Sample heating curing and heating auxiliary parallel enhancement control method and system

Abstract

The invention discloses a control method and system for heating curing and heating auxiliary parallel enhancement of a sample, and the method comprises the steps: selecting a plurality of process nodes from a heating curing process, and obtaining a sample preset temperature and a sample standard index of each process node; under the condition that the sample preset temperature between the adjacent process nodes is in a steady state, controlling the electromagnetic heating device to heat through a general control method or a PID-based preset frequency control method, otherwise, controlling the electromagnetic heating device to heat through a linear control method or a refined energy consumption control method; in the heating and curing process, the stirring speed is controlled based on the sample moisture content, the ultrasonic power is controlled based on the sample characteristic functional group content, and the vibration frequency is controlled based on the aroma functional group content. Real-time heating control can be carried out according to the sample temperature, the heating auxiliary device is controlled to work according to the sample moisture content and other indexes, and therefore the standardization of the heating and curing process is achieved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of sample heating and curing process control, and particularly relates to a sample heating and curing and heating auxiliary parallel enhancement control method and system. BACKGROUND

[0002] In the field of sample heating and curing, fire control is the core link to determine the quality of curing. The traditional process relies on manual observation of material color, hand feeling and frequency of turning, and the heating process is judged by experience. However, it has the following fundamental defects: first, it is strongly dependent on manual operation, and the individual experience difference leads to large fluctuations in curing degree between batches. Second, the heat conduction is uneven, and the traditional iron pot heating has the phenomenon of over-heating at the bottom and under-heating at the edge, and the local temperature deviation can reach more than 50℃, which easily causes carbonization of the material. Third, the energy utilization rate is low, there is no real-time energy consumption monitoring, and the heating power is fixed, which leads to large waste of energy consumption in the water evaporation stage.

[0003] The prior art Chinese patent application CN201410788734.0 discloses an electromagnetic heating control method, a control system and an electromagnetic heating device. The electromagnetic heating control method comprises: controlling the electromagnetic heating device to work according to a preset heating time and a preset power, and recording a heating termination time as a first time; after the electromagnetic heating device stops working, detecting whether the self-resonance voltage of the electromagnetic heating device in the self-resonance process is less than a preset reference voltage in real time; recording the time when the self-resonance voltage is detected for the first time as the second time; determining the reference heating time of the electromagnetic heating device under the preset power according to the time difference between the second time and the first time; and determining the working time of the electromagnetic heating device under any power heating instruction according to the reference heating time and the preset power to control the electromagnetic heating device to work.

[0004] In the above-mentioned prior art, the heating power and the heating time are both preset, and the heating power is not adjusted in real time according to the real-time change of the material characteristics, so that fine heating control is achieved, and the heating and curing effect is difficult to standardize. SUMMARY

[0005] The purpose of the present application is to provide a sample heating and curing and heating auxiliary parallel enhancement control method and system, which partially solves or alleviates the above-mentioned deficiencies in the prior art, can perform real-time heating control according to the sample temperature, and controls the heating auxiliary device to work according to the sample moisture content and other indicators, so as to achieve the standardization of the heating and curing process.

[0006] In order to solve the above-mentioned technical problems, the present application specifically adopts the following technical solutions: The first aspect of the present application is to provide a sample heating and curing and heating auxiliary parallel enhancement control method, comprising: selecting several process nodes from the heating and aging process, and obtaining a preset temperature of a sample at each process node and a sample standard index; the sample standard index includes a sample moisture content standard value, several characteristic functional group content standard values, and several aroma functional group content standard values; In the case that the sample preset temperature between adjacent process nodes is in a steady state, the electromagnetic heating device is controlled to heat by a general control method or a preset frequency control method based on PID, so that the sample temperature reaches the next process node from the current process node in a steady state; otherwise, the electromagnetic heating device is controlled to heat by a linear control method or a fine energy consumption control method, so that the sample preset temperature at the current process node reaches the sample preset temperature at the next process node; In the heating and aging process, the sample is stirred by a stirrer, vibrated by an ultrasonic oscillator, and shaken by a shaker; and the stirring speed is controlled based on the sample moisture content, the ultrasonic power is controlled based on the sample characteristic functional group content, and the shaking frequency is controlled based on the aroma functional group content.

[0007] Further, the method for obtaining the sample standard index at each process node includes: Parallel heating and aging of several samples is performed, and the moisture content, several characteristic functional group contents, and several aroma functional group contents of the samples are measured at each process node; The average values of the moisture content, several characteristic functional group contents, and several aroma functional group contents of the samples at a certain process node are taken as the sample moisture content standard value, several characteristic functional group content standard values, and several aroma functional group content standard values at the process node; The time functions of the sample indexes are fitted respectively using the standard indexes of the process nodes.

[0008] Further, the general control method includes using the formula:

[0009] The frequency of the electromagnetic heating device is controlled; wherein, is the real-time control frequency of the electromagnetic heating device, is the preset frequency of the electromagnetic heating device when the preset temperature of the process node Jn is in a steady state, OT is the real-time temperature of the sample, and T Jn is the preset temperature of the sample at the process node Jn; The formula is used:

[0010] The preset frequency of the electromagnetic heating device for the sample to maintain the preset temperature in a steady state at the process node Jn is calculated; wherein, is the preset frequency of the electromagnetic heating device when the preset temperature of the process node Jn is in a steady state, QEJn QW is the chemical energy of the sample change from process node Jn-1 to process node Jn; QW Jn QS is the energy required for the sample to evaporate moisture from process node Jn-1 to process node Jn; QS Jn QW is the energy of the sample released to the environment at a steady-state temperature from process node Jn-1 to process node Jn, N is the equivalent number of turns of the electromagnetic heating device, Φ is the maximum main magnetic flux of the electromagnetic heating device, k is the effective absorption coefficient of the sample to heat, t Jn T is the preset time from process node Jn-1 to process node Jn.

[0011] Further, the PID-based preset frequency control method includes using the formula:

[0012] The frequency of the electromagnetic heating device is controlled; wherein, f is the real-time control frequency of the electromagnetic heating device, T is the preset frequency of the electromagnetic heating device at the preset temperature steady state of process node Jn, Jn T is the preset temperature of the sample of process node Jn; u(t) is the output of the PID regulator, T is the upper limit of the electromagnetic heating frequency control from process node Jn-1 to process node Jn at the preset temperature steady state, T is the upper limit of the electromagnetic heating frequency control from process node Jn-1 to process node Jn at the preset temperature steady state; using the formula:

[0013] The output of the PID regulator is calculated; wherein, u(t) is the output of the PID regulator, k p k is the proportional gain, e(t) is the deviation of the sample temperature of process node Jn at time t from the preset value, t s t is the sampling time, t I t is the integral time, t d d is the differential symbol, t is the time t from process node Jn-1 to process node Jn.

[0014] Further, the linear control method includes using the formula:

[0015] The frequency of the electromagnetic heating device is controlled; wherein, f is the real-time control frequency of the electromagnetic heating device, Let Tt be the preset frequency of the electromagnetic heating device when the preset temperature changes from process node Jn-1 to process node Jn, and OT be the preset temperature of the sample at time t from process node Jn-1 to process node Jn. The difference between the preset sample temperature at process node Jn and process node Jn-1. The upper limit for electromagnetic heating frequency control is set to the preset temperature change from process node Jn-1 to process node Jn. The upper limit of electromagnetic heating frequency control is preset for temperature changes from process node Jn-1 to process node Jn. Using the formula:

[0016] Calculate the preset frequency of the electromagnetic heating device to maintain a preset temperature change for the sample at process node Jn; where... QY is the preset frequency of the electromagnetic heating device when the preset temperature changes from process node Jn-1 to process node Jn. Jn This represents the amount of heat required for the sample to travel from the preset temperature at process node Jn-1 to the preset temperature at process node Jn. This represents the equivalent number of turns of the electromagnetic heating device. This is the maximum main magnetic flux of the electromagnetic heating device. The power factor is given by k, where k is the effective heat absorption coefficient of the sample, and t is the power factor. Jn The preset time for the sample to travel from process node Jn-1 to process node Jn.

[0017] Furthermore, the refined energy consumption control method includes using the formula:

[0018] The frequency of the electromagnetic heating device is controlled; among which, QY is the real-time control frequency for the electromagnetic heating device. Jn t represents the amount of heat required for the sample to travel from the preset temperature at process node Jn-1 to the preset temperature at process node Jn. Jn The preset time is set from process node Jn-1 to process node Jn. For heat transfer thermal resistance, T EV OT represents the ambient temperature, while OT represents the real-time temperature of the sample. The difference between the preset sample temperature at process node Jn and process node Jn-1. This represents the equivalent number of turns of the electromagnetic heating device. This is the maximum main magnetic flux of the electromagnetic heating device. Here, d represents the power factor, and d is the differential symbol.

[0019] Further, the method for controlling the stirring speed of the stirrer based on the sample moisture content comprises using the formula:

[0020] controlling the stirring speed of the stirrer; wherein n st is the real-time control rotating speed of the stirrer, is the preset stirring rotating speed of the process node Jn, is the lower limit of the stirring rotating speed of the process node Jn, is the upper limit of the stirring rotating speed of the process node Jn, is the lower limit of the sample moisture content adjustment of the process node Jn, is the upper limit of the sample moisture content adjustment of the process node Jn; is the window period t IW of the monitoring value mean of the sample moisture content at the specified sampling time t0; is the standard value of the sample moisture content of the process node Jn, is the standard value of the sample moisture content of the process node Jn-1; using the formula:

[0021] calculating the lower limit of the sample moisture content adjustment of the process node Jn; wherein, is the lower limit of the sample moisture content adjustment of the process node Jn, is the time function of the sample moisture content standard value, is the sample moisture content adjustment value dead zone coefficient, is the standard value of the sample moisture content of the process node Jn, is the standard value of the sample moisture content of the process node Jn-1; using the formula:

[0022] calculating the upper limit of the sample moisture content adjustment of the process node Jn; wherein, is the upper limit of the sample moisture content adjustment of the process node Jn, is the time function of the sample moisture content standard value, is the sample moisture adjustment value dead zone coefficient, is the standard value of the sample moisture content of the process node Jn, is the standard value of the sample moisture content of the process node Jn-1.

