Refrigeration host energy efficiency intelligent monitoring and control system based on internet of things

Abstract

The application discloses a refrigeration host energy efficiency intelligent monitoring and regulation system based on Internet of Things, belongs to the technical field of Internet of Things, and comprises a quantum field environment sensing module, an energy efficiency tensor modeling module, a phase space trajectory prediction module, a critical state fuzzy control module, a dissipative structure optimization module and a holographic digital twin module. The application realizes high-precision and high-sensitivity environment parameter acquisition through a quantum tunneling effect sensor. Nonlinear responses of energy levels or tunneling probabilities of quantum states to weak environmental disturbances are utilized to map multidimensional parameters such as temperature, pressure and electromagnetic field into quantum state characteristics. In combination with laser excitation of a quantum system into a superposition state, the dynamic range and resolution of environment parameter detection are significantly improved. Meanwhile, through ground state characteristic calibration and environment compensation mechanism, non-target interference is effectively eliminated.

Description

Technical Field

[0001] This invention belongs to the field of Internet of Things (IoT) technology, specifically referring to an IoT-based intelligent monitoring and control system for the energy efficiency of refrigeration units. Background Technology

[0002] With the acceleration of urbanization and increasing pressure on energy consumption, the refrigeration system, as a major component of building energy consumption, directly affects the overall energy utilization efficiency. Traditional refrigeration unit control systems mostly rely on preset thresholds or simple feedback control, lacking the ability to perceive and dynamically respond to environmental disturbances, equipment aging, and load fluctuations in real time, resulting in low energy efficiency and accelerated equipment wear.

[0003] However, existing intelligent monitoring and control systems for refrigeration unit energy efficiency still have certain shortcomings. Current technologies rely on traditional sensors to collect environmental parameters, and their detection accuracy and sensitivity are limited by the physical characteristics of the sensors. They are unable to capture the nonlinear response of weak environmental disturbances, lack compensation mechanisms for non-target interference, and the collected data is easily contaminated by external noise. The dynamic range and resolution are insufficient, making it impossible to provide high-fidelity input for energy efficiency modeling. Using classical algorithms or shallow machine learning models for energy efficiency prediction makes it difficult to handle the multidimensional coupling relationship between environmental parameters and refrigeration system energy efficiency. In the face of nonlinear behavior in chaotic environments, they lack effective dynamic trajectory prediction capabilities and cannot capture the evolution trend of the system under critical states. They often use a single objective function, ignoring the multidimensional trade-off between energy efficiency and system stability. Therefore, an IoT-based intelligent monitoring and control system for refrigeration unit energy efficiency is proposed. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent monitoring and control system for the energy efficiency of a refrigeration unit based on the Internet of Things, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent monitoring and control system for energy efficiency of a refrigeration host based on the Internet of Things, comprising a quantum field environment sensing module, an energy efficiency tensor modeling module, a phase space trajectory prediction module, a critical state fuzzy control module, a dissipative structure optimization module, and a holographic digital twin module;

[0006] The quantum field environment sensing module collects multi-dimensional environmental parameters through a quantum tunneling effect sensor and outputs an environmental chaos coefficient matrix.

[0007] The energy efficiency tensor modeling module constructs a three-dimensional energy efficiency coupling tensor of the refrigeration system through tensor decomposition based on the environmental chaos coefficient matrix.

[0008] The phase space trajectory prediction module generates a future energy efficiency topological trajectory map in phase space based on the three-dimensional energy efficiency coupling tensor and simulates the Hamiltonian through a quantum annealing processor.

[0009] The critical state fuzzy control module dynamically generates a fuzzy control rule matrix based on the curvature characteristics of the phase space topological trajectory diagram and the Lyapunov exponent.

[0010] The dissipative structure optimization module is based on the fuzzy control rule matrix and solves the Pareto optimal solution set of the compressor and refrigerant flow by non-equilibrium thermodynamic entropy change calculation.

[0011] The holographic digital twin module is used to integrate the output data, correct the full-dimensional state of the cooling system in real time through a quantum entanglement synchronization protocol, and provide feedback for coordinated control.

[0012] Preferably, the quantum field environment sensing module is wirelessly connected to the energy efficiency tensor modeling module and the critical state fuzzy control module; the energy efficiency tensor modeling module is wirelessly connected to the phase space trajectory prediction module; the phase space trajectory prediction module is wirelessly connected to the critical state fuzzy control module; the critical state fuzzy control module is wirelessly connected to the dissipative structure optimization module; the dissipative structure optimization module is wirelessly connected to the holographic digital twin module; and the holographic digital twin module is wirelessly connected to the quantum field environment sensing module.

[0013] Preferably, the quantum field environment sensing module deploys a detection unit based on the quantum tunneling effect inside the sensor, maps environmental parameters to the energy level or tunneling probability of a quantum state, and excites the quantum system through laser or electromagnetic field to enter a stable superposition state;

[0014] Without external disturbance, the ground state characteristics of the quantum system are recorded to establish a reference value. Environmental compensation such as temperature and electromagnetic interference is performed on the sensor to eliminate interference from non-target parameters. When environmental parameters are disturbed, the energy level or tunneling probability of the quantum system will change nonlinearly.

[0015] Preferably, these changes are captured in real time by quantum measurement devices and converted into quantifiable signals. Multiple quantum sensor units are configured, each corresponding to a different environmental dimension. The collected multidimensional parameter signals are input into a chaotic analysis algorithm to extract nonlinear features, including Lyapunov exponents: quantifying the system's sensitivity to initial conditions; phase space reconstruction: constructing the phase space trajectory of parameters by embedding dimensions and delay time to observe whether there are strange attractors.

