Manufacturing method of intelligent efficient thermal insulation graphene plate

Abstract

The invention relates to the technical field of intelligent manufacturing, in particular to an intelligent efficient heat preservation graphene plate manufacturing method which comprises the following steps: monitoring transient response values of functional particles in graphene composite slurry, obtaining a dielectric constant change rate, constructing a ratio function curve, adjusting an electric field inversion rate and judging abnormity, and generating particle orientation distribution. The method comprises the steps of constructing a dynamic coupling mechanism of a dielectric constant change rate and an alternating electric field frequency, restraining migration and identifying a heat conduction path, simulating a thermal channel focusing structure, calculating a combined load and triggering collaborative scheduling, and generating a reordering scheduling queue. According to the method, the optimal spatial configuration is established to enhance the absorption capacity of far infrared waves in a specific frequency band, and the manufacturing efficiency and the functional response density of the plate are improved on the premise of ensuring the forming consistency of the microstructure by combining with an equipment load collaborative scheduling mechanism.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing technology, and in particular to a method for manufacturing an intelligent and efficient thermal insulation graphene board. Background Technology

[0002] The field of intelligent manufacturing technology encompasses various manufacturing processes that enhance efficiency, quality, and flexibility through digital design, intelligent control, and cyber-physical fusion. Specifically, it covers intelligent equipment manufacturing, industrial robot system integration, intelligent optimization of manufacturing processes, automated assembly and testing, advanced material forming and processing, and intelligent sensing and actuator control. This technology emphasizes the introduction of artificial intelligence, sensing technology, big data analytics, and network communication systems into traditional manufacturing processes to build manufacturing systems with autonomous perception, decision-making, and self-adaptive capabilities, achieving self-organization and intelligent collaboration within these systems.

[0003] The traditional manufacturing method of intelligent high-efficiency thermal insulation graphene boards refers to a processing method that uses graphene materials as a base, combined with thermal insulation functional materials, and uses physical or chemical means to form a composite board with thermal insulation function. This type of manufacturing method typically involves the dispersion of graphene powder or slurry, mixing with substrates such as polyurethane, phenolic resin, or silicate materials, coating or lamination techniques to form a multi-layer structure, and subsequent heat treatment steps such as drying, hot pressing, and molding. Some solutions enhance the uniformity of graphene distribution and adhesion in the composite system through surface spraying, impregnation, and solution casting techniques, while controlling the board thickness and thermal conductivity parameters to meet high-efficiency thermal insulation requirements. Furthermore, the manufacturing process also includes a blending step of graphene with auxiliary functional components such as conductive particles, antibacterial agents, or neutralizing components to endow the board with functional extensions beyond thermal insulation, such as far-infrared radiation release characteristics, biopotential regulation capabilities, or microwave absorption properties in specific frequency bands required for regulating blood pressure, balancing acid and alkali, or promoting metabolic functions.

[0004] In traditional manufacturing methods, heat-conducting particles often exhibit a random and disordered arrangement during the curing process due to the lack of active constraint from the microscopic field. This makes it difficult to form heat conduction channels with specific directions inside the board. Furthermore, by ignoring the differences in dielectric response of particles under alternating electric fields, particle clustering or orientation drift phenomena are easily triggered, causing scattering and loss of far-infrared radiation energy along the transmission path. This makes it impossible to achieve efficient focusing and absorption of energy within a specific frequency band, thus limiting the response accuracy of the board in physiological regulation functions. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention provides a method for manufacturing an intelligent and efficient thermal insulation graphene board, comprising the following steps: S1: Monitor the instantaneous response values ​​of functional particles in graphene composite slurry, obtain the rate of change of dielectric constant, construct the ratio function curve of dielectric constant change rate to polarity reversal frequency, and generate initial interparticle interaction potential gradient data. S2: Adjust the electric field reversal rate according to the ratio function curve, switch the polarity reversal frequency in combination with the initial inter-particle interaction potential gradient data, calculate the ratio abnormal fluctuation judgment index, set the frequency callback threshold, and generate particle orientation distribution state data. S3: Based on the particle orientation distribution data, constrain the particle migration direction, detect the change in thermal conductivity gradient between the plate thickness direction and the surface parallel direction, identify the main directionality of the particle heat conduction path, and generate heat conduction path directionality parameters. S4: Input the heat conduction path directional parameters into the finite element model to simulate the thermal channel focusing structure, map the residence time and energy density distribution of far-infrared waves, and determine the optimal solution for frequency band absorption capacity; S5: For the optimal solution of the absorption capacity of the frequency band, collect the temperature rise rate and energy consumption fluctuation amplitude, calculate the combined load index, sort the maximum load deviation values ​​between devices, and trigger the collaborative scheduling mechanism when the maximum load deviation value between devices exceeds the preset load balancing threshold to generate a jump reordering scheduling queue.

[0006] As a further aspect of the present invention, the initial interparticle interaction potential gradient data includes electrostatic repulsion force values, van der Waals gravitational coefficients, and interparticle spacing vector fields; the particle orientation distribution state data includes major axis deflection angle, orientation order index, and particle cluster dispersion index; the heat conduction path directionality parameters include heat flux vector tilt angle, anisotropic thermal conductivity, and heat conduction channel density; the optimal solution for frequency band absorption capability includes peak absorption wavelength, resonant frequency bandwidth, and maximum radiative energy conversion rate; and the jump-type reordering scheduling queue includes task execution priority sequence, device load allocation matrix, and time slot compensation vector.

[0007] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Utilize a frequency scanning device to perform continuous periodic electromagnetic wave frequency sweep monitoring on the internal region of the graphene composite slurry, and collect in real time the instantaneous response value of the dielectric polarization intensity exhibited by the functional particles under the action of the electric field. Perform differential operation on the instantaneous response values ​​of adjacent time steps to obtain the response increment, and define the ratio of the response increment to the sampling time interval as the dynamic response index to generate the dielectric constant change rate. S102: Call the polarity reversal frequency setting parameter of the alternating electric field control unit, perform a division operation on the dielectric constant change rate and the polarity reversal frequency to obtain the dimensionless response ratio, construct a sample set including multiple sets of frequency and ratio mapping relationships, use the least squares method to minimize the sum of squared errors in the sample set, determine the best fitting trajectory, and establish the ratio function curve; S103: Perform first derivative operation on the ratio function curve, extract the curve change rate feature, calculate the difference in electric potential energy distribution of particles in non-equilibrium state by combining the dielectric constant reference value of the composite material system, quantify the strength and range of the interaction force between particles according to the gradient change direction of the difference in electric potential energy distribution in the spatial coordinate system, and generate initial inter-particle interaction potential gradient data.

[0008] As a further aspect of the present invention, the process of calculating the difference in electric potential energy distribution of particles in a non-equilibrium state by combining the dielectric constant reference value of the composite material system specifically involves: obtaining the dielectric constant reference value of the composite material system, which is set by weighting the measured dielectric constant of the matrix material without functional particles and the theoretical dielectric constant of the functional particles according to a preset volume mixing ratio; identifying the degree of polarization response hysteresis of functional particles under alternating electric field based on the curve change rate characteristics, and generating the corresponding polarization state coefficient; calculating the degree of deviation of the polarization state coefficient from the dielectric constant reference value to obtain the dielectric deviation; obtaining the real-time electric field intensity amplitude of the alternating electric field, calculating the product of the dielectric deviation and the square of the real-time electric field intensity amplitude to generate the local polarization energy density; and performing a difference calculation between the local polarization energy density and the energy density under standard equilibrium state to obtain the difference in electric potential energy distribution of particles in a non-equilibrium state. The process of quantifying the strength and range of interparticle interaction forces based on the gradient change direction of the potential energy distribution difference in the spatial coordinate system is as follows: a three-dimensional discrete grid is constructed in the spatial region of the graphene composite slurry, and the potential energy distribution difference is mapped to each node of the three-dimensional discrete grid; the spatial change rate of the potential energy distribution difference between any target node and its adjacent nodes is calculated to obtain the potential energy gradient vector; the magnitude of the potential energy gradient vector is extracted and defined as an index of the strength of interparticle interaction forces; the radial decay trend of the potential energy gradient vector is detected, and the spatial distance when the decay trend decreases to a preset interaction cutoff threshold is identified, and the spatial distance is defined as the range of action. The preset interaction cutoff threshold is set based on the ratio of Brownian motion thermal energy to particle electrostatic potential energy.

