Method and system for controlling temperature return of constant-temperature incubator

Abstract

The invention provides a control method and system for temperature return of a constant-temperature incubator. Wherein the real-time temperature in the constant-temperature incubator is monitored, external environment data is combined, the real-time temperature and a preset threshold value are compared and analyzed, and a temperature return starting signal is generated; based on the temperature return starting signal, a control algorithm based on self-adaptive dynamic programming is adopted, a temperature gradient equalization technology is applied, and a heating control instruction is generated; based on the heating control instruction, constructing a three-dimensional temperature distribution model by using a temperature field reconstruction algorithm, predicting a temperature change trend through a recurrent neural network, and generating temperature prediction information; and on the basis of the temperature prediction information, after a set temperature is reached, fine temperature control is automatically switched to, environmental influence analysis is carried out, measures are taken to offset adverse factors, and a temperature control result is generated. According to the control method and system for temperature return of the constant-temperature incubator, the temperature control precision and the response speed are remarkably improved, long-term stability is ensured, and a reliable and efficient environment is provided for biological experiments.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of culture devices, and in particular to a control method and system for temperature recovery in a constant temperature incubator. Background Art

[0002] With the rapid development of biotechnology and life sciences, constant temperature incubators are increasingly used in laboratories and industrial production. In order to ensure the accuracy of experimental results and the stability of production, higher requirements are placed on the temperature control of constant temperature incubators. Constant temperature incubators are mainly used in biological cell culture, microbial research, and drug development, and need to maintain a stable temperature environment within a specific temperature range. In order to meet this demand, it is necessary to monitor the temperature in the constant temperature incubator in real time, and combine the external environment data with the preset temperature threshold for comparison and analysis, generate a temperature return start signal, and ensure rapid temperature recovery and long-term stability.

[0003] At present, most constant temperature incubators use traditional PID control algorithms for temperature control. This control method is simple and easy to implement, but its control accuracy and response speed are limited when faced with complex and changeable external environments and uneven temperature gradients. In addition, existing temperature control solutions usually lack the ability to predict temperature change trends and cannot take measures in advance to prevent temperature fluctuations.

[0004] Traditional PID control algorithms have insufficient control accuracy when dealing with complex environmental changes, and are prone to temperature fluctuations, affecting the accuracy of experimental results and the stability of production; existing temperature control schemes have a slow response speed during temperature recovery, especially when the temperature changes are large or the environmental conditions are complex, and it is difficult to quickly recover to the set temperature; existing control schemes lack the ability to predict temperature change trends and are unable to detect potential temperature fluctuations in advance, resulting in poor control effects; existing heating methods often lead to uneven temperature distribution, especially in large incubators, which are prone to local overheating or cold spots, affecting the culture effect. Summary of the invention

[0005] The embodiments of the present application provide a control method and system for the temperature recovery of a constant temperature incubator, so as to solve the problem of limited control accuracy and response speed in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a method for controlling the temperature return of a constant temperature incubator, comprising: Use the temperature sensor to monitor the real-time temperature in the constant temperature incubator, combine it with the external environment data, compare and analyze it with the preset temperature threshold, and generate a temperature recovery start signal; Based on the temperature recovery start signal, a control algorithm based on adaptive dynamic programming is used to automatically adjust the power output of the heating element, and a temperature gradient balancing technology is applied to ensure uniform temperature distribution during the heating process, and a heating control instruction is generated; Based on the heating control instruction, a temperature field reconstruction algorithm is used to construct a three-dimensional temperature distribution model, and a recursive neural network is used to predict the temperature change trend, accelerate the temperature recovery process, and generate temperature prediction information; Based on the temperature prediction information, the system automatically switches to fine temperature control after reaching the set temperature, performs an environmental impact analysis on the external environment, takes measures to offset adverse factors, ensures long-term stability of the temperature in the constant temperature incubator, and generates temperature control results.

