Temperature control method and system applied to heating and refrigerating constant temperature system

Abstract

The invention relates to the technical field of intelligent temperature control, in particular to a temperature control method and system applied to a heating and refrigerating constant-temperature system, which improve the precision and the stability of temperature control and improve the intelligence and the self-adaptability of the system. The method comprises the steps of obtaining temperature adjustment instruction information of a constant temperature system, performing target data extraction on the temperature adjustment instruction information, and obtaining a target temperature and a target timestamp of a load terminal of the constant temperature system; the real-time temperature of the load terminal is collected, and the temperature adjusting amount of the real-time temperature of the load terminal and the target temperature and the temperature adjusting duration are calculated; inputting the temperature regulation quantity and the temperature regulation duration into a pre-constructed load terminal temperature regulation pressure analysis model to obtain a temperature regulation pressure index; in response to the fact that the temperature regulation pressure index exceeds a preset threshold value, a time period between the target timestamp and the real-time time is converted into a temperature regulation time feature vector.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent temperature control, and in particular to a temperature control method and system applied to a heating and cooling constant temperature system. Background Art

[0002] With the advancement of science and technology and the development of industry, constant temperature systems are being used more and more widely in various fields, such as laboratories, hospitals, food processing, data centers, etc. These places have extremely high requirements for temperature control; the purpose of the constant temperature system is to maintain the temperature in a specific space within a constant range to ensure that the environment is suitable or the equipment operates normally; however, in actual applications, constant temperature systems often need to cope with complex and changeable working conditions and user needs, especially after receiving a temperature adjustment instruction, how to accurately and quickly achieve the set target temperature of the load terminal at a specific target time point, while considering the impact of changes in the surrounding environment, is a very challenging task.

[0003] Traditional constant temperature control methods are mostly based on simple time proportional integral differential algorithms or other linear control strategies, which are difficult to fully adapt to the nonlinear characteristics, dynamic disturbances and ambient temperature changes that occur during the temperature adjustment process, resulting in unsatisfactory temperature control effects. The temperature often fluctuates too much or the adjustment time is too long to meet the specific target time. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a temperature control method and system for a heating and cooling constant temperature system, which improves the accuracy and stability of temperature control and also improves the intelligence and adaptability of the system.

[0005] In a first aspect, the present invention provides a temperature control method for a heating and cooling constant temperature system, the method comprising:

[0006] Obtain temperature control instruction information of the constant temperature system, extract target data from the temperature control instruction information, and obtain the target temperature and target timestamp of the load terminal of the constant temperature system;

[0007] Collect the real-time temperature of the load terminal, calculate the temperature adjustment amount between the real-time temperature of the load terminal and the target temperature, and the temperature adjustment time;

[0008] The temperature adjustment amount and the temperature adjustment duration are input into a pre-built load terminal temperature adjustment pressure analysis model to obtain a temperature adjustment pressure index;

[0009] In response to the temperature adjustment pressure index exceeding a preset threshold, the time period between the target timestamp and the real time is converted into a temperature adjustment time feature vector, and input into a pre-built load terminal temperature influence behavior prediction model to obtain a quantized set of temperature influence behaviors of the load terminal;

[0010] Collect the surrounding environment impact parameter set of the load terminal, and convert the temperature impact behavior quantization set, the surrounding environment impact parameter set, the load terminal real-time temperature, the target temperature and the target timestamp into the temperature adjustment feature sequence of the temperature adjustment instruction information according to the preset data structure;

[0011] Input the temperature adjustment feature sequence into the pre-built constant temperature system temperature control parameter optimization model to obtain the working operation parameter set of the constant temperature system in response to the temperature adjustment instruction information;

[0012] The constant temperature system is controlled to adjust the temperature according to the set of working parameters.

[0013] Furthermore, the calculation formula of the temperature regulation pressure analysis model is:

[0014] P = k T f(ΔT)+k t h(t);

[0015] Where P represents the temperature regulation pressure index, k T and k t They represent the weight coefficients of temperature adjustment amount and temperature adjustment time respectively, ΔT represents the temperature adjustment amount, t represents the temperature adjustment time, and the f function and h function represent the standardized functions of the temperature adjustment amount and temperature adjustment time respectively.

[0016] Furthermore, the method for constructing the load terminal temperature regulation pressure analysis model includes:

[0017] Collect historical temperature control task data;

[0018] Clean the historical temperature control task data, remove outliers, fill in missing values, and perform standardization and normalization.

[0019] Perform feature engineering on the processed data to obtain the model training set;

[0020] Selecting a time series model as a basis of the model, the time series model includes a regression model, a moving average model, an autoregressive moving average model and an autoregressive integrated moving average model;

[0021] Use the training set to train and tune the model;

[0022] Evaluate the trained model using an independent test set;

[0023] Deploy the trained model to the constant temperature system.

[0024] Furthermore, the method for obtaining the temperature adjustment time feature vector includes:

[0025] Determine the time period between the target timestamp and the real time;

[0026] For the time period between the target timestamp and the real time, features that can reflect the time characteristics are extracted, and the extracted features include time interval features, time distribution features, time pattern features and time constraint features;

[0027] The extracted features are expressed in numerical form and arranged in a preset order to form a temperature adjustment time feature vector.