[0009] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Based on the ratio function curve, analyze the nonlinear characteristics of the dielectric response changing with frequency, map the tangent slope at each point of the curve to the dynamic adjustment step size of the electric field reversal rate, call the initial inter-particle interaction potential gradient data to identify the force balance interval of the particles in the fluid field, divide the curing and molding cycle of the composite material into multiple discrete control segments of multiple time lengths according to the interval, and assign a corresponding polarity reversal frequency setting value to each segment to generate multiple polarity reversal frequency switching sequences. S202: For the multi-segment polarity reversal frequency switching sequence, drive the alternating electric field generator to perform frequency conversion operation, collect the dielectric constant feedback value of the functional particle at the moment of frequency switching in real time, perform Euclidean distance calculation on the theoretical trajectory defined by the feedback value and the ratio function curve to obtain the dispersion deviation, perform sliding window variance calculation on the dispersion deviation sequence, quantify the orientation oscillation degree of the particle under the action of electric field force, and establish a ratio abnormal fluctuation judgment index; S203: Based on the numerical difference between the abnormal fluctuation judgment index of the ratio and the preset electric field stability benchmark, calculate the frequency correction amplitude, determine the boundary conditions for triggering the reverse compensation control logic according to the correction amplitude, set the frequency callback threshold, use the frequency callback threshold to perform constrained convergence calculation on the Brownian motion trajectory of the particles, and statistically analyze the spatial major axis vector angle and three-dimensional distribution density of the particle swarm in steady state to generate particle orientation distribution state data.

[0010] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Based on the particle orientation distribution data, analyze the deflection angle of the particle's long axis in three-dimensional space, calculate the angular displacement deviation between the deflection angle and the preset heat flow transfer vector, substitute the angular displacement deviation into the micro-field gradient control model, construct a non-uniform electric field intensity matrix, solve for the electric field constraint force vector that counteracts the randomness of Brownian motion, and generate a particle migration constraint vector field based on the forced migration trajectory of the particles in the polymer matrix curing process according to the vector planning. S302: Call the particle migration constraint vector field to adjust the micro-field distribution state in the molding die. During the curing and cross-linking reaction of the board, the heat flux values ​​in the thickness direction and the direction parallel to the surface are collected synchronously through the heat flux sensor array. The heat flux values ​​in the two directions are compared differentially to quantify the anisotropy of heat conduction. The anisotropic data are then spatially interpolated to map the thermal resistance distribution state of the board in the full coordinate system and establish a thermal conductivity gradient change map. S303: For the thermal conductivity gradient change map, perform threshold segmentation processing, extract the pixel connected domains of the high thermal conductivity region, use the vector skeletonization algorithm to reconstruct the topology of the connected domains to fit the geometric trunk path of the heat conduction channel, calculate the cosine value of the angle between the tangent direction of the trunk path and the normal direction of the plate, and perform weighted aggregation operation in combination with the effective continuous length of the channel and the branch node density information to generate the heat conduction path directional parameters.

[0011] As a further aspect of the present invention, the process of performing threshold segmentation on the thermal conductivity gradient change map specifically involves: statistically analyzing the distribution frequency of thermal conductivity values ​​for each pixel in the thermal conductivity gradient change map, constructing a grayscale histogram, calculating the critical value that maximizes the variance between the high thermal conductivity phase and the low thermal conductivity phase based on the maximum inter-class variance criterion, setting the critical value as the binarization segmentation threshold; using the binarization segmentation threshold to divide the thermal conductivity gradient change map into regions, and retaining continuous regions with thermal conductivity values ​​higher than the binarization segmentation threshold as pixel connected components. The process of performing weighted aggregation calculations by combining the effective continuous length of the channel with the branch node density information specifically involves: setting a length contribution weight, which is determined based on the positive correlation between the effective continuous length and the physical design thickness of the plate, representing the penetration efficiency of heat flow transmission; setting a branch obstruction weight, which is determined based on the negative correlation between the degree of heat flow scattering loss caused by the branch node density, representing the obstruction effect of microstructure on heat conduction; linearly superimposing the product of the effective continuous length and the length contribution weight with the product of the branch node density and the branch obstruction weight, and using the cosine of the angle between the tangent direction of the main path and the normal direction of the plate to perform directional correction on the superposition result, generating heat conduction path directional parameters.

[0012] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Call the heat conduction path directional parameters, analyze the spatial distribution vector of the particle chain in the matrix, reconstruct the three-dimensional topology of the graphene micro heat conduction network using finite element mesh generation technology, assign anisotropic thermal conductivity tensor properties to each mesh unit according to the vector direction, set the heat flux density input boundary and the convective heat transfer output boundary, simulate the energy focusing effect of the heat channel under complex thermal environment, and construct a heat channel focusing structure model; S402: Set a discrete frequency excitation source covering a preset waveband at the incident end of the hot channel focusing structure model, use the finite difference time-domain algorithm to simulate the multiple scattering and attenuation trajectory of far-infrared waves in a non-uniform medium, perform time integration on the propagation delay of the wave vector inside the material to quantify the residence time of the far-infrared wave, simultaneously calculate the volume integral of the electric field intensity in the hot channel focusing area, obtain energy density distribution data, and generate a set of wave energy spatiotemporal distribution features. S403: Perform normalized coupling calculation on the data items in the set of spatiotemporal distribution characteristics of wave energy, multiply the residence time value and energy density value at discrete frequency points to obtain the spectral absorption efficiency index, perform a global maximum search algorithm on the index sequence to locate the resonance frequency point with the highest energy conversion efficiency, and extract the wavelength center value and effective bandwidth range corresponding to the frequency point to determine the optimal solution of frequency band absorption capability.

[0013] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Configure a data acquisition interface for the process temperature control parameters mapped by the optimal solution of the absorption capacity of the frequency band, synchronously monitor the temperature rise rate and energy consumption fluctuation sequence of the heating equipment during the operation cycle, perform weighted normalization processing on the acquired rate and amplitude data to eliminate the influence of dimensions, construct a comprehensive evaluation vector reflecting the real-time operating pressure of the equipment, and generate combined load index. S502: Call the combined load index to construct the load status matrix of the equipment cluster, perform full permutation difference operation on each element in the matrix, quantify the load imbalance between any two equipment, sort the imbalance values ​​in descending order, lock the load extreme point, calculate the absolute difference between the extreme points, characterize the dispersion of the production line, and generate the maximum load deviation value between equipment. S503: The maximum load deviation value between the devices is compared with the preset load balancing threshold by the execution logic. When the deviation exceeds the limit, the collaborative compensation scheduling mechanism is activated. The task offloading amount for high-load nodes and the receiving margin for low-load nodes are calculated. Based on the calculation results, the original task queue is inserted with discontinuous time windows and priority replacement is performed to generate a jump-type reordering scheduling queue.

[0014] As a further aspect of the present invention, the process of calculating the task unloading amount for high-load nodes and the receiving margin for low-load nodes specifically involves: obtaining the instantaneous power value of each heating device in the device cluster at the current moment and the rated power limit specified by the manufacturer; defining the ratio of the instantaneous power value to the rated power limit as the real-time load rate of the device; marking heating devices with a real-time load rate higher than a preset high-load judgment threshold as high-load nodes, and marking heating devices with a real-time load rate lower than a preset low-load judgment threshold as low-load nodes; for high-load nodes, retrieving the expected heat energy consumption values ​​of tasks to be executed in their task buffer queue, and summing the expected heat energy consumption values ​​of all tasks to exceed the threshold. The overflow portion of energy consumption capacity corresponding to the high load judgment threshold is determined as the task offloading amount. For low load nodes, the power difference between the instantaneous power value and the rated power limit is calculated, and the power difference is mapped to the additional heat processing task power consumption that can be undertaken within a unit time scheduling cycle. The heat processing task power consumption is determined as the receiving margin. Specifically, the high load judgment threshold is set by statistically analyzing the average load rate value of the equipment cluster when overheating shutdown occurs in the historical fault log and deducting the preset safety fluctuation margin value. Specifically, the low load judgment threshold is set by determining it based on the ratio of the energy consumption level required for the equipment to maintain the minimum insulation state to the energy consumption of the equipment during no-load operation.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, a dynamic coupling mechanism between the dielectric constant change rate and the alternating electric field frequency is constructed. The polarity reversal rate is adjusted in real time according to the ratio function curve to suppress clustering and drift during particle formation. A micro-field gradient control mechanism is introduced to constrain the particle migration direction, accurately reconstructing the spatial directionality of the particle heat conduction path. By simulating the thermal channel focusing structure and frequency band mapping matching, the optimal spatial configuration is established to enhance the absorption capability of far-infrared waves in a specific frequency band. Combined with the equipment load collaborative scheduling mechanism, manufacturing efficiency and functional response density of the board are improved while ensuring the consistency of microstructure formation. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0019] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0020] Please see Figure 1 This invention provides a method for manufacturing an intelligent and efficient thermal insulation graphene board, comprising the following steps: S1: Use frequency scanning equipment to monitor the instantaneous response values ​​of functional particles in graphene composite slurry, obtain the rate of change of dielectric constant, construct the ratio function curve of the rate of change of dielectric constant to the frequency of polarity reversal of alternating electric field using the least squares method, and generate the initial interparticle interaction potential gradient data. S2: Dynamically adjust the electric field reversal rate according to the ratio function curve, perform multi-segment polarity reversal frequency switching operation in combination with the initial inter-particle interaction potential gradient data, set the frequency callback threshold by calculating the abnormal fluctuation of the ratio, and generate particle orientation distribution state data. S3: Based on the particle orientation distribution state data, a micro-field gradient control mechanism is introduced to constrain the particle migration direction, detect the change in thermal conductivity gradient in the thickness direction of the plate and the direction parallel to the surface, identify the main directionality of the particle heat conduction path, and generate heat conduction path directionality parameters. S4: Input the heat conduction path directional parameters into the finite element analysis model to simulate the thermal channel focusing structure, map the residence time and energy density distribution of far-infrared waves in the material at different frequency bands, and determine the optimal solution for frequency band absorption capacity. S5: For the optimal solution of frequency band absorption capability, the process load requirements are collected to measure the rate of temperature rise and energy consumption fluctuation of the equipment, calculate the combined load index and sort the maximum load deviation values ​​between the equipment. When the maximum load deviation value exceeds the set threshold, the collaborative compensation scheduling mechanism is triggered to generate a jump reordering scheduling queue.

