twin uses the optimized calculation path parameters to process the physical field observation matrix, and outputs the thermal gradient distribution prediction value and the material crystallization stress risk map.

4. The method for preparing a coating material for an electric ceramic stove according to claim 3, characterized in that, Step 3 comprises: Input the acoustic emission spectrum feature and the thermal expansion coefficient into the reinforcement learning strategy; The reinforcement learning strategy executes the acoustic emission spectrum feature and the thermal expansion coefficient fusion calculation, and calculates the partition power compensation vector; Implant the nanosecond-level delay compensation factor into the partition power compensation vector to generate the compensation vector data package.

5. The method for preparing a coating material for an electric ceramic stove according to claim 4, characterized in that, Step 3 further comprises: Input the response delay history data into the delay compensation calculation; The delay compensation calculation outputs the nanosecond-level delay compensation factor; The compensation factor is implanted into the partition power compensation vector to update the compensation vector data package.

6. The method for preparing a coating material for an electric ceramic hob according to claim 5, characterized in that, Step 4 comprises: Receive the compensation vector data package output in step 3; Analyze the data package to extract the magnetron array driving parameter; The driving parameter controls the tunable magnetron array to output the interference heating beam geometric configuration data matched with the coating diffusion threshold.

7. The method for preparing a coating material for an electric ceramic hob according to claim 6, characterized in that, Step 5 comprises: Receive the interference heating beam geometric configuration data output in step 4; Apply the beam data to the coating precursor powder to generate the microwave non-thermal effect; The microwave non-thermal effect excites the atomic migration response data to fill the microcrack gap space coordinate data, and outputs the thermal stress distribution update data.

8. The method for preparing a coating material for an electric ceramic hob according to claim 7, characterized in that, Step 2 further comprises: Receive the risk map output by the digital twin; Perform the thermal force coupling cloud map rendering operation, and the rendering operation outputs the spatial thermal stress distribution visual signal.

9. The method for preparing a coating material for an electric ceramic hob according to claim 8, characterized in that, Step 1 further comprises: Input the acoustic emission signal collected by the piezoelectric transducer network into the Kalman filter processing; The Kalman filter processing outputs a noise-reduced transmit signal; The noise-reduced transmit signal is input to a fusion operation to generate a physical field observation matrix.

10. The method for preparing a coating material for an electric ceramic hob according to claim 9, characterized in that, Step 3 further includes: performing a state space definition operation, the state space being a crystallization phase transition inflection point coordinate data set; performing an action space definition operation, the action space being a heating coil quantity dimension partitioned compensation vector set; the state space data set is input to a reinforcement learning policy, the policy outputting an action space partitioned compensation vector.

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