[0028] Furthermore, the method for obtaining the temperature impact behavior quantization set includes:

[0029] Construct a prediction model for load terminal temperature impact behavior;

[0030] The obtained temperature adjustment time feature vector is used as input to the load terminal temperature influence behavior prediction model. The model predicts the temperature behavior of the load terminal in this time period based on the input data and internal parameters.

[0031] Key features are extracted from the predicted temperature behavior, including maximum temperature deviation, minimum temperature deviation, average temperature change rate, temperature fluctuation frequency and temperature stability time ratio, to form a quantitative set of temperature-affecting behaviors.

[0032] Furthermore, the method of controlling the constant temperature system to adjust the temperature according to the set of working parameters includes:

[0033] Analyze the control instructions of each component of the constant temperature system from the set of working operation parameters;

[0034] Allocate the parsed control instructions to the corresponding control units according to the system architecture and device interface;

[0035] According to the assigned control instructions, the working status and parameters of each component of the constant temperature system are adjusted through the control system;

[0036] While executing control commands, continuously monitor the status of each device, the overall system operating parameters, and environmental impact parameters;

[0037] Based on real-time monitoring data, evaluate the current temperature control effect of the constant temperature system.

[0038] Furthermore, factors influencing the setting of the preset threshold include system response speed requirements, temperature fluctuation tolerance, system performance characteristics, environmental factor change patterns and intensity, historical data and experience accumulation.

[0039] On the other hand, the present application also provides a temperature control system for a heating and cooling constant temperature system, the system comprising:

[0040] The temperature control instruction parsing module is used to obtain the temperature control instruction information of the constant temperature system, extract the target data of the temperature control instruction information, and obtain the target temperature and target timestamp of the load terminal of the constant temperature system; the target timestamp is the time point at which the load terminal is required to reach the target temperature at the latest in the temperature control instruction information;

[0041] A real-time status monitoring module is used to collect the real-time temperature of the load terminal, calculate the temperature adjustment amount between the real-time temperature of the load terminal and the target temperature, and the temperature adjustment time; the temperature adjustment time is the time length between the target timestamp and the real-time time;

[0042] A temperature regulation pressure analysis module is used to input the temperature regulation amount and the temperature regulation duration as inputs to a pre-built load terminal temperature regulation pressure analysis model to obtain a temperature regulation pressure index;

[0043] The temperature influence behavior prediction module converts the time period between the target timestamp and the real time into a temperature adjustment time feature vector when the temperature adjustment pressure index exceeds the threshold, and inputs it into the pre-built load terminal temperature influence behavior prediction model to obtain a quantitative set of temperature influence behaviors of the load terminal;

[0044] A feature sequence construction module is used to collect a set of ambient environment impact parameters of the load terminal, and convert the temperature impact behavior quantization set, the ambient environment impact parameter set, the load terminal real-time temperature, the target temperature and the target timestamp into a temperature adjustment feature sequence of the temperature adjustment instruction information according to a preset data structure;

[0045] The temperature control parameter optimization module is used to input the temperature adjustment feature sequence into the pre-built constant temperature system temperature control parameter optimization model to obtain the working operation parameter set of the constant temperature system in response to the temperature adjustment instruction information;

[0046] The control execution module is used to control the constant temperature system to adjust the temperature according to the set of working operation parameters.

[0047] Compared with the prior art, the present invention has the following beneficial effects: the method can intelligently adjust the temperature to cope with different working conditions and user needs by analyzing the temperature adjustment pressure index and the temperature influence behavior, thereby improving the flexibility and adaptability of the temperature control system;

[0048] It can collect the real-time temperature of the load terminal and calculate the temperature adjustment amount and adjustment time to quickly respond to the temperature adjustment instruction and ensure that the load terminal reaches the set target temperature at a specific target time point;

[0049] By collecting and taking into account the surrounding environment influencing parameter set, it can better adapt to changes in the surrounding environment and improve the stability and reliability of the temperature control system;

[0050] Through the temperature control parameter optimization model, the system can optimize the working parameters to make the temperature control effect more ideal, reduce temperature fluctuations and adjustment time, thereby improving the efficiency and performance of the system;

[0051] In summary, this method can effectively solve the problems existing in traditional constant temperature control methods, improve the accuracy and stability of temperature control, and also improve the intelligence and adaptability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a flow chart of the present invention;

[0053] Figure 2 is a flow chart of a method for constructing a load terminal temperature regulation pressure analysis model;

[0054] Figure 3 It is a structural diagram of the temperature control system used in the heating and cooling constant temperature system. DETAILED DESCRIPTION

[0055] In the description of the present application, those skilled in the art should know that the present application can be implemented as a method, an apparatus, an electronic device, and a computer-readable storage medium. Therefore, the present application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), a combination of hardware and software. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable storage media, and the computer-readable storage medium contains computer program code.

[0056] The above-mentioned computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, computer-readable storage media can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device.

[0057] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws.

[0058] The present application describes the provided methods, devices, and electronic devices through flowcharts and / or block diagrams.

[0059] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer-readable program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified by the boxes in the flowchart and / or block diagram.

