Air conditioner heat transfer intelligent adjusting system and method

Abstract

The invention discloses an air conditioner heat transfer intelligent adjusting system and method, and relates to the technical field of air conditioner intelligent adjusting. Performing entropy compression processing on the thermodynamic non-equilibrium parameters by using a quantum compression entropy reduction algorithm to generate an entropy reduction regulation and control coefficient; constructing a pulse decision-making model, inputting the entropy subtraction regulation and control coefficient into the pulse decision-making model, and generating an electric pulse instruction; lorentz force vector calculation is executed on the electric pulse instruction through an electromagnetic force conversion method, and a mechanical motion signal is generated; and flow velocity parameter adjustment is executed on the refrigerant flow density through the mechanical motion signal, when the refrigerant flow density exceeds the preset critical flow density, refrigerant molecule space coherent motion is triggered, and a cooling capacity one-way conduction path is established. According to the method, entropy compression processing is performed on the thermodynamic non-equilibrium state parameters through the quantum compression entropy reduction algorithm, and the problems of expansion valve adjustment lag and energy efficiency fluctuation are solved.

Description

Technical Field

[0001] This invention relates to the field of intelligent air conditioning control technology, and in particular to an intelligent air conditioning heat transfer control system and method. Background Technology

[0002] In recent years, intelligent heat transfer regulation technology for air conditioning systems has gradually evolved from macroscopic thermodynamic control to microscopic energy efficiency optimization. Research focuses on three main areas: refrigerant flow phase change dynamics, heat transfer interface nanostructure engineering, and dynamic load prediction algorithms. Latent heat regulation technology for phase change materials (PCMs) achieves precise matching of solid-liquid phase change temperatures; microchannel heat exchangers employ biomimetic fractal flow channel design to reduce turbulence resistance while enhancing the condensation heat transfer coefficient; and deep learning-based load prediction models, through the fusion of meteorological parameters and building thermal inertia data using LSTM neural networks, significantly reduce the root mean square error of dynamic cooling capacity allocation.

[0003] Existing air conditioning heat transfer technologies have shortcomings. Non-equilibrium thermodynamic control is inaccurate. Traditional methods rely on macroscopic temperature-pressure parameter feedback, which cannot accurately quantify the local entropy increase dynamics in the microscale phase change process of refrigerants. Especially under near-critical conditions, neglecting the quantum tunneling effect leads to lag in expansion valve regulation and energy efficiency fluctuations. In addition, the energy efficiency conversion of quantum effects is lacking. Due to the lack of coherent induction mechanisms and the ability to monitor quantum vortex topological defects, the refrigerant flow process is dominated by classical viscosity, resulting in a serious loss of superfluid entropy reduction potential. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an intelligent regulation method for air conditioning heat transfer to solve the problems of non-equilibrium entropy increase inaccuracy and lack of quantum energy efficiency conversion.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides an intelligent regulation method for air conditioning heat transfer, comprising: defining thermodynamic non-equilibrium parameters based on temperature difference data; performing entropy compression processing on the thermodynamic non-equilibrium parameters using a quantum compression entropy reduction algorithm to generate an entropy reduction regulation coefficient; constructing a pulse decision model, inputting the entropy reduction regulation coefficient into the pulse decision model to generate an electrical pulse command; performing Lorentz force vector calculation on the electrical pulse command using an electromagnetic force conversion method to generate a mechanical motion signal; regulating the refrigerant flow density using the mechanical motion signal, and triggering spatial coherent motion of refrigerant molecules when the refrigerant flow density exceeds a preset critical flow density to establish a unidirectional cold energy conduction path; generating a thermal conductivity enhancement coefficient by monitoring quantized heat flux and a decoherence coefficient by monitoring quantum decoherence rate according to the unidirectional cold energy conduction path, and merging the thermal conductivity enhancement coefficient and the decoherence coefficient to generate a heat transfer performance parameter package; and performing instruction priority allocation and machine code compilation on the heat transfer performance parameter package to generate a quantum intelligent regulation instruction set.

[0007] In a preferred embodiment of the intelligent air conditioning heat transfer regulation method of the present invention, the specific steps for generating the entropy reduction regulation coefficient are as follows: Real-time acquisition of evaporation and condensation pressures, and retrieval of refrigerant thermodynamic properties table to obtain evaporator cold end temperature and condenser hot end temperature, and calculate temperature difference data; Real-time environmental data is collected, and temperature difference data is standardized using the environmental data to generate thermodynamic non-equilibrium parameters. A baseline compression factor is defined by the refrigerant type. A negative exponential entropy compression operation is performed on the thermodynamic non-equilibrium parameters and the baseline compression factor to generate the initial control coefficient. When the initial control coefficient is within the effective control range, the output is the entropy reduction control coefficient; otherwise, the nearest interval endpoint value is taken as the output entropy reduction control coefficient.

[0008] In a preferred embodiment of the intelligent air conditioning heat transfer adjustment method of the present invention, the specific steps for generating the electrical pulse command are as follows: The input layer is defined based on linear scaling, the accumulation layer is defined based on time-domain integral operation, and the decision layer is defined based on neural activation threshold triggering logic. An impulse decision model is constructed based on the input layer, accumulation layer and decision layer. The entropy reduction control coefficient is input into the pulse decision model, and an electrical pulse command is generated through the membrane potential accumulation mechanism of the accumulation layer and the preset neural activation threshold of the decision layer.

[0009] In a preferred embodiment of the intelligent air conditioning heat transfer adjustment method of the present invention, the specific steps for generating the mechanical motion signal are as follows: The current characteristic parameters of the electrical pulse command are analyzed, and the magnetic induction intensity is calculated based on the Biot-Savart law; The electromagnetic force vector is calculated based on the Ampere force formula using magnetic induction intensity. The electromagnetic force vector is then mapped to the axial displacement of the valve core through a linear mapping relationship and encoded as a mechanical motion signal.

