Refrigerator load temperature control system and method and temperature controller

Abstract

The invention discloses a refrigerator load temperature control system, a refrigerator load temperature control method and a temperature controller, relates to the technical field of refrigerator load temperature control, and solves the problems that the temperature control load of a refrigerator load cannot be coordinated and controlled, and temperature parameters set by a user cannot be accurately achieved. The time-frequency domain conjoint analysis mechanism carries out feature analysis on the temperature output signal to obtain temperature feature data, the particle swarm optimization PID control algorithm analyzes the temperature feature data and set temperature parameters to generate a PID control instruction, and the load optimization scheduling control model realizes temperature control load coordination control on the refrigerator load. According to the refrigerator load temperature control system and method, the refrigerator load temperature control load coordination control is achieved, and the temperature parameters set by the user are accurately achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of refrigerator load temperature control, and more specifically to a refrigerator load temperature control system, method and thermostat. Background Art

[0002] The function of a refrigerator load temperature control system is to intelligently monitor and control the temperature inside the refrigerator, and optimize and adjust the temperature parameters, so as to improve the operating efficiency and stability of the refrigerator load. Its main principle is to set the target temperature value through the human-machine interface module, and the temperature acquisition module collects real-time temperature data and transmits it to the information transmission module. The control center module then performs intelligent processing according to the preset algorithm, and sends corresponding instructions to the execution unit-temperature control response module, and finally controls the refrigeration device, fan and other equipment inside the refrigerator to achieve temperature regulation. In short, the refrigerator load temperature control system uses advanced technical means and intelligent algorithms to achieve accurate monitoring and control of the internal temperature of the refrigerator, and converts electrical signals into physical quantities through devices such as thermostats to achieve regulation and optimization of the internal environmental parameters of the refrigerator.

[0003] In the prior art, there are many disadvantages in the refrigerator load temperature control system, method and thermostat. On the one hand, when the temperature difference is too large, the temperature change cannot be accurately detected, and the control instruction cannot be accurately output according to the temperature output signal. On the other hand, the refrigerator load temperature control load cannot be coordinated and controlled according to the control instruction to accurately reach the temperature parameters set by the user. After the refrigerator load temperature control load is coordinated and controlled, the refrigerator load cannot be protected, resulting in overload during the refrigerator load temperature control, causing the refrigerator to be easily damaged. Therefore, the present invention proposes a refrigerator load temperature control system, method and thermostat, aiming to provide a method that can realize the coordinated control of the refrigerator load temperature control load and accurately reach the temperature parameters set by the user. Summary of the invention

[0004] In view of the shortcomings of the above-mentioned technology, the present invention discloses a refrigerator load temperature control system, method and temperature controller. The data acquisition module uses a fiber grating edge filter demodulation model of an integrated photonic chip (1) to accurately collect temperature output signals, thereby solving the problem that the temperature change cannot be accurately detected. The control center module uses a time-frequency domain joint analysis mechanism to perform feature analysis on the temperature output signal to obtain temperature feature data. The control center module uses a particle swarm optimization PID control algorithm to analyze and schedule the temperature feature data and the set temperature parameters to generate PID control instructions, thereby solving the problem that the control instructions cannot be accurately output according to the temperature output signal. The temperature control response module uses an open-loop response control circuit to sensitively respond to and execute nonlinear control instructions. The open-loop response control circuit uses a load optimization scheduling control model to coordinately control the refrigerator load temperature control load, thereby solving the problem that the refrigerator load temperature control load cannot be coordinated according to the control instructions to accurately reach the user set temperature parameters. The load monitoring model monitors the overload phenomenon on the refrigerator load through a load current monitoring circuit. The load monitoring model performs safety protection on the overload phenomenon on the refrigerator load through an embedded load safety protection circuit, thereby solving the problem that the overload phenomenon occurs during the refrigerator load temperature control.

[0005] The present invention adopts the following technical solutions: A refrigerator load temperature control system comprises: A human-machine interface module is used for users to set temperature parameters and display current temperature information. The human-machine interface module uses a combination of a liquid crystal display and operation buttons to enable users to set temperature parameters and display current temperature information; A temperature acquisition module is used to collect real-time temperature information of the area inside the refrigerator. The data acquisition module uses an integrated photonic chip to accurately collect temperature output signals. The integrated photonic chip uses a fiber grating edge filter demodulation model to capture and process optical signals of changes in optical path length and reflectivity caused by thermal expansion of the area inside the refrigerator to obtain temperature output signals; An information transmission module, used for transmitting the temperature output signal and the set temperature parameters, wherein the data transmission module uses a radio wave propagation network to accurately and reliably transmit the temperature output signal and the set temperature parameters to the control center module; A control center module is used to process the transmitted temperature output signal and the set temperature parameters, and generate control instructions. The control center module uses a time-frequency domain joint analysis mechanism to perform feature analysis on the temperature output signal to obtain temperature feature data. The temperature feature data at least includes a temperature rise amplitude, a temperature drop amplitude, a temperature peak value, a temperature output signal frequency, and a temperature output signal phase. The control center module uses a particle swarm optimization PID control algorithm to analyze and schedule the temperature feature data and the set temperature parameters, and generates a PID control instruction; A temperature control response module is used to respond to PID control instructions to realize the temperature drive control of the refrigerator load. The temperature control response module adopts an open-loop response control circuit to sensitively respond to and execute the PID control instructions. The open-loop response control circuit adopts a load optimization scheduling control model to coordinate the temperature control load of the refrigerator load. The load optimization scheduling control model controls the refrigerator refrigeration equipment according to the temperature control load of the refrigerator load to achieve the temperature parameters set by the user; The output end of the temperature acquisition module is connected to the input end of the information transmission module, the output end of the human-machine interface module is connected to the input end of the information transmission module, the output end of the information transmission module is connected to the input end of the control center module, and the output end of the control center module is connected to the input end of the temperature control response module. As a further technical solution of the present invention, the fiber Bragg grating edge filter demodulation model comprises a light source module, a fiber Bragg grating sensor module and a signal processing module, the output end of the light source module is connected to the input end of the fiber Bragg grating sensor module, the output end of the fiber Bragg grating sensor module is connected to the input end of the signal processing module, the light source module uses a laser light source to emit laser light into the incident port of the integrated photonic chip (1), the laser light source enters the first optical fiber through the incident port and is continuously reflected to form an interference pattern, the interference pattern undergoes displacement and morphological changes as the temperature changes, the fiber Bragg grating sensor module uses an optical fiber cable with a periodic refractive index change to generate interference effects of reflected and transmitted waves to capture optical signals due to temperature changes, the signal processing module uses an optical signal detector to detect the changing optical signal, the signal processing module uses a photoelectric conversion circuit to convert the optical signal into a temperature output signal, the photoelectric conversion circuit uses a space-varying Wiener pre-filtering mechanism to convert the temperature output signal without distortion, and the space-varying Wiener pre-filtering mechanism removes the temperature output signal noise by introducing a weighted geometric mean iterative process.