[0060] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product including functions / operations specified in the blocks in the flowchart and / or block diagram.

[0061] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0062] The present application is described below in conjunction with the drawings in the present application.

[0063] Embodiment 1: Figure 1 to Figure 2 As shown, the temperature control method applied to the heating and cooling constant temperature system of the present invention specifically includes the following steps:

[0064] S1. Obtain temperature control instruction information of a constant temperature system, extract target data from the temperature control instruction information, and obtain a target temperature and a target timestamp of a load terminal of the constant temperature system; the target timestamp is the time point at which the load terminal is required to reach the target temperature at the latest in the temperature control instruction information;

[0065] Through step S1, the intelligent temperature control system successfully and accurately analyzes the received temperature control instruction information and extracts the key target temperature and target timestamp data. These data provide clear target guidance and time constraints for subsequent real-time status monitoring, pressure analysis, behavior prediction, parameter optimization and final temperature control operations;

[0066] The method for obtaining the target temperature and the target timestamp includes:

[0067] S11, using sensors to monitor the temperature of the load terminal in the constant temperature system in real time, wherein the sensors include a temperature sensor, a humidity sensor, a pressure sensor, a flow sensor, and a positioning sensor;

[0068] S12, using wireless transmission technology to transmit the real-time collected temperature data to the control center;

[0069] S13, in the control center, using a data processing algorithm to parse and process the received temperature control instruction information, and extracting a target temperature and a target timestamp from the temperature control instruction information; the data processing algorithm includes data extraction, decoding and verification steps;

[0070] In this step, by extracting the target temperature and target timestamp data, the intelligent temperature control system can accurately control the constant temperature system to ensure that the load terminal reaches the specified temperature within the specified time, thereby improving the accuracy and reliability of temperature regulation; by monitoring the temperature in real time through sensors and transmitting the data to the control center, the state of the load terminal can be monitored in real time, and temperature changes and abnormal conditions can be discovered in time; based on real-time monitoring data, stress analysis and behavior prediction can be performed to identify the challenges and needs that may be faced during system operation, so as to take corresponding measures to deal with them; by parsing and processing the received temperature control instruction information, the system can optimize parameters according to the real-time monitoring data and the characteristics of the load terminal, adjust the temperature control strategy and parameter settings, and improve the performance and efficiency of the system; the target timestamp provides a time constraint for the temperature adjustment operation, so that the system can complete the temperature adjustment task within the specified time, improving the system's time management and execution efficiency;

[0071] In summary, this step enables the intelligent temperature control system to operate and manage the constant temperature system more efficiently, provides clear target guidance and time constraints, and thus improves the reliability, efficiency and performance of the system.

[0072] S2. Collect the real-time temperature of the load terminal, calculate the temperature adjustment amount of the real-time temperature of the load terminal and the target temperature, and the temperature adjustment time; the temperature adjustment time is the time length between the target timestamp and the real-time time;

[0073] Through step S2, the temperature state of the load terminal is monitored in real time, and the temperature adjustment amount between the target temperature and the temperature adjustment time from the current moment to the target time point is accurately calculated, which provides important basic information for subsequent pressure analysis, behavior prediction, parameter optimization and temperature adjustment operations, ensuring that the constant temperature system can quickly and accurately adjust the temperature according to the real-time working conditions and user needs to meet the temperature control requirements at a specific target time point;

[0074] The method for acquiring the real-time temperature of the load terminal includes:

[0075] S21. Deploy high-precision temperature sensors at the load terminals of the constant temperature system to monitor and record temperature changes at the locations in real time;

[0076] S22, the temperature data collected by the sensor is sent to the control center via wireless transmission;

[0077] S23, the control center filters and smoothes the received temperature data to remove noise interference;

[0078] Calculate the temperature difference between the current temperature of the load terminal and the target temperature, i.e., the temperature adjustment amount, from the acquired real-time temperature data;

[0079] The difference between the target timestamp and the real time, that is, the time length between the target time point and the current time, is used as the temperature adjustment duration. The calculation of the temperature adjustment duration needs to take into account the system response speed, load terminal characteristics, and the difference between the target temperature and the current temperature to ensure that the temperature adjustment task is completed within the specified time.

[0080] In this step, by deploying high-precision temperature sensors and monitoring temperature changes in real time, the real-time temperature state of the load terminal can be accurately obtained, so as to better grasp the actual situation of the temperature adjustment task; by calculating the temperature difference between the real-time temperature and the target temperature, the degree of deviation of the current temperature state relative to the target temperature can be accurately grasped, providing accurate reference data for subsequent temperature adjustment; using the difference between the target timestamp and the real-time time, the time length from the current time to the target time point can be determined as the temperature adjustment time; considering the system response speed, load terminal characteristics and the difference between the target temperature and the current temperature, it is ensured that the temperature adjustment task is completed within the specified time, thereby improving the timeliness and accuracy of the temperature adjustment;

[0081] In summary, the implementation of step S2 helps the constant temperature system to quickly and accurately adjust the temperature according to the real-time working conditions and user needs, thereby meeting the temperature control requirements at a specific target time point, and provides important basic data and guidance for the optimization and improvement of the system;

