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CN-121983201-A - Manufacturing method of intelligent efficient heat-preservation graphene plate

CN121983201ACN 121983201 ACN121983201 ACN 121983201ACN-121983201-A

Abstract

The invention relates to the technical field of intelligent manufacturing, in particular to a manufacturing method of an intelligent efficient thermal insulation graphene plate, which comprises the following steps of monitoring the transient response value of functional particles in graphene composite slurry, obtaining the dielectric constant change rate, constructing a ratio function curve, adjusting the electric field inversion rate and judging abnormality, generating particle orientation distribution, restricting migration and identifying a heat conduction path, simulating a heat channel focusing structure, calculating combined load and triggering cooperative scheduling to generate a reorder scheduling queue.

Inventors

  • LI SHAOKUN

Assignees

  • 珠海立衡科技有限公司

Dates

Publication Date
20260505
Application Date
20260130

Claims (10)

  1. 1. The manufacturing method of the intelligent efficient heat-preservation graphene board is characterized by comprising the following steps of: S1, monitoring an instantaneous response value of functional particles in graphene composite slurry, obtaining a dielectric constant change rate, constructing a ratio function curve of the dielectric constant change rate and polarity inversion frequency, and generating initial inter-particle action potential gradient data; S2, adjusting the electric field inversion rate according to the ratio function curve, combining the initial inter-particle action potential gradient data to switch the polarity inversion frequency, calculating a ratio abnormal fluctuation judgment index, setting a frequency callback threshold value, and generating particle orientation distribution state data; S3, restraining particle migration directions based on the particle orientation distribution state data, detecting thermal conductivity gradient changes in the thickness direction and the surface parallel direction of the plate, identifying main directionality of a particle thermal conduction path, and generating a thermal conduction path directionality parameter; s4, inputting the directivity parameters of the heat conduction path into a finite element model to simulate a heat channel focusing structure, mapping the residence time and energy density distribution of far infrared waves, and determining the optimal solution of the absorption capacity of a frequency band; And S5, aiming at the optimal solution of the absorption capacity of the frequency band, acquiring the temperature rise rate and the energy consumption fluctuation amplitude, calculating a combined load index, sequencing the maximum load deviation value among the devices, triggering a cooperative scheduling mechanism when the maximum load deviation value among the devices exceeds a preset load balancing threshold, and generating a jump type reordering scheduling queue.
  2. 2. The method of manufacturing an intelligent efficient thermal insulation graphene panel according to claim 1, wherein the initial inter-particle action potential gradient data comprises electrostatic repulsive force values, van der waals attraction coefficients and inter-particle distance vector fields, the particle orientation distribution state data comprises long axis deflection angles, orientation order indexes and particle cluster dispersion indexes, the thermal conduction path directivity parameters comprise thermal flow vector inclination angles, anisotropic thermal conductivity and thermal conduction channel densities, the frequency band absorption capacity optimal solution comprises peak absorption wavelengths, resonance frequency bandwidths and maximum radiant energy conversion rates, and the jump type reordering schedule queue comprises task execution priority sequences, equipment load distribution matrices and time slot compensation vectors.
  3. 3. The method for manufacturing the intelligent efficient heat-preservation graphene board according to claim 1, wherein the specific steps of S1 are as follows: S101, performing continuous periodic electromagnetic wave sweep monitoring on an internal area of the graphene composite slurry by utilizing frequency scanning equipment, collecting dielectric polarization intensity transient response values of functional particles under the action of an electric field in real time, performing differential operation on the transient response values of adjacent time steps, obtaining response increment, defining the ratio of the response increment to a sampling time interval as a dynamic response index, and generating a dielectric constant change rate; s102, calling a polarity inversion frequency setting parameter of an alternating electric field control unit, performing division operation on the dielectric constant change rate and the polarity inversion frequency to obtain a dimensionless response ratio, constructing a sample set comprising a plurality of groups of frequency and ratio mapping relations, performing minimum iterative solution on the error square sum in the sample set by adopting a least square method, determining a best fit track, and establishing a ratio function curve; S103, performing first derivative operation on the ratio function curve, extracting curve change rate characteristics, calculating potential energy distribution differences of particles in an unbalanced state by combining dielectric constant reference values of a composite material system, and quantifying strength and action range of interaction force among the particles according to gradient change directions of the potential energy distribution differences in a space coordinate system to generate initial inter-particle action potential gradient data.