[0006] As a further technical solution of the present invention, the time-frequency domain joint analysis mechanism adopts a time-domain difference analysis algorithm to perform time-domain feature analysis on the temperature output signal. The time-domain difference analysis algorithm discretizes the temperature output signal into time series data by sampling at equal intervals. The time-domain difference analysis algorithm calculates the difference between two adjacent time series data by a time-domain difference function, and the difference is used as new sequence data. The time-domain difference analysis algorithm obtains the temperature peak value in the new sequence data by extracting the kurtosis statistical feature. The time-domain difference analysis algorithm obtains the temperature rise amplitude and the temperature drop amplitude in the new sequence data by extracting the variance statistical feature. The time-frequency domain joint analysis mechanism adopts a frequency domain difference analysis algorithm to perform frequency domain feature analysis on the temperature output signal. The frequency domain difference analysis algorithm obtains temperature spectrum information through equal-interval sampling and Fourier transform. The frequency domain difference analysis algorithm calculates the frequency difference between frequency points in two adjacent temperature spectrum information through a frequency domain difference function to obtain a frequency domain sequence. The frequency domain difference analysis algorithm calculates the response difference of the frequency domain sequence in different frequency ranges through a differential operation equation to determine the frequency of the temperature output signal. The frequency domain difference analysis algorithm performs a frequency scanning on the frequency domain sequence through a frequency scanning mechanism to determine the phase difference of the temperature output signal and obtain the phase of the temperature output signal.

[0007] As a further embodiment of the present invention, the working method of the particle swarm optimization PID control algorithm is: S1. Using a support vector machine training engine to learn and train the temperature characteristic data and the set temperature parameters to establish a PID control model. The PID control model includes a proportional controller, an integral controller and a differential controller. The support vector machine training engine automatically learns the optimal discrimination boundary by training the characteristic relationship between the temperature characteristic data and the set temperature parameters through the optimal discrimination hyperplane. The support vector machine training engine establishes the PID control model according to the discrimination boundary. The length calculation formula of the discrimination boundary is: S2, then use the particle swarm optimization mechanism to search for the optimal PID parameter combination, and apply the optimal PID parameter combination to the PID control model, the PID parameter combination includes at least a proportional control coefficient, an integral control coefficient and a differential control coefficient, the particle swarm optimization mechanism uses an overshoot fitness function to iteratively optimize the proportional control coefficient, the integral control coefficient and the differential control coefficient, and outputs the optimal proportional control coefficient, the integral control coefficient and the differential control coefficient, and the proportional control coefficient calculation formula is: The integral control coefficient calculation formula is: The differential control coefficient calculation formula is: S3, then the PID control model balances the proportional control coefficient, the integral control coefficient and the differential control coefficient through the self-tuning function, the self-tuning function adaptively adjusts the PID parameter combination according to the dynamic characteristics of the controlled object, the PID control model analyzes the deviation between the current temperature and the set temperature through the time-varying weighted analysis function to obtain the PID parameter combination weighted value, the PID control model performs weighted summation on the proportional control coefficient, the integral control coefficient and the differential control coefficient according to the PID parameter combination weighted value, and generates a PID control instruction to achieve accurate control of the refrigerator load on the temperature, and the PID parameter combination weighted value calculation formula is: .

[0008] As a further embodiment of the present invention, the load optimization scheduling control model includes a load prediction module, a load scheduling module and a drive control module. The output end of the load prediction module is connected to the input end of the load scheduling module, and the output end of the load scheduling module is connected to the input end of the drive control module. The load prediction module uses a multivariate regression analysis network to establish a refrigerator load prediction sample. The refrigerator load prediction sample includes at least temperature, humidity, door opening and closing conditions, types of stored items and the number of stored items. The load prediction module uses a Gaussian process load prediction algorithm to predict the refrigerator load temperature control load consumption. The Gaussian process load prediction algorithm regards the temperature change inside the refrigerator as a random process that obeys a Gaussian distribution through a Bayesian non-parametric protocol. The Gaussian process load prediction algorithm observes the refrigerator load prediction sample through a mean covariance matrix to obtain the mean and covariance function of the random process. The Gaussian process load prediction algorithm analyzes the temperature change according to the mean and covariance function to determine the refrigerator load temperature control load consumption. As a further embodiment of the present invention, the load scheduling module adopts a dual-parameter collaborative intelligent optimization mechanism to perform rolling optimization on the refrigerator load temperature control load according to the real-time refrigerator load temperature control load consumption. The dual-parameter collaborative intelligent optimization mechanism uses the Lyapunov index spectrum of the refrigerator load temperature control load as the research object through the target difference optimization protocol. The dual-parameter collaborative intelligent optimization mechanism analyzes the correlation between the refrigerator load temperature control load consumption and the refrigerator load temperature control load through the association network. The dual-parameter collaborative intelligent optimization mechanism determines the refrigerator load temperature control load scheduling strategy that meets the temperature control target requirements through the radial basis function neural network. The refrigerator load temperature control load consumption and the refrigerator load temperature control load are analyzed. The correlation between the cabinet load and the temperature control load is used as the input variable of the radial basis function neural network, and the radial basis function neural network adjusts the cabinet load temperature control load through nonlinear mapping; the drive control module adopts a compressor control mechanism to control the refrigeration of the cabinet refrigeration equipment, and the compressor control mechanism dynamically adjusts the refrigeration output power according to the cabinet load temperature control load and the PID control instruction to achieve the adjustment of the cabinet temperature, and the compressor control mechanism adopts a nonlinear decoupling control protocol to eliminate the interaction and coupling effects inside the compressor, and the nonlinear decoupling control protocol maps the PID control instruction to the internal control variable through the generalized minimum variance control law to achieve precise control and adjustment of the cabinet temperature.

[0009] As a further embodiment of the present invention, a refrigerator load temperature control method is applied to the refrigerator load temperature control system, and the method comprises the following steps: Step 1: Install an integrated photonic chip in the refrigerator to monitor the temperature of the location of the goods in real time. The integrated photonic chip transmits the temperature output signal and the set temperature parameters to the control center module accurately and reliably through the radio wave propagation network; Step 2: The control center module uses a time domain difference analysis algorithm to perform time domain feature analysis on the temperature output signal to determine the temperature peak value, the temperature rise amplitude and the temperature drop amplitude; the control center module uses a frequency domain difference analysis algorithm to perform frequency domain feature analysis on the temperature output signal to determine the temperature output signal frequency and the temperature output signal phase; Step 3, the control center module uses a particle swarm optimization PID control algorithm to learn and train the temperature characteristic data and the set temperature parameters to establish a PID control model, the particle swarm optimization PID control algorithm searches for the optimal PID parameter combination applied to the PID control model through a particle swarm optimization mechanism, and the particle swarm optimization PID control algorithm generates a PID control instruction according to the weighted value of the PID parameter combination; Step 4: The temperature control response module adopts a load optimization scheduling control model to coordinate the control of the refrigerator load temperature control load. The load optimization scheduling control model determines the consumption of the refrigerator load temperature control load through the load prediction module. The load optimization scheduling control model then adjusts the refrigerator load temperature control load through the load scheduling module. The load optimization scheduling control model finally controls the refrigeration of the refrigerator refrigeration equipment through the drive control module. Step 5. Finally, a load monitoring model is used to monitor the real-time load changes of the refrigerator in real time. The load monitoring model monitors the overload phenomenon on the refrigerator load through a load current monitoring circuit. The load monitoring model performs safety protection on the overload phenomenon on the refrigerator load through an embedded load safety protection circuit.

[0010] As a further embodiment of the present invention, the load current monitoring circuit uses a current transmitter to convert the current on the refrigerator load into a voltage signal, the load current monitoring circuit uses an integrated operational amplifier signal processing protocol to enhance and filter the voltage signal, the load current monitoring circuit then uses a sensor array model of a PC terminal to analyze the voltage signal to determine the overload phenomenon on the refrigerator load, the sensor array model of the PC terminal uses an HHT nonlinear time-frequency analysis engine to analyze the frequency characteristics of the voltage signal, and the sensor array model of the PC terminal uses an overload analysis protocol to analyze the frequency characteristics of the voltage signal. Overload feature analysis realizes overload diagnosis of refrigerator load; the embedded load safety protection circuit adopts optocoupler isolation protection mechanism to isolate and protect the overload of refrigerator load, the optocoupler isolation protection mechanism realizes monolithic integrated overload protection through cooperative current detector and drive circuit, the cooperative current detector performs vector detection on the overload amplitude of refrigerator load through vector detection protocol, the drive circuit drives optocoupler isolator to isolate the overload of refrigerator load according to the overload amplitude of refrigerator load, the optocoupler isolation protection mechanism integrates cooperative current detector and drive circuit into embedded load safety protection circuit through monolithic integration mode.