[0082] S3, taking the temperature adjustment amount and the temperature adjustment duration as inputs to a pre-built load terminal temperature adjustment pressure analysis model to obtain a temperature adjustment pressure index;

[0083] Step S3 can effectively utilize the real-time temperature data and target temperature information of the load terminal, combined with the pre-established model, to quickly and accurately analyze the pressure situation during the temperature adjustment process, providing an important reference for subsequent temperature adjustment, thereby improving the temperature control effect and performance of the constant temperature system;

[0084] The method for constructing the load terminal temperature regulation pressure analysis model includes:

[0085] S31. Collect historical temperature adjustment task data, which covers various working conditions and operating conditions, including initial temperature, target temperature, actual time to reach the target temperature, maximum temperature deviation, ambient temperature, humidity, load terminal heat capacity, constant temperature system performance index, control actions taken during the temperature adjustment process, and timestamps corresponding to these actions;

[0086] S32. Clean the historical temperature control task data, remove outliers, fill in missing values, and perform standardization and normalization to ensure the accuracy and consistency of the data;

[0087] S33, performing feature engineering on the processed data, extracting key features, and obtaining a model training set; the extracted features include temperature adjustment amount, temperature adjustment duration, temperature change rate, environmental influencing factors, load terminal characteristics, and constant temperature system performance indicators;

[0088] S34, selecting a time series model as the basis of the model, wherein the time series model includes a regression model, a moving average model, an autoregressive moving average model, and an autoregressive integrated moving average model;

[0089] S35. Use the training set to train and tune the model to ensure the generalization performance of the model on unknown data;

[0090] S36. Evaluate the trained model using an independent test set, calculate the error index between the predicted pressure index and the actually observed pressure index, and evaluate the prediction accuracy of the model;

[0091] S37, deploying the trained model to the constant temperature system for real-time calculation of the temperature regulation pressure index of the new temperature regulation task;

[0092] The calculation formula of the temperature regulation pressure analysis model is:

[0093] P = k T f(ΔT)+k t h(t);

[0094] Where P represents the temperature regulation pressure index, k T and k t They represent the weight coefficients of temperature adjustment amount and temperature adjustment time respectively, ΔT represents the temperature adjustment amount, t represents the temperature adjustment time, and the f function and h function represent the standardized functions of the temperature adjustment amount and temperature adjustment time respectively.

[0095] In this step, through the collection and analysis of historical temperature control task data, the model can better understand the temperature control process under different working conditions, thereby providing more accurate temperature control guidance for the constant temperature system; it helps to optimize the temperature control algorithm, improve the temperature control effect and performance of the constant temperature system, and ensure that the load terminal can achieve the expected temperature target under various working conditions; by taking the temperature adjustment amount and adjustment time as input, combined with the real-time load terminal temperature data and target temperature information, the model can quickly and accurately analyze the pressure situation in the current temperature adjustment process; through cleaning, feature engineering and model training of historical data, the model can predict the pressure index in the temperature adjustment process and identify potential temperature adjustment problems and challenges in advance; using an independent test set to evaluate the trained model can effectively evaluate the generalization ability and prediction accuracy of the model on unknown data; ensuring the reliability and robustness of the model in practical applications, and improving the credibility and practicality of the system;

[0096] In summary, the construction and application of the load terminal temperature regulation pressure analysis model can effectively improve the temperature control effect and performance of the constant temperature system, realize the intelligent monitoring and optimization of the temperature regulation process, and provide important technical support and guarantee for industrial production and equipment operation;

[0097] S4, in response to the temperature adjustment pressure index exceeding a preset threshold, converting the time period between the target timestamp and the real time into a temperature adjustment time feature vector, and inputting the vector into a pre-built load terminal temperature influence behavior prediction model to obtain a quantized set of temperature influence behaviors of the load terminal;

[0098] Through step S4, a refined prediction is performed on the temperature control task with a large temperature control pressure, and a quantitative set of temperature influence behaviors of the load terminal during the temperature control process is generated. The set contains rich temperature behavior information, which provides detailed prediction data support for the subsequent temperature control parameter optimization model, and helps the system to adjust the control strategy more accurately under complex working conditions, ensuring that the set target temperature of the load terminal is achieved at a specific target time point;

[0099] The method for obtaining the temperature adjustment time feature vector includes:

[0100] S411, determining a time period between the target timestamp and the real time, the time period being the total time the constant temperature system needs to perform temperature adjustment;

[0101] S412, extracting features that can reflect time characteristics for the time period between the target timestamp and the real time, the extracted features including time interval features, time distribution features, time pattern features, and time constraint features;

[0102] S413, expressing the extracted features in numerical form and arranging them in a preset order to form a temperature adjustment time feature vector of a fixed dimension;

[0103] The method for obtaining the temperature impact behavior quantization set includes:

[0104] S421, constructing a load terminal temperature influence behavior prediction model, which can predict the temperature behavior of the load terminal under a given time characteristic according to an input temperature adjustment time characteristic vector;

[0105] S422, taking the acquired temperature adjustment time feature vector as input and sending it to the load terminal temperature influence behavior prediction model, the model predicts the temperature behavior of the load terminal in the time period according to the input data and internal parameters;