  4. 4. The method for manufacturing the intelligent high-efficiency thermal insulation graphene board according to claim 3 is characterized in that the process of calculating the potential energy distribution difference of the particles in the unbalanced state by combining the dielectric constant reference value of the composite material system is specifically to obtain the dielectric constant reference value of the composite material system, wherein the dielectric constant reference value is calculated and set by weighting according to the actual measured dielectric constant of the matrix material of the undoped functional particles and the theoretical dielectric constant of the functional particles and the preset volume mixing proportion, the polarization response hysteresis degree of the functional particles under the driving of an alternating electric field is identified based on the characteristic of the curve change rate, the corresponding polarization state coefficient is generated, the deviation degree of the polarization state coefficient relative to the dielectric constant reference value is calculated, the dielectric deviation amount is obtained, the real-time electric field intensity amplitude of the alternating electric field is obtained, the product of the dielectric deviation amount and the square term of the real-time electric field intensity amplitude is calculated, the local electric polarization energy density is generated, and the difference operation is carried out on the local electric polarization energy density and the energy density in the standard equilibrium state, so that the potential energy distribution difference of the particles in the unbalanced state is obtained; The process of quantifying strength and action range of interaction force among particles according to gradient change direction of potential energy distribution difference in a space coordinate system comprises the steps of constructing a three-dimensional discrete grid in a space region of graphene composite slurry, mapping the potential energy distribution difference to each node of the three-dimensional discrete grid, calculating space change rate of potential energy distribution difference between any one target node and adjacent nodes to obtain potential energy gradient vectors, extracting modular length of the potential energy gradient vectors, defining the modular length as strength index of interaction force among particles, detecting attenuation trend of the potential energy gradient vectors along radial direction, identifying space distance when the attenuation trend is reduced to a preset interaction cut-off threshold, and defining the space distance as the action range, wherein the preset interaction cut-off threshold is set according to the ratio relation of Brownian motion heat energy and particle electrostatic potential energy.
  5. 5. The method for manufacturing the intelligent efficient heat-preservation graphene board according to claim 1, wherein the specific steps of S2 are as follows: S201, analyzing nonlinear characteristics of dielectric response along with frequency change according to the ratio function curve, mapping tangential slope at each point of the curve into dynamic adjustment step length of electric field inversion rate, calling the initial inter-particle action potential gradient data to identify stress balance intervals of particles in a fluid field, dividing a curing molding period of a composite material into a plurality of discrete control fragments with time length according to the intervals, and distributing corresponding polarity inversion frequency set values for each fragment to generate a multi-segment polarity inversion frequency switching sequence; S202, aiming at the multi-section polarity inversion frequency switching sequence, driving an alternating electric field generator to perform frequency conversion operation, collecting dielectric constant feedback values of functional particles at frequency switching moments in real time, performing Euclidean distance calculation on theoretical tracks defined by feedback values and a ratio function curve to obtain dispersion deviation, performing sliding window variance operation on the dispersion deviation sequence, quantifying the orientation oscillation degree of the particles under the action of electric field force, and establishing a ratio abnormal fluctuation judgment index; And S203, calculating frequency correction amplitude based on the numerical difference between the ratio abnormal fluctuation judgment index and a preset electric field stability reference, determining a boundary condition triggering reverse compensation control logic according to the correction amplitude, setting a frequency callback threshold, executing constraint convergence calculation on the Brownian motion track of the particles by using the frequency callback threshold, and counting the space long axis vector angle and the three-dimensional distribution density of the particle swarm in a steady state to generate particle orientation distribution state data.
  6. 6. The method for manufacturing the intelligent efficient heat-preservation graphene board according to claim 1, wherein the specific steps of S3 are as follows: S301, analyzing deflection angles of long axes of particles in a three-dimensional space based on the particle orientation distribution state data, calculating angular displacement deviation between the deflection angles and a preset heat flow transmission vector, substituting the angular displacement deviation into a micro-field gradient control model, constructing a non-uniform electric field intensity matrix, solving an electric field constraint force vector for counteracting the randomness of Brownian motion, and generating a particle migration constraint vector field according to a forced migration track of the particles in a polymer matrix curing process by vector planning; S302, invoking the particle migration constraint vector field, adjusting the micro-field distribution state in a forming die, synchronously collecting heat flux values in the thickness direction and the surface parallel direction through a heat flux sensor array in the plate solidification crosslinking reaction process, performing differential pair operation on the heat flux values in the two directions, quantifying the anisotropy degree of heat conduction, performing spatial interpolation processing on the anisotropy data, mapping the thermal resistance distribution state under the plate full coordinate system, and establishing a thermal conductivity gradient change map; S303, performing threshold segmentation processing on the thermal conductivity gradient change map, extracting pixel connected domains of the high heat conduction region, performing topological reconstruction on the connected domains by adopting a vector skeletonization algorithm to fit a geometric trunk path of a heat conduction channel, calculating an included angle cosine value between the tangent direction of the trunk path and the normal direction of the plate, and performing weighted aggregation operation by combining the effective continuous length of the channel and the density information of branch nodes to generate a directivity parameter of the heat conduction path.