[0010] In a preferred embodiment of the intelligent air conditioning heat transfer regulation method of the present invention, the specific steps for establishing a unidirectional cold energy conduction path are as follows: The mechanical motion signal is analyzed to obtain the axial displacement of the valve core, and the opening of the electronic expansion valve is linearly adjusted according to the axial displacement of the valve core to change the refrigerant flow cross-sectional area. The refrigerant mass flow rate is obtained by a mass flow sensor, and the flow density is calculated by combining the refrigerant flow cross-sectional area. If the flow density exceeds the preset critical flow density, the spatial coherent motion of the refrigerant molecules is activated. A superfluid heat transfer channel is formed by the coherent spatial motion of refrigerant molecules, and the heat transfer direction is locked by quantum vortex arrangement to construct a unidirectional conduction path for cold energy.

[0011] As a preferred embodiment of the intelligent adjustment method for air conditioning heat transfer described in this invention, the specific steps for generating the heat transfer performance parameter package are as follows: Collect surface temperature gradient distribution data along the path and calculate the real-time heat flux by combining it with the preset material thermal conductivity. The reference heat flux is calibrated based on the standard heat transfer performance under rated operating conditions, and the thermal conductivity enhancement coefficient is calculated based on the real-time heat flux and the reference heat flux. The spin relaxation time of the unidirectional cold conduction path is measured, and the decoherence coefficient is calculated based on the spin relaxation time. The thermal conductivity enhancement coefficient and the decoherence coefficient are then combined into a heat transfer performance parameter package with a standardized floating-point data structure.

[0012] In a preferred embodiment of the intelligent air conditioning heat transfer regulation method of the present invention, the specific steps for generating the quantum intelligent regulation instruction set are as follows: The thermal conductivity enhancement coefficient and decoherence coefficient in the heat transfer performance parameter package are separated, and the command weight value is calculated. The command priority is determined according to the command weight value. The regulation intensity value is calculated based on the thermal conductivity enhancement coefficient and the decoherence coefficient, and the instruction priority is mapped to the opcode prefix. The environmental data, opcode prefix and regulation intensity value are compiled into machine code instructions using the triplet compilation method. The machine code instructions are sorted into an ordered sequence according to their execution time to generate a quantum intelligent regulation instruction set.

[0013] Secondly, this invention provides an intelligent air conditioning heat transfer regulation system, comprising an entropy difference control module, a pulse decision module, a flow state reconstruction module, a transfer effect monitoring module, and a quantum compilation module; the entropy difference control module is used to define thermodynamic non-equilibrium parameters based on temperature difference data; it uses a quantum compression entropy reduction algorithm to perform entropy compression processing on the thermodynamic non-equilibrium parameters to generate entropy reduction control coefficients; the pulse decision module is used to construct a pulse decision model, input the entropy reduction control coefficients into the pulse decision model, and generate electrical pulse commands; it uses an electromagnetic force conversion method to perform Lorentz force vector calculation on the electrical pulse commands to generate mechanical motion. The system comprises the following modules: a signal module and a flow state reconstruction module, which adjusts the flow rate parameter of the refrigerant flow density via mechanical motion signals. When the refrigerant flow density exceeds a preset critical flow density, it triggers coherent spatial motion of refrigerant molecules, establishing a unidirectional cold energy conduction path; a heat transfer performance monitoring module, which generates a thermal conductivity enhancement coefficient through quantized heat flux monitoring and a decoherence coefficient through quantum decoherence rate monitoring based on the unidirectional cold energy conduction path, and combines the thermal conductivity enhancement coefficient and the decoherence coefficient to generate a heat transfer performance parameter package; and a quantum compilation module, which performs instruction priority allocation and machine code compilation on the heat transfer performance parameter package to generate a quantum intelligent adjustment instruction set.

[0014] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the intelligent adjustment method for air conditioning heat transfer as described in the first aspect of the present invention.

[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the intelligent adjustment method for air conditioning heat transfer as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: by performing entropy compression processing on thermodynamic non-equilibrium parameters through a quantum compression entropy reduction algorithm, it breaks through the quantification bottleneck of the classical thermodynamic model for the local entropy increase of microscale phase transition, transforms the fluctuations of molecular kinetic energy distribution into controllable parameters, and solves the problems of expansion valve regulation lag and energy efficiency fluctuation; through the triggering mechanism of coherent spatial motion of refrigerant molecules, a superfluid heat transfer channel locked by a quantum vortex array is formed, reducing viscous loss and releasing the potential for superfluid entropy reduction. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of an intelligent adjustment method for air conditioning heat transfer.

[0019] Figure 2 This is a schematic diagram of an intelligent air conditioning heat transfer control system.

[0020] Figure 3 A flowchart for generating electrical pulse commands.