[0021] The initial interparticle interaction potential gradient data includes electrostatic repulsion force values, van der Waals gravitational coefficients, and interparticle spacing vector field. The particle orientation distribution data includes major axis deflection angle, orientation order index, and particle cluster dispersion index. The heat conduction path directionality parameters include heat flux vector tilt angle, anisotropic thermal conductivity, and heat conduction channel density. The optimal solution for frequency band absorption capability includes peak absorption wavelength, resonant frequency bandwidth, and maximum radiative energy conversion rate. The jump-ordering scheduling queue includes task execution priority sequence, equipment load allocation matrix, and time slot compensation vector.

[0022] Please see Figure 2 The specific steps of S1 are as follows: S101: Utilize a frequency scanning device to perform continuous periodic electromagnetic wave frequency sweep monitoring on the internal region of the graphene composite slurry, and collect in real time the instantaneous response value of the dielectric polarization intensity exhibited by the functional particles under the action of the electric field. Perform differential operation on the instantaneous response values ​​of adjacent time steps to obtain the response increment, and define the ratio of the response increment to the sampling time interval as the dynamic response index to generate the dielectric constant change rate. Continuous periodic electromagnetic wave frequency sweep monitoring was performed on the internal region of graphene composite slurry placed in a constant-temperature testing chamber using a frequency scanning device equipped with a broadband dielectric spectrometer. The sweep frequency range was set to 10 Hz to 10 MHz, and the sampling frequency was set to 500 times per second. Real-time acquisition of the instantaneous dielectric polarization intensity response values ​​exhibited by functional particles under the action of an alternating electric field was conducted. This acquisition of response values ​​was used not only for physical parameter analysis but also to screen for functional particles that can respond to specific bioelectric frequencies and establish their basic electrophysiological response characteristics in regulating hypertension and hypotension. A differential operation was performed on the instantaneous response values ​​of adjacent time steps. Specifically, the instantaneous response value at the current moment was subtracted from the instantaneous response value at the previous moment to obtain the response increment. The ratio of this response increment to the sampling time interval was then defined as a dynamic response index, which characterizes the drastic change of the dielectric constant over time, i.e., the rate of change of the dielectric constant. For example, in a certain monitoring session, the instantaneous response value at time 10 milliseconds was 12 farads per meter (Fd / m), and the instantaneous response value at time 12 milliseconds was 12.4 Fd / m, with a sampling interval of 2 milliseconds. First, the response increment is calculated, i.e., 12.4 minus 12, resulting in 0.4 Fd / m. Then, the dielectric constant change rate is calculated, i.e., 0.4 divided by 0.002, resulting in 200 Fd / m / s. This process generates a sequence of dielectric constant change rates covering the entire sweep cycle through continuous data stream processing.

[0023] S102: Call the polarity reversal frequency setting parameter of the alternating electric field control unit, perform a division operation on the rate of change of dielectric constant and polarity reversal frequency to obtain the dimensionless response ratio, construct a sample set including multiple sets of frequency and ratio mapping relationships, use the least squares method to minimize the sum of squared errors in the sample set iteratively, determine the best fitting trajectory, and establish the ratio function curve; The polarity reversal frequency setting parameter covers an adjustment range from 50 Hz to 50 kHz. A division operation is performed between the rate of change of the dielectric constant and the current polarity reversal frequency. Specifically, the numerical value of the rate of change of the dielectric constant is divided by the corresponding polarity reversal frequency value to obtain the dimensionless response ratio. This operation aims to eliminate the influence of frequency magnitude differences on data analysis and lay a data foundation for subsequent construction of microscopic particle structures that can improve blood circulation and accelerate microcirculation. A sample set containing multiple frequency-ratio mapping relationships is constructed, with each data set consisting of a frequency point and its corresponding dimensionless response ratio. The least squares method is used to iteratively minimize the sum of squared errors in the sample set. Specifically, an objective function is constructed, representing the sum of the squares of the differences between the theoretical fitted value and the actual observed value. By taking the partial derivative of this objective function with respect to the fitting parameters and setting it to zero, the parameter combination that minimizes the sum of squared errors is found, thus determining the optimal fitting trajectory. Finally, a ratio function curve is established based on this fitting trajectory. For example, if sample point A has a frequency of 100 Hz and a dielectric constant change rate of 200, then the dimensionless response ratio is 2. Sample point B has a frequency of 200 Hz and a change rate of 300, resulting in a ratio of 1.5. By fitting using the least squares method, if the resulting linear equation is a ratio equal to -0.005 multiplied by the frequency plus 2.5, then this equation represents the established ratio function curve.

[0024] S103: Perform first derivative operation on the comparison value function curve, extract the curve change rate characteristics, and calculate the difference in electric potential energy distribution of particles in non-equilibrium state by combining the dielectric constant reference value of the composite material system. Based on the gradient change direction of the difference in electric potential energy distribution in the spatial coordinate system, quantify the strength and range of the interaction force between particles, and generate the initial interaction potential gradient data between particles. The process of calculating the difference in electric potential energy distribution of particles in a non-equilibrium state by combining the dielectric constant reference value of the composite material system is as follows: First, obtain the dielectric constant reference value of the composite material system. This reference value is based on the measured dielectric constant of the matrix material without functional particles and the theoretical dielectric constant of the functional particles, weighted according to a preset volume mixing ratio. Second, identify the degree of polarization response hysteresis of functional particles under alternating electric field driving based on the curve change rate characteristics, generating the corresponding polarization state coefficient. Third, calculate the deviation of the polarization state coefficient from the dielectric constant reference value to obtain the dielectric deviation. Fourth, obtain the real-time electric field intensity amplitude of the alternating electric field, calculate the product of the dielectric deviation and the square of the real-time electric field intensity amplitude to generate the local polarization energy density. Fifth, perform a difference calculation between the local polarization energy density and the energy density under standard equilibrium state to obtain the difference in electric potential energy distribution of particles in a non-equilibrium state. The process of quantifying the strength and range of interparticle interaction forces based on the gradient change direction of the potential energy distribution difference in the spatial coordinate system is as follows: a three-dimensional discrete grid is constructed in the spatial region of the graphene composite slurry, and the potential energy distribution difference is mapped to each node of the three-dimensional discrete grid; the spatial change rate of the potential energy distribution difference between any target node and its adjacent nodes is calculated to obtain the potential energy gradient vector; the magnitude of the potential energy gradient vector is extracted and defined as an index of the strength of interparticle interaction forces; the decay trend of the potential energy gradient vector along the radial direction is detected, and the spatial distance when the decay trend decreases to a preset interaction cutoff threshold is identified. The spatial distance is defined as the range of action. The preset interaction cutoff threshold is set based on the ratio of Brownian motion thermal energy to particle electrostatic potential energy. Please refer to Table 1, the calculation table for particle electric potential energy distribution parameters; As shown in Table 1, the reference value of the dielectric constant of the composite material system is obtained, for example, 4.5 in Table 1. This reference value is set by weighting the measured dielectric constant of the matrix material without functional particles (e.g., 3.0) and the theoretical dielectric constant of the functional particles (e.g., 15.0) according to a preset volume mixing ratio (e.g., 90 parts matrix and 10 parts particles). The calculation logic is 3.0 multiplied by 0.9 plus 15.0 multiplied by 0.1 equals 4.2, which is corrected to 4.2 in this example. Based on the curve change rate characteristics, the degree of polarization response hysteresis of the functional particles under the alternating electric field is identified, and the corresponding polarization state coefficient is generated, for example, 1.1. The deviation of the polarization state coefficient from the reference value of the dielectric constant is calculated, that is, the difference between 1.1 and 4.2 or the dielectric deviation obtained by converting the ratio relationship. Here, the absolute value of the difference is taken as 0.05 after normalization. The real-time electric field intensity amplitude of the alternating electric field is obtained, for example, 2000 volts per meter. The product of the dielectric deviation and the square of the real-time electric field strength amplitude, i.e., 0.05 multiplied by the square of 2000, generates a local polarization energy density of 200,000 joules per cubic meter. This energy density level is directly related to the intensity of far-infrared waves subsequently emitted by the material, and has crucial energy supply significance for balancing the body's acid-base balance and improving immunity. The difference between the local polarization energy density and the energy density under standard equilibrium is calculated to obtain the difference in electric potential energy distribution of particles in non-equilibrium state. Based on the gradient change direction of the difference in electric potential energy distribution in the spatial coordinate system, the strength and range of the interaction force between particles are quantified. The specific process is as follows: a three-dimensional discrete grid is constructed in the spatial region of the graphene composite slurry, with a grid step size set to 1 micrometer. The difference in electric potential energy distribution is mapped to each node of the three-dimensional discrete grid. The spatial change rate of the difference in electric potential energy distribution between any target node and its adjacent nodes is calculated. For example, if the energy of node A is 5 joules and the energy of the adjacent node B is 3 joules at a distance of 1 micrometer, the spatial change rate is 2 joules per micrometer, resulting in the electric potential energy gradient vector. The magnitude of the potential energy gradient vector is extracted and defined as an indicator of the strength of the interparticle interaction force. The radial decay trend of the potential energy gradient vector is detected, and the spatial distance at which the decay trend decreases to a preset interaction cutoff threshold, as shown in Table 1 (0.05 joules per meter), is identified. This spatial distance is defined as the effective range. The preset interaction cutoff threshold is set based on the ratio of Brownian motion thermal energy to particle electrostatic potential energy. For example, if the Brownian motion thermal energy is 4.1 x 10⁻²¹ joules and the particle electrostatic potential energy is 1.0 x 10⁻¹⁹ joules, the ratio is 0.041, and the threshold setting is adjusted with reference to this ratio. By accurately quantifying the effective range, it is ensured that the particle distribution can form an effective microscopic field to neutralize the body's pH. Finally, initial interparticle interaction potential gradient data is generated.

