Intelligent temperature sensing and regulating device

Abstract

The embodiment of the invention provides intelligent temperature sensing and regulating equipment. The intelligent temperature sensing and regulating equipment comprises a temperature sensing module, a data processing module, a display and interaction module, a regulation strategy module, an execution module, a communication module and a power management module, and the regulation and control strategy module comprises a prediction model sub-module, a self-adaptive regulation sub-module, an intelligent decision sub-module, a control algorithm sub-module and the like. The prediction model sub-module can predict a future power generation equipment temperature change trend by analyzing historical power generation equipment temperature data, thereby avoiding severe fluctuation of the temperature of the power generation equipment and keeping the temperature of the environmental power generation equipment stable. If the temperature of the power generation equipment is predicted to rise sharply, the system can start a cooling mechanism in advance to prevent overheating. The response speed of the system is improved, the energy waste is reduced, and the energy efficiency is improved. The self-adaptive adjustment sub-module can automatically adjust control parameters according to the actual operation condition of the system and the environment change, and the adaptability and robustness of the system are enhanced.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of temperature monitoring of power generation equipment, and specifically to intelligent temperature sensing and control equipment. Background Art

[0002] With the continuous improvement of industrial automation level, the operating efficiency and safety of power generation equipment in power plants have become the focus of attention in the power industry. During the operation of power plants, temperature control of power generation equipment is a key factor in ensuring the normal operation of equipment and extending its service life. Since power generation equipment in power plants usually works in a high temperature and high pressure environment, slight fluctuations in temperature may cause equipment performance to deteriorate or even cause safety accidents. Traditional temperature control methods for power generation equipment in power plants mostly rely on manual monitoring and manual adjustment. Against the background of growing electricity demand and rising energy costs, power plants have an increasingly urgent need for intelligent and automated temperature control systems for power generation equipment.

[0003] However, existing temperature control systems often lack sufficient predictability and adaptability, and are unable to perform real-time and effective temperature adjustment based on the actual operating status of the power generation equipment and changes in the external environment. Failure to make decisions in advance may cause the temperature of the power generation equipment to fluctuate, affecting the stability of the temperature of the power generation equipment. Summary of the invention

[0004] The embodiment of the present invention aims to solve at least one of the technical problems existing in the prior art, and provides an intelligent temperature sensing and regulating device.

[0005] The embodiment of the present invention provides an intelligent temperature sensing and control device, including: a temperature sensing module, a data processing module, a display and interaction module, a control strategy module, an execution module, a communication module and a power management module; the control strategy module includes a prediction model submodule, an adaptive adjustment submodule, an intelligent decision submodule and a control algorithm submodule;

[0006] The temperature sensing module is used to collect temperature data and transmit it to the data processing module;

[0007] The data processing module is used to clean, normalize and analyze the temperature data, and transmit the processed temperature data to the display and interaction module and the control strategy module;

[0008] The control strategy module is used to generate control instructions based on the prediction model and the adaptive control algorithm; these instructions are then sent to the execution module to drive the heater or cooler actuator to adjust the temperature;

[0009] The communication module is used to establish data transmission channels between modules and with external systems to ensure smooth exchange of information;

[0010] The power management module is used to provide necessary power support for all other modules and monitor the power status to ensure the continuous operation of the system;

[0011] The display and interaction module is used to display the processed temperature data and system status information to the user in the form of graphics or text;

[0012] The prediction model submodule is used to predict future temperature trends based on historical data and pass these prediction results to the adaptive adjustment submodule;

[0013] The adaptive adjustment submodule is used to adjust the control parameters according to the prediction results and the current state of the system;

[0014] The intelligent decision submodule is used to generate a decision instruction based on the adjusted control parameters using fuzzy logic or other decision algorithms;

[0015] The control algorithm submodule is used to calculate a specific control signal according to the decision instruction;

[0016] The execution module actually adjusts the temperature of the ambient power generation equipment according to the control signal output by the control algorithm submodule.

[0017] In some possible embodiments, the temperature sensing module is further used for:

[0018] The built-in temperature sensor detects temperature data in real time and converts the detected temperature data into electrical signals;

[0019] amplifying the electrical signal by a signal amplifier;

[0020] The amplified electrical signal is converted into a digital signal through an analog-to-digital converter;

[0021] The digitized temperature data is transmitted to the data processing module for subsequent analysis and control.