[0021] Figure 4 A flowchart for generating a heat transfer performance parameter package. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides an intelligent adjustment method for air conditioning heat transfer, comprising the following steps: S1. Define thermodynamic non-equilibrium parameters based on temperature difference data; use the quantum compression entropy reduction algorithm to perform entropy compression processing on the thermodynamic non-equilibrium parameters to generate entropy reduction control coefficients; S1.1. Real-time acquisition of evaporation pressure and condensation pressure through pressure sensor, and retrieval of refrigerant thermodynamic property table, obtaining evaporator cold end temperature and condenser hot end temperature through linear interpolation, and calculating the absolute temperature difference data. It should be noted that, in a sealed, constant-temperature, high-pressure container, a gradient pressure of 0.1 MPa to 5.0 MPa was applied to a specific refrigerant (0.1 MPa to 5.0 MPa is jointly calibrated by industry safety standards and the physical properties of the refrigerant; 0.1 MPa corresponds to standard atmospheric pressure, which is the natural equilibrium pressure of the refrigerant in a shut-down state or at ambient temperature; 5.0 MPa covers the subcritical operating range of the refrigerant and is strictly below the critical pressure of the refrigerant). A platinum resistance thermometer and a laser interferometer were used to simultaneously record the saturation temperature and specific volume data. The liquid phase density was measured using a vibrating tube densitometer, and a magnetic levitation system was used to measure the liquid phase density. The gas phase density was measured using a flat-state method, and the phase transition point data was verified using the sound velocity method. Specific heat capacity was measured in 1℃ increments within the range of -40℃ to 120℃ using an adiabatic calorimeter (the temperature range was set based on the refrigerant safety boundary [-40℃~120℃]). The latent heat value was then defined using the Clausius-Clapeyron equation. Absolute temperature data was collected using a high-precision temperature sensor, and the volume difference was measured using a gas and liquid phase volume measurement device. Differential calculations were performed on the gradient pressure to generate the pressure change rate. The absolute temperature data, volume difference, and pressure change rate were then substituted into the Clausius-Clapeyron equation to generate the latent heat value, expressed as follows: ; in, Indicates latent heat value; This represents the volume difference between the gas phase and the liquid phase; Represents absolute temperature data; Indicates gradient pressure; Indicates the compressive strength; After cross-validation of all data, including saturation temperature, specific volume, liquid density, gas density, phase transition point, specific heat capacity, and latent heat value, by three different laboratories, a Boltzmann machine neural network was constructed based on the quantum annealing algorithm. This network encodes discrete data points such as saturation temperature, specific volume, liquid density, gas density, phase transition point, specific heat capacity, and latent heat value into a superposition state of qubits. By optimizing the weight matrix through Hamiltonian, a quantum state mapping relationship between pressure, temperature, and volume is established, and a thermodynamic property table of the refrigerant is generated through decoding. The evaporator outlet pressure and condenser inlet pressure are collected in real time using a high-precision pressure sensor. A refrigerant thermodynamic property table is called up, and the corresponding pressure-saturation temperature mapping relationship is matched according to the refrigerant type. Linear interpolation calculations are performed on the evaporation and condensation pressures to obtain the evaporator cold-end temperature and condenser hot-end temperature. The interpolation calculation uses Newton's forward difference method to ensure that the temperature conversion error is less than ±0.3℃ (the conversion error threshold is defined as ±0.3℃ based on the requirements of thermodynamic control accuracy and quantum algorithm sensitivity). The temperature difference data is obtained by calculating the absolute value difference between the evaporator cold-end temperature and the condenser hot-end temperature.

[0026] S1.2. Real-time acquisition of environmental data using a platinum resistance temperature sensor, standardization of temperature difference data using environmental data to generate thermodynamic non-equilibrium parameters; It should be noted that environmental data is collected in real time behind the air intake grille of the outdoor unit of the air conditioner using a Pt1000 platinum resistance temperature sensor, with a sampling frequency of 2Hz and a temperature resolution of ±0.1℃. The collected environmental data is converted to Kelvin temperature scale, and thermodynamic non-equilibrium parameters are calculated using standardized formulas. The calculation uses 32-bit floating-point arithmetic to ensure the accuracy of the thermodynamic non-equilibrium parameters reaches ±0.0005. When the ambient temperature is below -40℃, a default value of 298K is used (based on the global climate zone distribution and the definition of the thermodynamic optimal efficiency point) to prevent division by zero errors. The final output is the thermodynamic non-equilibrium parameters, and the expression for calculating the thermodynamic non-equilibrium parameters is as follows: ; ; in, Represents thermodynamic non-equilibrium parameters; This represents temperature difference data; The Kelvin temperature scale represents the absolute ambient temperature, converted from environmental data. Represents environmental data; This represents the constant for converting Celsius temperature scale to absolute temperature scale; it is an exact value.

[0027] S1.3 Define the reference compression factor by refrigerant type, perform negative exponential entropy compression operation on thermodynamic non-equilibrium parameters and reference compression factor to generate initial control coefficients; It should be noted that the corresponding reference compressibility factor (e.g., 1.25 for R32 refrigerant and 1.28 for R410A refrigerant) is retrieved by using the refrigerant type identifier code preset in the non-volatile memory of the air conditioner controller. The reference compressibility factor is calculated and stored by normalizing the refrigerant molecular mass and characteristic vibrational frequencies using the Boltzmann constant. The refrigerant molecular mass is determined by neutron diffraction experiments, and the fundamental frequency of molecular bond vibrations is measured using a Raman spectrometer under standard operating conditions (23℃±2℃, 50%±5% humidity). The reference compressibility factor is calculated according to the formula, which is: ; in, Indicates the baseline compression factor; Represents the normalization coefficient; Indicates the molecular weight of the refrigerant; Indicates the fundamental frequency of molecular bond vibration; Represents the Boltzmann constant; The thermodynamic non-equilibrium parameters and the reference compressibility factor are input into a floating-point arithmetic unit to perform negative exponential entropy compression calculation, generating the initial control coefficient, expressed as follows: ; in, Indicates the initial control coefficient; Indicates the baseline compression factor; This represents a thermodynamic non-equilibrium parameter.

[0028] S1.4 When the initial control coefficient is within the effective control range, the output is the entropy reduction control coefficient; otherwise, the nearest interval endpoint value is taken as the output entropy reduction control coefficient.

