Data monitoring method and system for real-time control of heat exchange unit

Abstract

The invention discloses a data monitoring method and system for real-time control of a heat exchange unit, and the method comprises the steps: collecting the real-time operation data of the heat exchange unit through a sensor module, carrying out the periodic global optimization of a multivariable heat transfer model of the heat exchange unit through quantum calculation, and forming a global scheduling parameter; an ultra wide band (UWB) sensor is used for collecting micro-vibration data in fluid, and a digital twin model and a chaos theory are combined to carry out short-period dynamic prediction on a heat exchange state; performing abnormal mode recognition on the multi-dimensional sensing data through high-dimensional topological data analysis TDA; and operating parameters of the heat exchange unit are adjusted by integrating calculation results. According to the method, global target parameters are obtained through quantum calculation optimization scheduling, short-period dynamic state prediction is realized by using ultra-wideband sensing and digital twinning combined chaos theory, and abnormal states are analyzed and detected by using high-dimensional topological data, so that final control parameters are generated after data fusion, and the control precision is improved. Precise adjustment and real-time control of key operation parameters of the heat exchange unit are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical digital data processing, and particularly to a data monitoring method and system for real-time control of a heat exchange unit. Background Art

[0002] Existing real-time monitoring and control technologies for heat exchange units mainly rely on traditional sensors and static control models. These technologies have limitations in data acquisition, status monitoring, and fault diagnosis, and cannot fully capture the multivariable non-linear dynamic changes involved in the heat exchange process. At the same time, traditional methods have deficiencies in real-time performance, accuracy, and response to minor anomalies, and cannot meet the requirements of modern industrial automation and intelligent manufacturing for efficient and precise monitoring and control. In recent years, although advanced technologies such as quantum computing, digital twin, ultra-wideband sensing, chaos theory, and high-dimensional topological data analysis have been introduced, these technologies still lack organic integration in existing applications and have not formed a comprehensive and dynamic data monitoring and control system. Summary of the Invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Aiming at the problems of incomplete data acquisition, poor real-time performance, insufficient status prediction, and lagging fault detection in the real-time monitoring and control of heat exchange units, the present invention proposes a real-time control method combining multi-source data fusion and advanced data processing technologies. This method obtains global target parameters through quantum computing optimization scheduling, realizes short-cycle dynamic state prediction by combining ultra-wideband sensing and digital twin with chaos theory, and detects abnormal states by using high-dimensional topological data analysis, so as to generate final control parameters after data fusion and achieve precise adjustment and real-time control of the key operating parameters of the heat exchange unit (such as fluid flow rate, pump speed, and temperature difference).

[0005] To solve the above technical problems, the present invention provides the following technical solution. A data monitoring method for real-time control of a heat exchange unit includes:

[0006] Collecting real-time operation data of the heat exchange unit through a sensor module, where the real-time operation data includes fluid temperature, pressure, flow rate parameters, and heat transfer path structure information inside the heat exchange pipe obtained by picosecond laser interferometry measurement;

[0007] Performing periodic global optimization on the multivariable heat transfer model of the heat exchange unit by using quantum computing to form global scheduling parameters;

[0008] Collecting internal micro-vibration data of the fluid by using an ultra-wideband UWB sensor, and combining with a digital twin model and chaos theory to perform short-cycle dynamic prediction on the heat exchange state;

[0009] Anomaly pattern recognition of multi-dimensional sensing data through high-dimensional topological data analysis (TDA);

[0010] Integrate the global scheduling parameters obtained by quantum computing, real-time monitoring data, and TDA fault detection results to adjust the operating parameters of the heat exchange unit, thereby achieving data monitoring and real-time control.

[0011] As a preferred solution of the data monitoring method for real-time control of the heat exchange unit described in the present invention, wherein: the picosecond laser interferometry is used for analyzing the internal heat transfer path of the heat exchange unit. A picosecond laser pulse is used to irradiate the heat exchange pipeline or the corresponding structure to obtain high-resolution thermal imaging data, and the optical coherence tomography technology is used to reconstruct the internal temperature distribution and heat flow path information of the heat exchange medium, and the obtained data is input as the system structure parameters.

