Building material fluid equipment heating method and system based on multi-dimensional data analysis

Abstract

The invention discloses a building material fluid equipment heating method and system based on multi-dimensional data analysis, and particularly relates to the field of industrial heating, comprising data acquisition and sensor deployment, data preprocessing, multi-dimensional data analysis and control strategy design. Through data acquisition and sensor deployment, the working state of the fluid equipment is monitored in real time, accurate data is provided for subsequent analysis, then, data preprocessing and establishment of a multi-dimensional data analysis model reveal the interaction relationship among physical fields in the fluid equipment, a scientific basis is provided for optimizing the heating process, and the working state of the fluid equipment is monitored in real time. And finally, according to a control strategy designed according to a multi-dimensional data analysis result, precise control over the heating process of the fluid equipment is achieved, and the energy efficiency and the production efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of building material processing technology, and more specifically, to a heating method and system for building material fluid equipment based on multidimensional data analysis. Background Technology

[0002] The operating process of existing building material fluid equipment is roughly as follows: First, the material to be heated is loaded into the main body of the equipment; then, the main body of the equipment begins to rotate to ensure that the material is evenly distributed and fully heated within the equipment; at the same time, an external electromagnetic generator produces a high-frequency alternating magnetic field that penetrates the main body of the equipment and induces eddy currents inside the material; the eddy currents flow inside the material and generate heat, thereby heating the material. During the heating process, the heating temperature and heating rate of the material can be controlled by adjusting the intensity and frequency of the electromagnetic field.

[0003] However, despite the many advantages of existing electromagnetic heating fluid equipment, there are still some shortcomings in practical applications. On the one hand, the monitoring and control of the electromagnetic field, temperature field and material heating process inside the fluid equipment are not precise enough, resulting in room for improvement in heating efficiency and relatively high energy consumption. On the other hand, existing technologies lack an effective means to adjust and optimize the heating process in real time to adapt to changes in the requirements of different materials and processes, thus limiting further improvement in production efficiency. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a heating method and system for building material fluid equipment based on multidimensional data analysis, which solves the problems mentioned in the background art through the following solutions.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a heating method for building material fluid equipment based on multidimensional data analysis, comprising: Step 1: Data Acquisition and Sensor Deployment: Monitor the electromagnetic field coupling characteristics, interface energy conversion, and multi-physics coupling parameters of the target fluid device by deploying sensors; Step 2: Data Preprocessing: This step involves preprocessing the data collected in Step 1. Step 3: Multidimensional data analysis: This step is used to establish a multidimensional data analysis model to perform multidimensional data analysis on the preprocessed data. Step 4: Control Strategy Design: This step involves designing control strategies based on the results of multidimensional data analysis.

[0006] Preferably, the electromagnetic field coupling characteristics in step 1 include the resonant frequency response value of the main body of the device, the electromagnetic induction coefficient of the material, the electromagnetic shielding interference degree, and the magnetic field penetration attenuation curve exponent; the interface energy conversion includes the interparticle capacitance effect value, the interface polarization intensity, the double-layer energy density, and the dynamic change rate of the dielectric loss factor; and the multi-physics coupling parameters include the thermo-mechanical-electric multi-field coupling coefficient, the plasma conductivity, the Maxwell stress tensor, and the eddy current-heat flow-mass flow mutual feedback intensity coefficient.

[0007] Preferably, the resonant frequency response value of the device body is obtained by connecting a Keysight spectrum analyzer to an induction coil installed inside the device body, gradually adjusting the frequency of an external signal generator, scanning from the lowest point, and recording the frequency at which the device body's response current reaches its peak value. This frequency is the resonant frequency response of the device body. The electromagnetic induction coefficient of the material is obtained by selecting a material sample of known size in the laboratory, using an Agilent LCR Meter, winding a coil around the material sample and passing a known alternating current through it, measuring the induced electromotive force through the material, and calculating the electromagnetic induction coefficient according to the induction coefficient formula. The electromagnetic shielding interference level is obtained by placing two EMI probes inside and outside the device body simultaneously, activating the transmitter to emit electromagnetic interference of known intensity inside the device body, measuring the field strength at the probe outside the device body, and calculating the electromagnetic shielding attenuation value, expressed in decibels. The magnetic field penetration attenuation curve exponent is obtained by fixing a magnetic field probe on an embedded slide rail, inserting it into the device body at a controllable rate, measuring the magnetic field strength at each depth with a high-resolution magnetometer, plotting the curve using software tools after the measurement is completed, and calculating the attenuation exponent of the curve.