[0029] It should be noted that the initial control coefficient is determined using a floating-point comparator. Whether it is within the effective control range [0.001, 0.999]: If If ∈ [0.001, 0.999], then output directly. As an entropy reduction regulation coefficient; if Then the output entropy reduction control coefficient ;like Then the output entropy reduction control coefficient ; It should also be noted that the definition of the effective control range [0.001, 0.999] is based on the essential constraints of the quantum effect mechanism and the thermodynamic stability boundary: the lower limit of the range corresponds to the minimum critical threshold for the quantum tunneling effect to trigger the refrigerant molecules to become ordered. Below the lower limit, the disorder of molecular thermal motion cannot be effectively suppressed, and the entropy reduction control effect fails; the upper limit of the range corresponds to the quantum Bose condensate saturation critical point. Exceeding the upper limit will cause the collective behavior of the refrigerant to become unstable, resulting in oscillation of the heat transfer path; the effective control range is determined by experiments of the refrigerant within the allowable operating temperature range, covering all effective working states of the air conditioner from the lowest load to the highest load.

[0030] S2. Construct a pulse decision model, input the entropy reduction control coefficient into the pulse decision model, and generate an electrical pulse command; perform Lorentz force vector calculation on the electrical pulse command through the electromagnetic force conversion method to generate a mechanical motion signal; S2.1. Define the input layer based on linear proportional mapping, the accumulation layer based on time-domain integral operation, and the decision layer based on threshold triggering logic. Construct an impulse decision model based on the input layer, accumulation layer, and decision layer. It should be noted that the input layer is constructed based on the linear scaling relationship between the entropy reduction control coefficient and the voltage signal. The linear scaling relationship anchors the mathematical relationship between the input and output quantities with a fixed proportional constant: the linear scaling relationship ensures that for every 1 unit increase in the entropy reduction control coefficient value, the voltage signal increases by a constant 0.1 volt, forming a strict linear correspondence between the input and output quantities; the input interface is set to receive the entropy reduction control coefficient, and the output interface transmits the voltage signal, thus completing the definition of the input layer; An accumulation layer is constructed based on the accumulation function of the voltage signal in the time dimension. The accumulation function refers to the continuous accumulation operation of the voltage signal in the time dimension, which is accomplished by discretization integration formula. The accumulation function enables the input voltage signal to be continuously superimposed to the membrane potential voltage according to the time step, forming the characteristic of the voltage signal growing linearly with time. The input interface is set to receive the voltage signal, and the output interface transmits the membrane potential voltage. At the same time, an independent functional boundary for integration operation is established to complete the definition of the accumulation layer. A decision layer is constructed based on a comparison triggering mechanism between membrane potential voltage and neural activation threshold. The comparison triggering mechanism refers to the real-time comparison of membrane potential voltage and neural activation threshold and the pulse generation logic. The neural activation threshold is set based on the consistency between the quantum tunneling critical field strength and the action potential of biological neurons, with a value of 0.8V. Based on the comparison result between membrane potential voltage and neural activation threshold, the corresponding electrical pulse command is output. An input interface is defined to receive the membrane potential voltage, and an output interface is defined to transmit the electrical pulse command, thus completing the definition of the decision layer. A three-layer cascaded pulse decision model is constructed by receiving the entropy reduction control coefficient and outputting the voltage signal in the input layer, receiving the voltage signal and outputting the membrane potential voltage in the accumulation layer, and receiving the membrane potential voltage and outputting the electrical pulse command in the decision layer. A dynamic heat transfer mathematical description is established based on the partial differential equation of heat conduction in the evaporator. A non-Newtonian fluid constitutive equation is introduced to describe the variation of the viscous stress tensor with shear rate, taking into account the rheological properties of the refrigerant. The evaporator tube bundle is spatially discretized into a finite volume grid, and the coupled form of the Navier-Stokes equations and energy conservation equations is solved at each grid node. The Runge-Kutta fourth-order method is used to advance the solution in the time dimension, achieving a spatial discretization accuracy of a second-order upwind scheme. The solver step size is set to match the real-time requirements of the air conditioning response, thus completing the construction of the physical field simulation environment. The impulse decision model was loaded into a physical field simulation environment, and a fixed proportional constant and time step were set as adjustable parameters. A step temperature difference disturbance signal was injected, and the compressor frequency response and actual energy efficiency deviation were collected. A weighted loss function for the actual energy efficiency deviation and frequency response was established. The partial derivative of the weighted loss function with respect to the adjustable parameters was calculated using the gradient descent algorithm, and the adjustable parameters were iteratively updated using the Adam optimizer with a learning rate of 0.001. After each iteration, the stability of the adjustable parameters under low temperature and overload conditions was verified. Training was terminated when the maximum number of training iterations was reached, and the finally iteratively updated adjustable parameters were solidified, thus completing the training of the impulse decision model.

[0031] S2.2 Input the entropy reduction control coefficient into the pulse decision model, and generate electrical pulse commands through the membrane potential accumulation mechanism of the accumulation layer and the preset neural activation threshold of the decision layer; It should be noted that the entropy reduction control coefficient is input to the input layer of the pulse decision model to perform linear proportional mapping. A voltage signal is generated by multiplying the entropy reduction control coefficient with a fixed proportional constant of 0.1 (defined based on the thermodynamic range matching principle). The voltage signal is transmitted to the input of the accumulation layer via a shielded cable. A charging current is formed through a metal film resistor. The charging current charges the ceramic capacitor to accumulate the membrane potential and generate a membrane potential voltage. The membrane potential voltage is connected to the non-inverting input of the high-speed voltage comparator in the decision layer and is compared in real time with the neural activation threshold at the inverting input. When the membrane potential voltage is greater than or equal to the neural activation threshold, the high-speed voltage comparator outputs a high-level signal. The high-level signal is converted into a current amplitude through a current conversion circuit. The pulse period is captured by a timer, and the pulse frequency is calculated. The charging time of the ceramic capacitor is measured by a differentiating circuit as the rise time. The current amplitude, pulse frequency, and rise time are integrated to output a 16-bit binary encoded electrical pulse command.

[0032] S2.3 Analyze the current characteristic parameters of the electrical pulse command, calculate the magnetic induction intensity based on the Biot-Savart law, solve the electromagnetic force vector based on the Ampere force formula using the magnetic induction intensity, generate the axial displacement of the valve core through a linear mapping relationship and encode it as a mechanical motion signal.