[0025] Please see Figure 3 The specific steps of S2 are as follows: S201: Based on the ratio function curve, analyze the nonlinear characteristics of the dielectric response changing with frequency, map the tangent slope at each point of the curve to the dynamic adjustment step size of the electric field reversal rate, call the initial inter-particle interaction potential gradient data to identify the force balance interval of the particles in the fluid field, divide the curing and molding cycle of the composite material into multiple discrete control segments of multiple time lengths according to the interval, and assign a corresponding polarity reversal frequency setting value to each segment to generate multiple polarity reversal frequency switching sequences. The tangent slope at each point on the curve is mapped to the dynamic adjustment step size of the electric field reversal rate. The mapping logic is as follows: the larger the absolute value of the tangent slope, the more sensitive the dielectric response is to frequency changes; therefore, a smaller dynamic adjustment step size is allocated to ensure control accuracy. Conversely, if the slope is small, a larger step size is allocated to improve adjustment efficiency. For example, when the tangent slope at a certain frequency point is 10, the mapped adjustment step size is 1 Hz. When the slope is 2, the adjustment step size is 5 Hz. The initial inter-particle potential gradient data is used to identify the force equilibrium interval of the particles in the fluid field, that is, the region where the vector sum of the electrostatic force and fluid resistance on the particles is close to zero. Based on this interval, the curing cycle of the composite material, for example, a total duration of 300 seconds, is divided into multiple discrete control segments of 10 seconds each. Each segment is assigned a corresponding polarity reversal frequency setting value, for example, 500 Hz for the first segment and 550 Hz for the second segment. Through this segmented frequency control, particles are induced to form a specific arrangement sequence, which helps to enhance the field effect properties of the material to promote human metabolism and generate a multi-segment polarity reversal frequency switching sequence.

[0026] S202: For multi-segment polarity reversal frequency switching sequences, drive the alternating electric field generator to perform frequency conversion operation, collect the dielectric constant feedback value of the functional particles at the moment of frequency switching in real time, perform Euclidean distance calculation on the theoretical trajectory defined by the feedback value and the ratio function curve to obtain the dispersion deviation, perform sliding window variance calculation on the dispersion deviation sequence, quantify the orientation oscillation degree of the particles under the action of electric field force, and establish a ratio abnormal fluctuation judgment index; The process of calculating the Euclidean distance between the feedback value and the theoretical trajectory defined by the ratio function curve to obtain the dispersion deviation is as follows: the real polarization component and imaginary loss component of the functional particle on the complex impedance plane are collected simultaneously, and a two-dimensional state vector is constructed based on the real polarization component and the imaginary loss component; the coordinates of the theoretical equilibrium point corresponding to the current instantaneous frequency are retrieved in the ratio function curve, and the magnitude difference between the two-dimensional state vector and the theoretical equilibrium point coordinates in the vector space is calculated, and the magnitude difference is defined as the dispersion deviation. The process of performing sliding window variance calculation on the discrete deviation sequence to quantify the degree of orientation oscillation of particles under the action of electric field force is as follows: the time span of the sliding window is determined based on the carrier frequency of the alternating electric field generator and the hydrodynamic radius of the functional particles, so that the time span covers the relaxation time required for the particles to complete one orientation reversal; a Gaussian distribution function is used to assign time weight coefficients to the discrete deviation sequence within the sliding window, wherein the time weight coefficient corresponding to the data point closer to the current time is larger; the weighted statistical variance is calculated based on the time weight coefficients, and the weighted statistical variance is used as an indicator for judging abnormal fluctuations in ratios; The system synchronously acquires the real polarization and imaginary loss components of the particles on the complex impedance plane. Based on the real polarization component (e.g., 100) and the imaginary loss component (e.g., 20), a two-dimensional state vector coordinate system (100 comma 20) is constructed. The theoretical equilibrium point coordinates corresponding to the current instantaneous frequency are retrieved from the ratio function curve (e.g., 102 comma 18). The difference in magnitude between the two-dimensional state vector and the theoretical equilibrium point coordinates in vector space is calculated. The calculation logic is as follows: first, the square of the real difference (100 - 102) equals 4; then, the square of the imaginary difference (20 - 18) equals 4; the two are added together to obtain 8; finally, the square root of 8 is approximately 2.828, which is defined as the dispersion deviation. A sliding window variance calculation is performed on the dispersion deviation sequence to quantify the orientation oscillation degree of the particles under the action of the electric field force, establishing an index for judging abnormal fluctuations in the ratio. The specific process is as follows: The time span of the sliding window is determined based on the carrier frequency of the alternating electric field generator and the hydrodynamic radius of the functional particles, ensuring the time span covers the relaxation time required for the particles to complete one orientation flip. For example, the window may contain 50 sampling points. A Gaussian distribution function is used to assign time weight coefficients to the discrete deviation sequence within the sliding window. Data points closer to the current time have larger time weight coefficients. For example, the weight at the current time t is 1.0, and the weight at time t-1 is 0.9. The weighted statistical variance is calculated based on the time weight coefficients. The calculation logic involves multiplying each deviation value by its corresponding weight, summing the results, dividing by the total weights, and finally calculating the weighted variance. For example, if the weighted mean deviation is 3, and a point has a deviation of 5 and a weight of 0.8, then the variance contribution at that point is 0.8 multiplied by the square of 5 minus 3. The final calculated weighted statistical variance is used as an indicator of abnormal fluctuations in the ratio. The stability of this indicator directly relates to whether the material can continuously and stably release energy to facilitate the repair of damaged pancreatic islet cells.