[0008] Preferably, the interparticle capacitance value is obtained by uniformly distributing the material particles on an APC automated capacitance testing platform, using a CH Instruments capacitance tester, measuring the interparticle capacitance under pressure, and recording the measured capacitance value. The interface polarization intensity is obtained by applying an enhanced electric field to the sample surface, recording the polarization current using a Radiant Technologies polarization analyzer, and calculating the interface polarization intensity in conjunction with the material surface area. The double-layer energy density is obtained by applying progressively increasing potentials to the material surface on a Metrohm Autolab, recording the current response, calculating the total energy stored in the double layer by integrating the potential and current, and then converting it into energy density per unit area. The dielectric loss factor dynamic rate of change is obtained by measuring the dielectric loss factor in a frequency range from low to high using a Novocontrol dielectric frequency analyzer, calculating the difference between each frequency point, and analyzing the average dynamic rate of the loss factor change with frequency.

[0009] Preferably, the thermo-mechanical-electrical multi-field coupling coefficient is obtained by using COMSOL Multiphysics, combined with experimental test data, setting multi-field interaction conditions, and extracting the coupling coefficient by comparing simulation with actual measurements. The plasma conductivity is obtained by placing a Langmuir probe in the discharge experiment, immersing the probe in the plasma inside the device, recording the current and voltage characteristic curves, and calculating the conductivity by combining the slope of the curve with plasma theory formulas. The Maxwell stress tensor is obtained by using ANSYS Maxwell to perform stress analysis on the object under the action of the magnetic field inside the device, and comparing the stress tensor with the experimental data through model comparison. The eddy current-heat flow-mass flow mutual feedback intensity is obtained by using the National Instruments distributed sensing system, which simultaneously records the eddy current induction, current flow, and heat flow in the material, and calculates the mutual feedback intensity coefficient between the three by the relative time difference and amplitude difference.

[0010] Preferably, step 2 preprocesses the electromagnetic field coupling characteristics, interface energy conversion, and multiphysics coupling parameters collected in step 1 using electromagnetic field coupling characteristic formulas, interface energy conversion formulas, and multiphysics coupling parameter formulas.

[0011] Preferably, the electromagnetic field coupling characteristic formula is specifically expressed as follows: F1 represents the electromagnetic field coupling value, KRFR represents the resonant frequency response value of the main body of the equipment, EIC represents the electromagnetic induction coefficient of the material, ESID represents the electromagnetic shielding interference degree, and MFPAI represents the magnetic field penetration attenuation curve exponent.

[0012] Preferably, the interface energy conversion formula is specifically expressed as follows: F2 represents the overall energy conversion value of the interface, IPCE represents the capacitance effect value between material particles, IPI represents the interface polarization intensity, DLED represents the double layer energy density, and DCRDLF represents the dynamic change rate of dielectric loss factor.

[0013] Preferably, the formula for the multiphysics coupling parameters is specifically expressed as follows: F3 represents the multiphysics field coupling parameter value, TMECC represents the thermo-mechanical-electric multi-field coupling coefficient, PC represents the plasma conductivity, MST represents the Maxwell stress tensor, and ETMFCC represents the eddy current-heat flow-mass flow mutual feedback intensity coefficient.

[0014] Preferably, the multidimensional data analysis model includes an electromagnetic-thermal coupling efficiency analysis model, an interface energy conversion analysis model, and a multi-field coupling strength analysis model.