[0016] Preferably, the chaos coefficient is calculated for each environmental parameter. If the Lyapunov index ,but , representing the degree of chaos over time exponential growth, if ,but This indicates that the system is in a periodic state, and the chaos coefficients of each parameter are... Arranged dimensionally as matrix elements, forming an environmental chaos coefficient matrix. , where n represents the number of environmental parameters and m represents the number of time sampling points. Wavelet transform is applied to the chaotic coefficient matrix C to eliminate random noise in quantum measurement and retain the high-frequency components of chaotic characteristics.

[0017] Preferably, the energy efficiency tensor modeling module receives the environmental chaos coefficient matrix output from the quantum field environment sensing module, and sets each element in the environmental chaos coefficient matrix... Mapped to the input variables of the refrigeration system;

[0018] Based on the operating characteristics of the refrigeration system, a three-dimensional energy efficiency tensor is defined. E represents the energy efficiency state dimension, m represents the number of time sampling points, and k represents the environmental parameter subspace dimension. The environmental chaos coefficient matrix C is fused with the real-time energy efficiency data of the cooling system and filled into the corresponding position of the tensor T.

[0019] Specifically, the high-dimensional tensor T is decomposed into a core tensor G and a factor matrix. , , , where r is the low-dimensional rank after decomposition, the core tensor G represents the coupling relationship between energy efficiency state and environmental parameters, and the factor matrices A, B, and C correspond to the feature vectors of energy efficiency state, time dimension, and environmental parameters, respectively.

[0020] The decomposed factor matrix and the core tensor G are reconstructed into a three-dimensional energy efficiency coupling tensor. The feature vectors of each dimension are associated through interaction terms in G, and the absolute values ​​of the elements of the core tensor G are calculated. As energy efficiency status ,time Environmental parameters An indicator of the coupling strength among the three.

[0021] Preferably, the phase space trajectory prediction module, in constructing the quantum state initialization step, includes: obtaining the three-dimensional energy efficiency coupling tensor. And extract energy efficiency status. The chaos coefficients of the environmental chaos coefficient matrix Mapped to a quantum state, the initial quantum state is formed by tensor product of the quantum encoding of the energy efficiency state and the chaotic coefficient of the environment, as shown in the formula:

[0022] ,

[0023] In the formula, The initial quantum state represents the initial quantum state of the quantum annealing processor, and N represents the dimension of the energy efficiency state of the cooling system. The quantum amplitude represents the i-th energy-efficient state. The quantum code representing the i-th energy efficiency state. This represents the quantum encoding of the i-th environmental chaos coefficient.

[0024] Preferably, the phase space trajectory prediction module includes a quantum annealing step comprising: designing an energy function of the Hamiltonian based on the energy efficiency optimization target of the refrigeration system, reflecting the energy efficiency distribution of the system under different states;

[0025] The Hamiltonian needs to include the physical constraints and dynamic characteristics of the cooling system. A transverse field term is added to the Hamiltonian to simulate the quantum tunneling effect, enabling the system to overcome local energy efficiency extremes. The initial temperature, transverse field strength and annealing time of the quantum annealing processor are preset, and the qubits are initialized as a uniform superposition state, covering all possible energy efficiency state combinations.

[0026] By gradually reducing the transverse field strength through a quantum annealing processor while increasing the dominance of the Hamiltonian, the qubit state dynamically evolves during annealing, gradually converging to the energy-optimal low-energy state, as shown in the formula:

[0027] ,

[0028] In the formula, Representing quantum states, Represents the time calculus, Denotes the reduced Planck constant. Represents the effective Hamiltonian related to time. Represented as , This represents the initial Hamiltonian. Represented as ;

[0029] Through transverse field To achieve global exploration capability of quantum states and avoid getting trapped in local energy efficiency extrema, Denotes the target Hamiltonian, and represents By using the projection operator of energy efficiency state and chaotic coefficients, an energy efficiency function coupled with the environment is constructed. , This represents the annealing progress function.

[0030] Preferably, the phase space trajectory prediction module, in the phase space topological trajectory diagram generation step, includes: at the end of annealing, performing quantum state... Measurements were performed to obtain M energy efficiency states sampling results;

[0031] Each energy efficiency state is calculated based on the measurement results. probability amplitude , This indicates its weight in the future energy efficiency trajectory;

[0032] Each energy efficiency status Mapped to phase space coordinates, and combined with probability amplitude weighted summation to generate the phase space trajectory point at time t. The formula is as follows:

[0033] ,

[0034] In the formula, This represents the phase space coordinates corresponding to the i-th energy efficiency state. To represent the probability amplitude, repeat the above process for multiple time points. Generate continuous phase space trajectories to form a future energy efficiency phase space topology trajectory map;

[0035] By weighted mapping of quantum measurement probability amplitude and phase space coordinates, the quantum annealing results are transformed into intuitive phase space trajectories, revealing the nonlinear behavior of the refrigeration system in chaotic environments. Combined with real-time environmental data, the phase space trajectory diagram is dynamically corrected.

[0036] Preferably, the critical state fuzzy control module, the fuzzy control rule matrix step includes: acquiring a phase space topology trajectory map, including the branches, convergence regions and critical abrupt change points of the energy efficiency trajectory, and extracting the curvature features of the phase space topology trajectory map. This is used to determine whether a system is approaching a chaotic critical state.

[0037] Obtain the current Lyapunov exponent from the environmental chaos coefficient matrix. To quantify the degree of chaos, the Lyapunov exponent is positive when the system is in a chaotic state, 0 when it is in a critical state, and negative when it is in a stable state.