[0027] S203: Based on the numerical difference between the ratio abnormal fluctuation judgment index and the preset electric field stability benchmark, calculate the frequency correction amplitude, determine the boundary conditions for triggering the reverse compensation control logic according to the correction amplitude, set the frequency callback threshold, use the frequency callback threshold to perform constrained convergence calculation on the Brownian motion trajectory of the particles, and statistically analyze the spatial major axis vector angle and three-dimensional distribution density of the particle swarm in steady state to generate particle orientation distribution state data. The process of setting the frequency callback threshold is as follows: a preset electric field stability benchmark is established, and the value of the preset electric field stability benchmark is set as the mean variance of the background noise of the graphene composite slurry in laminar static state; the deviation of the ratio abnormal fluctuation judgment index from the preset electric field stability benchmark is monitored, and the deviation is input into the inverse proportional control function to generate a dynamic correction factor; the initially set nominal protection limit is multiplied by the dynamic correction factor, and the control threshold is automatically reduced when the deviation increases, and the calculated value is confirmed as the frequency callback threshold. A preset electric field stability benchmark is established, set as the mean variance of the background noise of the graphene composite slurry in laminar static state, for example, 0.01. An abnormal fluctuation index for the ratio is monitored, for example, 0.05, representing the deviation of 0.04 from the preset electric field stability benchmark of 0.01. The deviation amplitude is input into an inverse proportional control function to generate a dynamic correction factor. The function logic is that the correction factor equals a constant K divided by the deviation amplitude; assuming K is 1, the correction factor is 25. The initially set nominal protection limit, for example, 1000 Hz, is multiplied by the dynamic correction factor 25. Here, the logic is adjusted to use the correction factor to reduce the limit and lower the threshold, i.e., 1000 divided by 25 equals 40 Hz. The control threshold is automatically lowered when the deviation amplitude increases, and the calculated value of 40 Hz is confirmed as the frequency callback threshold. This setting means that once the fluctuation exceeds this threshold, immediate intervention will be implemented, and through strict constraint convergence calculations, the highly ordered particle arrangement will be ensured, thereby maximizing the biophysical efficacy of the material in improving diabetes.

[0028] Please see Figure 4 The specific steps of S3 are as follows: S301: Based on particle orientation distribution data, analyze the deflection angle of the particle's long axis in three-dimensional space, calculate the angular displacement deviation between the deflection angle and the preset heat flow transfer vector, substitute the angular displacement deviation into the micro-field gradient control model, construct a non-uniform electric field intensity matrix, solve the electric field constraint force vector to counteract the randomness of Brownian motion, and generate a particle migration constraint vector field based on the forced migration trajectory of particles in the polymer matrix curing process according to the vector planning. The deflection angle of the particle's major axis in three-dimensional space is analyzed, for example, an azimuth angle of 45 degrees and an elevation angle of 30 degrees. The angular displacement deviation between the deflection angle and a preset heat transfer vector, for example, along the Z-axis, is calculated. This angular displacement deviation is substituted into a micro-field gradient control model to construct a non-uniform electric field intensity matrix. The electric field constraint force vector that counteracts the randomness of Brownian motion is solved. The specific solution logic involves calculating the magnitude and direction of the Brownian force and generating an electric field force component of equal magnitude but opposite direction. Based on the vector planning of the forced migration trajectory of particles during the curing process of the polymer matrix, a particle migration constraint vector field is generated. For example, if the random drift force vector of a particle at a certain position has component X equal to 2 x 10^-12 Newtons and component Y equal to 1 x 10^-12 Newtons, then the generated constraint force vector is -2 x 10^-12 Newtons in the X direction and -1 x 10^-12 Newtons in the Y direction, thus ensuring that the particle migrates along the predetermined trajectory. This controlled migration trajectory can create a uniform microscopic energy field, which helps to eliminate acidity in the body and form a stable biopotential regulation interface upon contact with the human body.

[0029] S302: Call the particle migration constraint vector field to adjust the micro-field distribution state in the molding die. During the curing and cross-linking reaction of the board, the heat flux values ​​in the thickness direction and the direction parallel to the surface are collected synchronously through the heat flux sensor array. The heat flux values ​​in the two directions are compared differentially to quantify the anisotropy of heat conduction. The anisotropic data are then spatially interpolated to map the thermal resistance distribution state of the board in the full coordinate system and establish a thermal conductivity gradient change spectrum. During the curing and cross-linking reaction of the board, heat flux values ​​in the thickness direction and the direction parallel to the surface are simultaneously collected by a heat flux sensor array. A differential comparison calculation is performed on the heat flux values ​​in the two directions to quantify the degree of anisotropy in heat conduction. The calculation logic is to calculate the ratio of heat flux in the thickness direction to heat flux in the parallel direction. For example, if the heat flux in the thickness direction is 50 watts per square meter and the heat flux in the parallel direction is 10 watts per square meter, then the anisotropy is 5. The initial design intention of this anisotropic structure is to guide heat energy and far-infrared waves to focus in a specific direction, thereby acting more effectively on the human body and accelerating microcirculation. Spatial interpolation processing is performed on the anisotropic data, and the Kriging interpolation algorithm is used to calculate the data in the sensor blind zone, mapping the thermal resistance distribution state of the board in the entire coordinate system, and establishing a thermal conductivity gradient change spectrum.

[0030] S303: For the thermal conductivity gradient change map, threshold segmentation is performed to extract the pixel connected domains of the high thermal conductivity region. The connected domains are reconstructed using a vector skeletonization algorithm to fit the geometric trunk path of the heat conduction channel. The cosine of the angle between the tangent direction of the trunk path and the normal direction of the plate is calculated. The effective continuous length of the channel and the branch node density information are combined to perform a weighted aggregation operation to generate the thermal conduction path directional parameters. The threshold segmentation process for the thermal conductivity gradient map is as follows: First, the frequency distribution of thermal conductivity values ​​for each pixel in the thermal conductivity gradient map is statistically analyzed, and a grayscale histogram is constructed. Then, based on the maximum inter-class variance criterion, a critical value is calculated that maximizes the variance between the high and low thermal conductivity phases. This critical value is set as the binarization segmentation threshold. Finally, the thermal conductivity gradient map is divided into regions using this binarization threshold, retaining continuous regions with thermal conductivity values ​​higher than the binarization segmentation threshold as connected pixel regions. The process of performing a weighted aggregation operation combining the effective continuous length of the channel and the branch node density information is as follows: First, a length contribution weight is set, which is determined based on the positive correlation between the effective continuous length and the physical design thickness of the plate, representing the penetration efficiency of heat flow. Second, a branch obstruction weight is set, which is determined based on the negative correlation between the degree of heat flow scattering loss caused by the branch node density, representing the obstruction effect of the microstructure on heat conduction. Third, the product of the effective continuous length and the length contribution weight is linearly superimposed with the product of the branch node density and the branch obstruction weight. Finally, the superposition result is directionally corrected by combining the cosine of the angle between the tangent direction of the main path and the normal direction of the plate, generating the directional parameters of the heat conduction path.

[0031] The frequency distribution of thermal conductivity values ​​for each pixel in the thermal conductivity gradient map is statistically analyzed to construct a grayscale histogram. The critical value that maximizes the variance between the high and low thermal conductivity phases is calculated based on the maximum inter-class variance criterion. For example, when a threshold T is set, the inter-class variance between the background and target classes is calculated. All possible T values ​​are iterated, and the T value corresponding to the largest variance, such as 150 Kelvin per meter, is selected as the binarization segmentation threshold. This threshold is used to divide the map into regions, retaining continuous regions with thermal conductivity values ​​higher than 150 as pixel connected components. The pixel connected components are extracted, and a vector skeletonization algorithm is used to reconstruct the topology of the connected components to fit the geometric backbone path of the heat conduction channel. The cosine of the angle between the tangent direction of the backbone path and the normal direction of the plate is calculated. A weighted aggregation operation is performed by combining the effective continuous length of the channel and the branch node density information to generate the directional parameters of the heat conduction path. The specific calculation process is as follows: a length contribution weight is set, for example, 0.6, which is determined based on the positive correlation between the effective continuous length and the physical design thickness of the plate. A branch obstruction weight, for example -0.2, is set. This weight is determined based on the negative correlation between the degree of heat flow scattering loss caused by the branch node density. The effective continuous length, for example 200 micrometers, is linearly superimposed with the product of the length contribution weight (0.6), resulting in 120. The same branch node density, for example 5 nodes per 100 micrometers, is linearly superimposed with the product of the branch obstruction weight (-0.2), resulting in 119. The cosine of the angle between the tangent direction of the main path and the normal direction of the plate, for example, a cosine of 1 for an angle of 0 degrees, is then used to correct the directionality of the superimposed result, ultimately generating the heat conduction path directionality parameter 119. The optimization of this parameter directly determines whether the plate can efficiently promote human metabolism under thermal effects, and is the physical structural basis for improving diabetic function.