[0015] Preferably, the electromagnetic-thermal coupling efficiency analysis model is specifically expressed as follows: η represents the electromagnetic-thermal conversion coefficient, k1 represents the electromagnetic-thermal conversion coefficient, μ represents the magnetic permeability of the material, μ0 represents the vacuum magnetic permeability, σ represents the electrical conductivity of the material, E represents the applied electric field strength, ρ represents the material density, Cp represents the material specific heat capacity, d represents the actual thickness of the material, and δ represents the skin depth of the electromagnetic wave in the material.

[0016] Preferably, the interface energy conversion analysis model is specifically expressed as follows: P represents the multi-field coupling energy absorption coefficient, k2 represents the interface energy conversion coefficient, εr represents the material's relative permittivity, ε0 ​​represents the vacuum permittivity, ω represents the electromagnetic field angular frequency, |E| represents the electric field strength modulus, tanδ represents the dielectric loss tangent, and σ represents the material's conductivity.

[0017] Preferably, the multi-field coupling strength analysis model is specifically expressed as follows: I represents the multi-field coupling comprehensive eigenvalue, k3 represents the multi-field coupling adjustment coefficient, ∂T / ∂t represents the partial derivative of temperature with respect to time, v represents the material velocity vector, ∇T represents the temperature gradient, α represents the material thermal diffusivity, and ∇ 2 T represents the Laplace operator for temperature, Q(E,H) represents the electromagnetic field energy conversion function, k4 represents the energy conversion adjustment coefficient, and |E| represents the magnitude of the electric field strength.

[0018] Preferably, the control strategy includes an energy efficiency control strategy, a power balance control strategy, and a field strength distribution control strategy.

[0019] Preferably, the energy efficiency control strategy is as follows: when 0.4η < η_ref, the voltage control command UV = UV_base + 0.15UV_base, the frequency control command f = f_base + 2000, and the speed control command v = 0.9v_base; when 0.4η_ref ≤ η ≤ 1.2η_ref, the voltage maintenance command UV = UV_base, the frequency maintenance command f = f_base, and the speed maintenance command v = v_base; when η > 1.2η_ref, the voltage control command UV = UV_base - 0.12UV_base, the frequency control command f = f_base - 1500, and the speed control command v = 1.08v_base.

[0020] Preferably, the power balance control strategy is as follows: when P < 0.85P_ref, the power boost instruction P_in = P_in_base + 0.2P_in_base, the resonance compensation instruction C = C_base + 0.1C_base, and the impedance matching instruction Z = Z_base - 0.15Z_base; when 0.85P_ref ≤ P ≤ 1.15P_ref, the power maintenance instruction is: P_in = P_in_base, the resonance maintenance instruction C = C_base, and the impedance maintenance instruction Z = Z_base; when P > 1.15P_ref, the power reduction instruction P_in = P_in_base - 0.18P_in_base, the resonance adjustment instruction C = C_base - 0.08C_base, and the impedance adjustment instruction Z = Z_base + 0.12Z_base.

[0021] Preferably, the field strength distribution control strategy is as follows: when I < 0.9I_ref, the field strength enhancement command E = E_base + 0.25E_base, the phase adjustment command φ = φ_base + 30°, and the waveguide adjustment command h = h_base - 0.1h_base; when 0.9I_ref ≤ I ≤ 1.1I_ref, the field strength maintenance command E = E_base, the phase maintenance command φ = φ_base, and the waveguide maintenance command h = h_base; when I > 1.1I_ref, the field strength reduction command E = E_base - 0.22E_base, the phase adjustment command φ = φ_base - 25°, and the waveguide adjustment command h = h_base + 0.08h_base.

[0022] Preferably, a heating system for building material fluid equipment based on multidimensional data analysis includes: Data acquisition and sensor deployment module: Monitors the electromagnetic field coupling characteristics, interface energy conversion, and multi-physics coupling parameters of the target fluid device by deploying sensors, and transmits the data to the data preprocessing module; Data preprocessing module: Used to preprocess the data collected by the data acquisition and sensor deployment module and transmit it to the multidimensional data analysis module; Multidimensional data analysis module: Used to establish a multidimensional data analysis model to perform multidimensional data analysis on the preprocessed data and transmit it to the control strategy design module; Control strategy design module: Used to design control strategies based on the results of multidimensional data analysis.