[0022] In some possible embodiments, the data processing module is further configured to:

[0023] Performing data cleaning on the temperature data to remove possible noise and outliers;

[0024] Normalize or standardize the cleaned temperature data to make it suitable for algorithm processing;

[0025] Combine data from other sensors or external data sources to get more accurate temperature readings of power generation equipment;

[0026] Run prediction models and adaptive adjustment algorithms to predict the temperature trend of power generation equipment and optimize adjustment parameters;

[0027] The processed temperature data will be passed to the display and interaction module and the control algorithm submodule for display and further control decision.

[0028] In some possible embodiments, the prediction model submodule predicts the temperature change trend in the future period of time based on historical data and environmental factors through the autoregressive integrated moving average model algorithm in time series analysis, and the specific steps include:

[0029] a. Data preparation: collect temperature data within a certain time range and perform preprocessing to ensure data quality;

[0030] b. Stationarity test: Check whether the temperature time series is stationary. If it is not stationary, differential processing is required;

[0031] c. Model identification: Determine the parameters of the ARIMA model, including the number of autoregressive terms p, the number of differences d, and the number of moving average terms q;

[0032] d. Parameter estimation: Use statistical methods to estimate the parameters of the ARIMA model;

[0033] e. Model testing: test the applicability of the model through residual analysis method;

[0034] f. Prediction: Use the model to predict future temperatures;

[0035] The basic formula of the ARIMA model is as follows:

[0036] Yt=c+φ 1 Y t-1 +φ 2 Y t-2 +...+φ p Y t-p +θ 1 ∈ t-1 +θ 2 ∈ t-2 +...+θ q ∈ t-q +∈ t ;

[0037] Where: Yt is the value of the time series at time t;

[0038] c is a constant term;

[0039] φ 1 ,φ 2 ,...,φ p is the coefficient of the autoregressive term;

[0040] θ 1 ,θ 2 ,...,θ q is the coefficient of the moving average term;

[0041] ∈ t is a white noise sequence.

[0042] In some possible embodiments, the adaptive regulation submodule automatically adjusts control parameters and optimizes control effects according to environmental changes and user needs through a model reference adaptive control algorithm. The specific steps include:

[0043] a. Determine the reference model: Select an ideal reference model that describes the behavior of the system in an ideal situation without interference and uncertainty;

[0044] b. Design the adaptive law: Determine the adaptive law that will adjust the controller parameters based on the error between the system output and the reference model output;

[0045] c. Implement control: Use an adaptive controller to control the system and continuously adjust the controller parameters to reduce the error between the system output and the reference model output;

[0046] d. Error feedback: Calculate the error between the system output and the reference model output, and use the error signal to update the controller parameters;

[0047] e. Parameter update: Update controller parameters according to the adaptive law;

[0048] The basic formula of the model reference adaptive control algorithm is:

[0049] u(t) = -K(t) [r(t) - y(t)]; where u(t) is the control input at time t; K(t) is the controller gain matrix at time t; r(t) is the reference model output at time t; y(t) is the actual system output at time t; [r(t) - y(t)] is the tracking error at time t;

[0050] The update formula of the adaptive law is:

[0051] K˙(t)=-Γ·φ(t)·[r(t)-y(t)] T ;

[0052] Where: K˙(t) is the update rate of the controller gain matrix K(t); Γ is a positive definite matrix that determines the convergence rate of the adaptive law; φ(t) is the adjustable parameter vector at time t; [r(t)-y(t)] T is the transpose of the tracking error.

[0053] In some possible embodiments, the intelligent decision submodule uses a fuzzy logic control algorithm combined with a prediction model and the results of adaptive adjustment to make an optimal control decision, specifically including:

[0054] a. Input fuzzification: converting clear input variables into fuzzy sets;

[0055] b. Establish a fuzzy rule base: Based on expert knowledge and experience, establish a series of IF-THEN rules to describe the logic of temperature control;

[0056] c. Fuzzy reasoning: Based on the fuzzified input and fuzzy rule base, fuzzy reasoning is performed to obtain fuzzy output;

[0057] d. Defuzzification: converting the fuzzy output obtained by fuzzy reasoning into a clear control signal;