[0023] Further, the method for controlling the ultrasonic power of the ultrasonic oscillator based on the sample characteristic functional group content comprises the master control functional group control method and the multi-characteristic functional group control method; The master control functional group control method comprises using the formula:

[0024] The voltage of the ultrasonic oscillator is controlled; wherein, V ul is the real-time control voltage of the ultrasonic oscillator, k v is a proportional coefficient, P ul is the real-time control power of the ultrasonic oscillator, f v is the frequency of the ultrasonic wave, is the dielectric constant of the sample, σ is the conductivity of the sample, T v is the propagation time of the ultrasonic wave, ρ is the average density of the sample; or, using the formula:

[0025] The output frequency of the ultrasonic oscillator is controlled; wherein, f ul is the real-time output frequency of the ultrasonic oscillator, P ul is the real-time control power of the ultrasonic oscillator, A is the amplitude of the ultrasonic wave, is the dielectric constant of the sample, σ is the conductivity of the sample, T v is the propagation time of the ultrasonic wave, ρ is the average density of the sample; using the formula:

[0026] The power of the ultrasonic oscillator is controlled; wherein, P ul is the real-time control power of the ultrasonic oscillator, is the preset ultrasonic power of the process node Jn, is the lower limit of the ultrasonic power of the process node Jn, is the upper limit of the ultrasonic power of the process node Jn, is the window period t ICn of the monitoring value average of the sample n number characteristic functional group content within the specified sampling time t0; is the average change rate of the standard value of the n number characteristic functional group content within the window period t ICn of the specified sampling time t0, is the standard value of the n number characteristic functional group content of the process node Jn sample, is the standard value of the n number characteristic functional group content of the process node Jn-1 sample, is the lower limit of the adjustment of the n number characteristic functional group content of the process node Jn sample, is the upper limit of the adjustment of the n number characteristic functional group content of the process node Jn sample; using the formula:

[0027] The average rate of change of the standard value of the characteristic functional group content of sample n is calculated; wherein, is the window period t of the prescribed sampling time t0 ICn is the average rate of change of the standard value of the characteristic functional group content of sample n, is the time function of the standard value of the characteristic functional group content of sample n, t0 is the prescribed sampling time, and t ICn is the sampling window period of the characteristic functional group; The lower limit of the adjustment of the characteristic functional group content is calculated by using the formula:

[0028] The lower limit of the adjustment of the characteristic functional group content is calculated by using the formula: is the lower limit of the adjustment of the characteristic functional group content of sample n of process node Jn, is the time function of the standard value of the characteristic functional group content of sample n, is the adjustment dead zone coefficient of the characteristic functional group content of sample n, is the standard value of the characteristic functional group content of sample n of process node Jn, is the standard value of the characteristic functional group content of sample n of process node Jn-1; The lower limit of the adjustment of the characteristic functional group content is calculated by using the formula:

[0029] The upper limit of the adjustment of the characteristic functional group content is calculated by using the formula: is the upper limit of the adjustment of the characteristic functional group content of sample n of process node Jn, is the time function of the standard value of the characteristic functional group content of sample n, is the adjustment dead zone coefficient of the characteristic functional group content of sample n, is the standard value of the characteristic functional group content of sample n of process node Jn, is the standard value of the characteristic functional group content of sample n of process node Jn-1; The multi-characteristic functional group control method comprises using the formula:

[0030] The voltage of the ultrasonic oscillator is controlled; wherein, V ul is the real-time control voltage of the ultrasonic oscillator, k v is a proportional coefficient, P ul is the real-time control power of the ultrasonic oscillator, f v is the frequency of the ultrasonic wave, is the dielectric constant of the sample, and σ is the conductivity of the sample, T v is the propagation time of the ultrasonic wave, and ρ is the average density of the sample; Alternatively, the formula:

[0031] controlling the output frequency of the ultrasonic oscillator; wherein, f ul is the real-time output frequency of the ultrasonic oscillator, P c is the real-time control power of the ultrasonic oscillator, A is the amplitude of the ultrasonic wave, ε is the dielectric constant of the sample, σ is the conductivity of the sample, Tv is the propagation time of the ultrasonic wave, and ρ is the average density of the sample; using the formula:

[0032] controlling the power of the ultrasonic oscillator; wherein, P ul is the real-time control power of the ultrasonic oscillator, is the preset ultrasonic power of the process node Jn, is the lower limit of the ultrasonic power of the process node Jn, is the upper limit of the ultrasonic power of the process node Jn, is the normalized value of the plurality of characteristic functional groups; The method for normalizing the plurality of characteristic functional groups in the multi-characteristic functional group control method includes using the formula:

[0033] calculating the normalized value of the plurality of characteristic functional groups; wherein, is the normalized value of the plurality of characteristic functional groups, is the comprehensive control amount of the plurality of characteristic functional groups; using the formula: ;

[0034] calculating the comprehensive control amount of the plurality of characteristic functional groups; wherein, is the comprehensive control amount of the n characteristic functional groups, u i-ul is the control amount of the i-th characteristic functional group, is the window period t ICn of the monitoring value average of the characteristic functional group content of the sample n within the window period t ICn of the specified sampling time t0, is the average change rate of the standard value of the characteristic functional group content n within the window period t ICn of the specified sampling time t0, is the standard value of the characteristic functional group content of the sample n of the process node Jn, is the standard value of the characteristic functional group content of the sample n of the process node Jn-1, is the lower limit of the adjustment of the characteristic functional group content of the sample n of the process node Jn, The upper limit of the adjustment of the characteristic functional group content of the sample n of the process node Jn.

[0035] Further, the method for controlling the vibration frequency of the vibrator based on the aroma functional group content of the sample includes a master aroma functional group control method and a multi-aroma functional group control method. The master aroma functional group control method includes using the formula:

[0036] The vibration frequency of the vibrator is controlled; wherein f sh The real-time control frequency of the vibrator, The preset vibration frequency of the process node Jn, The lower limit of the vibration frequency of the process node Jn, The upper limit of the vibration frequency of the process node Jn, The window period t OCm The average of the monitoring values of the aroma functional group content of the sample m within the window period t The window period t OCm The average change rate of the standard value of the aroma functional group content m within the window period t The standard value of the aroma functional group content of the sample m of the process node Jn, The standard value of the aroma functional group content of the sample m of the process node Jn-1, The lower limit of the adjustment of the aroma functional group content of the sample m of the process node Jn, The upper limit of the adjustment of the aroma functional group content of the sample m of the process node Jn; Using the formula:

[0037] The average change rate is calculated; wherein The window period t The average change rate of the standard value of the aroma functional group content m within the window period t The time function of the standard value of the aroma functional group content m, t0 is the specified sampling time, and t OCm The aroma functional group sampling window period; Using the formula:

[0038] The lower limit of the adjustment of the master aroma functional group content is calculated; wherein The lower limit of the adjustment of the aroma functional group content of the sample m of the process node Jn, The time function of the standard value of the aroma functional group content m, The aroma functional group content adjustment dead zone coefficient of the sample m, The standard value of the aroma functional group content of sample m at process node Jn, The standard value of the aroma functional group content of sample m at process node Jn-1; The standard value of the aroma functional group content of sample m at process node Jn,

[0039] The upper limit of the aroma functional group content of sample m at process node Jn is calculated by the formula: The upper limit of the aroma functional group content of sample m at process node Jn, The time function of the standard value of the aroma functional group content of sample m, The adjustment dead zone coefficient of the aroma functional group content of sample m, The standard value of the aroma functional group content of sample m at process node Jn, The standard value of the aroma functional group content of sample m at process node Jn-1; The multi-aroma functional group control method comprises the formula:

[0040] The vibration frequency of the vibrator is controlled; wherein f sh The real-time control frequency of the vibrator, The preset vibration frequency of process node Jn, The lower limit of the vibration frequency of process node Jn, The upper limit of the vibration frequency of process node Jn, u sh The multi-aroma functional group normalization value; The method for normalizing the multi-aroma functional groups in the multi-aroma functional group control method comprises the formula:

[0041] The multi-aroma functional group normalization value is calculated; wherein The multi-aroma functional group normalization value, The comprehensive control amount of the multi-aroma functional groups; The formula: ;

[0042] The comprehensive control amount of the multi-aroma functional groups is calculated; wherein The comprehensive control amount of the m aroma functional groups, u i-sh The control amount of the i-th aroma functional group, The monitoring value average of the aroma functional group content of sample m in the window period t OCm of the specified sampling time t0; The monitoring value average of the aroma functional group content of sample m in the window period t OCmAverage change rate of standard value of aroma functional group content of sample m in process node Jn, Standard value of aroma functional group content of sample m in process node Jn, Standard value of aroma functional group content of sample m in process node Jn-1, Lower limit of adjustment of aroma functional group content of sample m in process node Jn, Upper limit of adjustment of aroma functional group content of sample m in process node Jn.

[0043] The application also provides a control system for sample heating aging and heating auxiliary parallel enhancement, comprising: A standard index acquisition module is configured to select a plurality of process nodes from a heating aging process and acquire a preset temperature of a sample at each process node and a sample standard index; the sample standard index comprises a sample moisture content, a plurality of characteristic functional group contents, and a plurality of aroma functional group contents. A heating control module is configured to, in the case that the preset temperature of the sample between adjacent process nodes is stable, control an electromagnetic heating device to heat by a general control method or a preset frequency control method based on PID, so that the temperature of the sample reaches the next process node from the current process node in a stable state; otherwise, the electromagnetic heating device is controlled to heat by a linear control method or a refined energy consumption control method, so that the preset temperature of the sample at the current process node reaches the preset temperature of the sample at the next process node. A heating auxiliary control module is configured to, in the heating aging process, stir the sample by a stirrer, vibrate the sample by an ultrasonic oscillator, and vibrate the sample by a vibrator; and control the stirring speed based on the sample moisture content, control the ultrasonic power based on the characteristic functional group content of the sample, and control the vibration frequency based on the aroma functional group content.

[0044] Advantages: The electromagnetic heating technology is adopted in the scheme, compared with the traditional heating mode, the heat conduction efficiency can be significantly improved. The electromagnetic induction principle makes the internal material of the heated material directly generate eddy current to heat, reduces the loss in the heat transfer process, greatly shortens the heating time, and improves the production efficiency. In terms of temperature control precision, the disadvantages of large temperature fluctuation and difficult accurate control in the traditional heating are abandoned, the temperature can be accurately adjusted, the temperature can be controlled within a very small error range, the process requirements sensitive to temperature are met, and a solid guarantee is provided for product quality.

[0045] An innovative high-frequency vibration device is introduced, which works at a specific frequency and amplitude, generating high-frequency vibrations that can effectively break the interaction forces between the materials, dispersing the agglomerated material particles. In the traditional heating process, material agglomeration easily leads to blocked heat conduction, resulting in local overheating or even carbonization, which seriously affects product quality. However, through high-frequency vibration, this scheme ensures uniform heating of the material, overcoming this long-standing problem and ensuring product quality stability and reducing the rate of defective products.

[0046] With the synergistic effect of ultrasonic cavitation and mechanical stirring, the mass and heat transfer is strengthened. The cavitation bubbles generated by ultrasonic waves in the liquid will produce high temperature, high pressure and strong micro-jet and shock wave in the instant of collapse, greatly enhancing the turbulence degree of the liquid, accelerating the transfer of matter and the diffusion of heat. At the same time, mechanical stirring further promotes the mixing of materials, and the combination of the two greatly improves the mass and heat transfer efficiency. In addition, this synergistic effect can also stimulate molecular activation energy, reduce the activation energy barrier of chemical reaction, and improve the chemical reaction kinetics efficiency by orders of magnitude, speed up the reaction process, and improve the product output efficiency.