[0012] As a preferred solution of the data monitoring method for real-time control of the heat exchange unit described in the present invention, wherein: the quantum computing includes establishing a basic physical model describing the heat exchange process, and its core is the total heat exchange amount of the heat exchange unit under different control parameters and structure parameters;

[0013] It is set that the total heat exchange amount of the heat exchange unit is given by the following formula:

[0014]

[0015] Wherein, represents the operating control parameters of the heat exchange unit, and N represents the total number of parameters in the operating control parameter vector of the heat exchange unit; represents the structure parameters of the heat exchanger, and M represents the total number of parameters in the structure parameter vector of the heat exchanger; represents the thermal physical properties parameters of the environment and the fluid, represents the hot-side inlet temperature, cold-side inlet temperature, inner-side convective heat transfer coefficient, and outer-side convective heat transfer coefficient; represents the total heat exchange amount of the heat exchange unit; represents the overall heat transfer coefficient; represents the effective heat transfer area; represents the logarithmic mean temperature difference;

[0016] Overall heat transfer coefficient Expressed by the series thermal resistance model as:

[0017]

[0018] Wherein, represents the overall heat transfer coefficient; and are respectively the inner and outer side convective heat transfer coefficients of the heat exchanger, and is the thermal conductivity of the material;

[0019] Logarithmic mean temperature difference It is expressed as:

[0020]

[0021] Wherein, is the hot-side inlet temperature, is the cold-side inlet temperature; and are the hot-side and cold-side outlet temperatures respectively.

[0022] As a preferred solution of the data monitoring method for real-time control of the heat exchange unit described in the present invention, wherein: the quantum computing includes introducing an energy consumption model to reflect the energy consumption situation during the operation of the equipment, and the expression is:

[0023]

[0024] Wherein, represents the efficiency of the pump; represents a function reflecting the actual operating load; represents the equipment energy consumption; represents the operating load function;

[0025] To simultaneously optimize the heat exchange efficiency and energy consumption and meet the operating conditions constraints, the objective function is constructed as:

[0026] Wherein, represents the comprehensive objective function; represents the target heat transfer amount; represents the target energy consumption; represents the deviation of the i-th operating condition constraint; , , represent the weight coefficients of each item; represents the total number of operating condition constraints;

[0027] Since the quantum annealing or variational quantum algorithm requires the problem to be expressed in a discrete binary form, it is necessary to discretize the above continuous optimization problem;

[0028] Suppose that through encoding, and are discretized into a binary vector , then the original optimization problem is transformed into the QUBO form:

[0029]

[0030] Wherein, is from the objective function The symmetric matrix obtained after discretization and quadratic processing; b represents the binary vector obtained by discretizing continuous variables. Represents the objective function value of the quadratic unconstrained binary optimization problem.

[0031] As a preferred solution of the data monitoring method for real-time control of the heat exchange unit described in the present invention, wherein: the short-term dynamic prediction includes arranging UWB sensors outside the fluid pipeline of the heat exchange unit, using the UWB to emit high-frequency short pulse signals to penetrate the fluid, and receiving the reflected signals, and collecting and performing spectral analysis on the micro-vibration state inside the fluid based on the frequency change of the signals to obtain real-time information on the internal turbulence and microscopic flow state of the fluid.

[0032] Based on computational fluid dynamics (CFD) simulation and physical sensor data, a real-time digital twin model of the heat exchange unit is constructed, and the real-time data collected is fused using the real-time digital twin model, and the Lyapunov exponent and fractal dimension of the system state are calculated to predict the short-term dynamic changes of the heat exchange state.

[0033] As a preferred solution of the data monitoring method for real-time control of the heat exchange unit described in the present invention, wherein: the abnormal pattern recognition includes collecting multi-dimensional time series data during the operation of the heat exchange unit, mapping the collected data to a topological space using topological data analysis methods, calculating topological features, identifying the abnormal patterns existing in the data, and forming a data output for indicating the abnormal state of the system.

[0034] As a preferred solution of the data monitoring method for real-time control of the heat exchange unit described in the present invention, wherein: adjusting the operating parameters of the heat exchange unit includes fusing the global scheduling parameters obtained by quantum computing, the real-time operating state obtained based on UWB sensing and digital twin chaos prediction, and the TDA anomaly detection results, and comprehensively forming the final control parameters of the heat exchange unit.