[0058] In the fuzzification process, for the input variable x, the fuzzification process formula is:

[0059]

[0060] Where: μA(x) is the membership of the input variable x in the fuzzy set A; c is the center of the fuzzy set A; σ is the standard deviation of the fuzzy set A;

[0061] Fuzzy reasoning adopts the Mamdani reasoning method, and its basic form is:

[0062] IF x IS A THEN y IS B; where A and B are the fuzzy sets of input and output respectively;

[0063] The defuzzification formula is:

[0064] in:

[0065] y defuzzified is the clear output after defuzzification;

[0066] u i is the membership degree of the i-th fuzzy output set;

[0067] y i is the center of the i-th fuzzy output set;

[0068] n is the number of fuzzy output sets.

[0069] In some possible embodiments, the control algorithm submodule outputs a control signal to an actuator, including a heater or a cooler, to adjust the ambient temperature so that it approaches or reaches a set target; the entire process is closed-loop and will be repeated continuously to achieve continuous monitoring and precise control of the ambient temperature.

[0070] In some possible embodiments, the display and interaction module is further used to:

[0071] Receive temperature data from the data processing module and visualize the data through user interface design;

[0072] Receive interactive information input by the user through a touch screen, keyboard or other input device, including setting a temperature target for power generation equipment, adjusting system parameters or viewing historical data;

[0073] Update displayed information in real time to ensure users can obtain the latest system status;

[0074] Record the user's operation instructions and pass them to the control algorithm submodule or data processing module for corresponding processing.

[0075] In some possible embodiments, the execution module is further configured to:

[0076] receiving the control signal, wherein the control signal instructs the actuator how to act;

[0077] The control signal is converted into an instruction understood by the actuator through the driving circuit, so that the actuator performs corresponding actions according to the instruction to change the temperature of the ambient power generation equipment;

[0078] Monitors the status of the actuator to ensure it is functioning properly and provides fault feedback when necessary.

[0079] In some possible embodiments, the communication module establishes and maintains communication links, receives data transmission requests from other modules, and packages the data into a format suitable for transmission; during the data transmission process, the communication module is also responsible for data encryption, compression and decompression to ensure data security and transmission efficiency; after the receiving end receives the data, the communication module will unpack and verify it to ensure the integrity and correctness of the data, and finally pass the data to the corresponding module;

[0080] The power management module converts it into the voltage and current required by the system through a power adapter; the power management module distributes the power to ensure that each module obtains an appropriate amount of power; the power management module monitors the power status, including voltage, current and power consumption, to prevent overload and short circuit; in the event of unstable power supply or external power outage, the power management module will start the backup power supply and notify the system to perform necessary power management operations.

[0081] Compared with the prior art, the embodiments of the present invention mainly have the following beneficial effects:

[0082] 1. In the embodiment of the present invention, the prediction model submodule can predict the future temperature change trend of the power generation equipment by analyzing the historical power generation equipment temperature data, which provides forward-looking information for the system. This prediction capability enables the control strategy module to make decisions in advance, thereby avoiding drastic fluctuations in the temperature of the power generation equipment and maintaining the stability of the temperature of the ambient power generation equipment. If it is predicted that the temperature of the power generation equipment will rise sharply, the system can start the cooling mechanism in advance to prevent overheating. This forward-looking regulation not only improves the response speed of the system, but also reduces energy waste and improves energy efficiency. The adaptive adjustment submodule can automatically adjust the control parameters according to the actual operating conditions of the system and environmental changes, which greatly enhances the adaptability and robustness of the system. In the face of external interference or changes in internal system parameters, adaptive regulation can ensure the effectiveness of the control strategy and maintain the accuracy of the temperature control of the power generation equipment. This self-adjustment capability reduces the need for manual intervention, improves the automation level of the system, and also reduces the risk of control failure due to improper parameters.