  7. 7. The method for manufacturing the intelligent efficient thermal insulation graphene board according to claim 6, wherein the process of performing the threshold segmentation process for the thermal conductivity gradient change map is specifically to count the thermal conductivity value distribution frequency of each pixel point in the thermal conductivity gradient change map, construct a gray level histogram, calculate a critical value capable of maximizing the variance between the high thermal conductivity phase and the low thermal conductivity phase based on a maximum inter-class variance criterion, and set the critical value as a binary segmentation threshold; the method comprises the steps of setting a length contribution weight, wherein the length contribution weight is set according to positive correlation of the effective continuous length relative to the duty ratio of the physical design thickness of the plate, representing the penetration efficiency of heat flow transmission, setting a branch blocking weight, wherein the branch blocking weight is set according to negative correlation of the heat flow scattering loss degree caused by the density of the branch nodes and used for representing the blocking effect of a microstructure on heat conduction, linearly superposing the product of the effective continuous length and the length contribution weight and the product of the branch node density and the branch blocking weight, and directionally correcting the superposition result by combining the included angle cosine value of the tangential direction of a trunk path and the normal direction of the plate to generate a directional parameter of a heat conduction path.
  8. 8. The method for manufacturing the intelligent efficient heat-preservation graphene board according to claim 1, wherein the specific steps of S4 are as follows: s401, calling the directivity parameters of the heat conduction paths, analyzing the spatial distribution vectors of particle chains in a matrix, reconstructing a three-dimensional topological structure of a graphene microscopic heat conduction network by utilizing a finite element mesh division technology, endowing each mesh unit with anisotropic heat conductivity tensor attribute according to the vector direction, setting a heat flow density input boundary and a heat convection output boundary, simulating the energy focusing effect of a heat channel in a complex thermodynamic environment, and constructing a heat channel focusing structure model; S402, setting a discrete frequency excitation source covering a preset wave band at an incident end of the thermal channel focusing structure model, simulating multiple scattering and attenuation tracks of far infrared waves in a non-uniform medium by using a time domain finite difference algorithm, performing time integration on the transmission delay of wave vectors in a material to quantify the residence time of the far infrared waves, synchronously counting the volume integral of electric field intensity in a thermal channel focusing area, obtaining energy density distribution data, and generating a wave energy space-time distribution feature set; S403, performing normalized coupling calculation on the data items in the wave energy space-time distribution feature set, multiplying the residence time value and the energy density value under the discrete frequency points to obtain a spectrum absorption efficiency index, performing a global maximum search algorithm on the index sequence, positioning the resonance frequency point with highest energy conversion efficiency, extracting the wavelength center value and the effective bandwidth range corresponding to the frequency point, and determining the optimal solution of the frequency band absorption capacity.
  9. 9. The method for manufacturing the intelligent efficient heat-preservation graphene board according to claim 1, wherein the specific steps of S5 are as follows: S501, configuring a data acquisition interface aiming at a process temperature control parameter mapped by the optimal solution of the absorption capacity of the frequency band, synchronously monitoring a temperature rising rate value and an energy consumption fluctuation amplitude sequence of heating equipment in an operation period, performing weighted normalization processing on the acquired rate and amplitude data to eliminate dimension influence, constructing a comprehensive evaluation vector reflecting real-time operation pressure of the equipment, and generating a combined load index; S502, calling the combined load index, constructing a load state matrix of the equipment cluster, executing full arrangement difference operation on each element in the matrix, quantifying the load unbalance degree between any two pieces of equipment, executing descending arrangement on the unbalance degree value, locking load extreme points, calculating the absolute difference between the extreme points, representing the discrete degree of the production line, and generating the maximum load deviation value between the equipment; And S503, performing logic comparison on the maximum load deviation value between the devices and a preset load balance threshold, activating a cooperative compensation scheduling mechanism when deviation out-of-range is judged, calculating the task unloading amount of a high-load node and the receiving allowance of a low-load node, and performing discontinuous time window insertion and priority replacement on an original task queue according to a calculation result to generate a jump type reordering scheduling queue.
  10. 10. The method for manufacturing the intelligent efficient thermal insulation graphene board according to claim 9 is characterized in that the process of calculating the task unloading capacity of the high load node and the receiving margin of the low load node is specifically to obtain an instantaneous power value of each heating device in a device cluster at the current moment and a rated power upper limit of device factory calibration, define a ratio of the instantaneous power value to the rated power upper limit as a real-time load rate of the device, mark heating devices with the real-time load rate higher than a preset high load judging threshold value as high load nodes, mark heating devices with the real-time load rate lower than a preset low load judging threshold value as low load nodes, search an expected heat energy consumption value of a task to be executed in a task buffer queue of the high load nodes, calculate an overflow part of the expected heat energy consumption value sum of all tasks exceeding the high load judging threshold value corresponding to the energy consumption capacity in an accumulated mode, calculate the power difference between the instantaneous power value and the rated power upper limit as the low load node, map the power difference as an additional heat treatment task load limit in a unit time scheduling period, mark the heating devices with the real-time load rate higher than the preset high load judging threshold value as the high load nodes, mark the heating devices with the real-time load rate lower load than the preset low load judging threshold value as the low load nodes, and calculate the expected heat consumption value of the task to be executed in the task buffer queue, accumulate, calculate the expected heat consumption value of all tasks to be calculated, and the expected to be calculated, and the rated load value is determined to be calculated, and the rated.