[0035] As a preferred solution of the data monitoring system for real-time control of the heat exchange unit described in the present invention, wherein: it includes a sensor module, a computing module, and a control module. The sensor module includes sensors for collecting fluid temperature, pressure, and flow data, ultra-wideband UWB sensors for collecting micro-vibration data, and picosecond laser interferometry equipment for obtaining heat transfer path structure information; the computing module includes a global optimization unit based on quantum computing, a digital twin modeling and chaos theory processing unit, and a high-dimensional topological data analysis processing unit; the control module is used to adjust the operating state of the heat exchange unit according to the control parameters output by the computing module.

[0036] A computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of a data monitoring method for real-time control of a heat exchange unit are implemented.

[0037] A computer-readable storage medium stores a computer program thereon. When the computer program is executed by a processor, the steps of a data monitoring method for real-time control of a heat exchange unit are implemented.

[0038] Advantages of the present invention: By organically integrating quantum computing, ultra-wideband sensing, digital twin, chaos theory, and high-dimensional topological data analysis technology, the present invention constructs a real-time control system for a heat exchange unit based on multi-source data fusion. This system can comprehensively and real-time collect and process the operation state information of the heat exchange unit, accurately capture dynamic changes and abnormal situations, and generate final control parameters through comprehensive data fusion, providing a reliable basis for the precise adjustment of the fluid flow rate, pump speed, and temperature difference of the heat exchange unit, thereby improving the overall operation efficiency and safety of the system, and forming an intelligent monitoring and control solution with high practical value and scalability. Description of the Drawings

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0040] Figure 1 It is a schematic flow diagram of a data monitoring method for real-time control of a heat exchange unit provided by an embodiment of the present invention. Detailed Embodiments

[0041] To make the above objects, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0042] Many specific details are set forth in the following description to facilitate a thorough understanding of the present invention. However, the present invention may be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0043] Example 1, referring to Figure 1, which is the first embodiment of the present invention. This embodiment provides a data monitoring method for real-time control of a heat exchange unit, including:

[0044] S1: Collect the real-time operation data of the heat exchange unit through the sensor module. The real-time operation data includes fluid temperature, pressure, flow rate parameters, and the heat transfer path structure information inside the heat exchange pipe obtained by picosecond laser interferometry.

[0045] The picosecond laser interferometry is used for analyzing the internal heat transfer path of the heat exchange unit. A picosecond laser pulse is used to irradiate the heat exchange pipe or the corresponding structure to obtain high-resolution thermal imaging data, and the internal temperature distribution and heat flow path information of the heat exchange medium are reconstructed through optical coherence tomography technology. The obtained data is input as the system structure parameters.

[0046] Furthermore, in this picosecond laser interferometry step, a picosecond pulse laser is used as the light source, and its laser pulse is transmitted to the surface of the heat exchange pipe or related structure through an optical system including a beam splitter, a collimator, and a focusing lens; after the laser pulse irradiates the target area, due to the interfaces with dielectric constant changes inside the heat exchange medium, part of the laser energy is reflected, scattered, and interfered at these interfaces, forming a reflected signal containing interference fringes. This signal is collected by a detector equipped with a low-noise amplifier; the collected analog signal is processed by a band-pass filter and a preamplifier, and then digitized by a high-speed analog-to-digital converter; the digitized signal is transformed from the time domain to the frequency domain by the signal processing unit using the fast Fourier transform (FFT) algorithm, and the frequency domain data is deeply decomposed by combining with optical coherence tomography (OCT) technology. By correcting the optical path difference between the reference arm and the sample arm and applying the phase unwrapping algorithm, the interference fringes are converted into spatial distribution data, thereby reconstructing the internal temperature distribution and heat flow path information of the heat exchange medium; the reconstructed three-dimensional data is represented in the form of a matrix or volume data, and each data unit corresponds to the temperature or heat flow rate at a specific position. This data is then transmitted to the data processing module, where the data is processed for noise suppression, dynamic range adjustment, and spatial resolution optimization, and is input as part of the system structure parameters to the global optimization module and the data fusion platform for subsequent monitoring use.

[0047] S2: Use quantum computing to perform periodic global optimization on the multivariable heat transfer model of the heat exchange unit to form global scheduling parameters.

[0048] The quantum computing includes establishing a basic physical model describing the heat exchange process, and its core is the total heat transfer amount of the heat exchange unit under different control parameters and structure parameters.