[0047] The intelligent stirring system is constructed based on the calculation of Newton cooling model. This model fully considers various factors in the heat transfer process, such as convection, radiation and conduction, etc. Through accurate calculation and simulation, the intelligent stirring system can adjust the stirring speed, direction and time according to different process requirements, so as to optimize the temperature flow field distribution. Ensure that the temperature gradient can be accurately controlled in the whole reaction space, avoid the situation of local temperature being too high or too low, provide a more stable and suitable temperature environment for chemical reaction, which is beneficial to improve the selectivity and conversion rate of reaction.

[0048] The node thermodynamic modeling technology is adopted to conduct fine thermodynamic analysis on each node in the process. By pre-setting the electromagnetic field parameters of dynamic frequency, the energy input can be calibrated at nanometer level according to the needs of different process stages. This precise energy control avoids waste and excessive input of energy, improves energy utilization efficiency. At the same time, it ensures that the material can obtain just the right amount of energy at each process stage, so that the reaction can proceed in the expected direction, further improving product quality and process stability.

[0049] A multi-parameter coupling control model is established, and multiple indexes such as water activity, aroma threshold, and functional group concentration are comprehensively considered. These indexes are interrelated and influence each other, and jointly determine the quality and performance of the product. Through real-time monitoring and analysis of these parameters, the intelligent temperature control system can automatically adjust the temperature, power and other control parameters according to the actual situation, and realize accurate control of the process. Moreover, the system has self-learning ability, and can learn the best control strategy under different working conditions by continuously accumulating production data. With the increase of use time, the control precision and effect will be continuously improved, and it is suitable for various complex and changeable production demands. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. In all the drawings, similar elements or parts are generally identified by similar reference signs. In the drawings, each element or part is not necessarily drawn according to the actual proportion. Obviously, the drawings described below are some embodiments of the present application, and for those skilled in the art, other drawings can be obtained from these drawings without creative labor.

[0051] Figure 1 The flowchart of the first embodiment is shown in Figure 1. Figure 2 The process node schematic diagram is shown in Figure 2. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0053] In this paper, the suffix such as "module", "component" or "unit" used to represent the element is only for the convenience of the description of the present application, and has no specific meaning. Therefore, "module", "component" or "unit" can be mixedly used.

[0054] In this article, the terms "upper", "lower", "inner", "outer", "front", "back", "one end", "the other end" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0055] In this article, unless otherwise explicitly specified and limited, the terms "mounting", "provided with", "connection" and the like should be understood broadly, for example, "connection" can be fixed connection, can also be detachable connection, or integral connection; can be mechanical connection, can be direct connection, can also be indirect connection through intermediate medium, can be internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0056] In this article, "and / or" includes any and all combinations of one or more listed related items.

[0057] In this article, "multiple" means two or more, that is, it includes two, three, four, five, etc.

[0058] Embodiment one: Electromagnetic induction heating has the advantages of high heating efficiency, fast speed, good controllability and easy automation, and is widely used in industrial production processes such as metal smelting, heat penetration, heat treatment and welding, and has become an indispensable technical means in the departments of metallurgy, national defense, mechanical processing, and casting, forging, and shipbuilding, aircraft, automobile manufacturing industries.

[0059] The principle of induction heating is to generate alternating current, thereby generating alternating magnetic field, and using alternating magnetic field to generate eddy current to achieve heating effect. Induction heating is different from other heating methods such as gas heating and resistance furnace heating. It directly sends electric energy to the inside of the induction receptor to become heat energy to heat the induction receptor. Other heating methods first heat the surface of the induction receptor, and then conduct heat to heat the inside.

[0060] Generally speaking, the material that needs to be heated and matured is not an effective electromagnetic induction receptor, so the metal pot body should be used as the induction receptor in the material heating process, that is, the metal pot body is heated by the electromagnetic coil. Compared with the traditional electric heating tube heating, this heating method reduces one heat transfer compared with the traditional electric heating tube heating, greatly improving the heat utilization efficiency, and the final heating system efficiency can reach more than 90%. Based on the above advantages, electromagnetic induction is used in the present application to heat and mature the material.

[0061] In addition, the present application relates to real-time monitoring of material indicators during the heating aging process, including moisture content, characteristic functional group content, and aroma functional group content. The detection methods of the three indicators are as follows: (1) The detection method of moisture content.

[0062] The sample in the heating aging process is irradiated with infrared light III with a wavelength of N5-N6; the incident light intensity and diffuse reflectance absorbance of the infrared light III are obtained, and the moisture content in the sample is calculated using the incident light intensity and diffuse reflectance absorbance. The heating aging sample is irradiated with infrared light III with a wavelength of N5-N6, and the incident light intensity and diffuse reflectance absorbance of the infrared light III are measured. According to the diffuse reflection theory, the content of moisture in the sample is calculated by establishing a diffuse reflection regression model, so as to master the real-time moisture state of the sample.

[0063] When infrared light irradiates a solid sample, part of the light will produce specular reflection on the surface of the sample and cannot enter the interior of the sample, so it does not carry sample information; most of the light enters the interior of the sample through refraction, transmission, or internal surface reflection of particles, and interacts with sample molecules to produce reflection, refraction, scattering, and absorption, etc., and finally radiates from the surface of the sample. The infrared light after multiple refraction, transmission, and scattering in the space of the sample surface radiates in all directions, which is called diffuse reflection.

[0064] Diffuse reflection measurement can be used for various samples, especially for solid samples. When incident light irradiates the surface of a solid or material particle, two different reflection phenomena usually occur, namely specular reflection and diffuse reflection. Specular reflection is like mirror reflection, in which the light is not absorbed by the material and the reflection angle is equal to the incident angle; diffuse reflection is the light from the light source that enters the interior of the sample, returns to the surface of the sample after multiple reflections, refractions, diffractions, and absorptions. When diffuse reflection occurs, each light interacts with the molecules in the interior of the sample, and a certain amount of light is absorbed by the chemical substances in the sample. Therefore, the diffuse reflected light contains information about the composition of the sample, i.e., different components absorb different amounts of light at a specific wavelength.

[0065] Diffuse reflection and specular reflection coexist. If the sample surface is rough, the mirror reflection will be reduced and the diffuse reflection energy will be increased. Therefore, when measuring diffuse reflection infrared spectra, mirror reflection should be reduced as much as possible.

[0066] The diffuse reflection light intensity is usually very weak, because the diffuse reflected light is in all directions, and only a small directional light can be detected. Therefore, the design of the diffuse reflection accessory should improve the signal-to-noise ratio as much as possible.

[0067] Diffuse reflection spectra and transmission spectra are similar, and diffuse reflection spectra are usually represented by diffuse reflectance R (%) at different wave numbers, which is defined as the diffuse reflection light intensity I collected by the detector.fin The ratio of the intensity of diffuse reflection of background I fout .

[0068] The ordinate of the diffuse reflectance spectrum can also be expressed as diffuse reflectance absorbance (A):

[0069] The two forms of diffuse reflectance infrared spectra are similar to the transmittance spectrum and the absorbance spectrum in the transmission method, respectively. However, due to the existence of specular reflection, the absorbance of the diffuse reflectance infrared spectrum does not conform to the Lambert-Beer law between the sample component concentration. In order to make them linear, it is necessary to reduce or eliminate the specular reflection light, and the model elimination is usually used in engineering applications, that is:

[0070] Where C3 is the measured value of the moisture content, G1 is the absorbance coefficient of the sample to infrared III, k1 is the moisture diffuse reflectance regression coefficient, and b1 is the moisture offset.

[0071] Because the diffuse reflectance spectrum measured for a solid does not conform to the Lambert-Beer law, there is a diffuse reflectance theory and a diffuse reflectance absorbance calculation formula, which is shown as follows: ; Where A is the diffuse reflectance absorbance, G is the absorbance coefficient of the sample, and S is the scattering coefficient of the sample, which can be set as a constant value when the sample concentration is small, and is obtained by sample experiment. Therefore, it can be simplified as:

[0072] Where G1 is the absorbance coefficient of the sample to infrared III, K is the error coefficient, A1 is the diffuse reflectance absorbance of the sample to infrared III, and S1 is the scattering coefficient of the sample to infrared III.

[0073] In addition, the moisture diffuse reflectance regression coefficient k1 and the moisture offset b1 can be obtained by experimental methods.

[0074] (2) Detection method of characteristic functional group content.

[0075] ​The sample in the heating maturation process is irradiated by infrared rays IV with a wavelength of N7-N8; the incident light intensity and diffuse reflection absorbance of the infrared rays IV are obtained, and the content of the characteristic functional group in the sample is calculated by using the incident light intensity and diffuse reflection absorbance. For example, for the characteristic functional group (phenolic hydroxyl group) of tea polyphenols and catechin, the sample is irradiated by infrared rays IV with a wavelength of 2778 nm-3125 nm; for the characteristic functional group (carbonyl group) of caffeine, the sample is irradiated by infrared rays IV with a wavelength of 5797 nm-5952 nm; for the characteristic functional group (aromatic ring) of quercetin, the sample is irradiated by infrared rays IV with a wavelength of 6250 nm-6900 nm; the incident light intensity and diffuse reflection absorbance are obtained, and then the content of the characteristic functional group in the sample is calculated through diffuse reflection theory and corresponding formula, thereby providing data support for judging the change of effective components of the sample.

[0076] More specifically, the content of the characteristic functional group in the sample is calculated by using the formula:

[0077] wherein C4 is the measured value of the content of the characteristic functional group, G2 is the light absorption coefficient of the sample to the infrared rays IV, k2 is the diffuse reflection regression coefficient of the characteristic functional group, and b2 is the offset of the characteristic functional group.

[0078] The light absorption coefficient of the sample to the infrared rays IV is calculated by using the formula:

[0079] wherein G2 is the light absorption coefficient of the sample to the infrared rays IV, K is the error coefficient, A2 is the diffuse reflection absorbance of the sample to the infrared rays IV, and S2 is the scattering coefficient of the sample to the infrared rays IV.

[0080] (3) Detection method of aroma functional group.

[0081] The gas volatilized in the heating and aging process is irradiated by infrared II with a wavelength of N3~N4; the incident light intensity and the outgoing light intensity of the gas irradiated by the infrared II are obtained, and the concentration of the aroma functional group in the gas is calculated by using the incident light intensity and the outgoing light intensity. The volatilized gas is irradiated by infrared II with a wavelength of N3~N4, and the wavelength is selected according to the characteristic absorption peak of the aroma functional group. For example, for the aroma functional group (aldehyde group) in ethyl vanillin, infrared with a wavelength of 3425nm~3676nm can be used for irradiation, for the aroma functional group (ketone group) of boryline and borylone, infrared with a wavelength of 1751nm~1945nm can be used for irradiation, and for the aroma functional group (furan ring) of furan coumarin, infrared with a wavelength of 5952nm~16250nm can be used for irradiation. The incident light intensity and the outgoing light intensity are obtained, and the concentration of the aroma functional group in the gas is determined by using the Beer's law calculation, so as to monitor the change of the aroma component in real time.