[0038] The system acquires the real-time energy efficiency status and environmental disturbance parameters of the refrigeration system, quantifies the curvature characteristics of the phase space topology trajectory diagram, divides it into fuzzy sets, maps the Lyapunov exponent values ​​into fuzzy linguistic variables, maps the energy efficiency status and environmental parameters into fuzzy sets, and quantifies the degree of fuzziness through membership functions.

[0039] By combining the fuzzy states of the Lyapunov exponent with the curvature characteristics of the phase space topological trajectory graph, fuzzy control rules are generated, and dynamic weights are generated. This is used to adjust the priority of fuzzy control rules, and the formula is as follows:

[0040] ,

[0041] In the formula, Represents the exponentially decaying term, when As the dynamic weights increase, the system approaches a chaotic state, and the dynamic weights... It decays exponentially, reducing the sensitivity of control rules. This represents the chaos decay coefficient, used to adjust the sensitivity of chaos to weights. Represents the curvature gain term, when When the weight increases, the dynamic weight Linear increase, enhancing the ability to respond to mutations. This represents the curvature gain coefficient, used to adjust the amplification effect of curvature on weights. It analyzes real-time data through fuzzy clustering, automatically generates new fuzzy rules, and dynamically updates the rule matrix.

[0042] Preferably, the critical state fuzzy control module includes a fuzzy control output dynamic adjustment step comprising: weighting the fuzzy control output according to dynamic weights, as shown in the formula:

[0043] ,

[0044] In the formula, This represents the final control output at time t. This indicates fuzzy control output. This indicates the default control activation coefficient, balancing the fuzzy control output. With default control Contribution This indicates a conservative default control value. After the control signal is sent to the refrigeration system for execution, the actual operating effect is monitored in real time.

[0045] Preferably, the dissipative structure optimization module includes the following steps for calculating non-equilibrium thermodynamic entropy change: obtaining a dynamically generated fuzzy control rule matrix, which includes fuzzy rules for control parameters such as compressor speed and refrigerant flow rate, and the rule matrix includes input variables, output variables and rule weights;

[0046] Based on the thermodynamic characteristics of the refrigeration system, a coupled model of the compressor and refrigerant flow is established to quantify the entropy change of the system under different operating conditions. The coupled model distinguishes between reversible and irreversible processes, and the entropy change is calculated by designing a virtual reversible path. Based on the thermodynamic model, the entropy change under different combinations of control parameters is calculated. This includes compressor entropy increase and refrigerant entropy dissipation;

[0047] By applying the local equilibrium assumption of non-equilibrium thermodynamics, the system is decomposed into multiple sub-regions. The entropy change of each region is calculated and summed. A mapping relationship is established between the output variables in the fuzzy control rule matrix and the entropy change index, including rule matching and entropy change prediction. Rule matching: Based on the current system state, the control strategy in the fuzzy rules is matched. Entropy change prediction: Based on the control parameters output by the rules, the entropy change corresponding to the strategy is predicted.

[0048] Preferably, the dissipative structure optimization module generates a Pareto optimal solution set by: using compressor speed and refrigerant flow rate as decision variables to construct a multi-objective optimization problem; generating parameter combinations covering all possible control strategies through a fuzzy control rule matrix; and calculating the corresponding entropy change for each parameter combination. and COP value;

[0049] By screening for non-dominated solutions through dominance relations, if solution A... and If solution A dominates solution B, then all undominated solutions are retained, forming a Pareto optimal solution set.

[0050] Based on real-time environmental disturbances, the fuzzy rule matrix is ​​dynamically adjusted to regenerate the Pareto optimal solution set, as shown in the formula:

[0051] ,

[0052] In the formula, This represents the overall optimization objective; the smaller the value, the better. This represents the total entropy change at compressor speed N and refrigerant flow rate Q. The Coefficient of Performance (COP) represents the cooling capacity to the input power. The fuzzy weights for time t are in the range [0, 1]. The fuzzy control rule matrix is ​​derived from the system state and implemented as follows:

[0053] ;

[0054] when When the formula degenerates into Prioritize energy efficiency, when When the formula degenerates into Prioritize minimizing entropy production for all feasible... Combinatorial calculations are performed, and the solution set corresponding to the minimum value is retained as the Pareto optimal solution.

[0055] Preferably, the holographic digital twin module receives real-time sensor data from the physical devices of the refrigeration system, constructs a high-precision virtual model based on BIM, GIS and 3D modeling technologies, and maps the key state variables of the physical system and the digital twin model to quantum states through a quantum entanglement synchronization protocol;

[0056] Quantum entanglement pairs enable instantaneous state synchronization between physical devices and virtual models. When the state of the physical system changes, the quantum entanglement synchronization protocol immediately triggers the corresponding parameter update of the digital twin model. The digital twin model then recalculates the system performance indicators and generates control suggestions based on the corrected state.

[0057] By leveraging the simulation capabilities of digital twin models, the nonlinear characteristics of physical systems can be dynamically calibrated, correcting deviations in the virtual model.

[0058] Compared with the prior art, the beneficial effects of the present invention are:

[0059] 1. To address the challenges of multidimensional coupling modeling, chaotic environment prediction, critical state control, and energy efficiency optimization in refrigeration systems, and to improve energy efficiency and stability, this invention utilizes a quantum tunneling effect sensor to achieve high-precision and high-sensitivity environmental parameter acquisition. By leveraging the nonlinear response of quantum state energy levels or tunneling probabilities to weak environmental disturbances, multidimensional parameters such as temperature, pressure, and electromagnetic fields are mapped to quantum state characteristics. Combined with laser or electromagnetic field excitation of the quantum system into a superposition state, the dynamic range and resolution of environmental parameter detection are significantly improved. Simultaneously, through ground state characteristic calibration and environmental compensation mechanisms, non-target interference is effectively eliminated.