[0083] 2. In the embodiment of the present invention, the intelligent decision-making submodule uses algorithms such as fuzzy logic to deal with uncertainty and nonlinear problems, providing flexible and robust decision support for the system. Through fuzzy rules and reasoning, the intelligent decision-making submodule can simulate the decision-making process of human experts, making the temperature control of power generation equipment more humane. This decision-making mechanism is particularly important when dealing with complex environmental conditions and user needs. It can balance multiple control objectives, such as comfort, energy saving and rapid response, thereby improving user satisfaction and the overall performance of the system. The control algorithm submodule converts intelligent decisions into specific control signals and directly acts on the actuator. This step ensures the effective execution of the decision, so that the output of the entire control strategy module can affect the temperature of the environmental power generation equipment in real time and achieve precise control. The efficiency of the control algorithm submodule directly affects the dynamic performance and steady-state accuracy of the system, and its optimization and adjustment are the key to ensuring the long-term stable operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0085] Figure 1 is the overall system block diagram of the present invention;

[0086] Figure 2 This is a system block diagram of the control strategy module in the present invention. DETAILED DESCRIPTION

[0087] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0088] Unless otherwise specified, the technical terms or scientific terms used in the embodiments of the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. "Including" or "comprising" used in the embodiments of the present invention neither limit the shapes, numbers, steps, actions, operations, components, originals and / or their groups mentioned, nor exclude the appearance or addition of one or more other different shapes, numbers, steps, actions, operations, components, originals and / or their groups, or the addition of these. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number and order of the indicated technical features. Thus, the features defined as "first" and "second" can explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0089] Unless otherwise specifically stated, the relative arrangement of the components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention. At the same time, it should be understood that for ease of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship, and the techniques, methods and devices known to ordinary technicians in the relevant fields may not be discussed in detail, but where appropriate, the techniques, methods and devices shown should be considered as part of the authorized specification. In all examples shown and discussed here, any specific other examples may have different values. It should be noted that similar symbols and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0090] In the description of the embodiments of the present invention, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In the embodiments of the present invention, the schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in the embodiments of the present invention and the features of the different embodiments or examples, without contradiction.

[0091] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described here.

[0092] See also Figure 1 and Figure 2 An embodiment of the present invention relates to an intelligent temperature sensing and control device, including: a temperature sensing module, a data processing module, a display and interaction module, a control strategy module, an execution module, a communication module and a power management module; the control strategy module includes a prediction model submodule, an adaptive adjustment submodule, an intelligent decision-making submodule and a control algorithm submodule.

[0093] The temperature sensing module is responsible for collecting ambient temperature data and transmitting it to the data processing module for cleaning, normalization and analysis. The processed data is sent to the display and interaction module for user monitoring and interaction, and is also sent to the control strategy module.

[0094] The control strategy module generates control instructions based on the prediction model and the adaptive adjustment algorithm. These instructions are then sent to the execution module to drive the actuators such as heaters or coolers to adjust the temperature.

[0095] The communication module is responsible for establishing data transmission channels between modules and external systems to ensure smooth information exchange. The power management module provides necessary power support for all other modules and monitors the power status to ensure the continuous operation of the system.

[0096] The prediction model submodule is responsible for predicting future temperature trends based on historical data, and passing these prediction results to the adaptive adjustment submodule, which adjusts the control parameters based on the prediction and the current state of the system. The output of the adaptive adjustment submodule is sent to the intelligent decision submodule, which generates decision instructions using fuzzy logic or other decision algorithms. Finally, the decision instructions of the intelligent decision submodule are passed to the control algorithm submodule, which calculates specific control signals based on these instructions and outputs them to the actuator to achieve temperature control.

[0097] The temperature sensing module detects the ambient temperature in real time through a built-in temperature sensor (such as a thermocouple, thermistor, etc.). The sensor converts the ambient temperature into an electrical signal, which is usually very weak and needs to be amplified by a signal amplifier. Then, the analog signal is converted into a digital signal by an analog-to-digital converter (ADC) for processing by a microprocessor or controller. Finally, the digitized temperature data is transmitted to the data processing module for subsequent analysis and control. Throughout the process, the temperature sensing module also needs to be calibrated regularly to ensure measurement accuracy.

[0098] The data processing module receives the raw power plant temperature data from the temperature sensing module and first cleans the data to remove possible noise and outliers. The data is then normalized or standardized to make it suitable for algorithm processing. Next, the module may perform data fusion to combine information from other sensors or external data sources to obtain more accurate power plant temperature readings. The data processing module also runs prediction models and adaptive regulation algorithms to predict power plant temperature trends and optimize regulation parameters. The processed data will be passed to the display and interaction module and the control algorithm submodule for display and further control decisions.