[0049] It is assumed that the total heat transfer amount of the heat exchange unit is given by the following formula:

[0050]

[0051] Among them, represents the operation control parameters of the heat exchange unit, and N represents the total number of parameters in the operation control parameter vector of the heat exchange unit; represents the structural parameters of the heat exchanger, and M represents the total number of parameters in the structural parameter vector of the heat exchanger; represents the thermal physical properties parameters of the environment and fluid, represents the hot-side inlet temperature, cold-side inlet temperature, inner-side convective heat transfer coefficient, and outer-side convective heat transfer coefficient; represents the total heat transfer amount of the heat exchange unit; represents the overall heat transfer coefficient; represents the effective heat transfer area; represents the logarithmic mean temperature difference;

[0052] Overall heat transfer coefficient is expressed by the series thermal resistance model as:

[0053]

[0054] Among them, represents the overall heat transfer coefficient; and are the inner and outer side convective heat transfer coefficients of the heat exchanger respectively, and is the thermal conductivity of the material;

[0055] Logarithmic mean temperature difference is expressed as:

[0056]

[0057] Among them, is the hot-side inlet temperature, is the cold-side inlet temperature; and are the hot-side and cold-side outlet temperatures respectively.

[0058] The quantum computing includes introducing an energy consumption model to reflect the energy consumption situation during the operation of the device, and the expression is:

[0059]

[0060] Among them, represents the efficiency of the pump; represents a function reflecting the actual operation load; represents the device energy consumption; represents the operation load function;

[0061] To simultaneously optimize the heat transfer efficiency and energy consumption and meet the working condition constraints, the objective function is constructed as:

[0062] Among them, represents the comprehensive objective function; represents the target heat exchange quantity; represents the target energy consumption; represents the deviation of the i-th operating condition constraint; and and represent the weight coefficients of each item; represents the total number of operating condition constraints;

[0063] Since the quantum annealing or variational quantum algorithm requires the problem to be expressed in a discrete binary form, it is necessary to discretize the above continuous optimization problem;

[0064] Suppose that through encoding, and are discretized into the binary vector , then the original optimization problem is transformed into the QUBO form:

[0065]

[0066] Among them, is the symmetric matrix obtained after discretizing and quadraticizing the objective function ; b represents the binary vector obtained by discretizing the continuous variable; represents the objective function value of the quadratic unconstrained binary optimization problem.

[0067] S3: Use the ultra-wideband UWB sensor to collect the micro-vibration data inside the fluid, and combine the digital twin model and chaos theory to perform short-term dynamic prediction on the heat exchange state.

[0068] The short-term dynamic prediction includes arranging UWB sensors outside the fluid pipeline of the heat exchange unit, using the UWB to emit high-frequency short pulse signals to penetrate the fluid, and receiving its reflected signals, and collecting and analyzing the spectrum of the micro-vibration state inside the fluid according to the frequency change of the signals to obtain the real-time information of the turbulence and micro-flow state inside the fluid.

[0069] Based on the computational fluid dynamics CFD simulation and physical sensor data, construct a real-time digital twin model of the heat exchange unit, use the real-time digital twin model to perform data fusion on the collected real-time data, and calculate the Lyapunov exponent and fractal dimension of the system state, so as to predict the short-term dynamic change of the heat exchange state and provide a reference basis for the adjustment of control parameters.

[0070] Furthermore, the digital twin state equation in the real-time digital twin model is expressed as:

[0071]

[0072] Among them, represents the state vector of the heat exchange unit at time t, is the system dynamics model obtained by CFD simulation, represents measurement noise or model error.

[0073] The calculation of the Lyapunov exponent includes defining that the adjacent trajectory perturbation satisfies:

[0074]

[0075] And obtaining the local Lyapunov exponent through the calculation formula:

[0076]

[0077] Among them, represents the small perturbation of the state vector at time t ; represents the local Lyapunov exponent, indicating the exponential growth rate of the perturbation; represents the vector norm; represents the time interval; represents time at the moment of the state vector ;

[0078] The calculation of the fractal dimension includes adopting the idea of the correlation dimension and defining the correlation function:

[0079]

[0080] Among them, is the Heaviside step function, and is the state in the discrete sampling points in the phase space; represents the total number of discrete state points sampled from the state data of the heat exchange unit; i, j represent the indices of the sampling points; represents the correlation function of the state trajectory; r represents the distance threshold;

[0081] The fractal dimension is obtained by the following formula:

[0082]

[0083] Among them, represents the fractal dimension;

[0084] The short-term dynamic prediction includes constructing a prediction function in order to apply the chaos index to the short-term heat exchange state prediction:

[0085]

[0086] Among them, represents the heat exchange quantity calculated under the current state, and are respectively the local Lyapunov exponent and the fractal dimension of the current state; represents the future time within the predicted heat exchange quantity; and are weight coefficients. This prediction model adds chaos indicators on the basis of traditional heat exchange quantity calculation to reflect the influence of state sensitivity and nonlinear complexity, so as to provide a mathematical basis for short-term dynamic prediction.