[0060] 2. This invention uses a quantum annealing processor to simulate the Hamiltonian, generating a phase space topological trajectory diagram of future energy efficiency. It maps the environmental chaos coefficient matrix to quantum states, constructs an initial quantum state through quantum state initialization and tensor product, designs a target Hamiltonian that includes physical constraints of the cooling system, and combines transverse field terms to simulate the quantum tunneling effect, enabling the system to cross local extrema. During the annealing process, the qubits dynamically evolve to low-energy states. Finally, phase space trajectory points are generated by measuring probability amplitude weighting, forming an intuitive topological trajectory diagram, which significantly improves prediction efficiency and accuracy.

[0061] 3. This invention achieves dynamic regulation through a fuzzy control rule matrix. By combining the curvature characteristics of the phase space trajectory diagram and the Lyapunov exponent, it quantifies the degree to which the system approaches the edge of chaos. Fuzzy linguistic variables are mapped to control rules. Through dynamic weights, the control sensitivity can be automatically adjusted according to the chaos sensitivity. Fuzzy clustering automatically generates new rules to ensure that the control strategy is updated in real time with changes in operating conditions. At the same time, the priority adjustment mechanism balances energy efficiency and stability, significantly improving the safe operating boundary of the system.

[0062] 4. This invention achieves holographic digital twins through a quantum entanglement synchronization protocol. This module constructs a high-fidelity virtual model based on BIM, GIS, and 3D modeling technologies. It utilizes quantum entanglement to instantaneously synchronize key state variables of the physical devices and the model. When the state of the physical system changes, the protocol triggers real-time updates to the model parameters, ensuring that the virtual model always reflects the real state. Simultaneously, the module dynamically calibrates nonlinear characteristic deviations through the simulation capabilities of digital twins. Its full-dimensional state feedback and collaborative control functions enable real-time response to environmental disturbances and optimization of global strategies, significantly improving the intelligence and adaptability of the refrigeration system. Attached Figure Description

[0063] Figure 1This is a schematic diagram of the intelligent monitoring and control system for energy efficiency of a refrigeration unit based on the Internet of Things (IoT) of the present invention.

[0064] Figure 2 The present invention describes the operation flow of the IoT-based intelligent monitoring and control system for refrigeration unit energy efficiency. Figure 1 ;

[0065] Figure 3 The present invention describes the operation flow of the IoT-based intelligent monitoring and control system for refrigeration unit energy efficiency. Figure 2 ;

[0066] Figure 4 The present invention describes the operation flow of the IoT-based intelligent monitoring and control system for refrigeration unit energy efficiency. Figure 3 . Detailed Implementation

[0067] 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.

[0068] Example:

[0069] Please see Figures 1-4 As shown, the present invention provides a technical solution including a quantum field environment sensing module, an energy efficiency tensor modeling module, a phase space trajectory prediction module, a critical state fuzzy control module, a dissipative structure optimization module, and a holographic digital twin module;

[0070] The quantum field environment sensing module collects multi-dimensional environmental parameters through a quantum tunneling effect sensor and outputs an environmental chaos coefficient matrix.

[0071] The energy efficiency tensor modeling module constructs a three-dimensional energy efficiency coupling tensor of the refrigeration system through tensor decomposition based on the environmental chaos coefficient matrix.

[0072] The phase space trajectory prediction module generates a future energy efficiency topological trajectory map in phase space based on the three-dimensional energy efficiency coupling tensor and simulates the Hamiltonian through a quantum annealing processor.

[0073] The critical state fuzzy control module dynamically generates a fuzzy control rule matrix based on the curvature characteristics of the phase space topological trajectory diagram and the Lyapunov exponent.

[0074] The dissipative structure optimization module is based on the fuzzy control rule matrix and solves the Pareto optimal solution set of the compressor and refrigerant flow by non-equilibrium thermodynamic entropy change calculation.

[0075] The holographic digital twin module is used to integrate the output data, correct the full-dimensional state of the cooling system in real time through a quantum entanglement synchronization protocol, and provide feedback for coordinated control.

[0076] Preferably, the quantum field environment sensing module is wirelessly connected to the energy efficiency tensor modeling module and the critical state fuzzy control module; the energy efficiency tensor modeling module is wirelessly connected to the phase space trajectory prediction module; the phase space trajectory prediction module is wirelessly connected to the critical state fuzzy control module; the critical state fuzzy control module is wirelessly connected to the dissipative structure optimization module; the dissipative structure optimization module is wirelessly connected to the holographic digital twin module; and the holographic digital twin module is wirelessly connected to the quantum field environment sensing module.

[0077] Preferably, the quantum field environment sensing module deploys a detection unit based on the quantum tunneling effect inside the sensor, maps environmental parameters to the energy level or tunneling probability of a quantum state, and excites the quantum system through laser or electromagnetic field to enter a stable superposition state;

[0078] Without external disturbance, the ground state characteristics of the quantum system are recorded to establish a reference value. Environmental compensation such as temperature and electromagnetic interference is performed on the sensor to eliminate interference from non-target parameters. When environmental parameters are disturbed, the energy level or tunneling probability of the quantum system will change nonlinearly.

[0079] In this embodiment, these changes are captured in real time by a quantum measurement device and converted into quantifiable signals. Multiple quantum sensor units are configured, each corresponding to a different environmental dimension. The collected multidimensional parameter signals are input into a chaotic analysis algorithm to extract nonlinear features, including the Lyapunov exponent: quantifying the system's sensitivity to initial conditions; phase space reconstruction: constructing the phase space trajectory of parameters by embedding dimensions and delay time to observe whether there are strange attractors.