[0082] More specifically, the formula is used:

[0083] to calculate the concentration of the aroma functional group in the gas; wherein c2 is the measured value of the concentration of the aroma functional group, I3 is the incident intensity of the infrared II, I4 is the outgoing intensity of the infrared II, 2 is the molar absorption coefficient of the aroma functional group, and d is the detection distance.

[0084] In addition to the electromagnetic heating device, the application additionally provides an auxiliary device, including a stirrer, an ultrasonic oscillator and a vibrator.

[0085] The stirring device is widely used in industrial production equipment, and its core function is to realize uniform mixing of various components in the sample through mechanical stirring, so as to ensure that the sample can be uniformly heated during the heating process. This feature is of great significance to improve the sample processing efficiency, optimize the reaction conditions and ensure the product quality.

[0086] During the heating process, the stirring device breaks the temperature gradient in the sample through the rotating or reciprocating stirring blades, so that heat can be uniformly and quickly transmitted to every corner of the sample. Such uniform heating not only helps to improve the rate and uniformity of chemical reactions, but also effectively prevents the sample from burning or deteriorating due to local overheating.

[0087] In addition, the stirring device can also promote the sufficient contact and mixing between various components in the sample, providing more reaction interfaces and activation collision opportunities for chemical reactions, thereby further accelerating the reaction process. This effect is particularly significant in cases where high uniformity mixing and rapid reaction are required, such as the synthesis of high polymer materials, the activation of catalysts, etc.

[0088] In summary, the stirring device realizes uniform heating of the sample during heating through its unique stirring action, not only improves the processing efficiency, but also optimizes the reaction conditions, ensures the stability and consistency of product quality. Therefore, in industrial production and experimental research, the stirring device has become one of the indispensable important equipment, providing strong technical support for scientific research and production.

[0089] The ultrasonic oscillator performs well in the field of sample processing, especially in heat transfer and ripening promotion. The device significantly accelerates the internal heat transfer process by exciting ultrasonic vibrations inside the sample, ensuring uniform and rapid heating of the sample, and thus promoting the rapid progress of its internal chemical reactions. The role of ultrasonic vibration is not limited to heat transfer, it can also greatly promote frequent collisions between molecules, enhance the interaction between molecules, significantly improve the chemical reaction rate, and make the sample ripening process more rapid and efficient.

[0090] The introduction of ultrasonic technology not only optimizes the heating and reaction conditions of the sample, making the reaction more uniform and controllable, but also greatly improves the processing efficiency and reduces the processing time. At the same time, due to the uniform distribution of heat and reactants, the product quality has also been significantly improved, reducing the quality problems caused by local overheating or uneven reaction.

[0091] To deal with the problem of sample clumping during heating, which leads to uneven heat transfer, local high temperature and may cause coking, the motor-driven vibrator is used as an efficient and practical solution in this invention. The device uses the power provided by the motor to drive the internal vibration elements to produce regular vibrations, effectively breaking the adhesion between sample particles and preventing clumping, while improving the sample loose degree and promoting uniform distribution of heat energy. When vibration and heating are synchronized, heat energy smoothly penetrates the sample, reducing local hot spots, ensuring that the heating rate and temperature of each part of the sample are similar, achieving balanced heating. Therefore, the motor-driven vibration device is crucial in sample heating, it effectively prevents clumping, optimizes heat transfer efficiency, ensures uniform temperature, avoids local coking, and thus improves the quality of product heating treatment, providing a solid guarantee for the accuracy and reliability of experimental results.

[0092] The invention not only provides a control method for the electromagnetic heating device during the entire heating and ripening process, but also provides a control method for the above-mentioned stirrer, ultrasonic oscillator and vibrator. Specifically, as shown in Figure 1 The embodiment provides a control method for parallel enhancement of sample heating and ripening and heating assistance, comprising: S1 selects several process nodes from the heating and ripening process, and obtains the preset temperature of each process node sample and the sample standard index; the sample standard index includes the sample moisture content standard value, the content standard value of several characteristic functional groups, and the content standard value of several aroma functional groups.

[0093] In the heating and aging process, the process node is a key time point or state point with special significance. Several process nodes are selected because the entire heating and aging process is dynamic, and the physical and chemical properties of the sample at different stages change at different rates, and the reaction process is different. By determining these nodes, the continuous heating and aging process can be discretized, and targeted monitoring and control can be performed at each node.

[0094] In this embodiment, the adjacent process nodes, for example, J1-J2, can be regarded as a process section, and the preset temperature can remain unchanged, that is, temperature steady state, or can change unidirectionally, for example, heating or cooling, as shown in Figure 2 .

[0095] After setting the process nodes, the temperature of the sample at each process node is measured as the preset temperature, and the standard indicators of the sample at each process node are obtained, including the standard value of the moisture content of the sample, the standard value of the content of several characteristic functional groups, and the standard value of the content of several aroma functional groups. The above standard indicators are used to guide the control of the heating device and the heating auxiliary device.

[0096] The method for obtaining the standard indicators of the sample at each process node includes: S11 parallel heating and aging of several samples, and measuring the moisture content, the content of several characteristic functional groups, and the content of several aroma functional groups of the sample at each process node.

[0097] By setting multiple parallel samples, the influence of individual differences and accidental factors on the experimental results can be eliminated. For example, in pharmaceutical processes, there may be slight differences between different batches of raw materials, and parallel experiments can make the results more representative. Under the same heating and aging process conditions (such as the same heating equipment, temperature program, and environmental conditions), multiple samples are processed at the same time to ensure that each sample experiences the same process.

[0098] The moisture content, the content of characteristic functional groups, and the content of aroma functional groups of the sample are measured at each process node. In this embodiment, the number of parallel samples is 6, and of course the number of parallel samples can be increased according to requirements, but it is not recommended to be less than 6.

[0099] S12 the average value of the moisture content, the average value of the content of several characteristic functional groups, and the average value of the content of several aroma functional groups of several samples at a certain process node are taken as the standard value of the moisture content, the standard value of the content of several characteristic functional groups, and the standard value of the content of several aroma functional groups of the sample at the process node. Taking the average value can reduce the measurement error and improve the accuracy and stability of the data.

[0100] S13 the time function of each indicator of the sample is fitted using the standard indicators of each process node.

[0101] By establishing a mathematical model, the change rule of each index with time in the whole process can be intuitively reflected. Common fitting methods include linear regression, polynomial fitting, exponential fitting, etc. According to the change trend of the index, a suitable fitting method is selected, and the obtained function can be used for real-time monitoring and control. When the index deviates from the expected value of the function in actual production, the process parameters can be adjusted in time to ensure the stability of product quality, and also provide data support and theoretical basis for process optimization and new product development.

[0102] S2, in the heating and aging process, under the condition that the preset temperature of the sample between adjacent process nodes is stable, the electromagnetic heating device is controlled by a general control method or a preset frequency control method based on PID to heat, so that the sample temperature reaches the next process node from the current process node with stable state; otherwise, the electromagnetic heating device is controlled by a linear control method or a fine energy consumption control method to heat, so that the preset temperature of the sample from the current process node reaches the preset temperature of the next process node.

[0103] The preset temperature of the sample between adjacent process nodes can be stable or unidirectionally changed, so for the two cases, the corresponding heating control methods are provided in this embodiment. For the case that the preset temperature of the sample between adjacent process nodes is stable, the core target is to maintain the temperature stable to avoid fluctuations affecting the chemical reaction or physical state of the sample. The electromagnetic heating device can be controlled by a general control method or a preset frequency control method based on PID to heat, so that the sample temperature smoothly transitions from the current process node to the next process node.

[0104] And for the process node where the temperature will change, the core target is to accurately track the temperature setting curve to avoid overshoot or lag. The electromagnetic heating device can be controlled by a linear control method or a fine energy consumption control method to heat, so that the preset temperature of the sample from the current process node reaches the preset temperature of the next process node.

[0105] The four heating control methods are introduced below.

[0106] (1) General control method.

[0107] Specifically, the general control method includes using the formula:

[0108] to control the frequency of the electromagnetic heating device; wherein, is the real-time control frequency of the electromagnetic heating device, is the preset frequency of the electromagnetic heating device when the preset temperature of the process node Jn is stable, OT is the real-time temperature of the sample, and T Jn is the preset temperature of the sample in the process node Jn.

[0109] When the sample temperature is equal to the preset temperature, the current frequency of the electromagnetic heating device is kept as the preset frequency. When the actual sample temperature is lower, i.e. OT < T Jn , the real-time control frequency is higher than the preset frequency, the heating power is increased, and the temperature is raised to the preset value. Similarly, when the actual sample temperature is higher, i.e. OT > T Jn , the real-time control frequency is lower than the preset frequency, the heating power is reduced, and the temperature is lowered to the preset value.

[0110] In order to maintain the sample temperature at the preset frequency of the preset temperature, in the embodiment, the formula is used to calculate the preset frequency of the electromagnetic heating device when the sample at the process node Jn maintains the preset temperature in a steady state; wherein,

[0111] QE is the preset frequency of the electromagnetic heating device when the process node Jn is in a steady state, QE Jn is the chemical energy of the sample changed from the process node Jn-1 to the process node Jn; QW Jn is the energy required for the sample to evaporate moisture from the process node Jn-1 to the process node Jn; QS Jn is the energy released by the sample to the environment at a steady state temperature from the process node Jn-1 to the process node Jn, is the equivalent number of turns of the electromagnetic heating device, is the maximum main magnetic flux of the electromagnetic heating device, is the power factor, k is the effective absorption coefficient of the sample to heat, t Jn is the preset time from the process node Jn-1 to the process node Jn.

[0112] In the above formula, QE Jn is the energy absorbed or released by the sample in the chemical reaction (such as decomposition, synthesis) at the node, which is the energy required to maintain a specific chemical state. QW Jn includes the sensible heat and latent heat of water vaporization, reflecting the energy consumption of the change of the moisture state of the sample. QS Jn is based on the energy lost to the environment by heat conduction, radiation, etc., and is related to the temperature difference with the environment, the contact area, etc. The sum of the three is the total energy that needs to be compensated by the electromagnetic heating to maintain the temperature in a steady state.

[0113] Wherein, the chemical energy QE Jn of the sample changed from the process node Jn-1 to the process node Jn can be calculated by the formula:

[0114] ; wherein, QE Jn is the chemical energy of the sample changed from the process node Jn-1 to the process node Jn, the mass of the sample at process node Jn, the average chemical energy of the sample at process node Jn, the mass of the sample at process node Jn-1, the average chemical energy of the sample at process node Jn-1, the initial mass of the sample, the initial average chemical energy of the sample, n is the process node number.

[0115] In the above formula, the average chemical energy of the sample can be calculated by the formula:

[0116] ; wherein, is the average chemical energy of the sample per unit mass, is the mass of CO2, is the molar mass of CO2, is the chemical bond energy of CO2 per unit mass, is the mass of H2O, is the molar mass of H2O, is the chemical bond energy of H2O per unit mass, is the mass of O2, is the molar mass of O2, is the chemical bond energy of O2 per unit mass, m is the mass of the sample.