[0007] In a second aspect, an embodiment of the present application provides a control system for returning temperature to a constant temperature incubator, comprising: The monitoring module uses a temperature sensor to monitor the real-time temperature in the constant temperature incubator, combines external environmental data, compares and analyzes it with the preset temperature threshold, and generates a temperature recovery start signal; A control module, based on the temperature recovery start signal, adopts a control algorithm based on adaptive dynamic programming to automatically adjust the power output of the heating element, applies a temperature gradient balancing technology to ensure uniform temperature distribution during the heating process, and generates a heating control instruction; A prediction module, based on the heating control instruction, uses a temperature field reconstruction algorithm to construct a three-dimensional temperature distribution model, predicts the temperature change trend through a recursive neural network, accelerates the temperature recovery process, and generates temperature prediction information; The analysis module, based on the temperature prediction information, automatically switches to fine temperature control after reaching the set temperature, performs environmental impact analysis on the external environment, takes measures to offset adverse factors, ensures the long-term stability of the temperature in the constant temperature incubator, and generates temperature control results.

[0008] In a third aspect, an embodiment of the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a control method for reheating a constant temperature incubator as described in the first aspect.

[0009] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a control method for returning the temperature of a constant temperature incubator as described in the first aspect.

[0010] In an embodiment of the present application, the real-time temperature in the constant temperature incubator is monitored, combined with external environmental data, and compared and analyzed with a preset threshold value to generate a temperature recovery start signal; based on the temperature recovery start signal, a control algorithm based on adaptive dynamic programming is adopted, and the temperature gradient balancing technology is applied to generate a heating control instruction; based on the heating control instruction, a three-dimensional temperature distribution model is constructed using a temperature field reconstruction algorithm, and the temperature change trend is predicted through a recursive neural network to generate temperature prediction information; based on the temperature prediction information, after reaching the set temperature, fine temperature control is automatically switched to perform an environmental impact analysis, and measures are taken to offset adverse factors to generate a temperature control result. By real-time monitoring of the temperature in the constant temperature incubator and comparing and analyzing it with the preset temperature threshold, a temperature recovery start signal is generated to ensure the timeliness and accuracy of temperature control; based on the temperature recovery start signal, an adaptive dynamic programming control algorithm is used to automatically adjust the power output of the heating element, thereby improving the system's adaptability and control accuracy; temperature gradient balancing technology is applied to ensure uniform temperature distribution during the heating process, avoid local overheating or cold spots, and improve the consistency and reliability of the culture environment; recursive neural networks are used to predict temperature change trends, accelerate the temperature recovery process, shorten the time required to reach the set temperature, and improve the system's response speed and efficiency; after reaching the set temperature, the system automatically switches to fine temperature control mode, conducts environmental impact analysis on the external environment, and takes measures to offset adverse factors to ensure long-term stability of the temperature in the constant temperature incubator and provide a high-quality culture environment; the entire process realizes fully automated control from temperature monitoring, temperature recovery start, heating control to temperature prediction, reduces manual intervention, and improves the system's intelligence level and operating efficiency; temperature sensor monitoring data and external environment data are combined with advanced algorithms and technologies to achieve data-driven optimization control and improve the overall performance and reliability of the system.

[0011] Based on the temperature recovery start signal, the control algorithm of adaptive dynamic programming is used to analyze the internal and external environmental conditions and historical heating data, and calculate the optimal power output plan; the temperature gradient balancing technology is applied to finely control the power output of the heating element and generate the temperature gradient balancing parameters; the power output of the heating element is further optimized to ensure that the temperature gradient is minimized and generate the optimized heating strategy; the specific value of the power output of the heating element is determined, the working plan of the heating element is formulated, and the heating control instructions are generated. Through the control algorithm of adaptive dynamic programming, combined with the internal and external environmental conditions and historical heating data, the optimal power output value of the heating element is calculated, which improves the accuracy and response speed of temperature control; the temperature gradient balancing technology is applied to finely control the power output of the heating element to ensure uniform temperature distribution during the heating process, avoid local overheating or cold spots, and improve the consistency and reliability of the culture environment; based on the temperature gradient balancing parameters, the power output of the heating element is further optimized to ensure that the temperature gradient is minimized during the heating process, generate the optimized heating strategy, and improve the heating efficiency and safety; by generating heating control instructions, the working plan of the heating element is automatically adjusted to ensure the efficiency and safety of the heating process, reduce manual intervention, and improve the intelligence level of the system.