[0106] S423, extracting key features from the predicted temperature behavior, including maximum temperature deviation, minimum temperature deviation, average temperature change rate, temperature fluctuation frequency and temperature stable time ratio, to form a quantitative set of temperature impact behaviors;

[0107] The factors affecting the setting of the preset threshold include:

[0108] System response speed requirements: The preset threshold setting should match the response speed requirements of the constant temperature system to the temperature control command; if the application scenario has a high demand for rapid temperature adjustment (such as rapid heating or cooling during the experiment), the threshold should be set lower so that the system can quickly start the prediction model when the temperature adjustment pressure increases slightly and plan countermeasures in advance; on the contrary, if a relatively gentle temperature change process is allowed, the threshold can be appropriately increased to reduce unnecessary prediction operations and system resource consumption;

[0109] Temperature fluctuation tolerance. Different application scenarios have different tolerances for temperature fluctuations. For example, in a precision laboratory or high-precision manufacturing environment, very small temperature fluctuations may be required. In this case, the threshold should be set low to identify and suppress any trend that may cause the temperature to deviate significantly from the target as early as possible. In some scenarios that have a certain tolerance for short-term temperature fluctuations (such as some data centers or some food processing links), the threshold can be set relatively high, and the prediction model is triggered only when the temperature regulation pressure increases significantly.

[0110] The system's own performance characteristics. The preset threshold should take into account the performance characteristics of the constant temperature system, such as heating / cooling capacity, response speed, steady-state error, etc. If the system has strong performance, fast response and high control accuracy, a higher threshold can be set to fully utilize the system's regulation capabilities; on the contrary, if the system performance is limited or there is obvious lag, the threshold should be appropriately lowered to deal with possible temperature regulation difficulties in advance;

[0111] The regularity and intensity of environmental factors, the regularity and intensity of ambient temperature changes and their impact on the constant temperature system are also key factors to be considered when setting the threshold. In scenarios where the ambient temperature fluctuates frequently and with large amplitudes or there are obvious seasonal and diurnal changes, the threshold should be set lower to timely capture and respond to the pressure of environmental changes on temperature regulation. On the contrary, if the environmental conditions are relatively stable, the threshold can be appropriately increased.

[0112] Historical data and experience accumulation, based on historical operating data and practical experience, can analyze the relationship between the temperature regulation pressure index and the actual temperature control effect, so as to determine a threshold that can provide effective warning but not be too sensitive.

[0113] In this step, by presetting the threshold of the temperature regulation pressure index and extracting the temperature regulation time feature vector, the temperature behavior of the load terminal during the temperature regulation process can be accurately predicted, providing detailed prediction data support for the subsequent optimization of temperature control parameters; according to the method of obtaining the quantitative set of temperature influence behaviors, the control strategy can be adjusted more accurately under complex working conditions, and the challenges brought by temperature changes can be responded to in time to improve the temperature control effect; by making refined predictions of temperature regulation tasks with greater temperature regulation pressure, it can better ensure that the set target temperature of the load terminal is achieved at a specific target time point, thereby improving the reliability and stability of the system; through accurate temperature prediction and optimized temperature control strategy, unnecessary energy consumption can be reduced and energy utilization efficiency can be improved; through the prediction model and feature extraction method, the system has a certain level of intelligence, and can automatically adjust the control strategy according to different working conditions and needs, thereby improving the system's adaptability and intelligence level.

[0114] S5, collecting the surrounding environment impact parameter set of the load terminal, and converting the temperature impact behavior quantization set, the surrounding environment impact parameter set, the load terminal real-time temperature, the target temperature and the target timestamp into the temperature adjustment feature sequence of the temperature adjustment instruction information according to the preset data structure;

[0115] Step S5 involves integrating the temperature influence behavior quantization set, the surrounding environment influence parameter set, the load terminal real-time temperature, the target temperature and the target timestamp into the temperature adjustment feature sequence of the temperature adjustment instruction information. This sequence comprehensively and systematically reflects the characteristics of the temperature adjustment task and the environmental influence factors, and provides rich and accurate input data for the subsequent temperature control parameter optimization model, which helps the model make more accurate and efficient temperature control decisions.

[0116] The method for acquiring the temperature adjustment characteristic sequence comprises:

[0117] S51, collecting a set of parameters affecting the surrounding environment of the load terminal, and preparing a set of temperature impact behavior quantification, real-time temperature of the load terminal, target temperature and target timestamp data;

[0118] S52, presetting the data structure of the temperature adjustment feature sequence, clarifying each component and its arrangement order;

[0119] S53, according to the preset data structure, orderly integrate the prepared data to obtain a temperature adjustment feature sequence;

[0120] Check whether the constructed feature sequence contains all the fields required in the preset data structure, whether the data types of each field are correct, and whether the data values ​​are within a reasonable range.