[0023] The technical effects and advantages of this invention are as follows: This invention enables real-time monitoring of the electromagnetic field coupling characteristics, interface energy conversion, and multi-physics coupling parameters of fluid equipment through data acquisition and sensor deployment. This provides precise data support for subsequent data analysis and control strategy design. The acquired data covers multiple key parameters, including the resonant frequency response of the equipment body, the electromagnetic induction coefficient of the material, and the electromagnetic shielding interference level, facilitating a comprehensive understanding of the fluid equipment's operating status. Data preprocessing, using formulas for electromagnetic field coupling characteristics, interface energy conversion, and multi-physics coupling parameters, further refines key information, laying the foundation for subsequent multi-dimensional data analysis. Multi-dimensional data analysis models are established, including electromagnetic-thermal coupling efficiency analysis models and interface energy conversion analysis models. The system employs a multi-field coupling strength analysis model to comprehensively and deeply analyze the working state of fluid equipment. Through multi-dimensional data analysis, it reveals the interaction relationships between various physical fields within the fluid equipment, providing a scientific basis for optimizing the heating process and improving energy efficiency. By designing energy efficiency control strategies, power balance control strategies, and field strength distribution control strategies, it achieves precise control of the fluid equipment heating process. The control strategy design considers the needs under various operating conditions, such as energy efficiency, power balance, and field strength distribution, ensuring that the fluid equipment maintains efficient and stable operation under different conditions. By adjusting control commands such as voltage, frequency, and speed, it can achieve real-time adjustment of the fluid equipment heating process, optimizing the heating effect and improving production efficiency. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the method structure of the present invention.

[0025] Figure 2 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] refer to Figure 1 The heating method for building material fluid equipment based on multidimensional data analysis, as shown, includes the following steps: Step 1: Data Acquisition and Sensor Deployment: Monitor the electromagnetic field coupling characteristics, interface energy conversion, and multi-physics coupling parameters of the target fluid equipment by deploying sensors.

[0028] In step 1, the electromagnetic field coupling characteristics include the resonant frequency response value of the main body of the equipment, the electromagnetic induction coefficient of the material, the electromagnetic shielding interference degree, and the magnetic field penetration attenuation curve exponent. The interface energy conversion includes the capacitance effect value between material particles, the interface polarization intensity, the double electric layer energy density, and the dynamic change rate of the dielectric loss factor. The multi-physics coupling parameters include the thermo-mechanical-electric multi-field coupling coefficient, the plasma conductivity, the Maxwell stress tensor, and the eddy current-heat flow-mass flow mutual feedback intensity coefficient.

[0029] The resonant frequency response of the device body is determined by connecting a Keysight spectrum analyzer to an induction coil installed inside the device body, gradually adjusting the frequency of an external signal generator, scanning from the lowest point, and recording the frequency at which the device body's response current reaches its peak. This frequency is the resonant frequency response of the device body. The electromagnetic induction coefficient of the material is determined by selecting a material sample of known size in the laboratory, using an Agilent LCR Meter, winding a coil around the material sample and passing a known alternating current through it, measuring the induced electromotive force through the material, and calculating the electromagnetic induction coefficient according to the induction coefficient formula. The electromagnetic shielding interference level is determined by placing two EMI probes inside and outside the device body simultaneously, activating the transmitter to emit electromagnetic interference of known intensity inside the device body, measuring the field strength at the probes outside the device body, and calculating the electromagnetic shielding attenuation value, expressed in decibels. The magnetic field penetration attenuation curve exponent is determined by fixing a magnetic field probe on an embedded slide rail, inserting it into the device body at a controllable rate, measuring the magnetic field strength at each depth using a high-resolution magnetometer, plotting the curve using software tools after measurement, and calculating the attenuation exponent of the curve.