[0117] The energy QW required for the sample to evaporate water from process node Jn-1 to process node Jn Jn can be calculated by the formula:

[0118] ; wherein, QW Jn is the energy required for the sample to evaporate water from process node Jn-1 to process node Jn, C w is the specific heat capacity of water, is the difference between the preset temperature of the sample at process node Jn and process node Jn-1, is the mass of the evaporated water, and L is the latent heat of vaporization.

[0119] The energy QS released by the sample at process node Jn-1 to process node Jn to the environment at steady state temperature Jn can be calculated by the formula:

[0120] ; wherein, QS Jn is the energy released by the sample at process node Jn-1 to process node Jn to the environment at steady state temperature, k is the effective absorption coefficient of the sample to heat, T JnT is the preset temperature of the process node Jn sample EV t is the ambient temperature Jn T is the preset time from the process node Jn-1 to the process node Jn.

[0121] (2) PID-based preset frequency control method.

[0122] The PID-based preset frequency control method includes using the formula:

[0123] The frequency of the electromagnetic heating device is controlled; wherein, T is the real-time control frequency of the electromagnetic heating device, T is the preset frequency of the electromagnetic heating device when the process node Jn is in a preset temperature steady state Jn T is the preset temperature of the process node Jn sample; u(t) is the PID regulator output, T is the upper limit of the electromagnetic heating frequency control from the process node Jn-1 to the process node Jn when the preset temperature is in a steady state, T is the upper limit of the electromagnetic heating frequency control from the process node Jn-1 to the process node Jn when the preset temperature is in a steady state, which is usually set by system initialization; using the formula:

[0124] The PID regulator output is calculated; wherein u(t) is the PID regulator output, k p k is the proportional gain, e(t) is the deviation of the sample temperature of the process node Jn at time t from the preset value, t s t is the sampling time, t I t is the integral time, t d t is the differential time, d is the differential symbol, and t is the time t from the process node Jn-1 to the process node Jn.

[0125] The general control method has a simple algorithm structure, only needs to adjust the output in proportion to the temperature deviation, does not need complex mathematical models or parameter debugging, has low hardware requirements and low cost. It reacts directly to temperature changes, can quickly adjust the heating power, and is suitable for scenarios where the target temperature is quickly approached. However, when the system reaches equilibrium, the actual temperature will continue to deviate from the preset value (static error), and precise constant temperature cannot be achieved. It is sensitive to disturbances such as changes in environment and material characteristics, and the temperature fluctuation is large.

[0126] The PID-based preset frequency control method eliminates steady-state error through the integral element and suppresses overshoot through the differential element, has a small temperature fluctuation range, and achieves precise constant temperature. However, it needs to calculate the integral and differential in real time, and has high requirements for the performance of the controller.

[0127] Both methods have no advantage or disadvantage, and can be selected and applied according to actual needs.

[0128] (3) Linear control method.

[0129] The linear control method includes using the formula:

[0130] controlling the frequency of the electromagnetic heating device; wherein, is the real-time control frequency of the electromagnetic heating device, is the preset frequency of the electromagnetic heating device when the preset temperature changes from the process node Jn-1 to the process node Jn, Tt is the preset temperature of the sample at time t from the process node Jn-1 to the process node Jn, OT is the real-time temperature of the sample, is the difference between the preset temperature of the sample at the process node Jn and the process node Jn-1, is the upper limit of the electromagnetic heating frequency control when the preset temperature changes from the process node Jn-1 to the process node Jn, is the upper limit of the electromagnetic heating frequency control when the preset temperature changes from the process node Jn-1 to the process node Jn; using the formula:

[0131] calculating the preset frequency of the electromagnetic heating device when the sample maintains the preset temperature change at the process node Jn; wherein, is the preset frequency of the electromagnetic heating device when the preset temperature changes from the process node Jn-1 to the process node Jn, QY Jn is the heat required by the sample from the preset temperature at the process node Jn-1 to the preset temperature at the process node Jn, is the equivalent number of turns of the electromagnetic heating device, is the maximum main magnetic flux of the electromagnetic heating device, is the power factor, k is the effective absorption coefficient of the sample to heat, t Jn is the preset time of the sample from the process node Jn-1 to the process node Jn.

[0132] The preset frequency is proportional to the heat required for the sample to warm up, and inversely proportional to the energy transmission loss and device parameters. Through this formula, it is ensured that the output energy of the electromagnetic heating device matches the heat demand of the sample temperature change.

[0133] The heat QY required by the sample from the preset temperature at the process node Jn-1 to the preset temperature at the process node Jn Jn can be calculated using the formula:

[0134] ; wherein, QY JnC = (Cp * (Tn - Tn-1) * M) / (t * (Tn - Tn-1)) (1) s Cp is the specific heat capacity of the sample, which can be measured experimentally; M Jn M is the mass of the sample at process node Jn, (Tn - Tn-1) is the difference between the preset temperature of process node Jn and process node Jn-1, Qrad is the heat radiated by the sample to the environment during temperature change (when warming up) or absorbed from the environment (when cooling down), which can be calculated using the formula:

[0135] where, R is the heat transfer resistance, T t T is the sample temperature, T EV OT is the ambient temperature, and d is the differential symbol, t is the time t in process node Jn.

[0136] (4) A refined energy consumption control method.

[0137] The refined energy consumption control method includes controlling the frequency of the electromagnetic heating device using the formula:

[0138] where, QY is the real-time control frequency of the electromagnetic heating device, QY Jn C is the heat required by the sample from the preset temperature of process node Jn-1 to the preset temperature of process node Jn, t Jn t is the preset time from process node Jn-1 to process node Jn, R is the heat transfer resistance, T EV OT is the ambient temperature, and OT is the real-time temperature of the sample, (Tn - Tn-1) is the difference between the preset temperature of process node Jn and process node Jn-1, N is the equivalent number of turns of the electromagnetic heating device, Φm is the maximum main magnetic flux of the electromagnetic heating device, P is the power factor, and d is the differential symbol.

[0139] This method integrates the basic heat of sample warming, environmental heat exchange compensation, and temperature change rate adjustment, and calculates the real-time control frequency through electromagnetic device parameters. This makes the electromagnetic heating energy accurately match the actual needs of the sample (temperature change + heat exchange + dynamic change), avoids energy surplus or deficiency, and realizes refined energy consumption control.

[0140] The linear control method is based on the proportional adjustment frequency of the temperature deviation in the preset interval, so that the temperature changes linearly at a preset rate, the process is stable, and it is easy to track the preset temperature curve. The formula is relatively simple, the controller requires lower computing power, and it is easy to implement, and it is suitable for scenes that are sensitive to cost and have clear temperature change rules. However, it only adjusts according to the temperature difference and the preset change interval, and does not fully consider dynamic factors such as environmental heat dissipation and real-time heat exchange. If the environmental temperature fluctuates or the sample thermal characteristics change, the control accuracy will decrease. It is difficult to adapt to scenes with variable working conditions.

[0141] The fine energy consumption control method considers multiple factors such as sample variable temperature heat, environmental heat exchange, and temperature change rate, so that the heating energy accurately matches the actual demand, avoiding energy waste or deficiency. It can dynamically respond to complex working conditions such as changes in environmental temperature and sample thermal characteristics, such as compensating for heat loss and maintaining temperature stability when the environmental temperature is low in winter. However, it involves real-time calculation of multiple energy parameters, which requires high performance of sensors (real-time monitoring of temperature, environmental parameters, etc.) and controllers, increasing hardware costs.

[0142] Both have no advantages and disadvantages, and can be selected and applied according to actual conditions.

[0143] S3 During the heating and aging process, the sample is stirred by a stirrer, vibrated by an ultrasonic oscillator, and shaken by a shaker; and the stirring speed is controlled based on the sample moisture content, the ultrasonic power is controlled based on the sample characteristic functional group content, and the shaking frequency is controlled based on the aroma functional group content.

[0144] (1) Stirrer control method.

[0145] The method for controlling the stirring speed of the stirrer based on the sample moisture content in this embodiment includes using the formula:

[0146] The stirring speed of the stirrer is controlled; wherein n st is the real-time control speed of the stirrer, is the preset stirring speed of the process node Jn, is the lower limit of the stirring speed of the process node Jn, is the upper limit of the stirring speed of the process node Jn, is the lower limit of the sample moisture content adjustment of the process node Jn, is the upper limit of the sample moisture content adjustment of the process node Jn; is the monitoring value average of the sample moisture content in the window period t IW of the specified sampling time to; is the standard value of the sample moisture content of the process node Jn, The standard value of the moisture content of the process node Jn-1 sample.

[0147] The speed of the stirrer is controlled in segments by the above formula: If the average of the sample moisture monitoring values is lower than the lower limit of the adjustment, it indicates that the moisture is insufficient, and the stirring speed needs to be reduced. The preset speed is reduced by the adjustment amount to realize downward adjustment of the stirring speed, avoiding excessive loss of moisture.

[0148] If the average of the sample moisture monitoring values is between the lower limit and the upper limit of the adjustment, it indicates that the moisture content is in a reasonable range, and the stirring speed remains at the preset speed to maintain stable stirring effect and ensure uniform heating and mixing of the sample.

[0149] If the average of the sample moisture monitoring values is higher than the upper limit of the adjustment, it indicates that the moisture is excessive, and the stirring speed needs to be increased. The stirring is accelerated to promote evaporation of moisture, returning the moisture content to a reasonable range.

[0150] During the entire heating and maturation process, samples of the sample moisture are taken to guide the control of the stirring speed of the stirrer. In this embodiment, the average of the multiple sampling results in the window period of the specified sampling time point t0 is used as the control amount, rather than using the sampling result at a certain time point as the basis for controlling the stirrer. For example, the average of the multiple sampling results in the window period t IW of the specified sampling time point t0 is used as the control amount, i.e., the average of all sampling results in the time period of t IW / 2 before and t IW / 2 after the sampling time point t0, specifically, the formula:

[0151] is used to calculate the average of the monitoring values; wherein, is the average of the monitoring values of the sample moisture content in the window period t IW of the specified sampling time point t0, t0 is the specified sampling time point, t IW is the window period, d is the differential symbol, and t is the time t in the process node Jn. The formula calculates the average by integration to adapt to the characteristics of continuous real-time monitoring data and more accurately describe the average state of the sample moisture content in the window period, rather than using the ordinary average formula for discrete data.

[0152] The lower limit and the upper limit of the adjustment of the sample moisture content of the process node Jn together constitute a reasonable fluctuation range of the sample moisture content, and the calculation method is to use the formula:

[0153] to calculate the lower limit of the adjustment of the sample moisture content of the process node Jn; wherein, The lower limit of the sample moisture content adjustment value of the process node Jn, The time function of the sample moisture content standard value, The dead zone coefficient of the sample moisture content adjustment value, taking the value (0, 0.1) by default 0.005; The standard value of the sample moisture content of the process node Jn, The standard value of the sample moisture content of the process node Jn-1; The upper limit of the sample moisture content adjustment value of the process node Jn is calculated by the formula:

[0154] The upper limit of the sample moisture content adjustment value of the process node Jn, The upper limit of the sample moisture content adjustment value of the process node Jn, The time function of the sample moisture content standard value, The dead zone coefficient of the sample moisture adjustment value, taking the value (0, 0.1) by default 0.005; The standard value of the sample moisture content of the process node Jn, The standard value of the sample moisture content of the process node Jn-1.