[0080] Specifically, the chaos coefficient is calculated for each environmental parameter. If the Lyapunov index ,but , representing the degree of chaos over time exponential growth, if ,but This indicates that the system is in a periodic state, and the chaos coefficients of each parameter are... Arranged dimensionally as matrix elements, forming an environmental chaos coefficient matrix. , where n represents the number of environmental parameters and m represents the number of time sampling points. Wavelet transform is applied to the chaotic coefficient matrix C to eliminate random noise in quantum measurement and retain the high-frequency components of chaotic characteristics.

[0081] Preferably, the energy efficiency tensor modeling module receives the environmental chaos coefficient matrix output from the quantum field environment sensing module, and sets each element in the environmental chaos coefficient matrix... Mapped to the input variables of the refrigeration system;

[0082] In this embodiment, a three-dimensional energy efficiency tensor is defined based on the operating characteristics of the refrigeration system. E represents the energy efficiency state dimension, m represents the number of time sampling points, and k represents the environmental parameter subspace dimension. The environmental chaos coefficient matrix C is fused with the real-time energy efficiency data of the cooling system and filled into the corresponding position of the tensor T.

[0083] Specifically, the high-dimensional tensor T is decomposed into a core tensor G and a factor matrix. , , , where r is the low-dimensional rank after decomposition, the core tensor G represents the coupling relationship between energy efficiency state and environmental parameters, and the factor matrices A, B, and C correspond to the feature vectors of energy efficiency state, time dimension, and environmental parameters, respectively.

[0084] The decomposed factor matrix and the core tensor G are reconstructed into a three-dimensional energy efficiency coupling tensor. The feature vectors of each dimension are associated through interaction terms in G, and the absolute values ​​of the elements of the core tensor G are calculated. As energy efficiency status ,time Environmental parameters An indicator of the coupling strength among the three.

[0085] Preferably, the phase space trajectory prediction module, in constructing the quantum state initialization step, includes: obtaining the three-dimensional energy efficiency coupling tensor. And extract energy efficiency status. The chaos coefficients of the environmental chaos coefficient matrix Mapped to a quantum state, the initial quantum state is formed by tensor product of the quantum encoding of the energy efficiency state and the chaotic coefficient of the environment, as shown in the formula:

[0086] ,

[0087] In the formula, The initial quantum state represents the initial quantum state of the quantum annealing processor, and N represents the dimension of the energy efficiency state of the cooling system. The quantum amplitude represents the i-th energy-efficient state. The quantum code representing the i-th energy efficiency state. This represents the quantum encoding of the i-th environmental chaos coefficient.

[0088] Preferably, the phase space trajectory prediction module includes a quantum annealing step comprising: designing an energy function of the Hamiltonian based on the energy efficiency optimization target of the refrigeration system, reflecting the energy efficiency distribution of the system under different states;

[0089] Specifically, the Hamiltonian needs to include the physical constraints and dynamic characteristics of the cooling system. A transverse field term is added to the Hamiltonian to simulate the quantum tunneling effect, enabling the system to overcome local energy efficiency extremes. The initial temperature, transverse field strength, and annealing time of the quantum annealing processor are preset, and the qubits are initialized as a uniform superposition state, covering all possible energy efficiency state combinations.

[0090] It should be understood that by gradually reducing the transverse field strength through a quantum annealing processor while increasing the dominance of the Hamiltonian, the qubit state dynamically evolves during the annealing process, gradually converging to the energy-optimal low-energy state, as shown in the formula:

[0091] ,

[0092] In the formula, Representing quantum states, Represents the time calculus, Denotes the reduced Planck constant. Represents the effective Hamiltonian related to time. Represented as , This represents the initial Hamiltonian. Represented as ;

[0093] Through transverse field To achieve global exploration capability of quantum states and avoid getting trapped in local energy efficiency extrema, Denotes the target Hamiltonian, and represents By using the projection operator of energy efficiency state and chaotic coefficients, an energy efficiency function coupled with the environment is constructed. , which represents the annealing progress function.

[0094] Preferably, the phase space trajectory prediction module, in the phase space topological trajectory diagram generation step, includes: at the end of annealing, performing quantum state... Measurements were performed to obtain M energy efficiency states sampling results;

[0095] In this embodiment, each energy efficiency state is calculated based on the measurement results. probability amplitude , This indicates its weight in the future energy efficiency trajectory;

[0096] Each energy efficiency status Mapped to phase space coordinates, and combined with probability amplitude weighted summation to generate the phase space trajectory point at time t. The formula is as follows:

[0097] ,

[0098] In the formula, This represents the phase space coordinates corresponding to the i-th energy efficiency state. To represent the probability amplitude, repeat the above process for multiple time points. Generate continuous phase space trajectories to form a future energy efficiency phase space topology trajectory map;

[0099] By weighted mapping of quantum measurement probability amplitude and phase space coordinates, the quantum annealing results are transformed into intuitive phase space trajectories, revealing the nonlinear behavior of the refrigeration system in chaotic environments. Combined with real-time environmental data, the phase space trajectory diagram is dynamically corrected.

[0100] Preferably, the critical state fuzzy control module, the fuzzy control rule matrix step includes: acquiring a phase space topology trajectory map, including the branches, convergence regions and critical abrupt change points of the energy efficiency trajectory, and extracting the curvature features of the phase space topology trajectory map. This is used to determine whether a system is approaching a chaotic critical state.