Description

Manufacturing method of intelligent efficient heat-preservation graphene plate Technical Field The invention relates to the technical field of intelligent manufacturing, in particular to a manufacturing method of an intelligent efficient heat-insulation graphene plate. Background The intelligent manufacturing technical field comprises various manufacturing processes for improving manufacturing efficiency, quality and flexibility by means of digital design, intelligent control, information physical fusion and the like, and specifically comprises the aspects of intelligent equipment manufacturing, industrial robot system integration, intelligent optimization of manufacturing processes, automatic assembly and detection, advanced material forming and processing, intelligent perception, actuator control and the like. The technical field emphasizes that artificial intelligence, sensing technology, big data analysis and network communication system and the like are introduced in the traditional manufacturing flow to construct a manufacturing system with autonomous sensing, autonomous decision making and self-adaption capabilities, and self-organization and intelligent coordination of the manufacturing system are realized. The manufacturing method of the traditional intelligent high-efficiency heat-insulation graphene board is a processing mode that a graphene material is used as a base, and a heat-insulation functional material is combined, so that the graphene board is compounded by a physical or chemical means to form a composite board with a heat-insulation function. Such manufacturing methods typically involve dispersion treatment of the graphene powder or slurry, mixing processes with a substrate such as polyurethane, phenolic resin or silicate material, coating or lamination techniques to form a multi-layer structure, and subsequent heat treatment steps such as drying, hot pressing, compression molding, and the like. The distribution uniformity and the adhesion performance of graphene in a composite system are enhanced by the partial scheme through technologies such as surface spraying, dipping, solution casting and the like, and meanwhile, the thickness of a plate and heat conduction parameters are controlled to meet the high-efficiency heat preservation requirement. In addition, the manufacturing process also comprises a blending step of graphene and auxiliary functional components such as conductive particles, antibacterial factors or neutralizing components, and the blending step is used for endowing the board with function expansion on the basis of heat preservation, such as far infrared radiation release characteristics, biopotential adjustment capability or specific frequency band microwave absorption performance required for adjusting blood pressure, balancing acid and alkali or promoting metabolic functions. In the traditional manufacturing method, heat conducting particles often present a random unordered arrangement state due to lack of active constraint of a microscopic field in the curing and forming process, so that a heat conducting channel with specific directivity is difficult to form in a plate, and particle clusters or orientation drift phenomenon is easy to cause due to neglected dielectric response difference of the particles under an alternating electric field, so that far infrared radiation energy generates scattering and loss on a transmission path, high-efficiency focusing and absorption of the energy cannot be realized in a specific frequency band, and response accuracy of the plate in a physiological regulation function is limited. Disclosure of Invention In order to solve the technical problems in the prior art, the embodiment of the invention provides a manufacturing method of an intelligent efficient heat-insulation graphene plate, which comprises the following steps: S1, monitoring an instantaneous response value of functional particles in graphene composite slurry, obtaining a dielectric constant change rate, constructing a ratio function curve of the dielectric constant change rate and polarity inversion frequency, and generating initial inter-particle action potential gradient data; S2, adjusting the electric field inversion rate according to the ratio function curve, combining the initial inter-particle action potential gradient data to switch the polarity inversion frequency, calculating a ratio abnormal fluctuation judgment index, setting a frequency callback threshold value, and generating particle orientation distribution state data; S3, restraining particle migration directions based on the particle orientation distribution state data, detecting thermal conductivity gradient changes in the thickness direction and the surface parallel direction of the plate, identifying main directionality of a particle thermal conduction path, and generating a thermal conduction path directionality parameter; s4, inputting the directivity parameters of the heat conduction path into a f