[0155] (2) Ultrasonic oscillator control method.

[0156] The method for controlling the ultrasonic power of the ultrasonic oscillator based on the content of the characteristic functional group of the sample includes a master control characteristic functional group control method and a multi-characteristic functional group control method.

[0157] The so-called master control characteristic functional group control method is to adjust the ultrasonic power in real time by accurately calculating the upper and lower limits and the average change rate for a single key characteristic functional group, so as to ensure that the content of the functional group meets the process requirements. For example, the core component of Notopterygium, which is one of the characteristic functional groups of the furan ring, corresponds to the spectral wavelength: 9.09-10.00 μm.

[0158] The master control characteristic functional group control method includes using the formula:

[0159] The voltage of the ultrasonic oscillator is controlled; wherein V ul The real-time control voltage of the ultrasonic oscillator, k v The proportional coefficient, P ul The real-time control power of the ultrasonic oscillator, f v The frequency of the ultrasonic wave, The dielectric constant of the sample, σ is the conductivity of the sample, T v The propagation time of the ultrasonic wave, ρ is the average density of the sample; The formula establishes the relationship between the real-time control voltage of the ultrasonic oscillator and the power, sample characteristics, conductivity, average density, and ultrasonic parameters, and propagation time. The proportional coefficient is used to adjust the voltage output to ensure that the power matches the voltage. The voltage control can make the ultrasonic frequency constant but the amplitude not constant. This method is suitable for some chemical reactions inside the sample which have strict requirements on the ultrasonic frequency. A fixed frequency can trigger a specific molecular vibration mode to promote the target reaction. For example, the synthesis of a specific polymer requires a fixed frequency to make the monomer molecules collide and combine in a specific way. Although the amplitude fluctuates, as long as the frequency meets the requirements, the reaction direction can be ensured, and the amplitude change can be accepted within a certain range.

[0160] Alternatively, the formula is:

[0161] The output frequency of the ultrasonic oscillator is controlled; wherein f ul is the real-time output frequency of the ultrasonic oscillator, P ul is the real-time control power of the ultrasonic oscillator, A c is the amplitude of the ultrasonic wave, is the dielectric constant of the sample, σ is the conductivity of the sample, T v is the propagation time of the ultrasonic wave, and ρ is the average density of the sample.

[0162] This formula determines the real-time output frequency and realizes the correlation between the frequency and the power through the power, amplitude, sample characteristics, and propagation time to meet the requirements of different processes on the frequency. Output frequency adjustment is used to make the amplitude constant but the frequency not constant. Stable amplitude ensures consistent energy input everywhere, avoiding local overheating or uneven curing.

[0163] Regardless of voltage control or frequency adjustment, the power of the ultrasonic oscillator is actually used as the key parameter. Specifically, the formula is:

[0164] The power of the ultrasonic oscillator is controlled; wherein P ul is the real-time control power of the ultrasonic oscillator, is the preset ultrasonic power of the process node Jn, is the lower limit of the ultrasonic power of the process node Jn, is the upper limit of the ultrasonic power of the process node Jn, is the monitoring value average of the characteristic functional group content of the sample n number within the window period t ICn of the specified sampling time t0; is the average change rate of the standard value of the characteristic functional group content of the sample n number within the window period t ICn of the specified sampling time t0, The standard value of the characteristic functional group content of sample n for process node Jn, The standard value of the characteristic functional group content of sample n for process node Jn-1, The lower limit of the adjustment of the characteristic functional group content of sample n for process node Jn, The upper limit of the adjustment of the characteristic functional group content of sample n for process node Jn.

[0165] The above formula is a piecewise function for dynamically adjusting the real-time control power of the ultrasonic oscillator according to the relationship between the average value of the monitored value of the characteristic functional group content of sample n, i.e., the selected master characteristic functional group content, and the upper and lower limits of the adjustment.

[0166] If Below the lower limit , it indicates that the characteristic functional group content is insufficient, and the power is increased in proportion through the formula to enhance the ultrasonic effect to promote the change of the functional group.

[0167] If At this time Within a reasonable range, the preset power remains unchanged to avoid unnecessary power adjustment and ensure stable operation of the system.

[0168] If Above the upper limit , combined with The positive and negative (reflecting the change trend of the standard value) are adjusted in proportion through the formula.

[0169] In this embodiment, the average change rate of the standard value of the n characteristic functional group content within the window period t ICn At the sampling time t0 is calculated using the formula:

[0170] The average change rate is calculated; wherein, The average change rate of the n characteristic functional group content within the window period t ICn At the sampling time t0 is calculated using the formula: The standard value of the characteristic functional group content of sample n is a function of time, t0 is the specified sampling time, and t ICn Is the characteristic functional group sampling window period.

[0171] It reflects the average change of the standard value of the n characteristic functional group content within the window period (increasing or decreasing) with time. For example, during the heating and curing process of the sample, if the standard value of the master characteristic functional group content increases with the reaction, Is positive; if the standard value decreases due to certain process conditions, The average change rate makes the system respond to the change of the characteristic functional group content standard value in real time, avoiding the hysteresis of static control.

[0172] The upper limit of the adjustment of the master characteristic functional group content can be calculated by the formula:

[0173] The lower limit of the adjustment of the master characteristic functional group content is calculated; wherein, is the lower limit of the adjustment of the characteristic functional group content of sample n of process node Jn, is a time function of the characteristic functional group content standard value of sample n, is the characteristic functional group content adjustment dead zone coefficient of sample n, is the standard value of the characteristic functional group content of sample n of process node Jn, is the standard value of the characteristic functional group content of sample n of process node Jn-1. The upper limit of the adjustment of the master characteristic functional group content is calculated by the formula:

[0174] The upper limit of the adjustment of the master characteristic functional group content is calculated; wherein, is the upper limit of the adjustment of the characteristic functional group content of sample n of process node Jn, is a time function of the characteristic functional group content standard value of sample n, is the characteristic functional group content adjustment dead zone coefficient of sample n, is the standard value of the characteristic functional group content of sample n of process node Jn, is the standard value of the characteristic functional group content of sample n of process node Jn-1.

[0175] The multi-characteristic functional group control method refers to the comprehensive influence of multiple characteristic functional groups, and converts the multi-variable control quantity into a single adjustment signal through normalization processing, realizes multi-target collaborative control, and is suitable for scenes with complex correlation of characteristic functional groups. For example, the core component of Notopterygium is the characteristic functional group of the furan ring, the lactone ring, and the methoxy group, and the corresponding spectral wavelength is: 9.09-10.00 μm, 5.71-5.88 μm, and 3.38-3.53 μm.

[0176] The multi-characteristic functional group control method includes using the formula:

[0177] The voltage of the ultrasonic oscillator is controlled; wherein, V ul is the real-time control voltage of the ultrasonic oscillator, k v is a proportional coefficient, P ul is the real-time control power of the ultrasonic oscillator, fv is the frequency of the ultrasonic wave, is the dielectric constant of the sample, σ is the conductivity of the sample, T v is the propagation time of the ultrasonic wave, ρ is the average density of the sample; or, by using the formula:

[0178] controlling the output frequency of the ultrasonic oscillator; wherein, f ul is the real-time output frequency of the ultrasonic oscillator, P is the real-time control power of the ultrasonic oscillator, A is the amplitude of the ultrasonic wave, is the dielectric constant of the sample, σ is the conductivity of the sample, T is the propagation time of the ultrasonic wave, ρ is the average density of the sample; by using the formula:

[0179] controlling the power of the ultrasonic oscillator; wherein, P ul is the real-time control power of the ultrasonic oscillator, is the preset ultrasonic power of the process node Jn, is the lower limit of the ultrasonic power of the process node Jn, is the upper limit of the ultrasonic power of the process node Jn, is the multiple characteristic functional group normalization value.

[0180] Unlike the main control characteristic functional group control method, the multiple characteristic functional group control method needs to normalize the control quantity of multiple characteristic functional groups in the control formula, avoiding the influence of some functional groups being amplified or ignored due to dimensional differences.

[0181] The method for normalizing multiple characteristic functional groups in the multiple characteristic functional group control method includes using the formula:

[0182] calculating the multiple characteristic functional group normalization value; wherein, is the multiple characteristic functional group normalization value, is the comprehensive control quantity of the multiple characteristic functional groups; by using the formula: ;

[0183] calculating the comprehensive control quantity of the multiple characteristic functional groups; wherein, is the comprehensive control quantity of the n characteristic functional groups, u i-ul is the control quantity of the i-th characteristic functional group, is the window period t of the specified sampling time t0ICn the average of the monitoring values of the characteristic functional group content of the sample n, the window period t of the sampling time t0, ICn the average change rate of the standard value of the characteristic functional group content of the sample n, the standard value of the characteristic functional group content of the sample n of the process node Jn, the standard value of the characteristic functional group content of the sample n of the process node Jn-1, the lower limit of the adjustment of the characteristic functional group content of the sample n of the process node Jn, the upper limit of the adjustment of the characteristic functional group content of the sample n of the process node Jn.

[0184] (3) a vibrator control method.

[0185] In the embodiment, the method for controlling the vibration frequency of the vibrator based on the aroma functional group content of the sample includes a master aroma functional group control method and a multi-aroma functional group control method.

[0186] Like the characteristic functional group, the master aroma functional group control method focuses on a single key aroma functional group, and accurately adjusts the vibration frequency through comparison of the monitoring value with the preset interval and the change trend of the standard value. For example, the main aroma of Angelica is ligustilide, and the core functional group is lactone ring, and the corresponding wavelength is 5.65-5.71 μm.

[0187] The master aroma functional group control method includes using the formula:

[0188] control the vibration frequency of the vibrator; wherein f sh is the real-time control frequency of the vibrator, is the preset vibration frequency of the process node Jn, is the lower limit of the vibration frequency of the process node Jn, is the upper limit of the vibration frequency of the process node Jn, the window period t of the sampling time t0, OCm the average of the monitoring values of the aroma functional group content of the sample m, the window period t of the sampling time t0, OCm the average change rate of the standard value of the aroma functional group content of the sample m, the standard value of the aroma functional group content of the sample m of the process node Jn, the standard value of the aroma functional group content of the sample m of the process node Jn-1, the lower limit of the adjustment of the aroma functional group content of the sample m of the process node Jn, the upper limit of the adjustment of the aroma functional group content of the sample m of the process node Jn; using the formula:

[0189] The average change rate is calculated; wherein, is the average change rate of the standard value of the mth aroma functional group content in the window period of the specified sampling time t0, is a time function of the standard value of the mth aroma functional group content, t0 is the specified sampling time, and t is the current sampling time, OCm is the sampling window period of the aroma functional group; The formula is:

[0190] The lower limit of the adjustment of the main aroma functional group content is calculated; wherein, is the lower limit of the adjustment of the mth aroma functional group content of the sample of the process node Jn, is a time function of the standard value of the mth aroma functional group content, is the adjustment dead zone coefficient of the mth aroma functional group content of the sample, is the standard value of the mth aroma functional group content of the sample of the process node Jn, is the standard value of the mth aroma functional group content of the sample of the process node Jn-1. The formula is:

[0191] The upper limit of the adjustment of the main aroma functional group content is calculated; wherein, is the upper limit of the adjustment of the mth aroma functional group content of the sample of the process node Jn, is a time function of the standard value of the mth aroma functional group content, is the adjustment dead zone coefficient of the mth aroma functional group content of the sample, is the standard value of the mth aroma functional group content of the sample of the process node Jn, is the standard value of the mth aroma functional group content of the sample of the process node Jn-1.