[0030] The interparticle capacitance value was determined by uniformly distributing the material particles on an APC automated capacitance testing platform, using a CH Instruments capacitance tester, measuring the interparticle capacitance under pressure, and recording the measured capacitance value. Interface polarization intensity was calculated by applying an enhanced electric field to the sample surface, recording the polarization current using a Radiant Technologies polarization analyzer, and combining this with the material surface area. Double-layer energy density was determined by applying progressively increasing potentials to the material surface on a Metrohm Autolab, recording the current response, calculating the total energy stored in the double layer by integrating the potential and current, and then converting it to energy density per unit area. The dynamic rate of change of the dielectric loss factor was determined by measuring the dielectric loss factor in a frequency range from low to high using a Novocontrol dielectric frequency analyzer, calculating the difference between each frequency point, and analyzing the average dynamic rate of the loss factor change with frequency.

[0031] The thermo-mechanical-electrical multi-field coupling coefficient was obtained using COMSOL Multiphysics, combined with experimental test data. Multiple field conditions were set, and the coupling coefficient was extracted by comparing simulations with actual measurements. Plasma conductivity was calculated by placing a Langmuir probe in a discharge experiment, immersing the probe in the plasma within the device, and recording the current-voltage characteristic curves. The conductivity was calculated using the curve slope and plasma theory formulas. The Maxwell stress tensor was obtained using ANSYS Maxwell to analyze the stress of an object under the influence of a magnetic field within the device. The stress tensor was obtained by comparing the model with experimental data. The eddy current-heat flow-mass flow mutual feedback strength was determined using a National Instruments distributed sensing system, simultaneously recording eddy current induction, current flow, and heat flow in the material. The mutual feedback strength coefficient between the three was calculated using relative time difference and amplitude difference.

[0032] Step 1 involves handling missing data, detecting outliers, and standardizing the collected data; this embodiment will not provide specific details.

[0033] Step 2: Data preprocessing: This step is used to preprocess the data collected in Step 1.

[0034] Step 2 preprocesses the electromagnetic field coupling characteristics, interface energy conversion, and multiphysics coupling parameters collected in Step 1 using the electromagnetic field coupling characteristic formula, interface energy conversion formula, and multiphysics coupling parameter formula.

[0035] The electromagnetic field coupling characteristic formula is specifically expressed as follows: F1 represents the electromagnetic field coupling value, KRFR represents the resonant frequency response value of the main body of the equipment, EIC represents the electromagnetic induction coefficient of the material, ESID represents the electromagnetic shielding interference degree, and MFPAI represents the magnetic field penetration attenuation curve exponent.

[0036] The interface energy conversion formula is specifically expressed as follows: F2 represents the overall energy conversion value of the interface, IPCE represents the capacitance effect value between material particles, IPI represents the interface polarization intensity, DLED represents the double layer energy density, and DCRDLF represents the dynamic change rate of dielectric loss factor.

[0037] The formula for the multiphysics coupling parameters is specifically expressed as follows: F3 represents the multiphysics field coupling parameter value, TMECC represents the thermo-mechanical-electric multi-field coupling coefficient, PC represents the plasma conductivity, MST represents the Maxwell stress tensor, and ETMFCC represents the eddy current-heat flow-mass flow mutual feedback intensity coefficient.

[0038] Step 3: Multidimensional data analysis: This step is used to establish a multidimensional data analysis model to perform multidimensional data analysis on the preprocessed data.

[0039] The multidimensional data analysis model includes an electromagnetic-thermal coupling efficiency analysis model, an interface energy conversion analysis model, and a multi-field coupling strength analysis model.

[0040] The electromagnetic-thermal coupling efficiency analysis model is specifically expressed as follows: η represents the electromagnetic-thermal conversion coefficient, k1 represents the electromagnetic-thermal conversion coefficient, μ represents the magnetic permeability of the material, μ0 represents the vacuum magnetic permeability, σ represents the electrical conductivity of the material, E represents the applied electric field strength, ρ represents the material density, Cp represents the material specific heat capacity, d represents the actual thickness of the material, and δ represents the skin depth of the electromagnetic wave in the material.