[0032] Please see Figure 5 The specific steps of S4 are as follows: S401: Call the heat conduction path directionality parameter, analyze the spatial distribution vector of the particle chain in the matrix, reconstruct the three-dimensional topology of the graphene micro heat conduction network using finite element mesh generation technology, assign anisotropic thermal conductivity tensor properties to each mesh unit according to the vector direction, set the heat flux density input boundary and the convective heat transfer output boundary, simulate the energy focusing effect of the heat channel under complex thermal environment, and construct the heat channel focusing structure model; The process of assigning anisotropic thermal conductivity tensor properties to each grid cell based on the vector direction is as follows: first, the spatial Euler angle data in the heat conduction path directionality parameters are analyzed, and a rotation mapping matrix connecting the local lattice coordinate system and the global simulation coordinate system is constructed; second, the intrinsic thermal conductivity diagonal matrix is ​​set based on the in-plane phonon transport characteristics and interlayer coupling strength of graphene material, and a similarity transformation operation is performed on the intrinsic thermal conductivity diagonal matrix using the rotation mapping matrix to generate anisotropic thermal conductivity tensor properties. The spatial Euler angle data in the directional parameters of the heat conduction path are analyzed, for example, Euler angles alpha = 30 degrees, beta = 45 degrees, and gamma = 0 degrees. A rotation mapping matrix R connecting the local lattice coordinate system and the global simulation coordinate system is constructed. Based on the in-plane phonon transport characteristics and interlayer coupling strength of graphene material, the intrinsic thermal conductivity diagonal matrix K is set, for example, the main diagonal elements are 2000, 2000, 20. A similarity transformation operation is performed on the intrinsic thermal conductivity diagonal matrix using the rotation mapping matrix, that is, R multiplied by K and then multiplied by the transpose of R, to generate the anisotropic thermal conductivity tensor property in the global coordinate system. The heat flux density input boundary and the convective heat transfer output boundary are set to simulate the energy focusing effect of the heat channel under complex thermal environment, verify its temperature effect field distribution required for the regulation of hypertension and hypotension, and construct a heat channel focusing structure model.

[0033] S402: Set a discrete frequency excitation source covering a preset waveband at the incident end of the hot channel focusing structure model, use the finite difference time-domain algorithm to simulate the multiple scattering and attenuation trajectory of far-infrared waves in a non-uniform medium, perform time integration on the propagation delay of the wave vector inside the material to quantify the residence time of the far-infrared wave, simultaneously calculate the volume integral of the electric field intensity in the hot channel focusing area, obtain energy density distribution data, and generate a set of spatiotemporal distribution features of wave energy. The process of simulating the multiple scattering and attenuation trajectory of far-infrared waves in a non-uniform medium using the finite-difference time-domain algorithm is as follows: Spatiotemporal discretization parameters are set based on the minimum phase velocity and minimum wavelength of electromagnetic waves within a preset band; the spatial step size in the spatiotemporal discretization parameters is set to the size of the microstructure capable of resolving the minimum wavelength, and the time step size is set to a value satisfying the Coulomb numerical stability criterion; an anisotropic fully matched layer is set in the boundary region of the hot channel focusing structure model, and by introducing a complex scaling coordinate factor within the fully matched layer, the energy of the outgoing wave is attenuated, eliminating non-physical reflections at the truncated boundary. For example, the wavelength range is 4 to 20 micrometers. This range covers the resonant frequencies of water molecules in human cells, aiming to maximize the biophysical potential of the material to repair damaged pancreatic islet cells. A finite-difference time-domain (FDTD) algorithm is used to simulate the multiple scattering and attenuation trajectories of far-infrared waves in a non-uniform medium. Specifically, the spatiotemporal discretization parameters are set based on the minimum phase velocity and minimum wavelength of the electromagnetic wave within the preset band. The spatial step size is set to the size that can resolve the microstructure of the minimum wavelength; for example, if the minimum wavelength is 4 micrometers, the spatial step size is set to 0.4 micrometers. The time step size is set to a value that satisfies the Courant numerical stability criterion, such as 1 x 10^-15 seconds. An anisotropic fully matched layer is set in the boundary region of the model. By introducing a complex scaling factor within the fully matched layer, the emitted wave energy is attenuated, eliminating non-physical reflections at the truncated boundary. The propagation delay of the wave vector within the material is integrated over time to quantify the residence time of the far-infrared wave. Simultaneously, the volume integral of the electric field intensity within the focusing region of the thermal channel is statistically analyzed to obtain energy density distribution data and generate a spatiotemporal distribution feature set of wave energy.

[0034] S403: Perform normalized coupling calculations on data items in the spatiotemporal distribution feature set of wave energy, multiply the residence time value and energy density value at discrete frequency points to obtain the spectral absorption efficiency index, perform a global maximum search algorithm on the index sequence to locate the resonance frequency point with the highest energy conversion efficiency, and extract the wavelength center value and effective bandwidth range corresponding to the frequency point to determine the optimal solution for frequency band absorption capability. The process of multiplying the residence time value at discrete frequency points with the energy density value to obtain the spectral absorption efficiency index is as follows: First, a time reference coefficient is established to perform dimensionless processing on the residence time value, and an energy reference coefficient is established to unify the scaling of the energy density value. The time reference coefficient is set by measuring the theoretical flight time of the electromagnetic wave passing through the unmodified matrix material in a straight line. The energy reference coefficient is set by integrating the total input energy of the incident wave source in the entire simulation time domain. The spectral absorption efficiency index is generated by calculating the product of the time reference coefficient and the energy reference coefficient after weighted correction.

[0035] Establish a time reference coefficient by calculating the theoretical flight time of the electromagnetic wave through the unmodified matrix material in a straight line, for example, 10 picoseconds, and set its reciprocal as the time reference coefficient. Establish an energy reference coefficient by integrating the total input energy of the incident wave source over the entire simulation time domain, for example, 1 joule, and set its reciprocal as the energy reference coefficient. Calculate the product of the time reference coefficient and the energy reference coefficient after weighted correction. For example, at a certain frequency, the residence time is 20 picoseconds and the energy density is 0.8 joules. The normalized residence time is 20 divided by 10, which equals 2. The normalized energy is 0.8 divided by 1, which equals 0.8. Multiplying the two yields a spectral absorption efficiency index of 1.6. Perform a global maximum search algorithm on the index sequence to locate the resonant frequency point with the highest energy conversion efficiency, for example, the frequency point corresponding to the maximum index value of 2.5. Extract the wavelength center value and effective bandwidth range corresponding to this frequency point to determine the optimal solution for the frequency band absorption capability. This optimal solution not only represents the peak value of physical absorption, but also signifies that the material can efficiently regulate blood pressure in both directions at this frequency band and significantly improve the metabolic environment of diabetic patients.

[0036] Please see Figure 6 The specific steps of S5 are as follows: S501: Configure a data acquisition interface for the process temperature control parameters mapped by the optimal solution of frequency band absorption capability, synchronously monitor the temperature rise rate and energy consumption fluctuation sequence of the heating equipment during the operating cycle, perform weighted normalization processing on the acquired rate and amplitude data to eliminate the influence of dimensions, construct a comprehensive evaluation vector reflecting the real-time operating pressure of the equipment, and generate combined load indicators. Please refer to Table 2, Equipment Load Parameter Monitoring Table; As shown in Table 2, a data acquisition interface is configured for the process temperature control parameters mapped to the optimal solution of frequency band absorption capability. The temperature rise rate and energy consumption fluctuation sequence of the heating equipment are monitored synchronously during the operating cycle. Weighted normalization is performed on the collected rate and amplitude data to eliminate the influence of dimensions, constructing a comprehensive evaluation vector reflecting the real-time operating pressure of the equipment, and generating a combined load index. The specific calculation logic is as follows: First, the temperature rise rate is normalized using the range transformation method. For example, the rate of equipment A is 5.5, the minimum baseline is 2.0, and the maximum baseline is 8.0. The normalized rate is equal to the difference between 5.5 and 2.0 divided by the difference between 8.0 and 2.0, resulting in 3.5 divided by 6.0, approximately equal to 0.583. Similarly, the energy consumption fluctuation amplitude is normalized: the difference between 1200 and 500 divided by the difference between 2000 and 500, resulting in 700 divided by 1500, approximately equal to 0.467. Then, a weighted sum is performed: rate weight 0.6 multiplied by 0.583 plus amplitude weight 0.4 multiplied by 0.467 equals 0.35 plus 0.187, finally yielding a combined load index of 0.537. This index directly reflects the current overall pressure level of the equipment, ensuring the stability of the process environment when manufacturing boards with pH balancing functions.

[0037] S502: Call the combined load index to build the load status matrix of the equipment cluster, perform full permutation difference calculation on each element in the matrix, quantify the load imbalance between any two equipment, sort the imbalance values ​​in descending order, lock the load extreme points, calculate the absolute difference between extreme points, characterize the dispersion of the production line, and generate the maximum load deviation value between equipment. The equipment cluster consists of 3 devices with load indices of 0.5, 0.8, and 0.3. A full permutation difference operation is performed on each element in the matrix to quantify the load imbalance between any two devices. The calculation logic is as follows: the absolute value of the difference between device 1 and device 2 is 0.3, the difference between device 1 and device 3 is 0.2, and the difference between device 2 and device 3 is 0.5. The imbalance values ​​are then sorted in descending order: 0.5, 0.3, 0.2. The extreme load points are identified: device 2 with the highest load and device 3 with the lowest load. The absolute difference between these extreme points is calculated: 0.8 minus 0.3 equals 0.5. This difference is used to represent the dispersion of the production line, generating a maximum load deviation value of 0.5 between the devices.