[0192] The multi-aroma functional group control rule synthesizes multiple aroma functional group information, adjusts the frequency after normalization processing, and ensures the aroma quality of complex samples of multiple aroma functional groups. For example, the main aroma of Angelica is ligustilide, n-butyl phthalide, and senkyunolide, the core functional groups of which are lactone ring, olefin double bond, and benzene ring, and the corresponding wavelengths are 5.65-5.71 μm, 6.06-6.25 μm, and 11.1-14.3 μm.

[0193] The multi-aroma functional group control method includes using the formula:

[0194] ​controlling the vibration frequency of the vibrator; wherein, f sh controlling the vibration frequency of the vibrator in real time, preset vibration frequency for the process node Jn, lower limit of the vibration frequency for the process node Jn, upper limit of the vibration frequency for the process node Jn, u sh normalizing the plurality of aroma functional groups; The method for normalizing the plurality of aroma functional groups in the multi-aroma functional group control method comprises using the formula:

[0195] calculating the plurality of aroma functional group normalizing values; wherein, the plurality of aroma functional group normalizing values, the comprehensive control amount of the plurality of aroma functional groups; using the formula: ;

[0196] calculating the comprehensive control amount of the plurality of aroma functional groups; wherein, the comprehensive control amount of the m aroma functional groups, u i-sh the control amount of the i-th aroma functional group, the average change rate of the monitoring value of the sample m aroma functional group content in the window period t OCm within the specified sampling time t0; the average change rate of the monitoring value of the sample m aroma functional group content in the window period t OCm within the specified sampling time t0; the standard value of the sample m aroma functional group content of the process node Jn, the standard value of the sample m aroma functional group content of the process node Jn-1, the lower limit of the adjustment of the sample m aroma functional group content of the process node Jn, the upper limit of the adjustment of the sample m aroma functional group content of the process node Jn.

[0197] Embodiment two: The embodiment provides a sample heating and curing and heating auxiliary parallel enhanced control system, comprising: a standard index acquisition module, configured to select a plurality of process nodes from a heating and curing process, and acquire a preset temperature of a sample of each process node and a sample standard index; the sample standard index comprises a sample moisture content, a plurality of characteristic functional group contents, and a plurality of aroma functional group contents; The heating control module is configured to, in a case where a preset temperature of the sample between adjacent process nodes is a steady state, control the electromagnetic heating device to heat by a general control method or a preset frequency control method based on PID, so that the sample temperature reaches the next process node from the current process node in a steady state; otherwise, the electromagnetic heating device is controlled to heat by a linear control method or a fine energy consumption control method, so that the preset temperature of the sample from the current process node reaches the preset temperature of the next process node. The heating auxiliary control module is configured to, in a heating and aging process, stir the sample by a stirrer, vibrate the sample by an ultrasonic oscillator, and vibrate the sample by a vibrator; and control the stirring speed based on the moisture content of the sample, control the ultrasonic power based on the characteristic functional group content of the sample, and control the vibration frequency based on the aroma functional group content.

[0198] It should be noted that in this document, the terms "comprising", "including", or any other variant thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or device that comprises a list of elements does not only include those elements, but also other elements not expressly listed, or other elements inherent to such a process, method, article, or device. Without more limitations, an element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article, or device including the element.

[0199] From the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be realized by means of software and the necessary general hardware platform, of course, they can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes a plurality of instructions for causing a computer terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present application.

[0200] The embodiments of the present application are described above in combination with the drawings, but the present application is not limited to the above-mentioned specific embodiments, which are only illustrative and not limiting. Those skilled in the art can make many forms under the inspiration of the present application without departing from the purpose of the present application and the scope protected by the claims, which are all within the protection of the present application.

Claims

1. A method for controlling the parallel enhancement of sample heating and ripening, characterized in that... include: Several process nodes were selected from the heating and curing process, and the preset sample temperature and sample standard indicators for each process node were obtained. The sample standard indicators include the standard value of sample moisture content, the standard value of the content of several characteristic functional groups, and the standard value of the content of several aroma functional groups. When the sample temperature is in a steady state between adjacent process nodes, the electromagnetic heating device is controlled to heat the sample through a general control method or a PID-based preset frequency control method, so that the sample temperature reaches the next process node in a steady state from the current process node. Otherwise, the electromagnetic heating device is controlled by linear control method or refined energy consumption control method to heat the sample from the preset temperature of the current process node to the preset temperature of the next process node. During the heating and ripening process, the sample is stirred by a stirrer, vibrated by an ultrasonic oscillator, and vibrated by a vibrator. The stirring speed is controlled based on the sample moisture content, the ultrasonic power is controlled based on the content of characteristic functional groups in the sample, and the vibration frequency is controlled based on the content of aroma functional groups.

2. The control method for sample heating and ripening and parallel enhancement of heating assistance according to claim 1, characterized in that... Methods for obtaining the standard indicators of samples at each process node include: Several samples were subjected to parallel heating and aging, and the moisture content, content of several characteristic functional groups, and content of several aroma functional groups of the samples were measured at each process node. The average moisture content, average content of several characteristic functional groups, and average content of several aroma functional groups of several samples at a certain process node are used as the standard values ​​for moisture content, content of several characteristic functional groups, and content of several aroma functional groups of samples at that process node. The time functions of each sample index are fitted using the standard indices of each process node.

3. The control method for sample heating and ripening and parallel enhancement of heating assistance according to claim 1, characterized in that... The general control method includes using the formula: The frequency of the electromagnetic heating device is controlled; among which, For the real-time control frequency of the electromagnetic heating device, The preset frequency of the electromagnetic heating device is set for the steady-state temperature at process node Jn, where OT is the real-time temperature of the sample, and T is the preset frequency of the electromagnetic heating device. Jn The preset temperature for the sample at process node Jn; Using the formula: Calculate the preset frequency of the electromagnetic heating device that maintains the sample at a preset temperature steady state at process node Jn; where, The preset frequency of the electromagnetic heating device at steady-state temperature is set for process node Jn, QE Jn QW represents the chemical energy of the sample change from process node Jn-1 to process node Jn. Jn QS represents the energy required for moisture evaporation from the sample at process node Jn-1 to process node Jn. Jn This refers to the energy released into the environment by the sample at steady-state temperature from process node Jn-1 to process node Jn. This represents the equivalent number of turns of the electromagnetic heating device. This is the maximum main magnetic flux of the electromagnetic heating device. The power factor is given by k, where k is the effective heat absorption coefficient of the sample, and t is the power factor. Jn This is the preset time from process node Jn-1 to process node Jn.

4. The control method for parallel enhancement of sample heating and ripening and heating assistance according to claim 1, characterized in that... The preset frequency control method based on PID includes using the formula: The frequency of the electromagnetic heating device is controlled; among which, For the real-time control frequency of the electromagnetic heating device, For process node Jn, preset the frequency of the electromagnetic heating device when the temperature is steady-state, T Jn The preset temperature of the sample at process node Jn; u(t) is the output of the PID controller. To preset the upper limit of electromagnetic heating frequency control during steady-state temperature from process node Jn-1 to process node Jn, The upper limit of electromagnetic heating frequency control is preset for steady-state temperature from process node Jn-1 to process node Jn; Using the formula: Calculate the output of the PID controller; where u(t) is the output of the PID controller, k p Let e(t) be the proportional gain, and e(t) be the deviation of the sample temperature from the preset value at process node Jn at time t. s t is the sampling time. I Let t be the integration time. d Let d be the differential time, d be the differential symbol, and t be the time t from process node Jn-1 to process node Jn.

5. The control method for sample heating and ripening and parallel enhancement of heating assistance according to claim 1, characterized in that... The linear control method includes using the formula: The frequency of the electromagnetic heating device is controlled; among which, For the real-time control frequency of the electromagnetic heating device, Let Tt be the preset frequency of the electromagnetic heating device when the preset temperature changes from process node Jn-1 to process node Jn, and OT be the preset temperature of the sample at time t from process node Jn-1 to process node Jn. The difference between the preset sample temperature at process node Jn and process node Jn-1. The upper limit for electromagnetic heating frequency control is set to the preset temperature change from process node Jn-1 to process node Jn. The upper limit of electromagnetic heating frequency control is preset for temperature changes from process node Jn-1 to process node Jn. Using the formula: Calculate the preset frequency of the electromagnetic heating device to maintain a preset temperature change for the sample at process node Jn; where... QY is the preset frequency of the electromagnetic heating device when the preset temperature changes from process node Jn-1 to process node Jn. Jn This represents the amount of heat required for the sample to travel from the preset temperature at process node Jn-1 to the preset temperature at process node Jn. This represents the equivalent number of turns of the electromagnetic heating device. This is the maximum main magnetic flux of the electromagnetic heating device. The power factor is given by k, where k is the effective heat absorption coefficient of the sample, and t is the power factor. Jn This is the preset time from process node Jn-1 to process node Jn.

6. The control method for parallel enhancement of sample heating and ripening and heating assistance according to claim 1, characterized in that... The refined energy consumption control method includes using the following formula: The frequency of the electromagnetic heating device is controlled; among which, QY is the real-time control frequency for the electromagnetic heating device. Jn t represents the amount of heat required for the sample to travel from the preset temperature at process node Jn-1 to the preset temperature at process node Jn. Jn The preset time is set from process node Jn-1 to process node Jn. For heat transfer thermal resistance, T EV OT represents the ambient temperature, while OT represents the real-time temperature of the sample. The difference between the preset sample temperature at process node Jn and process node Jn-1. This represents the equivalent number of turns of the electromagnetic heating device. This is the maximum main magnetic flux of the electromagnetic heating device. Here, d represents the power factor, and d is the differential symbol.