[0041] The interface energy conversion analysis model is specifically represented as follows: P represents the multi-field coupling energy absorption coefficient, k2 represents the interface energy conversion coefficient, εr represents the material's relative permittivity, ε0 ​​represents the vacuum permittivity, ω represents the electromagnetic field angular frequency, |E| represents the electric field strength modulus, tanδ represents the dielectric loss tangent, and σ represents the material's conductivity.

[0042] The multi-field coupling strength analysis model is specifically expressed as follows: I represents the multi-field coupling comprehensive eigenvalue, k3 represents the multi-field coupling adjustment coefficient, ∂T / ∂t represents the partial derivative of temperature with respect to time, v represents the material velocity vector, ∇T represents the temperature gradient, α represents the material thermal diffusivity, and ∇ 2 T represents the Laplace operator for temperature, Q(E,H) represents the electromagnetic field energy conversion function, k4 represents the energy conversion adjustment coefficient, and |E| represents the magnitude of the electric field strength.

[0043] Step 4: Control Strategy Design: This step involves designing control strategies based on the results of multidimensional data analysis.

[0044] The control strategies include energy efficiency control strategy, power balance control strategy, and field strength distribution control strategy.

[0045] The energy efficiency control strategy specifies the following parameters: When 0.4η < η_ref, the voltage control command is UV = UV_base + 0.15UV_base, the frequency control command is f = f_base + 2000, and the speed control command is v = 0.9v_base. When 0.4η_ref ≤ η ≤ 1.2η_ref, the voltage maintenance command is UV = UV_base, the frequency maintenance command is f = f_base, and the speed maintenance command is v = v_base. When η > 1.2η_ref, the voltage control command is UV = UV_base - 0.12UV_base, the frequency control command is f = f_base - 1500, and the speed control command is v = 1.08v_base.

[0046] The power balance control strategy specifies the following commands for power balance: When P < 0.85P_ref, the power boost command is P_in = P_in_base + 0.2P_in_base, the resonance compensation command is C = C_base + 0.1C_base, and the impedance matching command is Z = Z_base - 0.15Z_base. When 0.85P_ref ≤ P ≤ 1.15P_ref, the power maintenance command is P_in = P_in_base, the resonance maintenance command is C = C_base, and the impedance maintenance command is Z = Z_base. When P > 1.15P_ref, the power reduction command is P_in = P_in_base - 0.18P_in_base, the resonance adjustment command is C = C_base - 0.08C_base, and the impedance adjustment command is Z = Z_base + 0.12Z_base.

[0047] The field strength distribution control strategy specifies the following: when I < 0.9I_ref, the field strength enhancement command is E = E_base + 0.25E_base, the phase adjustment command is φ = φ_base + 30°, and the waveguide adjustment command is h = h_base - 0.1h_base; when 0.9I_ref ≤ I ≤ 1.1I_ref, the field strength maintenance command is E = E_base, the phase maintenance command is φ = φ_base, and the waveguide maintenance command is h = h_base; when I > 1.1I_ref, the field strength reduction command is E = E_base - 0.22E_base, the phase adjustment command is φ = φ_base - 25°, and the waveguide adjustment command is h = h_base + 0.08h_base.

[0048] In this embodiment, it should be specifically noted that the _base suffix represents the baseline parameter value when the system is running normally, the _ref suffix represents the target reference value of the control system, and the percentage adjustment value is the adjustment coefficient obtained by optimization based on experimental data.

[0049] refer to Figure 2 A heating system for building material fluid equipment based on multidimensional data analysis, comprising: Data acquisition and sensor deployment module: Monitors the electromagnetic field coupling characteristics, interface energy conversion, and multi-physics coupling parameters of the target fluid device by deploying sensors, and transmits the data to the data preprocessing module; Data preprocessing module: Used to preprocess the data collected by the data acquisition and sensor deployment module and transmit it to the multidimensional data analysis module; Multidimensional data analysis module: Used to establish a multidimensional data analysis model to perform multidimensional data analysis on the preprocessed data and transmit it to the control strategy design module; Control strategy design module: Used to design control strategies based on the results of multidimensional data analysis.