[0099] The prediction model submodule predicts the temperature change trend in the future based on historical data and environmental factors through the autoregressive integrated moving average model algorithm in time series analysis. The specific steps include:

[0100] a. Data preparation: Collect temperature data within a certain time range and perform preprocessing to ensure data quality.

[0101] b. Stationarity test: Check whether the temperature time series is stationary. If it is not stationary, differential processing is required.

[0102] c. Model identification: Determine the parameters of the ARIMA model, including the number of autoregressive terms p, the number of differences d, and the number of moving average terms q.

[0103] d. Parameter estimation: Use statistical methods (such as maximum likelihood estimation) to estimate the parameters of the ARIMA model.

[0104] e. Model testing: The applicability of the model is tested through residual analysis method.

[0105] f. Prediction: Use the model to predict future temperatures;

[0106] The basic formula of the ARIMA model is as follows:

[0107] Yt=c+φ 1 Y t-1 +φ 2 Y t-2 +...+φ p Y t-p +θ 1 ∈ t-1 +θ 2 ∈ t-2 +...+θ q ∈ t-q +∈ t ;

[0108] Where: Yt is the value of the time series at time t;

[0109] c is a constant term;

[0110] φ 1 ,φ 2 ,...,φ p is the coefficient of the autoregressive term;

[0111] θ 1 ,θ 2 ,...,θ q is the coefficient of the moving average term;

[0112] ∈ t is a white noise sequence.

[0113] The adaptive regulation submodule automatically adjusts control parameters and optimizes control effects according to environmental changes and user needs through a model reference adaptive control algorithm. The specific steps include:

[0114] a. Determine the reference model: Select an ideal reference model that describes the behavior of the system under ideal conditions without interference and uncertainty.

[0115] b. Design the adaptive law: Determine the adaptive law that will adjust the controller parameters based on the error between the system output and the reference model output.

[0116] c. Implement control: Use an adaptive controller to control the system and continuously adjust the controller parameters to reduce the error between the system output and the reference model output.

[0117] d. Error feedback: Calculate the error between the system output and the reference model output, and use this error signal to update the controller parameters.

[0118] e. Parameter update: Update controller parameters according to the adaptive law.

[0119] The basic formula of the model reference adaptive control algorithm is:

[0120] u(t)=-K(t)·[r(t)-y(t)]; where u(t) is the control input at time t; K(t) is the controller gain matrix at time t; r(t) is the reference model output at time t; y(t) is the actual output of the system at time t; [r(t)-y(t)] is the tracking error at time t.

[0121] The update formula of the adaptive law is:

[0122] K˙(t)=-Γ·φ(t)·[r(t)-y(t)] T ;

[0123] Where: K˙(t) is the update rate of the controller gain matrix K(t); Γ is a positive definite matrix that determines the convergence rate of the adaptive law; φ(t) is the adjustable parameter vector at time t; [r(t)-y(t)] T is the transpose of the tracking error.

[0124] The intelligent decision submodule uses fuzzy logic control algorithm combined with the prediction model and the results of adaptive adjustment to make the optimal control decision, specifically including:

[0125] a. Input fuzzification: Convert crisp input variables (such as temperature error and temperature change rate) into fuzzy sets.

[0126] b. Establish a fuzzy rule base: Based on expert knowledge and experience, establish a series of IF-THEN rules to describe the logic of temperature control.

[0127] c. Fuzzy reasoning: Based on the fuzzified input and fuzzy rule base, fuzzy reasoning is performed to obtain fuzzy output.

[0128] d. Defuzzification: Convert the fuzzy output obtained by fuzzy reasoning into a clear control signal.

[0129] In the fuzzification process, for the input variable x, the fuzzification process formula is:

[0130]

[0131] Where: μA(x) is the membership of the input variable x in the fuzzy set A; c is the center of the fuzzy set A; σ is the standard deviation of the fuzzy set A.

[0132] Fuzzy reasoning adopts the Mamdani reasoning method, and its basic form is:

[0133] IF x IS A THEN y IS B; where A and B are the fuzzy sets of input and output respectively;

[0134] The defuzzification formula is:

[0135] in:

[0136] y defuzzified is the clear output after defuzzification;

[0137] u i is the membership degree of the i-th fuzzy output set;

[0138] y i is the center of the i-th fuzzy output set;

[0139] n is the number of fuzzy output sets.