[0101] Obtain the current Lyapunov exponent from the environmental chaos coefficient matrix. To quantify the degree of chaos, the Lyapunov exponent is positive when the system is in a chaotic state, 0 when it is in a critical state, and negative when it is in a stable state.

[0102] Specifically, the real-time energy efficiency status and environmental disturbance parameters of the refrigeration system are obtained, the curvature characteristics of the phase space topology trajectory diagram are quantified and divided into fuzzy sets, the Lyapunov exponent values ​​are mapped to fuzzy linguistic variables, the energy efficiency status and environmental parameters are mapped to fuzzy sets, and the degree of fuzziness is quantified by the membership function.

[0103] In this embodiment, the fuzzy state of the Lyapunov exponent is combined with the curvature characteristics of the phase space topological trajectory diagram to generate fuzzy control rules, and dynamic weights are generated. This is used to adjust the priority of fuzzy control rules, and the formula is as follows:

[0104] ,

[0105] In the formula, Represents the exponentially decaying term, when As the dynamic weights increase, the system approaches a chaotic state, and the dynamic weights... It decays exponentially, reducing the sensitivity of control rules. This represents the chaos decay coefficient, used to adjust the sensitivity of chaos to weights. Represents the curvature gain term, when When the weight increases, the dynamic weight Linear increase, enhancing the ability to respond to mutations. This represents the curvature gain coefficient, used to adjust the amplification effect of curvature on weights. It analyzes real-time data through fuzzy clustering, automatically generates new fuzzy rules, and dynamically updates the rule matrix.

[0106] Preferably, the critical state fuzzy control module includes a fuzzy control output dynamic adjustment step comprising: weighting the fuzzy control output according to dynamic weights, as shown in the formula:

[0107] ,

[0108] In the formula, This represents the final control output at time t. This indicates fuzzy control output. This indicates the default control activation coefficient, balancing the fuzzy control output. With default control Contribution This indicates a conservative default control value. After the control signal is sent to the refrigeration system for execution, the actual operating effect is monitored in real time.

[0109] Preferably, the dissipative structure optimization module includes the following steps for calculating non-equilibrium thermodynamic entropy change: obtaining a dynamically generated fuzzy control rule matrix, which includes fuzzy rules for control parameters such as compressor speed and refrigerant flow rate, and the rule matrix includes input variables, output variables and rule weights;

[0110] In this embodiment, based on the thermodynamic characteristics of the refrigeration system, a coupled model of the compressor and refrigerant flow is established to quantify the entropy change of the system under different operating conditions. The coupled model distinguishes between reversible and irreversible processes, calculates the entropy change by designing a virtual reversible path, and calculates the entropy change under different combinations of control parameters according to the thermodynamic model. This includes compressor entropy increase and refrigerant entropy dissipation;

[0111] By applying the local equilibrium assumption of non-equilibrium thermodynamics, the system is decomposed into multiple sub-regions. The entropy change of each region is calculated and summed. A mapping relationship is established between the output variables in the fuzzy control rule matrix and the entropy change index, including rule matching and entropy change prediction. Rule matching: Based on the current system state, the control strategy in the fuzzy rules is matched. Entropy change prediction: Based on the control parameters output by the rules, the entropy change corresponding to the strategy is predicted.

[0112] Preferably, the dissipative structure optimization module generates a Pareto optimal solution set by: using compressor speed and refrigerant flow rate as decision variables to construct a multi-objective optimization problem; generating parameter combinations covering all possible control strategies through a fuzzy control rule matrix; and calculating the corresponding entropy change for each parameter combination. and COP value;

[0113] Specifically, non-dominated solutions are selected through dominance relations. If solution A... and If solution A dominates solution B, then all undominated solutions are retained, forming a Pareto optimal solution set.

[0114] Based on real-time environmental disturbances, the fuzzy rule matrix is ​​dynamically adjusted to regenerate the Pareto optimal solution set, as shown in the formula:

[0115] ,

[0116] In the formula, This represents the overall optimization objective; the smaller the value, the better. This represents the total entropy change at compressor speed N and refrigerant flow rate Q. The Coefficient of Performance (COP) represents the cooling capacity to the input power. The fuzzy weights for time t are in the range [0, 1]. The fuzzy control rule matrix is ​​derived from the system state and implemented as follows:

[0117] ;

[0118] when When the formula degenerates into Prioritize energy efficiency, when When the formula degenerates into Prioritize minimizing entropy production for all feasible... Combinatorial calculation The solution set corresponding to the minimum value is retained as the Pareto optimal solution.

[0119] Preferably, the holographic digital twin module receives real-time sensor data from the physical devices of the refrigeration system, constructs a high-precision virtual model based on BIM, GIS and 3D modeling technologies, and maps the key state variables of the physical system and the digital twin model to quantum states through a quantum entanglement synchronization protocol;

[0120] Quantum entanglement pairs enable instantaneous state synchronization between physical devices and virtual models. When the state of the physical system changes, the quantum entanglement synchronization protocol immediately triggers the corresponding parameter update of the digital twin model. The digital twin model then recalculates the system performance indicators and generates control suggestions based on the corrected state.

[0121] By leveraging the simulation capabilities of digital twin models, the nonlinear characteristics of physical systems can be dynamically calibrated, correcting deviations in the virtual model.