[0087] S4: Perform anomaly pattern recognition on multi-dimensional sensing data through high-dimensional topological data analysis (TDA).

[0088] The anomaly pattern recognition includes collecting multi-dimensional time series data during the operation of the heat exchange unit, mapping the collected data to the topological space by using the topological data analysis method, calculating topological features, identifying the existing anomaly patterns in the data, and forming a data output for indicating the abnormal state of the system.

[0089] Furthermore, in this high-dimensional topological data analysis (TDA) fault detection step, first collect multi-dimensional time series data during the operation of the heat exchange unit, denoted as: , among which, represents the set of collected time series data; represents the total number of collected time series data; each represents the D-dimensional state vector measured at time , including key variables such as temperature, pressure, and flow rate. At the same time, according to the Takens embedding theory, by selecting the embedding dimension and the delay time construct the original time series data into an embedded data set:

[0090] Among them, is the number of data points after embedding; represents the data set after embedding;

[0091] Use the embedded data set to construct a distance matrix, and on this basis use the Vietoris-Rips method to establish a simplicial complex, and its construction process depends on a filtration parameter , and during the filtration process when the distance between any two data points is less than or equal to When, connect them into edges and then construct higher-dimensional simplices; on this basis, by changing the value range, a series of complexes are obtained, thereby constructing a filtration process, and calculating the persistent homology for this filtration process to obtain different homology dimensions of the persistence diagram where the points in each diagram respectively represent the scales at which the th topological feature appears and disappears during the filtration process; define the persistence of each topological feature as:

[0092]

[0093] and statistically analyze the persistence of all features to obtain a global persistence index:

[0094]

[0095] where, represents the number of features in dimension , is the highest homology dimension considered; and represent the birth and disappearance scales of the oth topological feature respectively; P represents the global persistence index; persistence index of the feature;

[0096] To compare the topological features of the current state with the preset normal state, construct a reference persistence diagram and use the bottleneck distance to calculate the difference between the current persistence diagram and , and its definition is:

[0097]

[0098] where, represents and all possible bijections between, represents the infinity norm; represents the bottleneck distance between the current persistence diagram and the reference persistence diagram; represents a point in the persistence diagram ;

[0099] To further quantify the geometric complexity of the state trajectory, define the Topological Anomaly Score (TAS) as:

[0100] where, represents dimension The average value of the following characteristic birth times, and is the weight coefficient determined by historical data calibration; is the topological anomaly score; if exceeds the preset threshold or significantly deviates from the normal value, a data output is formed to indicate that the system is in an abnormal state.

[0101] The multi-dimensional time series data is mapped to a high-dimensional phase space through Takens embedding to construct a simplicial complex, and persistent homology is used to calculate and obtain topological features. Then, the bottleneck distance and the custom topological anomaly score are used to comprehensively describe the change of the system state.

[0102] S5: Integrate the global scheduling parameters obtained by quantum computing, the real-time monitoring data, and the TDA fault detection results, and adjust the operating parameters of the heat exchange unit, so as to realize data monitoring and real-time control.

[0103] The adjustment of the operating parameters of the heat exchange unit includes fusing the global scheduling parameters obtained by quantum computing, the real-time operating state obtained based on UWB sensing and digital twin chaos prediction, and the TDA anomaly detection results, and comprehensively forming the final control parameters of the heat exchange unit.