[0011] As a further embodiment of the present invention, a thermostat used for refrigerator load temperature control is applied to the refrigerator load temperature control system and the refrigerator load temperature control method, and the thermostat includes: Integrated photonic chip for real-time monitoring of temperature changes inside the freezer; A control chip is used to process the temperature output signal and the set temperature parameters to generate corresponding PID control instructions, thereby realizing accurate control of the internal ambient temperature of the refrigerator. The control chip includes a control center module, a temperature control response module and a load monitoring module. The output end of the control center module is connected to the input end of the temperature control response module, and the output end of the load monitoring module is connected to the input end of the temperature control response module. The load monitoring module monitors the overload phenomenon on the refrigerator load through a load current monitoring circuit, and the load monitoring module then performs safety protection on the overload phenomenon on the refrigerator load through an embedded load safety protection circuit; Liquid crystal display screen, used to display the current temperature information of the internal environment of the refrigerator; Operation button, used by the user to set the temperature of the internal environment of the refrigerator; A communication interface is used to realize data interaction between the integrated photonic chip and the control chip. The communication interface uses a radio wave communication protocol to ensure that the temperature output signal and the set temperature parameters can be correctly transmitted; The housing and heat sink are used to protect the internal equipment from external interference and damage and ensure normal heat dissipation of the thermostat during operation. The housing and heat sink are made of ABS composite material, and the ABS composite material has excellent thermal conductivity through the graphene thermal conductive film; The integrated photonic chip is connected to the control chip via a communication interface, the liquid crystal display is fixedly connected to the operation button via a fixed interface and a connector, the liquid crystal display and the operation button are connected to the control chip via a communication interface, and the housing and the heat dissipation device are arranged outside the thermostat. The present invention has the following positive and beneficial effects compared with the prior art: The invention discloses a refrigerator load temperature control system, method and temperature controller. A data acquisition module adopts a fiber grating edge filter demodulation model of an integrated photon chip to accurately collect a temperature output signal, thereby realizing accurate detection of temperature changes. A control center module adopts a time-frequency domain joint analysis mechanism to perform feature analysis on the temperature output signal to obtain temperature feature data. The control center module adopts a particle swarm optimization PID control algorithm to analyze and schedule the temperature feature data and set temperature parameters, generates a PID control instruction, and realizes accurate output of control instructions according to the temperature output signal. A temperature control response module adopts an open-loop response control circuit to sensitively respond to and execute nonlinear control instructions. The open-loop response control circuit adopts a load optimization scheduling control model to coordinately control the refrigerator load temperature control load, thereby realizing coordinated control of the refrigerator load temperature control load according to the control instruction, and accurately reaching the user-set temperature parameter. A load monitoring model monitors the overload phenomenon on the refrigerator load through a load current monitoring circuit. The load monitoring model performs safety protection on the overload phenomenon on the refrigerator load through an embedded load safety protection circuit, thereby avoiding overload phenomenon during refrigerator load temperature control. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which: Figure 1 This is a schematic diagram of the overall architecture of a refrigerator load temperature control system of the present invention; Figure 2 This is a flow chart of a method for controlling the load temperature of a refrigerator according to the present invention; Figure 3 This is a schematic diagram of the structure of a thermostat used for load temperature control of a refrigerator according to the present invention; Figure 4 The flowchart of the particle swarm optimization PID control algorithm used in the present invention; Figure 5 This is a schematic diagram of the load optimization scheduling control model structure adopted by the present invention.

[0013] Description of the drawings in the specification: 1. Integrated photonic chip; 2. Control chip; 3. Liquid crystal display; 4. Operation button; 5. Communication interface; 6. Housing and heat dissipation device. DETAILED DESCRIPTION

[0014] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0015] like Figure 1-Figure 5 As shown, a refrigerator load temperature control system comprises: A human-machine interface module is used for the user to set temperature parameters and display the current temperature information. The human-machine interface module uses a combination of a liquid crystal display 3 and an operation button 4 to enable the user to set the temperature parameters and display the current temperature information; A temperature acquisition module is used to collect real-time temperature information of the area inside the refrigerator. The data acquisition module uses an integrated photonic chip 1 to accurately collect temperature output signals. The integrated photonic chip 1 uses a fiber grating edge filter demodulation model to capture and process optical signals of changes in optical path length and reflectivity caused by thermal expansion of the area inside the refrigerator to obtain a temperature output signal; An information transmission module, used for transmitting the temperature output signal and the set temperature parameters, wherein the data transmission module uses a radio wave propagation network to accurately and reliably transmit the temperature output signal and the set temperature parameters to the control center module; A control center module is used to process the transmitted temperature output signal and the set temperature parameters, and generate control instructions. The control center module uses a time-frequency domain joint analysis mechanism to perform feature analysis on the temperature output signal to obtain temperature feature data. The temperature feature data at least includes a temperature rise amplitude, a temperature drop amplitude, a temperature peak value, a temperature output signal frequency, and a temperature output signal phase. The control center module uses a particle swarm optimization PID control algorithm to analyze and schedule the temperature feature data and the set temperature parameters, and generates a PID control instruction; A temperature control response module is used to respond to PID control instructions to realize the temperature drive control of the refrigerator load. The temperature control response module adopts an open-loop response control circuit to sensitively respond to and execute the PID control instructions. The open-loop response control circuit adopts a load optimization scheduling control model to coordinate the temperature control load of the refrigerator load. The load optimization scheduling control model controls the refrigerator refrigeration equipment according to the temperature control load of the refrigerator load to achieve the temperature parameters set by the user; The output end of the temperature acquisition module is connected to the input end of the information transmission module, the output end of the human-machine interface module is connected to the input end of the information transmission module, the output end of the information transmission module is connected to the input end of the control center module, and the output end of the control center module is connected to the input end of the temperature control response module.

[0016] During the implementation of the present invention, firstly, the human-machine interface module is the interface for the user to interact with the refrigerator load temperature control system, and the internal temperature of the refrigerator can be set, queried and adjusted through the module. Secondly, the temperature acquisition module is responsible for collecting the temperature data inside the refrigerator and transmitting the collected data to the subsequent processing module for temperature control and optimization. After that, the information transmission module connects the bridges between the various subsystems, and is mainly responsible for transmitting the collected temperature data, user setting information, etc. to the control center module, and transmitting the instructions issued by the control center to the corresponding execution units. Then the control center module is responsible for monitoring, analyzing and controlling the temperature inside the refrigerator. After receiving the user setting information and real-time temperature data, the module will perform intelligent processing according to the preset algorithm, and issue corresponding instructions to the execution unit. At the same time, the module will also record historical data, and adjust and optimize parameters according to the historical data. Finally, the temperature control response module: This module is an execution unit, which is mainly responsible for receiving the instructions issued by the control center and making corresponding temperature adjustments according to the instructions. This module can achieve temperature regulation by controlling the refrigeration device, fan and other equipment inside the refrigerator. In short, this refrigerator load temperature control system adopts advanced technical means, which can intelligently monitor and control the temperature inside the refrigerator, and optimize and adjust the temperature parameters, thereby improving the operating efficiency and stability of the refrigerator load. In a further embodiment, the fiber Bragg grating edge filter demodulation model includes a light source module, a fiber Bragg grating sensor module and a signal processing module, the output end of the light source module is connected to the input end of the fiber Bragg grating sensor module, the output end of the fiber Bragg grating sensor module is connected to the input end of the signal processing module, the light source module uses a laser light source to emit laser into the incident port of the integrated photonic chip 1, the laser light source enters the first optical fiber through the incident port and is continuously reflected to form an interference pattern, the interference pattern is displaced and changes in shape as the temperature changes, the fiber Bragg grating sensor module uses an optical fiber cable with a periodic refractive index change to produce an interference effect of reflected and transmitted waves to capture the optical signal due to temperature changes, the signal processing module uses an optical signal detector to detect the changing optical signal, the signal processing module uses a photoelectric conversion circuit to convert the optical signal into a temperature output signal, the photoelectric conversion circuit uses a space-varying Wiener pre-filtering mechanism to convert the temperature output signal without distortion, and the space-varying Wiener pre-filtering mechanism removes the temperature output signal noise by introducing weighted geometric average iterative processing.