[0033] It should be noted that the process involves decoding the electrical pulse command, extracting the current amplitude, pulse frequency, and rise time; calling up pre-stored electronic expansion valve coil structural parameters (turns density 200 turns / meter, radius 0.1 meter, effective length 0.15 meter, and total number of turns 30); calculating the central magnetic field strength based on the current amplitude and coil structural parameters using the Biot-Savart law to generate the magnetic induction intensity; and calculating the electromagnetic force vector based on the magnetic induction intensity and current amplitude using the Ampere force formula. The electromagnetic force vector is converted into the axial displacement of the valve core through a linear mapping relationship: the electromagnetic force vector is applied to the end of the valve core to compress the return spring. The deformation of the return spring and the force value follow a direct proportional relationship. The proportionality coefficient is the reciprocal of the spring stiffness coefficient. The proportionality coefficient is calibrated by performing a tensile and compression test on the stainless steel valve core spring under standard working conditions using a universal testing machine to generate the axial displacement of the valve core. The axial displacement of the valve core is encoded into a 12-bit binary mechanical motion signal for output.

[0034] S3. The refrigerant flow density is adjusted by mechanical motion signal. When the refrigerant flow density exceeds the preset critical flow density, the spatial coherent motion of the refrigerant molecules is triggered, and a unidirectional cold energy conduction path is established. S3.1. Analyze the mechanical motion signal to obtain the axial displacement of the valve core, and linearly adjust the opening of the electronic expansion valve according to the axial displacement of the valve core to change the refrigerant flow cross-sectional area. It should be noted that, under standard operating conditions, the valve core is compressed at a uniform speed of 5 mm / min. The axial displacement of the valve core measured by a high-precision displacement sensor and the valve opening angle fed back by the rotary encoder are recorded simultaneously. The valve core axial displacement is collected at 0.1 mm intervals (a total of 100 sets) for each interval. The percentage ratio of the opening angle data to the full opening angle of the valve core (factory setting of the electronic expansion valve) is taken as the opening percentage. The least squares method is used to perform linear fitting on the opening percentage to generate a linear equation between the valve core axial displacement and the opening percentage. This equation is then stored in the controller's non-volatile memory, ultimately forming a displacement-opening linear mapping curve. The axial displacement of the valve core is obtained by parsing the mechanical motion signal using a 12-bit binary decoder. The displacement-opening linear mapping curve is then invoked to query the corresponding opening value based on the axial displacement (e.g., axial displacement 1.0 mm → opening value 12%). The opening value is converted into a pulse sequence by a stepper motor driver. This pulse sequence serves as a digital instruction sequence to control the rotation angle of the stepper motor; each pulse corresponds to a fixed microstep rotation of the motor rotor. The refrigerant flow cross-sectional area is calculated based on the axial displacement of the valve core, thus adjusting the refrigerant flow cross-sectional area. The expression for calculating the refrigerant flow cross-sectional area is as follows: ; ; in, Indicates the cross-sectional area for refrigerant flow; This indicates the minimum constraint diameter of the internal flow channel of the valve body, which is set by the factory drawings of the electronic expansion valve. Indicates the real-time diameter of the valve needle; This indicates the initial diameter of the valve needle, which is set according to the valve needle drawing. This represents the valve needle taper coefficient, which is set by optimizing the valve needle design cone angle and flow characteristics. This indicates the axial displacement of the valve core.

[0035] S3.2. Obtain the refrigerant mass flow rate through the mass flow sensor, and calculate the flow density in combination with the refrigerant flow cross-sectional area. If the flow density exceeds the preset critical flow density, activate the spatial coherent motion of the refrigerant molecules. It should be noted that the critical flow density is determined by observing the superfluid phase transition point of the refrigerant under extremely low temperatures in the liquid helium temperature range. A refrigerant sample is injected into a vacuum-insulated cavity near absolute zero. A neutron diffractometer is used to measure the abrupt inflection point of the change in intermolecular spacing with refrigerant density. When the de Broglie wavelength of the refrigerant molecules is greater than or equal to the average intermolecular spacing, the number of refrigerant molecules per unit volume is recorded. The number of refrigerant molecules, combined with Avogadro's constant and the molar mass of the refrigerant, is converted to dimensions using the mass density formula to generate the critical flow density, expressed as follows: ; in, Indicates the critical flow density; This indicates the number of refrigerant molecules per unit volume; It represents the molar mass of a refrigerant, which is determined by the type of refrigerant and is a fixed physical constant inherent to each type of refrigerant; denoted by Avogadro's constant, defined by the X-ray crystal density method for single-crystal silicon spheres; The refrigerant mass flow rate is collected in real time by a mass flow sensor, and the ratio of the refrigerant mass flow rate to the refrigerant flow cross-sectional area is used as the flow density. The flow density is compared with the critical flow density. When the flow density is greater than or equal to the critical flow density, a high-level trigger signal is output. The high-level trigger signal activates the terahertz electromagnetic field generator to generate high-frequency electromagnetic waves, which are directionally radiated to the evaporator pipeline through a waveguide antenna, inducing the refrigerant molecules to undergo spatial coherent motion. It should also be noted that spatial coherent motion refers to the collective quantum behavior of refrigerant molecules induced by terahertz electromagnetic waves, which manifests as the phase synchronization of the wave functions of a large number of refrigerant molecules to form a macroscopic quantum state. Specifically, when refrigerant molecules absorb the energy of photons of a specific frequency and jump to an excited state, the wave functions overlap spatially when the wavelength of the matter wave is greater than the average intermolecular distance, causing refrigerant molecules exceeding the critical flux density to condense into the same quantum ground state, forming a non-local coherent wave function. At this time, the motion of refrigerant molecules exhibits superfluid characteristics.