[0104] Furthermore, in this control adjustment step, the system fuses the global scheduling parameters obtained by quantum computing, the real-time operating state obtained based on UWB sensing and digital twin chaos prediction, and the TDA anomaly detection results, so as to construct a fusion model to generate the final control parameters of the heat exchange unit; specifically, assume that the global scheduling parameters are optimized by quantum computing and expressed as a vector

[0105]

[0106] where, represents the target fluid flow rate, represents the target pump speed, represents the target temperature difference; represents the global scheduling parameter vector optimized by quantum computing;

[0107] Let the real-time operating state be:

[0108]

[0109] respectively represent the real-time measured values of the fluid flow rate, pump speed, and temperature difference obtained through UWB sensing and digital twin chaos prediction; Denote the real-time operating state vector obtained by UWB sensing and digital twin chaotic prediction at time t;

[0110] Let the TDA anomaly detection result be a scalar , and this value is converted into an adjustment vector with the same dimension as the control parameter through the non-linear mapping function . The function can be defined as:

[0111]

[0112] Then the final control parameter vector is given by the following formula

[0113]

[0114] where represents the scalar anomaly detection value obtained through the TDA anomaly detection step, represents the hyperbolic tangent function, represents the final fluid flow rate, pump speed, and temperature difference, and are weight coefficients determined by historical data calibration, used to balance the influence of real-time state and TDA anomaly detection adjustment on the final control parameter; the finally obtained includes key operating parameters such as fluid flow rate, pump speed, and temperature difference, and is used as an instruction to be input into the actuator of the heat exchange unit to realize the adjustment of the state of the heat exchange unit.

[0115] Embodiment 2

[0116] The second embodiment of the present invention provides a data monitoring system for real-time control of a heat exchange unit, including: a sensor module, a calculation module, and a control module. The sensor module includes sensors for collecting fluid temperature, pressure, and flow data, an ultra-wideband UWB sensor for collecting micro-vibration data, and a picosecond laser interferometry device for obtaining heat transfer path structure information; the calculation module includes a global optimization unit based on quantum computing, a digital twin modeling and chaos theory processing unit, and a high-dimensional topological data analysis processing unit; the control module is used to adjust the operating state of the heat exchange unit according to the control parameters output by the calculation module.

[0117] The sensor module collects the operating data of each key area of the heat exchange unit in real time and transmits the data to the calculation module. After the calculation module performs quantum computing optimization, UWB micro-vibration monitoring, digital twin chaotic dynamic prediction, and TDA fault detection processing on the received data respectively, it fuses the calculation results of each unit. Finally, the control module sends an adjustment instruction to the heat exchange unit according to the data fusion result.

[0118] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

[0119] Embodiment 3

[0120] The third embodiment of the present invention is different from the previous two embodiments in that:

[0121] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the essence of the technical solution of the present invention, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0122] This application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of this application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0123] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements the specified functions in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0124] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the process Figure 1 one process or a plurality of processes and / or blocks Figure 1 steps for the functions specified in one block or a plurality of blocks.

[0125] Although the preferred embodiments of the present application have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic creative concept. Therefore, the appended claims are intended to be construed to cover the preferred embodiments as well as all changes and modifications that fall within the scope of the present application.

[0126] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A data monitoring method for real-time control of a heat exchange unit, characterized in that: Including Collecting the real-time operation data of the heat exchange unit through the sensor module, where the real-time operation data includes fluid temperature, pressure, flow parameters, and the heat transfer path structure information inside the heat exchange pipeline obtained by picosecond laser interferometry measurement; Performing periodic global optimization on the multi-variable heat transfer model of the heat exchange unit by using quantum computing to form global scheduling parameters; Collecting the internal micro-vibration data of the fluid by using an ultra-wideband UWB sensor, and combining the digital twin model and chaos theory to perform short-term dynamic prediction on the heat exchange state; Performing abnormal pattern recognition on multi-dimensional sensing data through high-dimensional topological data analysis TDA; Integrating the global scheduling parameters obtained by quantum computing, the real-time monitoring data, and the TDA fault detection results, and adjusting the operation parameters of the heat exchange unit, so as to realize data monitoring and real-time control.

2. The real-time control data monitoring method of a heat exchange unit according to claim 1, characterized in that: The picosecond laser interferometry measurement is used for the analysis of the internal heat transfer path of the heat exchange unit. The heat exchange pipeline or the corresponding structure is irradiated by picosecond laser pulses to obtain high-resolution thermal imaging data, and the temperature distribution and heat flow path information inside the heat exchange medium are reconstructed through optical coherence tomography technology, and the obtained data is used as the input of the system structure parameters.