[0017] In a further embodiment, the time-frequency domain joint analysis mechanism uses a time-domain difference analysis algorithm to perform time-domain feature analysis on the temperature output signal. The time-domain difference analysis algorithm discretizes the temperature output signal into time series data by sampling at equal intervals. The time-domain difference analysis algorithm calculates the difference between two adjacent time series data by a time-domain difference function, and the difference is used as new sequence data. The time-domain difference analysis algorithm obtains the temperature peak value in the new sequence data by extracting the kurtosis statistical feature. The time-domain difference analysis algorithm obtains the temperature rise amplitude and the temperature drop amplitude in the new sequence data by extracting the variance statistical feature. The time-frequency domain joint analysis mechanism uses a time-domain difference analysis algorithm to obtain the temperature rise amplitude and the temperature drop amplitude in the new sequence data. The domain joint analysis mechanism adopts a frequency domain difference analysis algorithm to perform frequency domain feature analysis on the temperature output signal. The frequency domain difference analysis algorithm obtains temperature spectrum information through equal-interval sampling and Fourier transform. The frequency domain difference analysis algorithm calculates the frequency difference between frequency points in two adjacent temperature spectrum information through a frequency domain difference function to obtain a frequency domain sequence. The frequency domain difference analysis algorithm calculates the response difference of the frequency domain sequence in different frequency ranges through a differential operation equation to determine the frequency of the temperature output signal. The frequency domain difference analysis algorithm performs a frequency scanning on the frequency domain sequence through a frequency scanning mechanism to determine the phase difference of the temperature output signal and obtain the phase of the temperature output signal.

[0018] In the specific implementation, the fiber Bragg grating edge filter demodulation model mainly includes the following three modules: light source module: a laser light source is used to emit laser into the incident port of the integrated photonic chip 1, and the first optical fiber is used to continuously reflect to form an interference pattern. As the temperature changes, the interference pattern will shift and change in shape. Fiber Bragg grating sensor module: an optical cable with periodic refractive index changes is used to produce the interference effect of reflected and transmitted waves, thereby capturing the reflected signal caused by temperature changes. This module will obtain external temperature information by detecting the change of the reflected signal. Signal processing module: an optical signal detector is used to detect the captured reflected signal, and it is converted into a temperature output signal through a photoelectric conversion circuit. In this process, a space-varying Wiener pre-filtering mechanism is introduced to remove the influence of noise. Specifically, the mechanism uses a weighted geometric average iterative processing method for filtering, and ensures that the temperature output signal is converted without distortion. Fiber Bragg grating sensor is a temperature measurement technology based on the principle of optical interference, with the advantages of high resolution, high precision and anti-interference.

[0019] The time-frequency domain joint analysis mechanism is a method for analyzing the time-domain characteristics of the temperature output signal. Specifically, its main process is as follows: Time domain difference analysis algorithm: discretize the temperature output signal into time series data, and calculate the difference between two adjacent time series data through the time domain difference function to obtain new series data. The algorithm mainly adopts the method of equal interval sampling. Kurtosis statistical feature extraction: The temperature peak is extracted from the new series data by the kurtosis statistical feature extraction method. Kurtosis is an indicator to measure the sharpness of the probability density curve. By calculating the kurtosis value of the probability density curve in the new series data, the temperature peak corresponding to the highest point can be found. Variance statistical feature extraction: The temperature rise amplitude and temperature drop amplitude are extracted from the new series data by the variance statistical feature extraction method. Variance is an indicator to measure the degree of discreteness or fluctuation of a set of data. In this method, it is used to evaluate the amplitude of temperature change in the new series data. The time-frequency domain joint analysis mechanism is a method based on the time domain difference analysis algorithm and statistical feature extraction. It can perform time domain feature analysis on the temperature output signal, and then extract information such as the temperature peak, rise amplitude and drop amplitude.

[0020] The method of frequency domain feature analysis of temperature output signal by time-frequency domain joint analysis mechanism. Specifically, its main process is as follows: Frequency domain difference analysis algorithm: The temperature spectrum information is obtained by equal-interval sampling and Fourier transform, and the frequency difference between the frequency points in two adjacent temperature spectrum information is calculated by frequency domain difference function to obtain the frequency domain sequence. This algorithm is mainly based on the principle of Fourier transform to convert the time domain signal into the frequency domain signal. Difference operation equation: The response difference of the frequency domain sequence in different frequency ranges is calculated by the difference operation equation to determine the main frequency of the temperature output signal. In different temperature measurement scenarios, the main frequency occupied by the output signal may be different, so it is necessary to adjust the response difference calculation method according to the actual situation. Frequency scanning mechanism: The phase difference of the temperature output signal can be determined by frequency scanning the frequency domain sequence. Phase refers to the offset of the position of the waveform relative to the reference waveform at a certain moment. In this method, it is used to measure the relative temperature difference between different measurement points. In summary, the time-frequency domain joint analysis mechanism is a method based on the frequency domain difference analysis algorithm, which can perform frequency domain feature analysis on the temperature output signal and then extract the main frequency and phase information therein. This method can effectively reduce temperature measurement errors and improve measurement accuracy and reliability.

[0021] In a further embodiment, the particle swarm optimization PID control algorithm works as follows: S1. Using a support vector machine training engine to learn and train the temperature characteristic data and the set temperature parameters to establish a PID control model. The PID control model includes a proportional controller, an integral controller and a differential controller. The support vector machine training engine automatically learns the optimal discrimination boundary by training the characteristic relationship between the temperature characteristic data and the set temperature parameters through the optimal discrimination hyperplane. The support vector machine training engine establishes the PID control model according to the discrimination boundary. The length calculation formula of the discrimination boundary is: S2, then use the particle swarm optimization mechanism to search for the optimal PID parameter combination, and apply the optimal PID parameter combination to the PID control model, the PID parameter combination includes at least a proportional control coefficient, an integral control coefficient and a differential control coefficient, the particle swarm optimization mechanism uses an overshoot fitness function to iteratively optimize the proportional control coefficient, the integral control coefficient and the differential control coefficient, and outputs the optimal proportional control coefficient, the integral control coefficient and the differential control coefficient, and the proportional control coefficient calculation formula is: The integral control coefficient calculation formula is: The differential control coefficient calculation formula is: S3, then the PID control model balances the proportional control coefficient, the integral control coefficient and the differential control coefficient through the self-tuning function, the self-tuning function adaptively adjusts the PID parameter combination according to the dynamic characteristics of the controlled object, the PID control model analyzes the deviation between the current temperature and the set temperature through the time-varying weighted analysis function to obtain the PID parameter combination weighted value, the PID control model performs weighted summation on the proportional control coefficient, the integral control coefficient and the differential control coefficient according to the PID parameter combination weighted value, and generates a PID control instruction to achieve accurate control of the refrigerator load on the temperature, and the PID parameter combination weighted value calculation formula is: . In a specific embodiment, an application platform of a particle swarm optimization PID control algorithm can be constructed. During the operation of the platform, a hardware platform can be built, such as by constructing the following components for processing, such as: sensors, actuators, controllers, data acquisition cards and computers. The platform can also design a corresponding software platform to realize functions such as processing, encoding and decoding of control commands. The following is a more detailed description of the implementation method: Sensor: used to collect real-time status data of the controlled object, such as temperature, pressure, flow, etc. The sensor needs to select appropriate models and parameters and connect to the control system.