[0036] S3.3. Superfluid heat transfer channels are formed through coherent molecular motion, and the direction of heat transfer is locked by quantum vortex arrangement to construct a unidirectional conduction path for cold energy.

[0037] It should be noted that the phase synchronization of the refrigerant molecular wavefunction is maintained by continuous radiation of the terahertz electromagnetic field, forming a Bose-Einstein condensate superfluid; the axial gradient magnetic field is applied to constrain the directional alignment of quantum vortices, so that the spacing between the quantum vortices matches an integer multiple of the refrigerant's de Broglie wavelength; the quantum vortex array forms a unidirectional heat transfer channel through topological phase transition, and the cold energy is directionally conducted along the core region of the vortex at the second speed of sound. The temperature difference and flow stability of the evaporator inlet and outlet are detected. When the temperature difference fluctuation is continuously lower than the temperature difference fluctuation threshold (based on the definition of the superfluid entropy generation rate limit, which is ±0.1℃), it is determined that the unidirectional conduction path of cold energy is completed.

[0038] S4. Based on the unidirectional conduction path of cold energy, the thermal conductivity enhancement coefficient is generated by quantized heat flux monitoring, and the decoherence coefficient is generated by quantum decoherence rate monitoring. The thermal conductivity enhancement coefficient and the decoherence coefficient are combined to generate a heat transfer performance parameter package. S4.1 Arrange a thermopile sensor array along the axial direction of the unidirectional cold conduction path, collect the temperature gradient distribution data of the path surface, perform time integration on the temperature difference between adjacent thermopile sensor nodes, and calculate the real-time heat flux in combination with the preset material thermal conductivity. It should be noted that, under standard operating conditions, a protective hot plate apparatus is used to apply a constant temperature difference to the material sample, measuring the heat passing through a unit area per unit time to generate heat flux. The thermal conductivity of the material is calculated according to Fourier's law of heat conduction: the product of the ratio of the material sample thickness to the constant temperature difference and the heat flux is used as the initial thermal conductivity. The test sample is pretreated under constant temperature and humidity for 48 hours, and the measurements are repeated three times, with the average value taken to generate the material's thermal conductivity, which is then pre-stored in memory. The expression for calculating the material's thermal conductivity is as follows: ; in, Indicates the thermal conductivity of the material; Heat flux; Indicates the thickness of the material sample; Indicates a constant temperature difference; A thermopile sensor array is embedded axially at equal intervals along the surface of the unidirectional cold conduction path, with the sensor node spacing matching the path width. A multiplexer cyclically scans the thermoelectric potential signals output from each sensor node, amplifies them using an instrumentation amplifier, and then converts them into digital temperature values ​​via an analog-to-digital converter, generating temperature gradient distribution data for the path surface. Time integration is performed on the real-time temperature difference values ​​between adjacent sensor nodes, and the temperature difference energy is accumulated using a numerical integration method. The material's thermal conductivity is then used to calculate the real-time heat flux according to Fourier's law of heat conduction, expressed as follows: ; in, Indicates real-time heat flux; Indicates the thermal conductivity of the material; This represents the temperature difference data between adjacent nodes; Indicates a specific moment in time; This indicates the spacing between sensor nodes.

[0039] S4.2. Based on the standard heat transfer performance under rated operating conditions, calibrate the reference heat flux and calculate the thermal conductivity enhancement coefficient according to the real-time heat flux and the reference heat flux. It should be noted that the rated operating conditions are defined based on the optimal balance point between air conditioning equipment specifications and thermodynamic performance, specifically an ambient temperature of 35℃, a condensing temperature of 54℃, and an evaporating temperature of 18℃. Under rated operating conditions, the air conditioning equipment is operated, and heat flux data is continuously collected over a fixed period using a thermopile sensor array. An arithmetic mean is calculated on all collected heat flux values ​​to generate a baseline heat flux value. The ratio of the real-time heat flux to the baseline heat flux is used as the original thermal conductivity enhancement coefficient. A time-window moving average filter is applied to the original thermal conductivity enhancement coefficient to suppress transient noise, and the thermal conductivity enhancement coefficient is then output.

[0040] S4.3 Arrange nitrogen vacancy color center sensors at nodes of the unidirectional cold energy conduction path, measure the spin relaxation time, calculate the decoherence coefficient based on the spin relaxation time, and combine the thermal conductivity enhancement coefficient and the decoherence coefficient into a heat transfer performance parameter package with a standardized floating-point data structure. It should be noted that nitrogen-vacancy color center sensors are equidistantly embedded on the surface of the unidirectional cold energy conduction path. The electron spin state in the diamond lattice is excited by a 532 nm laser pulse. A 2.87-2.93 GHz gigahertz microwave field is applied to scan the spin energy level resonance frequency. The free induction decay signal is collected and the spin relaxation time is solved by fitting an exponential function. The reciprocal of the spin relaxation time is used as the decoherence coefficient. The thermal conductivity enhancement coefficient and the decoherence coefficient are encoded as 32-bit binary data (the thermal conductivity enhancement coefficient occupies the high 16 bits and the decoherence coefficient occupies the low 16 bits) and packaged into a heat transfer performance parameter package.

[0041] S5. Assign instruction priority and compile machine code for the heat transfer performance parameter package to generate a quantum intelligent adjustment instruction set.