[0140] The control algorithm submodule outputs a control signal to an actuator, including a heater or a cooler, to adjust the ambient temperature so that it approaches or reaches a set target; the entire process is a closed loop and will be repeated continuously to achieve continuous monitoring and precise control of the ambient temperature.

[0141] The display and interaction module is also specifically used to: receive temperature data from the data processing module and visualize the data through user interface design; receive interactive information input by the user through a touch screen, keyboard or other input device, including setting the temperature target of the power generation equipment, adjusting system parameters or viewing historical data; update the display information in real time to ensure that the user can obtain the latest system status; record the user's operation instructions and pass them to the control algorithm submodule or the data processing module for corresponding processing.

[0142] The execution module is also specifically used to: receive the control signal, wherein the control signal instructs the actuator (such as a heater, a cooler, a fan, etc.) how to act; convert the control signal into an instruction understood by the actuator through a drive circuit, so that the actuator performs corresponding actions according to the instruction to change the temperature of the ambient power generation equipment; monitor the status of the actuator to ensure its normal operation, and provide fault feedback when necessary.

[0143] The communication module is responsible for data exchange between modules within the system and between the system and external devices or networks. First, the module establishes and maintains a communication link, which can be a wired (such as Ethernet, USB) or wireless (such as Wi-Fi, Bluetooth) connection. Then, the module receives data transmission requests from other modules and packages the data into a format suitable for transmission. During the data transmission process, the communication module is also responsible for data encryption, compression and decompression to ensure data security and transmission efficiency. After the receiving end receives the data, the communication module will unpack and verify it to ensure the integrity and correctness of the data, and finally pass the data to the corresponding module;

[0144] The power management module is responsible for providing stable and appropriate power to the entire system. First, the module receives power from an external power source (such as AC power or battery) and converts it into the voltage and current required by the system through a power adapter. Next, the power management chip distributes the power to ensure that each module obtains the appropriate amount of power. At the same time, the module monitors the power status, including voltage, current, and power consumption, to prevent overload and short circuit. In the event of unstable power supply or external power outage, the power management module will start the backup power supply (such as a battery) and notify the system to perform necessary power management operations, such as reducing power consumption or shutting down safely.

[0145] In the embodiment of the present invention, the prediction model submodule can predict the future temperature change trend of the power generation equipment by analyzing the historical power generation equipment temperature data, which provides forward-looking information for the system. This prediction capability enables the control strategy module to make decisions in advance, thereby avoiding drastic fluctuations in the temperature of the power generation equipment and maintaining the stability of the temperature of the ambient power generation equipment. If it is predicted that the temperature of the power generation equipment will rise sharply, the system can start the cooling mechanism in advance to prevent overheating. This forward-looking regulation not only improves the response speed of the system, but also reduces energy waste and improves energy efficiency. The adaptive adjustment submodule can automatically adjust the control parameters according to the actual operating conditions of the system and environmental changes, which greatly enhances the adaptability and robustness of the system. In the face of external interference or changes in internal system parameters, adaptive regulation can ensure the effectiveness of the control strategy and maintain the accuracy of the temperature control of the power generation equipment. This self-adjustment capability reduces the need for manual intervention, improves the automation level of the system, and also reduces the risk of control failure due to improper parameters.

[0146] In the embodiment of the present invention, the intelligent decision-making submodule uses algorithms such as fuzzy logic to deal with uncertainty and nonlinear problems, providing flexible and robust decision support for the system. Through fuzzy rules and reasoning, the intelligent decision-making submodule can simulate the decision-making process of human experts, making the temperature control of power generation equipment more humane. This decision-making mechanism is particularly important when dealing with complex environmental conditions and user needs. It can balance multiple control objectives, such as comfort, energy saving and rapid response, thereby improving user satisfaction and the overall performance of the system. The control algorithm submodule converts intelligent decisions into specific control signals and directly acts on the actuator. This step ensures the effective execution of the decision, so that the output of the entire control strategy module can affect the temperature of the environmental power generation equipment in real time and achieve precise control. The efficiency of the control algorithm submodule directly affects the dynamic performance and steady-state accuracy of the system, and its optimization and adjustment are the key to ensuring the long-term stable operation of the system.