[0022] Actuator: It is used to execute control commands and convert the signals output by the controller into physical actions. Common actuators include electric valves, motors, hydraulic motors, etc., which need to be selected according to the characteristics of the controlled object.

[0023] Controller: Responsible for calculating and outputting control commands, comparing the measured value with the set value, and adjusting the output signal according to the error. In the particle swarm optimization PID control algorithm, the controller mainly includes the PID regulator and the particle swarm optimization algorithm module.

[0024] Data acquisition card: used to convert the analog signals collected by the sensor into digital signals and send them to the computer for processing. The data acquisition card needs to have the characteristics of high precision, high speed and strong stability.

[0025] Computer: Responsible for running the particle swarm optimization PID control algorithm and related software, and processing and analyzing data. The computer needs to have sufficient computing power and storage capacity to meet the requirements of real-time control.

[0026] In the process of building the application platform, it is necessary to select and configure according to the specific control objects and control requirements, and perform system integration and testing to ensure the stability and reliability of the system.

[0027] The particle swarm optimization PID control algorithm combined with the support vector machine training engine realizes the learning and training of the relationship between the temperature feature data and the set temperature parameters, and establishes a PID control model. The particle swarm optimization PID control algorithm uses the support vector machine training engine to learn and train the relationship between the temperature feature data and the set temperature parameters. The SVM training engine can automatically learn the optimal discrimination boundary and establish a classification model. PID controller design: Design the PID controller according to the established classification model. The proportional controller, integral controller and differential controller are responsible for feedback, accumulated error, and predicted future errors, respectively, to achieve precise control of the temperature of the controlled object. Particle swarm optimization: The PID parameters are continuously adjusted through the particle swarm algorithm to gradually approach the optimal state. In this process, the particle swarm algorithm will continuously search for the global optimal solution based on the current state and historical experience, and update the position and speed of each particle. Real-time control: Under the joint action of the PID controller and the particle swarm optimization algorithm, the controlled object is controlled in real time so that its temperature is always kept within the set value range. The statistical table of the proportional control coefficient calculation results in the PID parameter combination is shown in Table 1: As shown in Table 1, four test groups are set up, and two methods are used to calculate the proportional control coefficient in the PID parameter combination. Method 1 uses statistical analysis to infer the proportional control coefficient in the PID parameter combination from historical data or sample data. Method 2 is a particle swarm optimization mechanism that uses an overshoot fitness function to iteratively optimize the proportional control coefficient and outputs the optimal proportional control coefficient. The error of method 1 is greater than the error of method 2. It can be seen that the particle swarm optimization mechanism uses an overshoot fitness function to iteratively optimize the proportional control coefficient and outputs the optimal proportional control coefficient, which has outstanding technical effects.

[0028] In a further embodiment, the load optimization scheduling control model includes a load prediction module, a load scheduling module and a drive control module. The output end of the load prediction module is connected to the input end of the load scheduling module, and the output end of the load scheduling module is connected to the input end of the drive control module. The load prediction module uses a multivariate regression analysis network to establish a refrigerator load prediction sample. The refrigerator load prediction sample includes at least temperature, humidity, door opening and closing conditions, types of stored items and the number of stored items. The load prediction module uses a Gaussian process load prediction algorithm to predict the refrigerator load temperature control load consumption. The Gaussian process load prediction algorithm regards the temperature change inside the refrigerator as a random process that obeys a Gaussian distribution through a Bayesian non-parametric protocol. The Gaussian process load prediction algorithm observes the refrigerator load prediction sample through a mean covariance matrix to obtain the mean and covariance function of the random process. The Gaussian process load prediction algorithm analyzes the temperature change according to the mean and covariance function to determine the refrigerator load temperature control load consumption. In a further embodiment, the load scheduling module uses a dual-parameter collaborative intelligent optimization mechanism to perform rolling optimization on the refrigerator load temperature control load according to the real-time refrigerator load temperature control load consumption. The dual-parameter collaborative intelligent optimization mechanism uses the Lyapunov index spectrum of the refrigerator load temperature control load as the research object through the target difference optimization protocol. The dual-parameter collaborative intelligent optimization mechanism analyzes the correlation between the refrigerator load temperature control load consumption and the refrigerator load temperature control load through the association network. The dual-parameter collaborative intelligent optimization mechanism determines the refrigerator load temperature control load scheduling strategy that meets the temperature control target requirements through the radial basis function neural network. The refrigerator load temperature control load consumption and the refrigerator load The correlation between the load and the temperature control load is used as the input variable of the radial basis function neural network, and the radial basis function neural network adjusts the temperature control load of the refrigerator load through nonlinear mapping; the drive control module adopts a compressor control mechanism to control the refrigeration of the refrigerator refrigeration equipment, and the compressor control mechanism dynamically adjusts the refrigeration output power according to the temperature control load of the refrigerator load and the PID control instruction to achieve the adjustment of the refrigerator temperature, and the compressor control mechanism adopts a nonlinear decoupling control protocol to eliminate the interaction and coupling effects inside the compressor, and the nonlinear decoupling control protocol maps the PID control instruction to the internal control variable through the generalized minimum variance control law to achieve precise control and adjustment of the refrigerator temperature.

[0029] In a specific embodiment, the load optimization scheduling control model includes three modules: a load prediction module, a load scheduling module and a drive control module. The load prediction module uses a multivariate regression analysis network to establish a refrigerator load prediction sample, which includes multiple factors that affect the refrigerator load, such as temperature, humidity, door opening and closing, the type of stored items and the number of stored items. By comprehensively analyzing these factors, the load consumption of the refrigerator in the future can be obtained. In the prediction process, the load prediction module uses a Gaussian process load prediction algorithm. The algorithm regards the temperature change inside the refrigerator as a random process that obeys the Gaussian distribution, and observes the refrigerator load prediction sample through the mean covariance matrix to obtain the mean and covariance function of the random process. Based on these statistical characteristics, the algorithm can analyze the temperature changes in the future and determine the corresponding refrigerator load temperature control load consumption.

[0030] The load scheduling module performs rolling optimization of the refrigerator load temperature control load according to the real-time refrigerator load temperature control load consumption, and adopts a dual-parameter collaborative intelligent optimization mechanism. This mechanism uses the Lyapunov index spectrum of the refrigerator load temperature control load as the research object through the target differential optimization protocol, and analyzes the correlation between the refrigerator load temperature control load consumption and the refrigerator load temperature control load through the association network to determine the refrigerator load temperature control load scheduling strategy that meets the temperature control target requirements. In the specific implementation, the dual-parameter collaborative intelligent optimization mechanism uses a radial basis function neural network to determine the scheduling strategy. The neural network uses the refrigerator load temperature control load consumption and the correlation between the refrigerator load temperature control load as input variables, and adjusts the refrigerator load scheduling capacity through nonlinear mapping to ensure that it meets the temperature control target requirements.