[0012] Start the temperature data acquisition system, collect temperature data of each point inside the constant temperature incubator, and generate a real-time temperature data set; use the temperature field reconstruction algorithm to build a three-dimensional temperature distribution model inside the constant temperature incubator and generate a three-dimensional temperature distribution map; combine the real-time temperature data set with historical heating records, use recursive neural networks to predict future temperature change trends, and generate temperature change prediction reports; correct heating control instructions through real-time feedback, accelerate the temperature recovery process, and generate temperature prediction information. By starting the temperature data acquisition system, the temperature data of each point inside the constant temperature incubator is collected in real time to ensure the accuracy and real-time nature of the data; use the temperature field reconstruction algorithm to build a three-dimensional temperature distribution model inside the constant temperature incubator, generate a detailed three-dimensional temperature distribution map, and provide intuitive temperature distribution visualization; combine the real-time temperature data set with historical heating records, use recursive neural networks to predict future temperature change trends, generate temperature change prediction reports, discover potential temperature fluctuations in advance, and improve the system's predictability and response speed; correct heating control instructions through real-time feedback, accelerate the temperature recovery process, ensure that the temperature quickly returns to the set value, and improve the system's control accuracy and stability.

[0013] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0015] Figure 1 A flow chart of a control method for returning the temperature of a constant temperature incubator provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a control system for a constant temperature incubator provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0017] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0018] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0019] Figure 1 A flow chart of a control method for returning the temperature of a constant temperature incubator is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes: 101. Use a temperature sensor to monitor the real-time temperature in the constant temperature incubator, combine it with external environmental data, compare and analyze it with the preset temperature threshold, and generate a temperature recovery start signal; The temperature sensor collects the temperature data of each point inside the constant temperature incubator in real time; the external sensor or weather station collects the external environment data such as room temperature, humidity, air pressure, etc. in real time; the control system compares and analyzes the collected temperature data with the preset temperature threshold; if the temperature of any point is detected to be lower than the minimum temperature threshold or higher than the maximum temperature threshold, the control system generates a temperature recovery start signal; the temperature recovery start signal triggers the heating element to start, so as to restore the temperature in the constant temperature incubator to the set range.

[0020] Suppose there is a biological laboratory that needs to use a constant temperature incubator to maintain the environmental temperature for cell culture; Temperature sensors are installed at multiple key locations of the constant temperature incubator, such as the top, middle and bottom, to collect temperature data at various points in the box in real time; environmental sensors are installed inside the laboratory to monitor external environmental data such as room temperature, humidity and air pressure; temperature thresholds are preset in the control system to ensure that the temperature in the incubator remains within this range; temperature sensors and environmental sensors continuously collect data and transmit the data to the control system; the control system compares and analyzes the real-time collected temperature data with the preset temperature thresholds, and if it is detected that the temperature at any point exceeds the threshold, the control system will immediately generate a temperature recovery start signal; once it is detected that the temperature exceeds the preset range, the control system will generate a temperature recovery start signal to trigger the heating element to start; after receiving the temperature recovery start signal, the heating element starts working and increases heat output to restore the temperature in the constant temperature incubator to the set range; the control system will also dynamically adjust the temperature threshold in combination with external environmental data to ensure that the temperature in the constant temperature incubator is more stable and accurate; the control system continuously monitors temperature data and external environmental data, and through a real-time feedback mechanism, continuously adjusts the power output of the heating element to ensure that the temperature is always maintained within the set range.

[0021] Through the above steps, biological laboratories can ensure that the temperature in the constant temperature incubator is always maintained within the required range, providing a stable environment for cell culture.

[0022] 102. Based on the temperature recovery start signal, a control algorithm based on adaptive dynamic programming is used to automatically adjust the power output of the heating element, and a temperature gradient balancing technology is applied to ensure uniform temperature distribution during the heating process, and a heating control instruction is generated; Read the current internal and external environmental conditions and historical heating data; calculate the optimal heating element power output value through an adaptive dynamic programming algorithm; preliminarily allocate the power output of the heating element; collect temperature data at various points inside the constant temperature incubator; analyze the uniformity of temperature distribution and identify abnormal areas; adjust the heating power of abnormal areas; and form the final heating control instruction to ensure uniform temperature distribution.