[0147] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present invention, but the present invention is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. An intelligent temperature sensing and control device, characterized in that: include: Temperature sensing module, data processing module, display and interaction module, control strategy module, execution module, communication module and power management module; The control strategy module includes a prediction model submodule, an adaptive adjustment submodule, an intelligent decision-making submodule and a control algorithm submodule; The temperature sensing module is used to collect temperature data and transmit it to the data processing module; The data processing module is used to clean, normalize and analyze the temperature data, and transmit the processed temperature data to the display and interaction module and the control strategy module; The control strategy module is used to generate control instructions according to the prediction model and the adaptive adjustment algorithm; These instructions are then sent to the execution module to drive the heater or cooler actuator to adjust the temperature; The communication module is used to establish data transmission channels between modules and with external systems to ensure smooth exchange of information; The power management module is used to provide necessary power support for all other modules and monitor the power status to ensure the continuous operation of the system; The display and interaction module is used to display the processed temperature data and system status information to the user in the form of graphics or text; The prediction model submodule is used to predict future temperature trends based on historical data and pass these prediction results to the adaptive adjustment submodule; The adaptive adjustment submodule is used to adjust the control parameters according to the prediction results and the current state of the system; The intelligent decision submodule is used to generate a decision instruction based on the adjusted control parameters using fuzzy logic or other decision algorithms; The control algorithm submodule is used to calculate a specific control signal according to the decision instruction; The execution module actually adjusts the temperature of the ambient power generation equipment according to the control signal output by the control algorithm submodule.

2. The intelligent temperature sensing and control device according to claim 1, characterized in that: The temperature sensing module is further used for: The built-in temperature sensor detects temperature data in real time and converts the detected temperature data into electrical signals; amplifying the electrical signal by a signal amplifier; The amplified electrical signal is converted into a digital signal through an analog-to-digital converter; The digitized temperature data is transmitted to the data processing module for subsequent analysis and control.

3. The intelligent temperature sensing and control device according to claim 1, characterized in that: The data processing module is further specifically used for: Performing data cleaning on the temperature data to remove possible noise and outliers; Normalize or standardize the cleaned temperature data to make it suitable for algorithm processing; Combine data from other sensors or external data sources to get more accurate temperature readings of power generation equipment; Run prediction models and adaptive adjustment algorithms to predict the temperature trend of power generation equipment and optimize adjustment parameters; The processed temperature data will be passed to the display and interaction module and the control algorithm submodule for display and further control decision.

4. The intelligent temperature sensing and control device according to claim 1, characterized in that: The prediction model submodule predicts the temperature change trend in the future based on historical data and environmental factors through the autoregressive integrated moving average model algorithm in time series analysis. The specific steps include: a. Data preparation: collect temperature data within a certain time range and perform preprocessing to ensure data quality; b.Stability test: Check whether the temperature time series is stable. If it is not stable, differential processing is required; c. Model identification: Determine the parameters of the ARIMA model, including the number of autoregressive terms p, the number of differences d, and the number of moving average terms q; d. Parameter estimation: Use statistical methods to estimate the parameters of the ARIMA model; e. Model testing: test the applicability of the model through residual analysis method; f. Prediction: Use the model to predict future temperatures; The basic formula of the ARIMA model is as follows: Yt=c+φ1Y t-1 +φ2Y t-2 +...+φ p Y t-p +θ1∈ t-1 +θ2∈ t-2 +...+θ q ∈ t-q +∈ t ; Where: Yt is the value of the time series at time t; c is a constant term; φ1,φ2,...,φ p is the coefficient of the autoregressive term; θ1,θ2,...,θ q is the coefficient of the moving average term; ∈ t is a white noise sequence.