[0122] Working principle: Multidimensional environmental parameters are collected through a quantum tunneling effect sensor. The nonlinear response characteristics of the quantum system are used to convert environmental disturbances into changes in the energy levels or tunneling probability of the quantum state. The sensor excites the quantum system into a superposition state through laser or electromagnetic field and records the ground state characteristics as a reference value to achieve environmental compensation such as temperature and electromagnetic interference, and eliminate non-target interference. When environmental parameters are disturbed, the energy levels or tunneling probability of the quantum system change nonlinearly. These changes are captured by quantum measurement equipment and converted into quantifiable signals. The multidimensional environmental parameter signals are processed by a chaotic analysis algorithm to extract the Lyapunov exponent and phase space reconstruction features, calculate the chaotic coefficients of each parameter, and finally form an environmental chaotic coefficient matrix. Noise is removed by wavelet transform, and high-frequency chaotic features are preserved.

[0123] The energy efficiency tensor modeling module receives the environmental chaos coefficient matrix and maps its elements to the input variables of the refrigeration system. Based on the system's operating characteristics, a three-dimensional energy efficiency tensor is defined. The environmental chaos coefficients and real-time energy efficiency data are fused and filled into the tensor. Through tensor decomposition, the core tensor and factor matrix are extracted to represent the low-dimensional feature vectors of energy efficiency state, time dimension, and environmental parameters, respectively. The reconstructed three-dimensional energy efficiency coupling tensor associates the features of each dimension through the interaction terms of the core tensor, and calculates the absolute value of the elements of the core tensor as a quantitative indicator of the coupling strength among energy efficiency state, time, and environmental parameters. The phase space trajectory prediction module is based on the three-dimensional energy efficiency coupling... Tensors map environmental chaos coefficients to quantum states. By initializing the quantum state and multiplying it with tensors, a target Hamiltonian containing the physical constraints of the refrigeration system is constructed in the initial quantum state. Combined with transverse field terms, the quantum tunneling effect is simulated, allowing the system to cross local energy efficiency extremes. The quantum annealing processor initializes the qubits as a uniform superposition state and gradually reduces the transverse field strength, allowing the quantum state to evolve to a low-energy state. After annealing, the energy efficiency state is sampled by measuring the quantum state. Combined with probability amplitude weighting, phase space coordinate points are generated to form a phase space topological trajectory diagram of future energy efficiency. Combined with real-time environmental data, the trajectory is dynamically corrected to reveal the nonlinear behavior of the refrigeration system in a chaotic environment.

[0124] Based on the analysis of the curvature characteristics of the phase space topological trajectory diagram using the critical state fuzzy control module, and combined with the Lyapunov exponent in the environmental chaos coefficient matrix, it is determined whether the system is close to the chaotic critical state. The curvature characteristics are quantized into fuzzy sets, and the Lyapunov exponent is mapped to fuzzy linguistic variables. The degree of fuzziness is quantified through membership functions to dynamically generate a fuzzy control rule matrix. The priority of the control rules is adjusted by combining the exponential decay term and the curvature gain term, reducing the sensitivity in chaotic states and enhancing the response capability to sudden changes. Finally, new rules are automatically generated through fuzzy clustering, the control strategy is dynamically updated, and the balance between fuzzy control and default control is achieved. Contribution: The dissipative structure optimization module, based on a fuzzy control rule matrix, establishes a coupled model of the compressor and refrigerant flow, distinguishes between reversible and irreversible processes, calculates entropy change through a virtual reversible path, and utilizes the local equilibrium assumption of non-equilibrium thermodynamics to decompose the system into sub-regions, calculates and sums the entropy changes, establishes a mapping relationship between control parameters and entropy change, constructs a multi-objective optimization problem with compressor speed and refrigerant flow as decision variables, filters non-dominated solutions through dominance relationships, forms a Pareto optimal solution set, dynamically adjusts the fuzzy rule matrix, and regenerates the solution set in conjunction with real-time environmental disturbances, balancing energy efficiency ratio and minimizing entropy production to achieve refrigeration. The system performs global optimization under complex operating conditions. Real-time sensor data from physical equipment is used to construct a high-precision virtual model based on BIM, GIS, and 3D modeling technologies. A quantum entanglement synchronization protocol is used to achieve instantaneous synchronization of key state variables between the physical system and the digital twin model through quantum entanglement pairs. When the state of the physical system changes, the protocol triggers an update to the virtual model parameters. The digital twin model recalculates performance indicators and generates control suggestions. Simulation capabilities dynamically calibrate the nonlinear characteristic deviations of the physical system, correcting the differences between the virtual model and the actual state, achieving real-time feedback and collaborative control of the entire state. The holographic digital twin module integrates real-time sensor data from physical equipment and constructs a high-precision virtual model based on BIM, GIS, and 3D modeling technologies. A quantum entanglement synchronization protocol is used to achieve instantaneous synchronization of key state variables between the physical system and the digital twin model through quantum entanglement pairs. When the state of the physical system changes, the protocol triggers an update to the virtual model parameters. The digital twin model recalculates performance indicators and generates control suggestions. Simulation capabilities dynamically calibrate the nonlinear characteristic deviations of the physical system, correcting the differences between the virtual model and the actual state, achieving real-time feedback and collaborative control of the entire state, improving system response speed and control accuracy.

[0125] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their likenesses.

[0126] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A smart energy efficiency monitoring and control system for refrigeration units based on the Internet of Things, characterized in that: It includes a quantum field environment perception module, an energy efficiency tensor modeling module, a phase space trajectory prediction module, a critical state fuzzy control module, a dissipative structure optimization module, and a holographic digital twin module; The phase space trajectory prediction module generates a future energy efficiency topological trajectory map in phase space based on the three-dimensional energy efficiency coupling tensor and simulates the Hamiltonian through a quantum annealing processor. The critical state fuzzy control module dynamically generates a fuzzy control rule matrix based on the curvature characteristics of the phase space topological trajectory diagram and the Lyapunov exponent. The dissipative structure optimization module is based on the fuzzy control rule matrix and solves the Pareto optimal solution set of the compressor and refrigerant flow by non-equilibrium thermodynamic entropy change calculation. The holographic digital twin module is used to integrate the output data, correct the full-dimensional state of the cooling system in real time through a quantum entanglement synchronization protocol, and provide feedback for coordinated control.