[0042] S5.1 Separate the thermal conductivity enhancement coefficient and decoherence coefficient from the heat transfer performance parameter package, calculate the instruction weight value, and divide the instruction priority according to the instruction weight value; It should be noted that the priority classification threshold is defined based on the quantum control stability theory and industry standard real-time specifications, including the quantum vortex critical value (e.g., 1.0 × 10⁻⁶). -6 ) and quantum flux baseline value (e.g., 0.5 × 10) −6The heat transfer performance parameter package is analyzed to extract the thermal conductivity enhancement coefficient and decoherence coefficient. The command weight value is calculated using the weight calculation formula. The command priority is divided according to the command weight value and the priority division threshold. When the command weight value is greater than or equal to the quantum vortex critical value, it is classified as a high-priority command; when the quantum flux baseline value is less than or equal to the command weight value and the quantum vortex critical value, it is classified as a medium-priority command; and when the command weight value is less than the quantum flux baseline value, it is classified as a low-priority command.

[0043] S5.2 Calculate the regulation intensity value based on the thermal conductivity enhancement coefficient and the decoherence coefficient, map the instruction priority to the opcode prefix, and compile the environmental data, opcode prefix and regulation intensity value into machine code instructions using the triplet compilation method; It should be noted that the modulation intensity value is generated by calculating the product of the thermal conductivity enhancement coefficient and the decoherence coefficient using a floating-point multiplier; it is then mapped to an opcode prefix according to instruction priority (high priority instruction → 01, medium priority instruction → 10, low priority instruction → 00); the real-time acquired environmental data is normalized and converted into a 16-bit environmental data word; the environmental data, opcode prefix, and modulation intensity value are integrated into a 32-bit machine code instruction using a triplet compilation method: the environmental data occupies the high 16 bits, the opcode prefix occupies bits 15-14, and the modulation intensity value occupies the low 14 bits; a 16-bit CRC checksum is added at the end, and finally integrated into a 48-bit machine code instruction.

[0044] S5.3. Sort the machine code instructions into an ordered sequence according to the execution time to generate a quantum intelligent regulation instruction set.

[0045] It should be noted that the opcode prefix in the machine code instruction is parsed by a priority decoder, and a multi-level instruction queue is constructed according to three priority levels. High-priority instructions are inserted into the hardware interrupt stack, medium-priority instructions are pushed into the real-time task queue, and low-priority instructions are stored in the circular buffer. A multi-level feedback queue scheduling algorithm is used to dynamically sort the instruction sequence: high-priority instructions in the hardware interrupt stack are processed first, followed by high-priority instructions in the real-time task queue, and finally low-priority instructions in the circular buffer are polled. The sorted machine code instructions are output in descending order of priority level to generate a quantum intelligent adjustment instruction set.

[0046] This embodiment also provides an intelligent air conditioning heat transfer regulation system, including: an entropy difference regulation module, a pulse decision module, a flow state reconstruction module, a transfer effect monitoring module, and a quantum compilation module; The entropy difference control module is used to define thermodynamic non-equilibrium parameters based on temperature difference data; it uses a quantum compression entropy reduction algorithm to perform entropy compression processing on the thermodynamic non-equilibrium parameters to generate entropy reduction control coefficients. The pulse decision module is used to construct a pulse decision model. It inputs the entropy reduction control coefficient into the pulse decision model to generate an electrical pulse command. It then performs Lorentz force vector calculation on the electrical pulse command through an electromagnetic force conversion method to generate a mechanical motion signal. The flow regime reconstruction module is used to adjust the flow rate parameter of the refrigerant flow density through mechanical motion signals. When the refrigerant flow density exceeds the preset critical flow density, it triggers the spatial coherent motion of refrigerant molecules and establishes a unidirectional conduction path for cooling. The heat transfer performance monitoring module is used to generate a thermal conductivity enhancement coefficient by monitoring quantized heat flux and a decoherence coefficient by monitoring quantum decoherence rate, based on the unidirectional conduction path of cold energy. The thermal conductivity enhancement coefficient and the decoherence coefficient are then combined to generate a heat transfer performance parameter package. The quantum compilation module is used to assign instruction priorities and compile machine code for the heat transfer performance parameter package, generating a quantum intelligent adjustment instruction set.

[0047] This embodiment also provides a computer device applicable to the intelligent adjustment method for air conditioning heat transfer, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the intelligent adjustment method for air conditioning heat transfer as proposed in the above embodiment.

[0048] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0049] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the intelligent adjustment method for air conditioning heat transfer as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0050] In summary, this invention achieves entropy compression processing of thermodynamic non-equilibrium parameters through a quantum compression entropy reduction algorithm, breaking through the quantification bottleneck of local entropy increase in microscale phase transitions in classical thermodynamic models, transforming molecular kinetic energy distribution fluctuations into controllable parameters, and solving the problems of expansion valve regulation lag and energy efficiency fluctuations; through the triggering mechanism of coherent spatial motion of refrigerant molecules, a superfluid heat transfer channel locked by a quantum vortex array is formed, reducing viscous losses and releasing the potential for superfluid entropy reduction.

[0051] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for intelligent regulation of heat transfer in air conditioning, characterized in that: include, Thermodynamic non-equilibrium parameters are defined based on temperature difference data; the entropy value compression processing of the thermodynamic non-equilibrium parameters is performed using a quantum compression entropy reduction algorithm to generate entropy reduction control coefficients; A pulse decision model is constructed, and the entropy reduction control coefficient is input into the pulse decision model to generate an electrical pulse command. The Lorentz force vector is calculated on the electrical pulse command through the electromagnetic force conversion method to generate a mechanical motion signal. The refrigerant flow density is adjusted by mechanical motion signals. When the refrigerant flow density exceeds the preset critical flow density, the spatial coherent motion of the refrigerant molecules is triggered, and a unidirectional conduction path of cold energy is established. Based on the unidirectional conduction path of cold energy, the thermal conductivity enhancement coefficient is generated by quantized heat flux monitoring, and the decoherence coefficient is generated by quantum decoherence rate monitoring. The thermal conductivity enhancement coefficient and the decoherence coefficient are combined to generate a heat transfer performance parameter package. The heat transfer performance parameter package is assigned instruction priority and machine code is compiled to generate a quantum intelligent adjustment instruction set.