5. The intelligent temperature sensing and control device according to claim 1, characterized in that: The adaptive regulation submodule automatically adjusts control parameters and optimizes control effects according to environmental changes and user needs through a model reference adaptive control algorithm. The specific steps include: a. Determine the reference model: Select an ideal reference model that describes the behavior of the system in an ideal situation without interference and uncertainty; b. Design the adaptive law: Determine the adaptive law that will adjust the controller parameters based on the error between the system output and the reference model output; c. Implement control: Use an adaptive controller to control the system and continuously adjust the controller parameters to reduce the error between the system output and the reference model output; d. Error feedback: Calculate the error between the system output and the reference model output, and use the error signal to update the controller parameters; e. Parameter update: Update controller parameters according to the adaptive law; The basic formula of the model reference adaptive control algorithm is: u(t) = -K(t) [r(t) - y(t)]; where u(t) is the control input at time t; K(t) is the controller gain matrix at time t; r(t) is the reference model output at time t; y(t) is the actual system output at time t; [r(t) - y(t)] is the tracking error at time t; The update formula of the adaptive law is: K ˙ (t)=-Γ·φ(t)·[r(t)-y(t)] T ; Where: K˙(t) is the update rate of the controller gain matrix K(t); Γ is a positive definite matrix that determines the convergence rate of the adaptive law; φ(t) is the adjustable parameter vector at time t; [r(t)-y(t)] T is the transpose of the tracking error.

6. The intelligent temperature sensing and control device according to claim 1, characterized in that: The intelligent decision submodule uses fuzzy logic control algorithm combined with the prediction model and the results of adaptive adjustment to make the optimal control decision, specifically including: a. Input fuzzification: converting clear input variables into fuzzy sets; b. Establish a fuzzy rule base: Based on expert knowledge and experience, establish a series of IF-THEN rules to describe the logic of temperature control; c. Fuzzy reasoning: Based on the fuzzified input and fuzzy rule base, fuzzy reasoning is performed to obtain fuzzy output; d. Defuzzification: converting the fuzzy output obtained by fuzzy reasoning into a clear control signal; In the fuzzification process, for the input variable x, the fuzzification process formula is: Where: μA(x) is the membership of the input variable x in the fuzzy set A; c is the center of the fuzzy set A; σ is the standard deviation of the fuzzy set A; Fuzzy reasoning adopts the Mamdani reasoning method, and its basic form is: IF xIS A THEN yIS B; where A and B are the fuzzy sets of input and output respectively; The defuzzification formula is: in: y defuzzified is the clear output after defuzzification; u i is the membership degree of the i-th fuzzy output set; y i is the center of the i-th fuzzy output set; n is the number of fuzzy output sets.

7. The intelligent temperature sensing and control device according to claim 1, characterized in that: The control algorithm submodule outputs a control signal to an actuator, including a heater or a cooler, to adjust the ambient temperature so that it approaches or reaches a set target; the entire process is a closed loop and will be repeated continuously to achieve continuous monitoring and precise control of the ambient temperature.

8. The intelligent temperature sensing and control device according to claim 1, characterized in that: The display and interaction module is further specifically used for: Receive temperature data from the data processing module and visualize the data through user interface design; Receive interactive information input by the user through a touch screen, keyboard or other input device, including setting a temperature target for power generation equipment, adjusting system parameters or viewing historical data; Update displayed information in real time to ensure users can obtain the latest system status; Record the user's operation instructions and pass them to the control algorithm submodule or data processing module for corresponding processing.

9. The intelligent temperature sensing and control device according to claim 1, characterized in that: The execution module is further specifically used for: receiving the control signal, wherein the control signal instructs the actuator how to act; The control signal is converted into an instruction understood by the actuator through the driving circuit, so that the actuator performs corresponding actions according to the instruction to change the temperature of the ambient power generation equipment; Monitors the status of the actuator to ensure it is functioning properly and provides fault feedback when necessary.

10. The intelligent temperature sensing and control device according to claim 1, characterized in that: The communication module establishes and maintains communication links, receives data transmission requests from other modules, and packages the data into a format suitable for transmission; during the data transmission process, the communication module is also responsible for data encryption, compression and decompression to ensure data security and transmission efficiency; after the receiving end receives the data, the communication module will unpack and verify it to ensure the integrity and correctness of the data, and finally pass the data to the corresponding module; The power management module converts it into the voltage and current required by the system through the power adapter; the power management module distributes the power to ensure that each module obtains the appropriate amount of power; The power management module monitors the power status, including voltage, current and power consumption, to prevent overload and short circuit; in the event of unstable power supply or external power failure, the power management module will start the backup power supply and notify the system to perform necessary power management operations.

Images