[0031] The drive control module uses a compressor control mechanism to control the refrigeration equipment of the refrigerator for refrigeration. The compressor control mechanism dynamically adjusts the refrigeration output power according to the refrigerator load temperature control load and PID control instructions, thereby realizing the regulation of the refrigerator temperature. At the same time, in order to eliminate the interaction and coupling effects inside the compressor, the compressor control mechanism adopts a nonlinear decoupling control protocol. Specifically, in the nonlinear decoupling control protocol, the PID control instructions are mapped to internal control variables through the generalized minimum variance control law to achieve precise control and regulation of the refrigerator temperature. In this process, it is necessary to consider a variety of factors, such as the working state of the compressor, load conditions, ambient temperature, etc., and these factors should be comprehensively analyzed and processed. In practical applications, it is necessary to adjust and optimize parameters according to specific circumstances to achieve the best temperature regulation effect.

[0032] In a further embodiment, a refrigerator load temperature control method is applied to the refrigerator load temperature control system, and the method comprises the following steps: Step 1: Install an integrated photonic chip 1 in the refrigerator to monitor the temperature of the location of the goods in real time. The integrated photonic chip 1 transmits the temperature output signal and the set temperature parameters to the control center module accurately and reliably through the radio wave propagation network; Step 2: The control center module uses a time domain difference analysis algorithm to perform time domain feature analysis on the temperature output signal to determine the temperature peak value, the temperature rise amplitude and the temperature drop amplitude; the control center module uses a frequency domain difference analysis algorithm to perform frequency domain feature analysis on the temperature output signal to determine the temperature output signal frequency and the temperature output signal phase; Step 3, the control center module uses a particle swarm optimization PID control algorithm to learn and train the temperature characteristic data and the set temperature parameters to establish a PID control model, the particle swarm optimization PID control algorithm searches for the optimal PID parameter combination applied to the PID control model through a particle swarm optimization mechanism, and the particle swarm optimization PID control algorithm generates a PID control instruction according to the weighted value of the PID parameter combination; Step 4: The temperature control response module adopts a load optimization scheduling control model to coordinate the control of the refrigerator load temperature control load. The load optimization scheduling control model determines the consumption of the refrigerator load temperature control load through the load prediction module. The load optimization scheduling control model then adjusts the refrigerator load temperature control load through the load scheduling module. The load optimization scheduling control model finally controls the refrigeration of the refrigerator refrigeration equipment through the drive control module. Step 5. Finally, a load monitoring model is used to monitor the real-time load changes of the refrigerator in real time. The load monitoring model monitors the overload phenomenon on the refrigerator load through a load current monitoring circuit. The load monitoring model performs safety protection on the overload phenomenon on the refrigerator load through an embedded load safety protection circuit.

[0033] In a further embodiment, the load current monitoring circuit uses a current transmitter to convert the current on the refrigerator load into a voltage signal, the load current monitoring circuit uses an integrated operational amplifier signal processing protocol to enhance and filter the voltage signal, the load current monitoring circuit then uses a sensor array model of a PC terminal to analyze the voltage signal to determine the overload phenomenon on the refrigerator load, the sensor array model of the PC terminal uses an HHT nonlinear time-frequency analysis engine to analyze the frequency characteristics of the voltage signal, and the sensor array model of the PC terminal uses an overload analysis protocol to analyze the frequency characteristics of the voltage signal. The load characteristic analysis realizes the overload diagnosis of the refrigerator load; the embedded load safety protection circuit adopts an optocoupler isolation protection mechanism to isolate and protect the overload of the refrigerator load, and the optocoupler isolation protection mechanism realizes monolithic integrated overload protection through a cooperative current detector and a drive circuit. The cooperative current detector performs vector detection on the overload amplitude of the refrigerator load through a vector detection protocol, and the drive circuit drives the optocoupler isolator to isolate the overload of the refrigerator load according to the overload amplitude of the refrigerator load. The optocoupler isolation protection mechanism integrates the cooperative current detector and the drive circuit into the embedded load safety protection circuit through a monolithic integration mode. In a specific embodiment, the load current monitoring circuit is used to monitor the load current of the refrigerator and to protect the refrigerator by detecting the overload phenomenon. Specifically, the circuit uses a current transmitter to convert the current on the refrigerator load into a voltage signal, and then uses an integrated operational amplifier signal processing protocol to enhance and filter this signal to improve its accuracy and reliability. Next, the load current monitoring circuit transmits the processed signal to the sensor array model of the PC terminal. In this model, the HHT nonlinear time-frequency analysis engine is used to analyze the frequency characteristics of the voltage signal, and the overload analysis protocol is used to perform overload characteristic analysis. Through these analysis and diagnosis methods, it is possible to accurately determine whether the refrigerator is overloaded and take corresponding protective measures in a timely manner. In short, the load current monitoring circuit is an indispensable part of the refrigerator protection system. It uses efficient and accurate technical means to monitor the load of the refrigerator, and can promptly detect and diagnose abnormal conditions such as overload, thereby effectively ensuring the safety and stable operation of the refrigerator equipment.

[0034] The embedded load safety protection circuit is a circuit for protecting the overload of the refrigerator load. The circuit adopts an optocoupler isolation protection mechanism, which can isolate and protect the overload of the refrigerator load. Specifically, the optocoupler isolation protection mechanism realizes monolithic integrated overload protection through a cooperative current detector and a drive circuit. In this mechanism, the cooperative current detector performs vector detection on the overload amplitude of the refrigerator load through a vector detection protocol, and transmits the detected result to the drive circuit. The drive circuit drives the optocoupler isolator to isolate the overload of the refrigerator load according to the overload amplitude of the refrigerator load. At the same time, the optocoupler isolation protection mechanism integrates the cooperative current detector and the drive circuit into the embedded load safety protection circuit through a monolithic integration mode, thereby improving the reliability and stability of the entire system. In short, the embedded load safety protection circuit adopts advanced technical means to achieve isolation and protection of the overload of the refrigerator load. The use of the optocoupler isolation protection mechanism can effectively prevent the overload current from damaging the refrigerator equipment. At the same time, through the monolithic integration mode, the reliability and stability of the entire system can be improved, and a strong guarantee can be provided for the safe operation of the refrigerator.

[0035] In a further embodiment, a thermostat for controlling the temperature of a refrigerator load is applied to the refrigerator load temperature control system and the refrigerator load temperature control method, and the thermostat comprises: The integrated photonic chip 1 is used to monitor the temperature changes inside the refrigerator in real time; The control chip 2 is used to process the temperature output signal and the set temperature parameters, generate corresponding PID control instructions, and thus realize accurate control of the internal environment temperature of the refrigerator. The control chip 2 includes a control center module, a temperature control response module, and a load monitoring module. The output end of the control center module is connected to the input end of the temperature control response module, and the output end of the load monitoring module is connected to the input end of the temperature control response module. The load monitoring module monitors the overload phenomenon on the refrigerator load through a load current monitoring circuit, and the load monitoring module then performs safety protection on the overload phenomenon on the refrigerator load through an embedded load safety protection circuit; LCD display 3, used to display the current temperature information of the internal environment of the refrigerator; Operation button 4 is used by the user to set the temperature of the internal environment of the refrigerator; The communication interface 5 is used to realize data exchange between the integrated photonic chip 1 and the control chip 2. The communication interface 5 uses a radio wave communication protocol to ensure that the temperature output signal and the set temperature parameters can be correctly transmitted; The housing and heat sink 6 are used to protect the internal equipment from external interference and damage and ensure that the thermostat dissipates heat normally during operation. The housing and heat sink 6 are made of ABS composite material, and the ABS composite material has excellent thermal conductivity through the graphene thermal conductive film; The integrated photonic chip 1 is connected to the control chip 2 via a communication interface 5, the LCD screen 3 is fixedly connected to the operation button 4 via a fixed interface and a connector, the LCD screen 3 and the operation button 4 are connected to the control chip 2 via a communication interface 5, and the housing and the heat dissipation device 6 are arranged outside the temperature controller.