[0050] This invention enables real-time monitoring of the electromagnetic field coupling characteristics, interface energy conversion, and multi-physics coupling parameters of fluid equipment through data acquisition and sensor deployment. This provides precise data support for subsequent data analysis and control strategy design. The acquired data covers multiple key parameters, including the resonant frequency response of the equipment body, the electromagnetic induction coefficient of the material, and the electromagnetic shielding interference level, facilitating a comprehensive understanding of the fluid equipment's operating status. Data preprocessing, using formulas for electromagnetic field coupling characteristics, interface energy conversion, and multi-physics coupling parameters, further refines key information, laying the foundation for subsequent multi-dimensional data analysis. Multi-dimensional data analysis models are established, including electromagnetic-thermal coupling efficiency analysis models and interface energy conversion analysis models. The system employs a multi-field coupling strength analysis model to comprehensively and deeply analyze the working state of fluid equipment. Through multi-dimensional data analysis, it reveals the interaction relationships between various physical fields within the fluid equipment, providing a scientific basis for optimizing the heating process and improving energy efficiency. By designing energy efficiency control strategies, power balance control strategies, and field strength distribution control strategies, it achieves precise control of the fluid equipment heating process. The control strategy design considers the needs under various operating conditions, such as energy efficiency, power balance, and field strength distribution, ensuring that the fluid equipment maintains efficient and stable operation under different conditions. By adjusting control commands such as voltage, frequency, and speed, it can achieve real-time adjustment of the fluid equipment heating process, optimizing the heating effect and improving production efficiency.

[0051] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other. In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A heating method for building material fluid equipment based on multidimensional data analysis, characterized in that, include: Step 1: Data Acquisition and Sensor Deployment: Monitor the electromagnetic field coupling characteristics, interface energy conversion, and multi-physics coupling parameters of the target fluid device by deploying sensors; Step 2: Data Preprocessing: This step involves preprocessing the data collected in Step 1. Step 3: Multidimensional data analysis: This step is used to establish a multidimensional data analysis model to perform multidimensional data analysis on the preprocessed data. Step 4: Control Strategy Design: This step involves designing control strategies based on the results of multidimensional data analysis.

2. The heating method for building material fluid equipment based on multidimensional data analysis according to claim 1, characterized in that: In step 1, the electromagnetic field coupling characteristics include the resonant frequency response value of the main body of the equipment, the electromagnetic induction coefficient of the material, the electromagnetic shielding interference degree, and the magnetic field penetration attenuation curve exponent. The interface energy conversion includes the capacitance effect value between material particles, the interface polarization intensity, the double electric layer energy density, and the dynamic change rate of the dielectric loss factor. The multi-physics coupling parameters include the thermo-mechanical-electric multi-field coupling coefficient, the plasma conductivity, the Maxwell stress tensor, and the eddy current-heat flow-mass flow mutual feedback intensity coefficient.

3. The heating method for building material fluid equipment based on multidimensional data analysis according to claim 1, characterized in that: Step 2 preprocesses the electromagnetic field coupling characteristics, interface energy conversion, and multiphysics field coupling parameters collected in Step 1 using electromagnetic field coupling characteristic formulas, interface energy conversion formulas, and multiphysics field coupling parameter formulas. The multidimensional data analysis model includes an electromagnetic-thermal coupling efficiency analysis model, an interface energy conversion analysis model, and a multi-field coupling strength analysis model. The control strategies include energy efficiency control strategy, power balance control strategy, and field strength distribution control strategy.

4. The heating method for building material fluid equipment based on multidimensional data analysis according to claim 3, characterized in that: The electromagnetic-thermal coupling efficiency analysis model is specifically expressed as follows: η represents the electromagnetic-thermal conversion coefficient, k1 represents the electromagnetic-thermal conversion coefficient, μ represents the magnetic permeability of the material, μ0 represents the vacuum magnetic permeability, σ represents the electrical conductivity of the material, E represents the applied electric field strength, ρ represents the material density, Cp represents the material specific heat capacity, d represents the actual thickness of the material, and δ represents the skin depth of the electromagnetic wave in the material.