[0121] In this step, the temperature regulation feature sequence can comprehensively and systematically reflect the characteristics of the temperature regulation task and environmental influencing factors, providing a more accurate basis for the subsequent optimization of temperature control parameters; by integrating multiple key information into the temperature regulation feature sequence, it provides rich and accurate input data for the temperature control parameter optimization model; such data input helps the model make more accurate and efficient temperature control decisions, improving the performance and efficiency of the overall system; the construction method of the temperature regulation feature sequence can guide the subsequent temperature control parameter optimization process; through the analysis of the sequence, the key factors affecting the temperature regulation effect can be identified, providing guidance and basis for optimizing the temperature control parameters; the last step checks to ensure that the constructed feature sequence contains all the fields required in the preset data structure, the data type of each field is correct, and the data value is within a reasonable range; this helps to ensure the accuracy and completeness of the data and improves the reliability and stability of the model.

[0122] S6, inputting the temperature adjustment feature sequence into a pre-built constant temperature system temperature control parameter optimization model to obtain a set of working operation parameters of the constant temperature system in response to the temperature adjustment instruction information;

[0123] Through step S6, the intelligent temperature control system uses the pre-built temperature control parameter optimization model to generate a set of working parameters for a specific temperature control task according to the temperature control feature sequence. These parameter sets comprehensively consider the characteristics of the temperature control task and the environmental impact, and are intended to guide the constant temperature system to achieve the set target temperature of the load terminal at a specific target time point in the most efficient and energy-saving manner; the following is a detailed description of step S6:

[0124] Construct a temperature control parameter optimization model for the constant temperature system to optimize the operating parameters of the constant temperature system;

[0125] The temperature regulation feature sequence contains information such as the temperature influence behavior of the load terminal, the surrounding environment influence parameters, the real-time temperature, the target temperature and the timestamp. After processing and integration, it is input into the temperature control parameter optimization model as input data;

[0126] The temperature control parameter optimization model of the constant temperature system predicts the set of working parameters of the constant temperature system in response to the temperature adjustment instruction according to the input temperature adjustment feature sequence;

[0127] The method for constructing the temperature control parameter optimization model of the constant temperature system includes:

[0128] S61, collecting historical operating data of the constant temperature system under different working conditions, and preprocessing the data, including removing noise, filling missing values ​​and normalizing;

[0129] S62, selecting a machine learning model as the basic architecture of the model, wherein the machine learning model includes a regression model, a support vector machine, a random forest, a recurrent neural network, and a long short-term memory network;

[0130] S63, extracting features from historical operation data to obtain a training set for the model;

[0131] S64. Use the training set to train and optimize the constructed model to improve the predictive ability and robustness of the model;

[0132] S65. Use the validation set to evaluate the trained model and assess its performance and generalization ability.

[0133] S66. Deploy the trained model to the actual constant temperature system for real-time temperature control.

[0134] In this step, the optimized working parameter set can enable the constant temperature system to respond to the temperature control instruction in a more efficient manner, thereby reducing energy consumption; by considering factors such as the temperature influencing behavior of the load terminal and the parameters influencing the surrounding environment, the model can adjust the working mode of the constant temperature system and improve energy utilization; the temperature control parameter optimization model can quickly predict the working parameters of the constant temperature system based on the real-time temperature adjustment feature sequence, thereby accelerating the system's response speed to the temperature control instruction; it helps to ensure that the load terminal reaches the set target temperature at a specific target time point; through the collection of historical operating data and the training and optimization of the model, the temperature control parameter optimization model can more accurately predict the working parameters of the constant temperature system under different working conditions, thereby improving the stability and accuracy of temperature control; it helps to reduce temperature fluctuations so that the system can maintain the target temperature more stably; through the selection and training of the machine learning model, the temperature control parameter optimization model can gradually learn and optimize, thereby improving the intelligence level of the system;

[0135] In summary, the temperature control parameter optimization model of the constant temperature system constructed in step S6 can effectively improve the performance and efficiency of the constant temperature system, achieve faster, more stable and more energy-saving temperature control, and thus meet various complex and changeable temperature adjustment requirements.

[0136] S7, controlling the constant temperature system to adjust the temperature according to the set of working parameters;

[0137] Step S7 is the last step of the entire temperature control method, that is, to control the constant temperature system in real time according to the set of operating parameters calculated by the temperature control parameter optimization model of the constant temperature system, so as to achieve the load terminal reaching the set target temperature at a specific target time point;

[0138] The method for controlling the constant temperature system to adjust the temperature according to the set of working parameters includes:

[0139] S71, parsing the control instructions of each component of the constant temperature system from the working operation parameter set;

[0140] S72, distributing the parsed control instructions to corresponding control units according to the system architecture and device interface, to ensure that each device receives a clear and accurate control signal;

[0141] S73, adjusting the working status and parameters of each component of the constant temperature system through the control system according to the assigned control instructions;

[0142] S74. While executing the control command, continuously monitor the status of each device, the overall system operating parameters, and the environmental impact parameters;

[0143] S75. Evaluate the current temperature control effect of the constant temperature system based on the real-time monitoring data.