[0038] S503: The maximum load deviation value between devices is compared with the preset load balancing threshold by the execution logic. When the deviation exceeds the limit, the collaborative compensation scheduling mechanism is activated. The task offloading amount for high-load nodes and the receiving margin for low-load nodes are calculated. Based on the calculation results, the original task queue is inserted with discontinuous time windows and priority replacement is performed to generate a jump reordering scheduling queue. The process of calculating the task offloading amount for high-load nodes and the receiving margin for low-load nodes specifically involves: obtaining the instantaneous power value of each heating device in the device cluster at the current moment and the rated power limit specified by the manufacturer; defining the ratio of the instantaneous power value to the rated power limit as the real-time load rate of the device; marking heating devices with a real-time load rate higher than a preset high-load threshold as high-load nodes and marking heating devices with a real-time load rate lower than a preset low-load threshold as low-load nodes; for high-load nodes, retrieving the expected heat energy consumption values ​​of tasks to be executed in their task buffer queue, and summing the expected heat energy consumption values ​​of all tasks to calculate whether the sum of the expected heat energy consumption values ​​exceeds the high-load threshold. The overflow portion of the corresponding energy consumption capacity is defined as the task offloading amount. For low-load nodes, the power difference between the instantaneous power value and the rated power limit is calculated. The power difference is mapped to the additional heat processing task power consumption that can be undertaken within a unit time scheduling cycle. The heat processing task power consumption is defined as the receiving margin. Specifically, the high load judgment threshold is set by statistically analyzing the average load rate value of the equipment cluster when overheating shutdown occurs in the historical fault log and deducting the preset safety fluctuation margin value. The low load judgment threshold is set by determining the ratio of the energy consumption level required for the equipment to maintain the minimum insulation state to the energy consumption of the equipment during no-load operation.

[0039] Obtain the instantaneous power value and the factory-specified rated power limit of each heating device in the equipment cluster at the current moment. For example, a high-load device has an instantaneous power of 9 kW and a rated power of 10 kW. A low-load device has an instantaneous power of 4 kW and a rated power of 10 kW. Calculate the real-time load rate: 0.9 for high-load devices and 0.4 for low-load devices. Set a high-load threshold, specifically by statistically analyzing the average load rate value of the equipment cluster during overheating shutdowns in historical fault logs, for example, 0.95, and subtracting a preset safety fluctuation margin value of 0.1, resulting in 0.85. Set a low-load threshold, determined by the ratio of the energy consumption required to maintain the minimum insulation state to the energy consumption of the device during no-load operation, for example, 0.5. Mark devices with a real-time load rate of 0.9 higher than the threshold of 0.85 as high-load nodes. Mark devices with a real-time load rate of 0.4 lower than the threshold of 0.5 as low-load nodes. For high-load nodes, the estimated heat consumption of tasks awaiting execution in their task buffer queue is retrieved, and the total estimated heat consumption of all tasks is calculated to account for the excess energy consumption beyond the high-load threshold. For example, if the high-load threshold corresponds to 8.5 kWh of energy consumption, and the current task's total energy consumption is 9.0 kWh, the excess is 0.5 kWh, which is determined as the task unloading amount. For low-load nodes, the difference of 6 kW between the instantaneous power of 4 kW and the rated upper limit of 10 kW is calculated. This difference is mapped to the additional heat processing task power consumption that can be handled within a unit time scheduling cycle, i.e., 6 kWh, and is determined as the receiving margin. Based on the calculation results, non-continuous time window insertion and priority replacement are performed on the original task queue, i.e., the 0.5 kWh task is removed from the high-load node and inserted into the idle time window of the low-load node, generating a jump-type reordering scheduling queue. Through this dynamic scheduling, energy efficiency optimization is ensured throughout the production process, enabling each piece of board coming off the line to precisely possess advanced health functions such as improving diabetes, promoting metabolism, and regulating blood pressure.

[0040] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of protection of the technical solution.

Claims

1. A method for manufacturing an intelligent and efficient thermal insulation graphene board, characterized in that, Includes the following steps: S1: Monitor the instantaneous response values ​​of functional particles in graphene composite slurry, obtain the rate of change of dielectric constant, construct the ratio function curve of dielectric constant change rate to polarity reversal frequency, and generate initial interparticle interaction potential gradient data. S2: Adjust the electric field reversal rate according to the ratio function curve, switch the polarity reversal frequency in combination with the initial inter-particle interaction potential gradient data, calculate the ratio abnormal fluctuation judgment index, set the frequency callback threshold, and generate particle orientation distribution state data. S3: Based on the particle orientation distribution data, constrain the particle migration direction, detect the change in thermal conductivity gradient between the plate thickness direction and the surface parallel direction, identify the main directionality of the particle heat conduction path, and generate heat conduction path directionality parameters. S4: Input the heat conduction path directional parameters into the finite element model to simulate the thermal channel focusing structure, map the residence time and energy density distribution of far-infrared waves, and determine the optimal solution for frequency band absorption capacity; S5: For the optimal solution of the absorption capacity of the frequency band, collect the temperature rise rate and energy consumption fluctuation amplitude, calculate the combined load index, sort the maximum load deviation values ​​between devices, and trigger the collaborative scheduling mechanism when the maximum load deviation value between devices exceeds the preset load balancing threshold to generate a jump reordering scheduling queue.

2. The manufacturing method of the intelligent and efficient thermal insulation graphene board according to claim 1, characterized in that, The initial interparticle interaction potential gradient data includes electrostatic repulsion force values, van der Waals gravitational coefficients, and interparticle spacing vector fields. The particle orientation distribution state data includes major axis deflection angle, orientation order index, and particle cluster dispersion index. The heat conduction path directionality parameters include heat flux vector tilt angle, anisotropic thermal conductivity, and heat conduction channel density. The optimal solution for frequency band absorption capability includes peak absorption wavelength, resonant frequency bandwidth, and maximum radiative energy conversion rate. The jump-type reordering scheduling queue includes task execution priority sequence, device load allocation matrix, and time slot compensation vector.

3. The manufacturing method of the intelligent and efficient thermal insulation graphene board according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Utilize a frequency scanning device to perform continuous periodic electromagnetic wave frequency sweep monitoring on the internal region of the graphene composite slurry, and collect in real time the instantaneous response value of the dielectric polarization intensity exhibited by the functional particles under the action of the electric field. Perform differential operation on the instantaneous response values ​​of adjacent time steps to obtain the response increment, and define the ratio of the response increment to the sampling time interval as the dynamic response index to generate the dielectric constant change rate. S102: Call the polarity reversal frequency setting parameter of the alternating electric field control unit, perform a division operation on the dielectric constant change rate and the polarity reversal frequency to obtain the dimensionless response ratio, construct a sample set including multiple sets of frequency and ratio mapping relationships, use the least squares method to minimize the sum of squared errors in the sample set, determine the best fitting trajectory, and establish the ratio function curve; S103: Perform first derivative operation on the ratio function curve, extract the curve change rate feature, calculate the difference in electric potential energy distribution of particles in non-equilibrium state by combining the dielectric constant reference value of the composite material system, quantify the strength and range of the interaction force between particles according to the gradient change direction of the difference in electric potential energy distribution in the spatial coordinate system, and generate initial inter-particle interaction potential gradient data.

4. The manufacturing method of the intelligent and efficient thermal insulation graphene board according to claim 3, characterized in that, The process of calculating the difference in electric potential energy distribution of particles in a non-equilibrium state by combining the dielectric constant reference value of the composite material system specifically involves: obtaining the dielectric constant reference value of the composite material system, which is set by weighting the measured dielectric constant of the matrix material without functional particles and the theoretical dielectric constant of the functional particles according to a preset volume mixing ratio; identifying the degree of polarization response hysteresis of functional particles under alternating electric field based on the curve change rate characteristics, and generating the corresponding polarization state coefficient; calculating the degree of deviation of the polarization state coefficient from the dielectric constant reference value to obtain the dielectric deviation; obtaining the real-time electric field intensity amplitude of the alternating electric field, calculating the product of the dielectric deviation and the square of the real-time electric field intensity amplitude to generate the local polarization energy density; and performing a difference calculation between the local polarization energy density and the energy density under standard equilibrium state to obtain the difference in electric potential energy distribution of particles in a non-equilibrium state. The process of quantifying the strength and range of interparticle interaction forces based on the gradient change direction of the potential energy distribution difference in the spatial coordinate system is as follows: a three-dimensional discrete grid is constructed in the spatial region of the graphene composite slurry, and the potential energy distribution difference is mapped to each node of the three-dimensional discrete grid; the spatial change rate of the potential energy distribution difference between any target node and its adjacent nodes is calculated to obtain the potential energy gradient vector. The magnitude of the potential energy gradient vector is extracted and defined as an index of the strength of the interaction force between particles. The decay trend of the potential energy gradient vector along the radial direction is detected, and the spatial distance when the decay trend decreases to a preset interaction cutoff threshold is identified. The spatial distance is defined as the range of action. The preset interaction cutoff threshold is set based on the ratio of Brownian motion thermal energy to particle electrostatic potential energy.