5. The heating method for building material fluid equipment based on multidimensional data analysis according to claim 3, characterized in that: The interface energy conversion analysis model is specifically represented as follows: P represents the multi-field coupling energy absorption coefficient, k2 represents the interface energy conversion coefficient, εr represents the material's relative permittivity, ε0 ​​represents the vacuum permittivity, ω represents the electromagnetic field angular frequency, |E| represents the electric field strength modulus, tanδ represents the dielectric loss tangent, and σ represents the material's conductivity.

6. The heating method for building material fluid equipment based on multidimensional data analysis according to claim 3, characterized in that: The multi-field coupling strength analysis model is specifically expressed as follows: I represents the comprehensive characteristic value of field coupling, k3 represents the multi-field coupling adjustment coefficient, ∂T / ∂t represents the partial derivative of temperature with respect to time, v represents the velocity vector of material motion, ∇T represents the temperature gradient, α represents the thermal diffusivity of the material, and ∇ 2 T represents the Laplace operator for temperature, Q(E,H) represents the electromagnetic field energy conversion function, k4 represents the energy conversion adjustment coefficient, and |E| represents the magnitude of the electric field strength.

7. The heating method for building material fluid equipment based on multidimensional data analysis according to claim 3, characterized in that: The energy efficiency control strategy specifies the following parameters: When 0.4η < η_ref, the voltage control command is UV = UV_base + 0.15UV_base, the frequency control command is f = f_base + 2000, and the speed control command is v = 0.9v_base. When 0.4η_ref ≤ η ≤ 1.2η_ref, the voltage maintenance command is UV = UV_base, the frequency maintenance command is f = f_base, and the speed maintenance command is v = v_base. When η > 1.2η_ref, the voltage control command is UV = UV_base - 0.12UV_base, the frequency control command is f = f_base - 1500, and the speed control command is v = 1.08v_base.

8. The heating method for building material fluid equipment based on multidimensional data analysis according to claim 3, characterized in that: The power balance control strategy specifies the following commands for power balance: When P < 0.85P_ref, the power boost command is P_in = P_in_base + 0.2P_in_base, the resonance compensation command is C = C_base + 0.1C_base, and the impedance matching command is Z = Z_base - 0.15Z_base. When 0.85P_ref ≤ P ≤ 1.15P_ref, the power maintenance command is P_in = P_in_base, the resonance maintenance command is C = C_base, and the impedance maintenance command is Z = Z_base. When P > 1.15P_ref, the power reduction command is P_in = P_in_base - 0.18P_in_base, the resonance adjustment command is C = C_base - 0.08C_base, and the impedance adjustment command is Z = Z_base + 0.12Z_base.

9. The heating method for building material fluid equipment based on multidimensional data analysis according to claim 3, characterized in that: The field strength distribution control strategy specifies the following: when I < 0.9I_ref, the field strength enhancement command is E = E_base + 0.25E_base, the phase adjustment command is φ = φ_base + 30°, and the waveguide adjustment command is h = h_base - 0.1h_base; when 0.9I_ref ≤ I ≤ 1.1I_ref, the field strength maintenance command is E = E_base, the phase maintenance command is φ = φ_base, and the waveguide maintenance command is h = h_base; when I > 1.1I_ref, the field strength reduction command is E = E_base - 0.22E_base, the phase adjustment command is φ = φ_base - 25°, and the waveguide adjustment command is h = h_base + 0.08h_base.

10. A heating method for building material fluid equipment based on multidimensional data analysis, used to implement the heating method for building material fluid equipment based on multidimensional data analysis as described in any one of claims 1-9, characterized in that, include: Data acquisition and sensor deployment module: Monitors the electromagnetic field coupling characteristics, interface energy conversion, and multi-physics coupling parameters of the target fluid device by deploying sensors, and transmits the data to the data preprocessing module; Data preprocessing module: Used to preprocess the data collected by the data acquisition and sensor deployment module and transmit it to the multidimensional data analysis module; Multidimensional data analysis module: Used to establish a multidimensional data analysis model to perform multidimensional data analysis on the preprocessed data and transmit it to the control strategy design module; Control strategy design module: Used to design control strategies based on the results of multidimensional data analysis.

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