2. The intelligent adjustment method for air conditioning heat transfer as described in claim 1, characterized in that: The specific steps for generating the entropy reduction control coefficient are as follows: Real-time acquisition of evaporation and condensation pressures, and retrieval of refrigerant thermodynamic properties table to obtain evaporator cold end temperature and condenser hot end temperature, and calculate temperature difference data; Real-time environmental data is collected, and temperature difference data is standardized using the environmental data to generate thermodynamic non-equilibrium parameters. A baseline compression factor is defined by the refrigerant type. A negative exponential entropy compression operation is performed on the thermodynamic non-equilibrium parameters and the baseline compression factor to generate the initial control coefficient. When the initial control coefficient is within the effective control range, the output is the entropy reduction control coefficient; otherwise, the nearest interval endpoint value is taken as the output entropy reduction control coefficient.

3. The intelligent adjustment method for air conditioning heat transfer as described in claim 2, characterized in that: The specific steps for generating the electrical pulse command are as follows: The input layer is defined based on linear scaling, the accumulation layer is defined based on time-domain integral operation, and the decision layer is defined based on neural activation threshold triggering logic. An impulse decision model is constructed based on the input layer, accumulation layer and decision layer. The entropy reduction control coefficient is input into the pulse decision model, and an electrical pulse command is generated through the membrane potential accumulation mechanism of the accumulation layer and the preset neural activation threshold of the decision layer.

4. The intelligent adjustment method for air conditioning heat transfer as described in claim 3, characterized in that: The specific steps for generating the mechanical motion signal are as follows: The current characteristic parameters of the electrical pulse command are analyzed, and the magnetic induction intensity is calculated based on the Biot-Savart law; The electromagnetic force vector is calculated based on the Ampere force formula using magnetic induction intensity. The electromagnetic force vector is then mapped to the axial displacement of the valve core through a linear mapping relationship and encoded as a mechanical motion signal.

5. The intelligent adjustment method for air conditioning heat transfer as described in claim 4, characterized in that: The specific steps for establishing a unidirectional cold energy conduction path are as follows. The mechanical motion signal is analyzed to obtain the axial displacement of the valve core, and the opening of the electronic expansion valve is linearly adjusted according to the axial displacement of the valve core to change the refrigerant flow cross-sectional area. The refrigerant mass flow rate is obtained by a mass flow sensor, and the flow density is calculated by combining the refrigerant flow cross-sectional area. If the flow density exceeds the preset critical flow density, the spatial coherent motion of the refrigerant molecules is activated. A superfluid heat transfer channel is formed by the coherent spatial motion of refrigerant molecules, and the heat transfer direction is locked by quantum vortex arrangement to construct a unidirectional conduction path for cold energy.

6. The intelligent adjustment method for air conditioning heat transfer as described in claim 5, characterized in that: The specific steps for generating the heat transfer performance parameter package are as follows: Collect surface temperature gradient distribution data along the path and calculate the real-time heat flux by combining it with the preset material thermal conductivity. The reference heat flux is calibrated based on the standard heat transfer performance under rated operating conditions, and the thermal conductivity enhancement coefficient is calculated based on the real-time heat flux and the reference heat flux. The spin relaxation time of the unidirectional cold conduction path is measured, and the decoherence coefficient is calculated based on the spin relaxation time. The thermal conductivity enhancement coefficient and the decoherence coefficient are then combined into a heat transfer performance parameter package with a standardized floating-point data structure.

7. The intelligent adjustment method for air conditioning heat transfer as described in claim 6, characterized in that: The specific steps for generating the quantum intelligent adjustment instruction set are as follows: The thermal conductivity enhancement coefficient and decoherence coefficient in the heat transfer performance parameter package are separated, and the command weight value is calculated. The command priority is determined according to the command weight value. The regulation intensity value is calculated based on the thermal conductivity enhancement coefficient and the decoherence coefficient, and the instruction priority is mapped to the opcode prefix. The environmental data, opcode prefix and regulation intensity value are compiled into machine code instructions using the triplet compilation method. The machine code instructions are sorted into an ordered sequence according to their execution time to generate a quantum intelligent regulation instruction set.

8. An intelligent air conditioning heat transfer control system, based on the intelligent air conditioning heat transfer control method according to any one of claims 1 to 7, characterized in that: It includes an entropy difference control module, an impulse decision module, a flow state reconstruction module, a transfer effect monitoring module, and a quantum compilation module; The entropy difference control module is used to define thermodynamic non-equilibrium parameters based on temperature difference data; it uses a quantum compression entropy reduction algorithm to perform entropy compression processing on the thermodynamic non-equilibrium parameters to generate entropy reduction control coefficients. The pulse decision module is used to construct a pulse decision model. It inputs the entropy reduction control coefficient into the pulse decision model to generate an electrical pulse command. It then performs Lorentz force vector calculation on the electrical pulse command through an electromagnetic force conversion method to generate a mechanical motion signal. The flow regime reconstruction module is used to adjust the flow rate parameter of the refrigerant flow density through mechanical motion signals. When the refrigerant flow density exceeds the preset critical flow density, it triggers the spatial coherent motion of refrigerant molecules and establishes a unidirectional conduction path for cooling. The heat transfer performance monitoring module is used to generate a thermal conductivity enhancement coefficient by monitoring quantized heat flux and a decoherence coefficient by monitoring quantum decoherence rate, based on the unidirectional conduction path of cold energy. The thermal conductivity enhancement coefficient and the decoherence coefficient are then combined to generate a heat transfer performance parameter package. The quantum compilation module is used to assign instruction priorities and compile machine code for the heat transfer performance parameter package, generating a quantum intelligent adjustment instruction set.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the intelligent adjustment method for air conditioning heat transfer as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the intelligent adjustment method for air conditioning heat transfer as described in any one of claims 1 to 7.

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