7. The control method for sample heating and ripening and parallel enhancement of heating assistance according to claim 1, characterized in that... Methods for controlling the stirring speed of a stirrer based on the moisture content of a sample include using the following formula: The stirring speed of the agitator is controlled; where n st This is for real-time control of the stirrer's rotation speed. The preset stirring speed for process node Jn This represents the lower limit of the stirring speed at process node Jn. This represents the upper limit of the stirring speed at process node Jn. This is the lower limit for adjusting the moisture content of the sample at process node Jn. This is the upper limit for adjusting the moisture content of the sample at process node Jn; To specify the window period t for sampling time t0 IW The average value of moisture content in the internal samples; This represents the standard value for the moisture content of the sample at process node Jn. This is the standard value for the moisture content of the sample at process node Jn-1; Using the formula: Calculate the lower limit of adjustment for moisture content in the sample at process node Jn; where, This is the lower limit for adjusting the moisture content of the sample at process node Jn. The time function of the standard value of sample moisture content The dead zone coefficient is the adjustment value for the moisture content of the sample. This represents the standard value for the moisture content of the sample at process node Jn. This is the standard value for the moisture content of the sample at process node Jn-1; Using the formula: Calculate the upper limit for adjusting the moisture content of the sample at process node Jn; where, This represents the upper limit for adjusting the moisture content of the sample at process node Jn. The time function of the standard value of sample moisture content The dead zone coefficient is the sample moisture adjustment value. This represents the standard value for the moisture content of the sample at process node Jn. This is the standard value for the moisture content of the sample at process node Jn-1.

8. The control method for parallel enhancement of sample heating and ripening and heating assistance according to claim 1, characterized in that... Methods for controlling the ultrasonic power of an ultrasonic oscillator based on the content of characteristic functional groups in a sample include the master characteristic functional group control method and the multi-characteristic functional group control method. The controlling feature functional group control method includes the use of the following formula: Control the voltage of the ultrasonic oscillator; Among them, V ul k is the real-time control voltage for the ultrasonic oscillator. v P is the proportionality coefficient. ul f is the real-time control power of the ultrasonic oscillator. v The frequency of ultrasound. Let σ be the dielectric constant of the sample, σ be the conductivity of the sample, and T be the dielectric constant of the sample. v ρ is the propagation time of the ultrasonic wave, and ρ is the average density of the sample. Alternatively, using the formula: The output frequency of the ultrasonic oscillator is controlled; where f ul P is the real-time output frequency of the ultrasonic oscillator. ul A is the real-time control power of the ultrasonic oscillator. c This represents the amplitude of the ultrasonic wave. Let σ be the dielectric constant of the sample, σ be the conductivity of the sample, and T be the dielectric constant of the sample. v ρ is the propagation time of the ultrasonic wave, and ρ is the average density of the sample. Using the formula: The power of the ultrasonic oscillator is controlled; where P ul This refers to the real-time control power of the ultrasonic oscillator. The preset ultrasonic power for process node Jn. This represents the lower limit of ultrasonic power at process node Jn. This represents the upper limit of ultrasonic power at process node Jn. To specify the window period t for sampling time t0 ICn The average value of the monitored content of characteristic functional group n in the sample; For the window period t at the specified sampling time t0 ICn The average rate of change of the standard value of the content of characteristic functional group n inside, This represents the standard value for the content of characteristic functional groups in sample n at process node Jn. This represents the standard value for the content of characteristic functional group n in sample n at process node Jn-1. This is the lower limit for adjusting the content of characteristic functional groups in sample n at process node Jn. This is the upper limit for adjusting the content of characteristic functional groups of sample n at process node Jn; Using the formula: Calculate the average rate of change; where, For the window period t at the specified sampling time t0 ICn The average rate of change of the standard value of the content of characteristic functional group n inside, The content of characteristic functional group n in sample n is a time function, where t0 is the specified sampling time, and t ICn The sampling window period for characteristic functional groups; Using the formula: Calculate the lower limit of regulation of the content of the main control functional groups; among which, This is the lower limit for adjusting the content of characteristic functional groups in sample n at process node Jn. This is a time function of the standard value of the content of characteristic functional group n in sample n. The dead zone coefficient is adjusted based on the content of characteristic functional group n in sample n. This represents the standard value for the content of characteristic functional groups in sample n at process node Jn. The standard value for the content of characteristic functional group n in sample n at process node Jn-1; Using the formula: Calculate the upper limit of regulation of the content of the main control functional groups; where, This represents the upper limit for adjusting the content of characteristic functional groups of sample n at process node Jn. This is a time function of the standard value of the content of characteristic functional group n in sample n. The dead zone coefficient is adjusted based on the content of characteristic functional group n in sample n. This represents the standard value for the content of characteristic functional groups in sample n at process node Jn. The standard value for the content of characteristic functional group n in sample n at process node Jn-1; The multi-feature functional group control method includes the use of the formula: The voltage of the ultrasonic oscillator is controlled; where V ul k is the real-time control voltage for the ultrasonic oscillator. v P is the proportionality coefficient. ul f is the real-time control power of the ultrasonic oscillator. v The frequency of ultrasound. Let σ be the dielectric constant of the sample, σ be the conductivity of the sample, and T be the dielectric constant of the sample. v ρ is the propagation time of the ultrasonic wave, and ρ is the average density of the sample. Alternatively, using the formula: The output frequency of the ultrasonic oscillator is controlled; where f ul Where is the real-time output frequency of the ultrasonic oscillator, Pul is the real-time control power of the ultrasonic oscillator, and A is the amplitude of the ultrasonic wave. σ is the dielectric constant of the sample, Tv is the propagation time of the ultrasound, and ρ is the average density of the sample. Using the formula: The power of the ultrasonic oscillator is controlled; where P ul This refers to the real-time control power of the ultrasonic oscillator. The preset ultrasonic power for process node Jn. This represents the lower limit of ultrasonic power at process node Jn. This represents the upper limit of ultrasonic power at process node Jn. Normalize the values ​​for multiple characteristic functional groups; In the multi-feature functional group control method, the normalization of multiple feature functional groups includes using the following formula: Calculate the normalization values ​​of multiple feature functional groups; where, Normalize the values ​​for multiple characteristic functional groups. It represents the comprehensive control quantity for multiple characteristic functional groups; Using the formula: ； Calculate the combined control quantity of multiple characteristic functional groups; among which, u is the combined control quantity for n characteristic functional groups. i-ul Let i be the control quantity for the i-th characteristic functional group. To specify the window period t for sampling time t0 ICn The average value of the monitored content of characteristic functional group n in the sample; For the window period t at the specified sampling time t0 ICn The average rate of change of the standard value of the content of characteristic functional group n inside, This represents the standard value for the content of characteristic functional groups in sample n at process node Jn. This represents the standard value for the content of characteristic functional group n in sample n at process node Jn-1. This is the lower limit for adjusting the content of characteristic functional groups in sample n at process node Jn. This is the upper limit for adjusting the content of characteristic functional groups of sample n at process node Jn.

9. The control method for parallel enhancement of sample heating and ripening and heating assistance according to claim 1, characterized in that... Methods for controlling the vibration frequency of a vibrator based on the content of aroma functional groups in a sample include the main aroma functional group control method and the multi-aroma functional group control method. The method for controlling the main aroma functional groups includes the use of the following formula: The vibration frequency of the vibrator is controlled; where f sh To control the frequency of the vibrator in real time, The vibration frequency preset for process node Jn This represents the lower limit of the vibration frequency at process node Jn. This represents the upper limit of the vibration frequency at process node Jn. To specify the window period t for sampling time t0 OCm The average value of the aroma functional group content of sample m. For the window period t at the specified sampling time t0 OCm The average rate of change of the standard value of aroma functional group content in component m. This represents the standard value for the aroma functional group content of sample m at process node Jn. This refers to the standard value for the aroma functional group content of sample m at process node Jn-1. This is the lower limit for adjusting the aroma functional group content of sample m at process node Jn. The upper limit for adjusting the aroma functional group content of sample m at process node Jn; Using the formula: Calculate the average rate of change; where, For the window period at the specified sampling time t0 The average rate of change of the standard value of aroma functional group content in component m. Let be the time function of the standard value of aroma functional group content m, where t0 is the specified sampling time, and t OCm The sampling window period for aroma functional groups; Using the formula: Calculate the lower limit of adjustment for the content of the main aroma functional groups; among which, This is the lower limit for adjusting the aroma functional group content of sample m at process node Jn. The time function represents the standard value of the content of aroma functional group m. The dead zone coefficient was adjusted to account for the aroma functional group content of sample m. This represents the standard value for the aroma functional group content of sample m at process node Jn. The standard value for the aroma functional group content of sample m at process node Jn-1; Using the formula: Calculate the upper limit of adjustment for the content of the main aroma functional groups; among which, This represents the upper limit for adjusting the aroma functional group content of sample m at process node Jn. The time function represents the standard value of the content of aroma functional group m. The dead zone coefficient was adjusted to account for the aroma functional group content of sample m. This represents the standard value for the aroma functional group content of sample m at process node Jn. The standard value for the aroma functional group content of sample m at process node Jn-1; The method for controlling the multi-aroma functional groups includes the use of the following formula: The vibration frequency of the vibrator is controlled; where f sh To control the frequency of the vibrator in real time, The vibration frequency preset for process node Jn This represents the lower limit of the vibration frequency at process node Jn. u is the upper limit of the vibration frequency at process node Jn. sh Normalize multiple aroma functional groups to a single value; In the multi-aroma functional group control method, the normalization of multiple aroma functional groups includes using formulas: Calculate the normalization values ​​of multiple aroma functional groups; among which, To normalize the values ​​of multiple aroma functional groups, It represents the combined control amount of multiple aroma functional groups; Using the formula: ； Calculate the combined control amount of multiple aroma functional groups; among which, u represents the overall control quantity for m aroma functional groups. i-sh Let be the control amount for the i-th aroma functional group. To specify the window period t for sampling time t0 OCm The average value of the aroma functional group content of sample m within the sample; For the window period t at the specified sampling time t0 OCm The average rate of change of the standard value of aroma functional group content in component m. This represents the standard value for the aroma functional group content of sample m at process node Jn. This refers to the standard value for the aroma functional group content of sample m at process node Jn-1. This is the lower limit for adjusting the aroma functional group content of sample m at process node Jn. This is the upper limit for adjusting the aroma functional group content of sample m at process node Jn.

10. A control system for parallel enhancement of sample heating and ripening with heating assistance, characterized in that... include: The standard index acquisition module is used to select several process nodes from the heating and aging process, and acquire the preset sample temperature and sample standard index for each process node; the sample standard index includes sample moisture content, content of several characteristic functional groups, and content of several aroma functional groups. The heating control module is used to control the electromagnetic heating device to heat the sample when the preset sample temperature is steady between adjacent process nodes, using a general control method or a preset frequency control method based on PID, so that the sample temperature can reach the next process node in a steady state from the current process node. Otherwise, the electromagnetic heating device is controlled by linear control method or refined energy consumption control method to heat the sample from the preset temperature of the current process node to the preset temperature of the next process node. The heating auxiliary control module is used to stir the sample with a stirrer, vibrate the sample with an ultrasonic oscillator, and vibrate the sample with a vibrator during the heating and ripening process; and to control the stirring speed based on the sample moisture content, the ultrasonic power based on the content of characteristic functional groups in the sample, and the vibration frequency based on the content of aroma functional groups.

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