2. The IoT-based intelligent monitoring and control system for refrigeration unit energy efficiency according to claim 1, characterized in that: The phase space trajectory prediction module constructs a quantum state initialization step, including: obtaining the three-dimensional energy efficiency coupling tensor. And extract energy efficiency status. The chaos coefficients of the environmental chaos coefficient matrix Mapped to a quantum state, the initial quantum state is formed by tensor product of the quantum encoding of the energy efficiency state and the chaotic coefficient of the environment, as shown in the formula: ， In the formula, N represents the initial quantum state, and N represents the dimension of the energy efficiency state of the refrigeration system. The quantum amplitude represents the i-th energy-efficient state. The quantum code representing the i-th energy efficiency state. This represents the quantum encoding of the i-th environmental chaos coefficient.

3. The IoT-based intelligent monitoring and control system for refrigeration unit energy efficiency according to claim 2, characterized in that: The phase space trajectory prediction module includes a quantum annealing step: presetting the initial temperature, transverse field strength, and annealing time of the quantum annealing processor; gradually reducing the transverse field strength through the quantum annealing processor while increasing the dominance of the Hamiltonian; during the annealing process, the qubit state dynamically evolves and gradually converges to the low-energy state with optimal energy efficiency, as shown in the formula: ， In the formula, Representing quantum states, Represents the time calculus, Denotes the reduced Planck constant. Represents the effective Hamiltonian related to time.

4. The IoT-based intelligent monitoring and control system for refrigeration unit energy efficiency according to claim 3, characterized in that: The phase space trajectory prediction module, the phase space topological trajectory diagram generation step includes: at the end of annealing, performing quantum state... Measurements were performed to obtain M energy efficiency states sampling results; each energy efficiency state was calculated based on the measurement results. probability amplitude Each energy efficiency state Mapped to phase space coordinates, and combined with probability amplitude weighted summation to generate the phase space trajectory point at time t. The formula is as follows: ， In the formula, This represents the phase space coordinates corresponding to the j-th energy efficiency state. Represents the probability amplitude over multiple time points. Continuous phase space trajectories are generated to form a future energy efficiency phase space topology trajectory map.

5. The IoT-based intelligent monitoring and control system for refrigeration unit energy efficiency according to claim 1, characterized in that: The critical state fuzzy control module includes the following steps for the fuzzy control rule matrix: acquiring the phase space topology trajectory map and extracting the curvature features of the phase space topology trajectory map. Obtain the current Lyapunov exponent from the environmental chaos coefficient matrix. To quantify the degree of chaos, the fuzzy states of the Lyapunov exponent are combined with the curvature characteristics of the phase space topological trajectory diagram to generate fuzzy control rules, and dynamic weights are generated. This is used to adjust the priority of fuzzy control rules, and the formula is as follows: ， In the formula, Represents the exponentially decaying term. Represents the chaos decay coefficient. Represents the curvature gain term. This represents the curvature gain coefficient. By analyzing real-time data through fuzzy clustering, new fuzzy rules are automatically generated, and the rule matrix is ​​dynamically updated.

6. The IoT-based intelligent monitoring and control system for refrigeration unit energy efficiency according to claim 5, characterized in that: The critical state fuzzy control module includes a dynamic adjustment step for the fuzzy control output, which involves weighting the fuzzy control output according to dynamic weights, using the following formula: ， In the formula, This represents the final control output at time t. This indicates fuzzy control output. This indicates the default control activation coefficient. This indicates a conservative default control value.

7. The IoT-based intelligent monitoring and control system for refrigeration unit energy efficiency according to claim 1, characterized in that: The dissipative structure optimization module includes the following steps for calculating non-equilibrium thermodynamic entropy change: obtaining a dynamically generated fuzzy control rule matrix; establishing a compressor-refrigerant flow coupling model based on the thermodynamic characteristics of the refrigeration system; quantifying the entropy change under different operating conditions; and calculating the entropy change under different combinations of control parameters based on the thermodynamic model. This includes compressor entropy increase and refrigerant entropy dissipation, and establishes a mapping relationship between the output variables in the fuzzy control rule matrix and the entropy change index.

8. The IoT-based intelligent monitoring and control system for refrigeration unit energy efficiency according to claim 7, characterized in that: The dissipative structure optimization module generates a Pareto optimal solution set by: using compressor speed and refrigerant flow rate as decision variables to construct a multi-objective optimization problem; generating parameter combinations of all possible control strategies through a fuzzy control rule matrix; and calculating the corresponding entropy change for each parameter combination. And COP value, through dominance relation screening for non-dominated solutions, if solution A and If solution A dominates solution B, then all undominated solutions are retained, forming a Pareto optimal solution set.

9. The IoT-based intelligent monitoring and control system for refrigeration unit energy efficiency according to claim 1, characterized in that: The holographic digital twin module receives real-time sensor data from the physical devices of the cooling system, constructs a high-precision virtual model based on BIM, GIS, and 3D modeling technologies, and maps key state variables of the physical system and the digital twin model to quantum states through a quantum entanglement synchronization protocol. Instantaneous state synchronization between the physical devices and the virtual model is achieved through quantum entanglement pairs. When the state of the physical system changes, the quantum entanglement synchronization protocol immediately triggers the corresponding parameter update of the digital twin model. The digital twin model recalculates the system performance indicators and generates control suggestions based on the corrected state.

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