[0036] Although the specific embodiments of the present invention are described above, it should be understood by those skilled in the art that these specific embodiments are only illustrative, and those skilled in the art may make various omissions, substitutions and changes to the details of the above methods and systems without departing from the principles and essence of the present invention. For example, merging the above method steps so as to perform substantially the same functions in substantially the same manner to achieve substantially the same results is within the scope of the present invention. Therefore, the scope of the present invention is limited only by the appended claims.

Claims

1. A refrigerator load temperature control system, characterized in that: The system comprises: A human-machine interface module, used for a user to set temperature parameters and display current temperature information at the same time, the human-machine interface module adopts a combination of a liquid crystal display screen (3) and an operation button (4) to enable a user to set temperature parameters and display current temperature information at the same time; A temperature acquisition module is used to acquire real-time temperature information of an area within the refrigerator, wherein the data acquisition module uses an integrated photonic chip (1) to accurately acquire a temperature output signal, and the integrated photonic chip (1) uses a fiber grating edge filter demodulation model to capture and process optical signals of changes in optical path length and reflectivity caused by thermal expansion of the area within the refrigerator to obtain a temperature output signal; An information transmission module, used for transmitting the temperature output signal and the set temperature parameters, wherein the data transmission module uses a radio wave propagation network to accurately and reliably transmit the temperature output signal and the set temperature parameters to the control center module; A control center module is used to process the transmitted temperature output signal and the set temperature parameters, and generate control instructions. The control center module uses a time-frequency domain joint analysis mechanism to perform feature analysis on the temperature output signal to obtain temperature feature data. The temperature feature data at least includes a temperature rise amplitude, a temperature drop amplitude, a temperature peak value, a temperature output signal frequency, and a temperature output signal phase. The control center module uses a particle swarm optimization PID control algorithm to analyze and schedule the temperature feature data and the set temperature parameters, and generates a PID control instruction; A temperature control response module is used to respond to PID control instructions to realize the temperature drive control of the refrigerator load. The temperature control response module adopts an open-loop response control circuit to sensitively respond to and execute the PID control instructions. The open-loop response control circuit adopts a load optimization scheduling control model to coordinate the temperature control load of the refrigerator load. The load optimization scheduling control model controls the refrigerator refrigeration equipment according to the temperature control load of the refrigerator load to achieve the temperature parameters set by the user; The output end of the temperature acquisition module is connected to the input end of the information transmission module, the output end of the human-machine interface module is connected to the input end of the information transmission module, the output end of the information transmission module is connected to the input end of the control center module, and the output end of the control center module is connected to the input end of the temperature control response module.

2. A refrigerator load temperature control system according to claim 1, characterized in that: The fiber Bragg grating edge filter demodulation model comprises a light source module, a fiber Bragg grating sensor module and a signal processing module. The output end of the light source module is connected to the input end of the fiber Bragg grating sensor module, and the output end of the fiber Bragg grating sensor module is connected to the input end of the signal processing module. The light source module uses a laser light source to emit laser light into the incident port of the integrated photonic chip (1). The laser light source enters the first optical fiber through the incident port and is continuously reflected to form an interference pattern. The interference pattern undergoes displacement and morphological changes as the temperature changes. The fiber Bragg grating sensor module uses an optical fiber cable with a periodically changing refractive index to generate interference effects of reflected and transmitted waves to capture optical signals due to temperature changes. The signal processing module uses an optical signal detector to detect the changing optical signal. The signal processing module uses a photoelectric conversion circuit to convert the optical signal into a temperature output signal. The photoelectric conversion circuit uses a space-varying Wiener pre-filtering mechanism to convert the temperature output signal without distortion. The space-varying Wiener pre-filtering mechanism removes the temperature output signal noise by introducing a weighted geometric mean iterative process.

3. A refrigerator load temperature control system according to claim 1, characterized in that: The time-frequency domain joint analysis mechanism adopts a time-domain difference analysis algorithm to perform time-domain feature analysis on the temperature output signal. The time-domain difference analysis algorithm discretizes the temperature output signal into time series data by sampling at equal intervals. The time-domain difference analysis algorithm calculates the difference between two adjacent time series data by a time-domain difference function, and the difference is used as new sequence data. The time-domain difference analysis algorithm obtains the temperature peak value in the new sequence data by extracting the kurtosis statistical feature. The time-domain difference analysis algorithm obtains the temperature rise amplitude and the temperature drop amplitude in the new sequence data by extracting the variance statistical feature. The time-frequency domain joint analysis mechanism adopts a time-domain difference analysis algorithm to discretize the temperature output signal into time series data by sampling at equal intervals. The time-domain difference analysis algorithm calculates the difference between two adjacent time series data by a time-domain difference function, and the difference is used as the new sequence data. The mechanism adopts a frequency domain difference analysis algorithm to perform frequency domain feature analysis on the temperature output signal. The frequency domain difference analysis algorithm obtains temperature spectrum information through equal-interval sampling and Fourier transform. The frequency domain difference analysis algorithm calculates the frequency difference between frequency points in two adjacent temperature spectrum information through a frequency domain difference function to obtain a frequency domain sequence. The frequency domain difference analysis algorithm calculates the response difference of the frequency domain sequence in different frequency ranges through a differential operation equation to determine the frequency of the temperature output signal. The frequency domain difference analysis algorithm determines the phase difference of the temperature output signal by performing a frequency scanning on the frequency domain sequence through a frequency scanning mechanism to obtain the phase of the temperature output signal.

4. A refrigerator load temperature control system according to claim 1, characterized in that: The working method of the particle swarm optimization PID control algorithm is: S1. Using a support vector machine training engine to learn and train the temperature characteristic data and the set temperature parameters to establish a PID control model. The PID control model includes a proportional controller, an integral controller and a differential controller. The support vector machine training engine automatically learns the optimal discrimination boundary by training the characteristic relationship between the temperature characteristic data and the set temperature parameters through the optimal discrimination hyperplane. The support vector machine training engine establishes the PID control model according to the discrimination boundary. The length calculation formula of the discrimination boundary is: S2, then use the particle swarm optimization mechanism to search for the optimal PID parameter combination, and apply the optimal PID parameter combination to the PID control model, the PID parameter combination includes at least a proportional control coefficient, an integral control coefficient and a differential control coefficient, the particle swarm optimization mechanism uses an overshoot fitness function to iteratively optimize the proportional control coefficient, the integral control coefficient and the differential control coefficient, and outputs the optimal proportional control coefficient, the integral control coefficient and the differential control coefficient, and the proportional control coefficient calculation formula is: S3, then the PID control model balances the proportional control coefficient, the integral control coefficient and the differential control coefficient through the self-tuning function, the self-tuning function adaptively adjusts the PID parameter combination according to the dynamic characteristics of the controlled object, the PID control model analyzes the deviation between the current temperature and the set temperature through the time-varying weighted analysis function to obtain the PID parameter combination weighted value, the PID control model performs weighted summation on the proportional control coefficient, the integral control coefficient and the differential control coefficient according to the PID parameter combination weighted value, and generates a PID control instruction to achieve accurate control of the refrigerator load on the temperature, and the PID parameter combination weighted value calculation formula is: 。 5. The refrigerator load temperature control system according to claim 1, characterized in that: The load optimization scheduling control model includes a load prediction module, a load scheduling module and a drive control module. The output end of the load prediction module is connected to the input end of the load scheduling module, and the output end of the load scheduling module is connected to the input end of the drive control module. The load prediction module uses a multivariate regression analysis network to establish a load prediction sample for the refrigerator load. The load prediction sample for the refrigerator load includes at least temperature, humidity, door opening and closing conditions, types of stored items and the number of stored items. The load prediction module uses a Gaussian process load prediction algorithm to predict the consumption of the refrigerator load temperature control load. The Gaussian process load prediction algorithm regards the temperature change inside the refrigerator as a random process that obeys the Gaussian distribution through a Bayesian non-parametric protocol. The Gaussian process load prediction algorithm observes the refrigerator load prediction sample through a mean covariance matrix to obtain the mean and covariance function of the random process. The Gaussian process load prediction algorithm analyzes the temperature change according to the mean and covariance function to determine the consumption of the refrigerator load temperature control load.