[0144] In this step, by parsing the working operation parameter set and assigning the control instructions to the corresponding control unit, it is ensured that each device works as expected; it can ensure that the constant temperature system can be adjusted according to the set target temperature and time point to meet user needs; by adjusting the working status and parameters of each component of the constant temperature system in real time, the system can be optimized according to actual conditions; thereby improving the energy efficiency ratio of the system, reducing energy consumption, and extending the service life of the equipment; while executing the control command, continuously monitoring the status of each device, the overall system operating parameters and environmental impact parameters; it can help to promptly discover problems in the system and take corresponding measures to adjust them to ensure the stability and reliability of the system; based on the real-time monitoring data, the current temperature control effect of the constant temperature system can be evaluated, and deviations in the temperature adjustment process can be promptly discovered and corrected, thereby improving the temperature control effect; this continuous monitoring and adjustment can ensure that the system can stably maintain within the target temperature range, reduce temperature fluctuations, and improve the accuracy and stability of temperature regulation;

[0145] In summary, the S7 step can help the constant temperature system achieve precise control, optimized operation, real-time monitoring and feedback, and improve the temperature control effect, thereby ensuring that the system can operate stably and reliably and meet specific temperature regulation requirements.

[0146] Embodiment 2: Figure 3 As shown, the temperature control system applied to the heating and cooling constant temperature system of the present invention specifically includes the following modules:

[0147] The temperature control instruction parsing module is used to obtain the temperature control instruction information of the constant temperature system, extract the target data of the temperature control instruction information, and obtain the target temperature and target timestamp of the load terminal of the constant temperature system; the target timestamp is the time point at which the load terminal is required to reach the target temperature at the latest in the temperature control instruction information;

[0148] A real-time status monitoring module is used to collect the real-time temperature of the load terminal, calculate the temperature adjustment amount between the real-time temperature of the load terminal and the target temperature, and the temperature adjustment time; the temperature adjustment time is the time length between the target timestamp and the real-time time;

[0149] A temperature regulation pressure analysis module is used to input the temperature regulation amount and the temperature regulation duration as inputs to a pre-built load terminal temperature regulation pressure analysis model to obtain a temperature regulation pressure index;

[0150] The temperature influence behavior prediction module converts the time period between the target timestamp and the real time into a temperature adjustment time feature vector when the temperature adjustment pressure index exceeds the threshold, and inputs it into the pre-built load terminal temperature influence behavior prediction model to obtain a quantitative set of temperature influence behaviors of the load terminal;

[0151] A feature sequence construction module is used to collect a set of ambient environment impact parameters of the load terminal, and convert the temperature impact behavior quantization set, the ambient environment impact parameter set, the load terminal real-time temperature, the target temperature and the target timestamp into a temperature adjustment feature sequence of the temperature adjustment instruction information according to a preset data structure;

[0152] The temperature control parameter optimization module is used to input the temperature adjustment feature sequence into the pre-built constant temperature system temperature control parameter optimization model to obtain the working operation parameter set of the constant temperature system in response to the temperature adjustment instruction information;

[0153] The control execution module is used to control the constant temperature system to adjust the temperature according to the set of working operation parameters.

[0154] The system can intelligently analyze the urgency of temperature regulation through the real-time status monitoring module and the temperature regulation pressure analysis module, and adjust the temperature control strategy according to the situation to respond to temperature regulation instructions quickly and accurately;

[0155] The feature sequence building module collects the environmental impact parameters around the load terminal, allowing the system to take into account the impact of ambient environmental changes on temperature regulation, improving the system's adaptability under complex and changing environmental conditions;

[0156] The temperature impact behavior prediction module can predict the temperature impact behavior of the load terminal and further optimize the temperature control strategy, so that the system can better cope with the nonlinear characteristics and dynamic disturbances that occur during the temperature regulation process;

[0157] The temperature control parameter optimization module uses the pre-built constant temperature system temperature control parameter optimization model to optimize the temperature control parameters, so that the system can adjust the temperature more efficiently when responding to the temperature adjustment command, thereby improving the temperature control effect and adjustment speed;

[0158] In summary, the system can effectively meet the application needs of constant temperature systems in various fields, improve the accuracy and stability of temperature control, and also improve the intelligence and adaptability of the system.

[0159] The various variations and specific embodiments of the temperature control method applied to the heating and cooling constant temperature system in the aforementioned embodiment 1 are also applicable to the temperature control system applied to the heating and cooling constant temperature system in this embodiment. Through the aforementioned detailed description of the temperature control method applied to the heating and cooling constant temperature system, those skilled in the art can clearly know the implementation method of the temperature control system applied to the heating and cooling constant temperature system in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0160] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A temperature control method applied to a heating and cooling constant temperature system, characterized in that: The method comprises: Obtain temperature control instruction information of the constant temperature system, extract target data from the temperature control instruction information, and obtain the target temperature and target timestamp of the load terminal of the constant temperature system; Collect the real-time temperature of the load terminal, calculate the temperature adjustment amount between the real-time temperature of the load terminal and the target temperature, and the temperature adjustment time; The temperature adjustment amount and the temperature adjustment duration are input into a pre-built load terminal temperature adjustment pressure analysis model to obtain a temperature adjustment pressure index; In response to the temperature adjustment pressure index exceeding a preset threshold, the time period between the target timestamp and the real time is converted into a temperature adjustment time feature vector, and input into a pre-built load terminal temperature influence behavior prediction model to obtain a quantized set of temperature influence behaviors of the load terminal; Collect the surrounding environment impact parameter set of the load terminal, and convert the temperature impact behavior quantization set, the surrounding environment impact parameter set, the load terminal real-time temperature, the target temperature and the target timestamp into the temperature adjustment feature sequence of the temperature adjustment instruction information according to the preset data structure; Input the temperature adjustment feature sequence into the pre-built constant temperature system temperature control parameter optimization model to obtain the working operation parameter set of the constant temperature system in response to the temperature adjustment instruction information; The constant temperature system is controlled to adjust the temperature according to the set of working parameters.