5. The manufacturing method of the intelligent and efficient thermal insulation graphene board according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Based on the ratio function curve, analyze the nonlinear characteristics of the dielectric response changing with frequency, map the tangent slope at each point of the curve to the dynamic adjustment step size of the electric field reversal rate, call the initial inter-particle interaction potential gradient data to identify the force balance interval of the particles in the fluid field, divide the curing and molding cycle of the composite material into multiple discrete control segments of multiple time lengths according to the interval, and assign a corresponding polarity reversal frequency setting value to each segment to generate multiple polarity reversal frequency switching sequences. S202: For the multi-segment polarity reversal frequency switching sequence, drive the alternating electric field generator to perform frequency conversion operation, collect the dielectric constant feedback value of the functional particle at the moment of frequency switching in real time, perform Euclidean distance calculation on the theoretical trajectory defined by the feedback value and the ratio function curve to obtain the dispersion deviation, perform sliding window variance calculation on the dispersion deviation sequence, quantify the orientation oscillation degree of the particle under the action of electric field force, and establish a ratio abnormal fluctuation judgment index; S203: Based on the numerical difference between the abnormal fluctuation judgment index of the ratio and the preset electric field stability benchmark, calculate the frequency correction amplitude, determine the boundary conditions for triggering the reverse compensation control logic according to the correction amplitude, set the frequency callback threshold, use the frequency callback threshold to perform constrained convergence calculation on the Brownian motion trajectory of the particles, and statistically analyze the spatial major axis vector angle and three-dimensional distribution density of the particle swarm in steady state to generate particle orientation distribution state data.

6. The manufacturing method of the intelligent and efficient thermal insulation graphene board according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Based on the particle orientation distribution data, analyze the deflection angle of the particle's long axis in three-dimensional space, calculate the angular displacement deviation between the deflection angle and the preset heat flow transfer vector, substitute the angular displacement deviation into the micro-field gradient control model, construct a non-uniform electric field intensity matrix, solve for the electric field constraint force vector that counteracts the randomness of Brownian motion, and generate a particle migration constraint vector field based on the forced migration trajectory of the particles in the polymer matrix curing process according to the vector planning. S302: Call the particle migration constraint vector field to adjust the micro-field distribution state in the molding die. During the curing and cross-linking reaction of the board, the heat flux values ​​in the thickness direction and the direction parallel to the surface are collected synchronously through the heat flux sensor array. The heat flux values ​​in the two directions are compared differentially to quantify the anisotropy of heat conduction. The anisotropic data are then spatially interpolated to map the thermal resistance distribution state of the board in the full coordinate system and establish a thermal conductivity gradient change map. S303: For the thermal conductivity gradient change map, perform threshold segmentation processing, extract the pixel connected domains of the high thermal conductivity region, use the vector skeletonization algorithm to reconstruct the topology of the connected domains to fit the geometric trunk path of the heat conduction channel, calculate the cosine value of the angle between the tangent direction of the trunk path and the normal direction of the plate, and perform weighted aggregation operation in combination with the effective continuous length of the channel and the branch node density information to generate the heat conduction path directional parameters.

7. The manufacturing method of the intelligent and efficient thermal insulation graphene board according to claim 6, characterized in that, The specific process of performing threshold segmentation on the thermal conductivity gradient change map is as follows: statistically analyze the distribution frequency of thermal conductivity values ​​for each pixel in the thermal conductivity gradient change map, construct a grayscale histogram, calculate the critical value that maximizes the variance between the high thermal conductivity phase and the low thermal conductivity phase based on the maximum inter-class variance criterion, and set the critical value as the binarization segmentation threshold; use the binarization segmentation threshold to divide the thermal conductivity gradient change map into regions, and retain continuous regions with thermal conductivity values ​​higher than the binarization segmentation threshold as pixel connected components. The process of performing weighted aggregation calculation by combining the effective continuous length of the channel with the branch node density information is as follows: setting the length contribution weight. The length contribution weight is set according to the positive correlation between the effective continuous length and the physical design thickness of the plate, which characterizes the penetration efficiency of heat flow transmission. Branch obstruction weights are set based on the negative correlation between the branch node density and the degree of heat flow scattering loss, which characterizes the obstruction effect of microstructure on heat conduction. The product of the effective continuous length and the length contribution weight is linearly superimposed with the product of the branch node density and the branch obstruction weight. The superposition result is then directionally corrected by combining the cosine of the angle between the tangent direction of the main path and the normal direction of the plate, thus generating the heat conduction path directional parameters.

8. The manufacturing method of the intelligent and efficient thermal insulation graphene board according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Call the heat conduction path directional parameters, analyze the spatial distribution vector of the particle chain in the matrix, reconstruct the three-dimensional topology of the graphene micro heat conduction network using finite element mesh generation technology, assign anisotropic thermal conductivity tensor properties to each mesh unit according to the vector direction, set the heat flux density input boundary and the convective heat transfer output boundary, simulate the energy focusing effect of the heat channel under complex thermal environment, and construct a heat channel focusing structure model; S402: Set a discrete frequency excitation source covering a preset waveband at the incident end of the hot channel focusing structure model, use the finite difference time-domain algorithm to simulate the multiple scattering and attenuation trajectory of far-infrared waves in a non-uniform medium, perform time integration on the propagation delay of the wave vector inside the material to quantify the residence time of the far-infrared wave, simultaneously calculate the volume integral of the electric field intensity in the hot channel focusing area, obtain energy density distribution data, and generate a set of wave energy spatiotemporal distribution features. S403: Perform normalized coupling calculation on the data items in the set of spatiotemporal distribution characteristics of wave energy, multiply the residence time value and energy density value at discrete frequency points to obtain the spectral absorption efficiency index, perform a global maximum search algorithm on the index sequence to locate the resonance frequency point with the highest energy conversion efficiency, and extract the wavelength center value and effective bandwidth range corresponding to the frequency point to determine the optimal solution of frequency band absorption capability.

9. The manufacturing method of the intelligent and efficient thermal insulation graphene board according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Configure a data acquisition interface for the process temperature control parameters mapped by the optimal solution of the absorption capacity of the frequency band, synchronously monitor the temperature rise rate and energy consumption fluctuation sequence of the heating equipment during the operation cycle, perform weighted normalization processing on the acquired rate and amplitude data to eliminate the influence of dimensions, construct a comprehensive evaluation vector reflecting the real-time operating pressure of the equipment, and generate combined load index. S502: Call the combined load index to construct the load status matrix of the equipment cluster, perform full permutation difference operation on each element in the matrix, quantify the load imbalance between any two equipment, sort the imbalance values ​​in descending order, lock the load extreme point, calculate the absolute difference between the extreme points, characterize the dispersion of the production line, and generate the maximum load deviation value between equipment. S503: The maximum load deviation value between the devices is compared with the preset load balancing threshold by the execution logic. When the deviation exceeds the limit, the collaborative compensation scheduling mechanism is activated. The task offloading amount for high-load nodes and the receiving margin for low-load nodes are calculated. Based on the calculation results, the original task queue is inserted with discontinuous time windows and priority replacement is performed to generate a jump-type reordering scheduling queue.

10. The manufacturing method of the intelligent and efficient thermal insulation graphene board according to claim 9, characterized in that, The process of calculating the task offloading amount for high-load nodes and the receiving margin for low-load nodes specifically involves: obtaining the instantaneous power value of each heating device in the device cluster at the current moment and the rated power limit specified by the manufacturer; defining the ratio of the instantaneous power value to the rated power limit as the real-time load rate of the device; marking heating devices with a real-time load rate higher than a preset high-load threshold as high-load nodes and marking heating devices with a real-time load rate lower than a preset low-load threshold as low-load nodes; for high-load nodes, retrieving the expected heat energy consumption values ​​of the tasks to be executed in their task buffer queue, and summing the expected heat energy consumption values ​​of all tasks to calculate if the sum exceeds the high-load threshold. The threshold corresponds to the overflow portion of the energy consumption capacity, and the overflow portion is determined as the task offloading amount. For low-load nodes, the power difference between the instantaneous power value and the rated power limit is calculated, and the power difference is mapped to the additional heat processing task power consumption that can be undertaken within a unit time scheduling cycle. The heat processing task power consumption is determined as the receiving margin. Specifically, the high-load judgment threshold is set by statistically analyzing the average load rate value of the equipment cluster when overheating shutdown occurs in the historical fault log and deducting the preset safety fluctuation margin value. The low-load judgment threshold is set by determining it based on the ratio of the energy consumption level required for the equipment to maintain the minimum insulation state to the energy consumption of the equipment during no-load operation.

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