6. A refrigerator load temperature control system according to claim 5, characterized in that: The load scheduling module uses a dual-parameter collaborative intelligent optimization mechanism to perform rolling optimization on the refrigerator load temperature control load according to the real-time refrigerator load temperature control load consumption. The dual-parameter collaborative intelligent optimization mechanism uses the Lyapunov index spectrum of the refrigerator load temperature control load as the research object through the target difference optimization protocol. The dual-parameter collaborative intelligent optimization mechanism analyzes the correlation between the refrigerator load temperature control load consumption and the refrigerator load temperature control load through the association network. The dual-parameter collaborative intelligent optimization mechanism determines the refrigerator load temperature control load scheduling strategy that meets the temperature control target requirements through the radial basis function neural network. The refrigerator load temperature control load consumption and the refrigerator load temperature control load The correlation relationship between them is used as the input variable of the radial basis function neural network, and the radial basis function neural network adjusts the temperature control load of the refrigerator load through nonlinear mapping; the drive control module adopts a compressor control mechanism to control the refrigeration of the refrigerator refrigeration equipment, and the compressor control mechanism dynamically adjusts the refrigeration output power according to the temperature control load of the refrigerator load and the PID control instruction to achieve the adjustment of the refrigerator temperature, and the compressor control mechanism adopts a nonlinear decoupling control protocol to eliminate the interaction and coupling effects inside the compressor, and the nonlinear decoupling control protocol maps the PID control instruction to the internal control variable through the generalized minimum variance control law to achieve precise control and adjustment of the refrigerator temperature.

7. A method for controlling the temperature of a refrigerator load, characterized in that: The method applied to a refrigerator load temperature control system as claimed in any one of claims 1 to 6 comprises the following steps: Step 1: installing an integrated photonic chip (1) in the refrigerator to monitor the temperature of the location of the goods in real time, wherein the integrated photonic chip (1) accurately and reliably transmits the temperature output signal and the set temperature parameters to the control center module through a radio wave propagation network; Step 2: The control center module uses a time domain difference analysis algorithm to perform time domain feature analysis on the temperature output signal to determine the temperature peak value, the temperature rise amplitude and the temperature drop amplitude; the control center module uses a frequency domain difference analysis algorithm to perform frequency domain feature analysis on the temperature output signal to determine the temperature output signal frequency and the temperature output signal phase; Step 3, the control center module uses a particle swarm optimization PID control algorithm to learn and train the temperature characteristic data and the set temperature parameters to establish a PID control model, the particle swarm optimization PID control algorithm searches for the optimal PID parameter combination applied to the PID control model through a particle swarm optimization mechanism, and the particle swarm optimization PID control algorithm generates a PID control instruction according to the weighted value of the PID parameter combination; Step 4: The temperature control response module adopts a load optimization scheduling control model to coordinate the control of the refrigerator load temperature control load. The load optimization scheduling control model determines the consumption of the refrigerator load temperature control load through the load prediction module. The load optimization scheduling control model then adjusts the refrigerator load temperature control load through the load scheduling module. The load optimization scheduling control model finally controls the refrigeration of the refrigerator refrigeration equipment through the drive control module. Step 5. Finally, a load monitoring model is used to monitor the real-time load changes of the refrigerator in real time. The load monitoring model monitors the overload phenomenon on the refrigerator load through a load current monitoring circuit. The load monitoring model performs safety protection on the overload phenomenon on the refrigerator load through an embedded load safety protection circuit.

8. A refrigerator load temperature control method according to claim 7, characterized in that: The load current monitoring circuit uses a current transmitter to convert the current on the refrigerator load into a voltage signal, and the load current monitoring circuit uses an integrated operational amplifier signal processing protocol to enhance and filter the voltage signal. The load current monitoring circuit then uses a sensor array model of a PC terminal to analyze the voltage signal to determine the overload phenomenon on the refrigerator load. The sensor array model of the PC terminal uses an HHT nonlinear time-frequency analysis engine to analyze the frequency characteristics of the voltage signal. The sensor array model of the PC terminal uses an overload analysis protocol to perform overload characteristic analysis on the frequency characteristics of the voltage signal to achieve overload diagnosis of the refrigerator load. The embedded load safety protection circuit adopts an optocoupler isolation protection mechanism to isolate and protect the refrigerator load from overload. The optocoupler isolation protection mechanism realizes monolithic integrated overload protection through a cooperative current detector and a drive circuit. The cooperative current detector performs vector detection on the overload amplitude of the refrigerator load through a vector detection protocol. The drive circuit drives the optocoupler isolator to isolate the overload of the refrigerator load according to the overload amplitude of the refrigerator load. The optocoupler isolation protection mechanism integrates the cooperative current detector and the drive circuit into the embedded load safety protection circuit through a monolithic integration mode.

9. A thermostat for controlling the load temperature of a refrigerator, characterized in that: A refrigerator load temperature control system and a refrigerator load temperature control method as described in any one of claims 1 to 8, wherein the temperature controller comprises: An integrated photonic chip (1) is used to monitor the temperature changes inside the refrigerator in real time; A control chip (2) is used to process the temperature output signal and the set temperature parameters to generate corresponding PID control instructions, thereby realizing accurate control of the internal environment temperature of the refrigerator, the control chip (2) comprising a control center module, a temperature control response module and a load monitoring module, the output end of the control center module is connected to the input end of the temperature control response module, the output end of the load monitoring module is connected to the input end of the temperature control response module, the load monitoring module monitors the overload phenomenon on the refrigerator load through a load current monitoring circuit, and the load monitoring module then performs safety protection on the overload phenomenon on the refrigerator load through an embedded load safety protection circuit; A liquid crystal display screen (3) is used to display the current temperature information of the internal environment of the refrigerator; An operation button (4) is used by the user to set the temperature of the internal environment of the refrigerator; A communication interface (5) is used to implement data interaction between the integrated photonic chip (1) and the control chip (2), wherein the communication interface (5) uses a radio wave communication protocol to ensure that the temperature output signal and the set temperature parameters can be correctly transmitted; The housing and the heat dissipation device (6) are used to protect the internal equipment from external interference and damage and ensure that the temperature controller dissipates heat normally during operation. The housing and the heat dissipation device (6) are made of ABS composite material. The ABS composite material has excellent thermal conductivity through the graphene thermal conductive film. The integrated photonic chip (1) is connected to the control chip (2) via a communication interface (5); the liquid crystal display screen (3) is fixedly connected to the operation button (4) via a fixed interface and a connector; the liquid crystal display screen (3) and the operation button (4) are connected to the control chip (2) via the communication interface (5); and the housing and the heat dissipation device (6) are arranged outside the temperature controller.

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