3. The data monitoring method for real-time control of a heat exchange unit according to claim 2, characterized in that: The quantum computing includes establishing a basic physical model describing the heat exchange process, and its core is the total heat exchange amount of the heat exchange unit under different control parameters and structure parameters; It is set that the total heat exchange amount of the heat exchange unit is given by the following formula: Among them, represents the operation control parameters of the heat exchange unit, and N represents the total number of parameters in the operation control parameter vector of the heat exchange unit; represents the structural parameters of the heat exchanger, and M represents the total number of parameters in the structural parameter vector of the heat exchanger; represents the thermal physical properties parameters of the environment and fluid, represents the hot side inlet temperature, cold side inlet temperature, inner convective heat transfer coefficient, and outer convective heat transfer coefficient; represents the total heat transfer amount of the heat exchange unit; represents the overall heat transfer coefficient; represents the effective heat transfer area; represents the logarithmic mean temperature difference; Overall heat transfer coefficient It is expressed by the series thermal resistance model as follows: Among them, represents the overall heat transfer coefficient; and are the convective heat transfer coefficients on the inner and outer sides of the heat exchanger respectively, while is the thermal conductivity of the material; Logarithmic mean temperature difference Expressed as: Among them, is the hot-side inlet temperature, is the cold-side inlet temperature; and are the hot-side and cold-side outlet temperatures respectively.

4. The data monitoring method for real-time control of a heat exchange unit according to claim 3, characterized in that: The quantum computing includes introducing an energy consumption model to reflect the energy consumption situation of the equipment during operation, and the expression is: Among them, represents the efficiency of the pump; represents a function reflecting the actual operating load; represents the energy consumption of the equipment; represents the operating load function; To simultaneously optimize the heat exchange efficiency and energy consumption and meet the working condition constraints, the objective function is constructed as: Among them, represents the comprehensive objective function; represents the target heat transfer amount; represents the target energy consumption; represents the deviation of the i-th operating condition constraint; , , represent the weight coefficients of each item; represents the total number of operating condition constraints; Since the quantum annealing or variational quantum algorithm requires the problem to be expressed in a discrete binary form, the above continuous optimization problem needs to be discretized; Suppose that through encoding and are discretized into binary vectors , then the original optimization problem is transformed into the QUBO form: Among them, is a symmetric matrix obtained after discretization and quadratic processing from the objective function ; b represents a binary vector obtained by discretizing continuous variables; represents the objective function value of the quadratic unconstrained binary optimization problem.

5. The data monitoring method for real-time control of a heat exchange unit according to claim 4, characterized in that: The short-term dynamic prediction includes arranging UWB sensors outside the fluid pipeline of the heat exchange unit, using the UWB to emit high-frequency short pulse signals to penetrate the fluid, and receiving its reflected signals, and collecting and analyzing the spectrum of the micro-vibration state inside the fluid according to the frequency change of the signals, so as to obtain the real-time information of the internal turbulence and microscopic flow state of the fluid; Constructing a real-time digital twin model of the heat exchange unit based on computational fluid dynamics CFD simulation and physical sensor data, using the real-time digital twin model to perform data fusion on the collected real-time data, and calculating the Lyapunov exponent and fractal dimension of the system state, so as to predict the short-term dynamic changes of the heat exchange state.

6. The data monitoring method for real-time control of a heat exchange unit according to claim 5, characterized in that: The abnormal pattern recognition includes collecting multi-dimensional time series data during the operation of the heat exchange unit, using the topological data analysis method to map the collected data to the topological space, calculating the topological features, identifying the abnormal patterns existing in the data, and forming a data output for indicating the abnormal state of the system.

7. The real-time control data monitoring method for a heat exchange unit according to claim 6, characterized in that: Adjusting the operation parameters of the heat exchange unit includes fusing the global scheduling parameters obtained by quantum computing, the real-time operation state obtained based on UWB sensing and digital twin chaos prediction, and the TDA abnormal detection results, and comprehensively forming the final control parameters of the heat exchange unit.

8. A system adopting a data monitoring method for real-time control of a heat exchange unit as described in any one of claims 1 to 7, characterized in that: It includes a sensor module, a computing module and a control module. The sensor module includes sensors for collecting fluid temperature, pressure and flow data, an ultra-wideband UWB sensor for collecting micro-vibration data, and a picosecond laser interferometry device for obtaining heat transfer path structure information; the computing module includes a global optimization unit based on quantum computing, a digital twin modeling and chaos theory processing unit, and a high-dimensional topological data analysis processing unit; the control module is used to adjust the operating state of the heat exchange unit according to the control parameters output by the computing module.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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