2. The temperature control method for a heating and cooling constant temperature system according to claim 1, characterized in that: The calculation formula of the temperature regulation pressure analysis model is: P=k T ·f(ΔT)+k t ·h(t); Where P represents the temperature regulation pressure index, k T and k t They represent the weight coefficients of temperature adjustment amount and temperature adjustment time respectively, ΔT represents the temperature adjustment amount, t represents the temperature adjustment time, and the f function and h function represent the standardized functions of the temperature adjustment amount and temperature adjustment time respectively.

3. The temperature control method for a heating and cooling constant temperature system according to claim 2, characterized in that: The method for constructing the load terminal temperature regulation pressure analysis model includes: Collect historical temperature control task data; Clean the historical temperature control task data, remove outliers, fill in missing values, and perform standardization and normalization. Perform feature engineering on the processed data to obtain the model training set; Selecting a time series model as a basis of the model, the time series model includes a regression model, a moving average model, an autoregressive moving average model and an autoregressive integrated moving average model; Use the training set to train and tune the model; Evaluate the trained model using an independent test set; Deploy the trained model to the constant temperature system.

4. The temperature control method for a heating and cooling constant temperature system according to claim 1, characterized in that: The method for obtaining the temperature adjustment time feature vector includes: Determine the time period between the target timestamp and the real time; For the time period between the target timestamp and the real time, features that can reflect the time characteristics are extracted, and the extracted features include time interval features, time distribution features, time pattern features and time constraint features; The extracted features are expressed in numerical form and arranged in a preset order to form a temperature adjustment time feature vector.

5. The temperature control method for a heating and cooling constant temperature system according to claim 1, characterized in that: The method for obtaining the temperature impact behavior quantization set includes: Construct a prediction model for load terminal temperature impact behavior; The obtained temperature adjustment time feature vector is used as input to the load terminal temperature influence behavior prediction model. The model predicts the temperature behavior of the load terminal in the time period based on the input data and internal parameters. Key features are extracted from the predicted temperature behavior, including maximum temperature deviation, minimum temperature deviation, average temperature change rate, temperature fluctuation frequency and temperature stability time ratio, to form a quantitative set of temperature-affecting behaviors.

6. The temperature control method for a heating and cooling constant temperature system according to claim 1, characterized in that: The method for controlling the constant temperature system to adjust the temperature according to the set of working parameters includes: Analyze the control instructions of each component of the constant temperature system from the set of working operation parameters; Allocate the parsed control instructions to the corresponding control units according to the system architecture and device interface; According to the assigned control instructions, the working status and parameters of each component of the constant temperature system are adjusted through the control system; While executing control commands, continuously monitor the status of each device, the overall system operating parameters, and environmental impact parameters; Based on real-time monitoring data, evaluate the current temperature control effect of the constant temperature system.

7. The temperature control method for a heating and cooling constant temperature system according to claim 1, characterized in that: The factors affecting the setting of the preset threshold include system response speed requirements, temperature fluctuation tolerance, system performance characteristics, environmental factor change patterns and intensity, historical data and experience accumulation.

8. A temperature control system for a heating and cooling constant temperature system, characterized in that: The system comprises: The temperature control instruction parsing module is used to obtain the temperature control instruction information of the constant temperature system, extract the target data of the temperature control instruction information, and obtain the target temperature and target timestamp of the load terminal of the constant temperature system; the target timestamp is the time point at which the load terminal is required to reach the target temperature at the latest in the temperature control instruction information; A real-time status monitoring module is used to collect the real-time temperature of the load terminal, calculate the temperature adjustment amount between the real-time temperature of the load terminal and the target temperature, and the temperature adjustment time; the temperature adjustment time is the time length between the target timestamp and the real-time time; A temperature regulation pressure analysis module is used to input the temperature regulation amount and the temperature regulation duration as inputs to a pre-built load terminal temperature regulation pressure analysis model to obtain a temperature regulation pressure index; The temperature influence behavior prediction module converts the time period between the target timestamp and the real time into a temperature adjustment time feature vector when the temperature adjustment pressure index exceeds the threshold, and inputs it into the pre-built load terminal temperature influence behavior prediction model to obtain a quantitative set of temperature influence behaviors of the load terminal; A feature sequence construction module is used to collect a set of ambient environment impact parameters of the load terminal, and convert the temperature impact behavior quantization set, the ambient environment impact parameter set, the load terminal real-time temperature, the target temperature and the target timestamp into a temperature adjustment feature sequence of the temperature adjustment instruction information according to a preset data structure; The temperature control parameter optimization module is used to input the temperature adjustment feature sequence into the pre-built constant temperature system temperature control parameter optimization model to obtain the working operation parameter set of the constant temperature system in response to the temperature adjustment instruction information; The control execution module is used to control the constant temperature system to adjust the temperature according to the set of working operation parameters.

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