[0023] Optionally, the step 102, based on the temperature recovery start signal, adopts a control algorithm based on adaptive dynamic programming to automatically adjust the power output of the heating element, applies a temperature gradient balancing technology to ensure uniform temperature distribution during the heating process, and generates a heating control instruction, including: Based on the temperature recovery start signal, a control algorithm based on adaptive dynamic programming is used to analyze internal and external environmental conditions and historical heating data, calculate the optimal power output value of the heating element, and generate an optimal power output plan; Based on the optimal power output scheme, the temperature gradient balancing technology is applied to finely control the power output of the heating element to ensure uniform temperature distribution during the heating process and generate temperature gradient balancing parameters; Based on the temperature gradient balancing parameters, further optimizing the power output of the heating element to ensure that the temperature gradient is minimized during the heating process, and generating an optimized heating strategy; Based on the optimized heating strategy, the specific value of the power output of the heating element is determined, a working plan for the heating element is formulated, heating efficiency and safety are ensured, and a heating control instruction is generated.

[0024] Suppose there is a pharmaceutical laboratory that needs to use a constant temperature incubator to maintain the ambient temperature for drug culture; When the temperature sensor detects that the temperature in the constant temperature incubator is lower than 36.5°C or higher than 37.5°C, the control system generates a temperature recovery start signal; after receiving the temperature recovery start signal, the control system starts the control algorithm based on adaptive dynamic programming, reads the current internal and external environmental conditions, and analyzes the impact of the current environmental conditions on temperature recovery in combination with historical heating data: based on the environmental condition analysis results and historical heating data, the optimal heating element power output value is calculated through the adaptive dynamic programming control algorithm; the optimal power output plan is generated to ensure that the set temperature is reached as quickly as possible under low energy consumption; based on the optimal power output plan, the temperature gradient balancing technology is applied to finely control the power output of the heating element; through the multi-point temperature sensor array, the temperature data of each point inside the constant temperature incubator is collected in real time to generate a real-time temperature distribution map; the uniformity of the current temperature distribution is analyzed, the temperature gradient abnormal area is identified, and the temperature gradient balancing parameters are generated; based on the temperature gradient balancing parameters, the power output of the heating element is further optimized to ensure that the temperature gradient is minimized during the heating process; the heating power of the abnormal area is adjusted, the local temperature correction is performed, and the optimized heating strategy is generated; based on the optimized heating strategy, the specific value of the power output of the heating element is determined; a heating element work plan is formulated to ensure heating efficiency and safety, and a heating control instruction is generated.

[0025] By following the above steps, pharmaceutical laboratories can ensure that the temperature in the constant temperature incubator not only quickly returns to the set range, but also is evenly distributed throughout the heating process, providing an ideal environment for drug culture.

[0026] Optionally, based on the temperature recovery start signal, a control algorithm based on adaptive dynamic programming is used to analyze internal and external environmental conditions and historical heating data, calculate the optimal power output value of the heating element, and generate an optimal power output solution, including: Based on the temperature recovery start signal, a control algorithm based on adaptive dynamic programming is used to read and analyze the current internal and external environmental conditions to generate an environmental condition analysis result; Based on the environmental condition analysis results and in combination with historical heating data, a control algorithm based on adaptive dynamic programming is used to simulate the temperature recovery process of different heating strategies and generate a multi-dimensional heating strategy model; Based on the multi-dimensional heating strategy model, iterative optimization is performed to find the best heating strategy to ensure that the set temperature is reached as quickly as possible with low energy consumption and to generate the optimal power output value; Based on the optimal power output value, the specific working parameters of the heating element are determined to guide subsequent heating control and generate an optimal power output solution.

[0027] Suppose there is an agricultural laboratory that needs to use a constant temperature incubator to maintain the environmental temperature for plant seeds to germinate; When the temperature sensor detects that the temperature in the constant temperature incubator exceeds the threshold, the control system generates a temperature recovery start signal; after receiving the temperature recovery start signal, the control system starts the control algorithm based on adaptive dynamic programming; reads the current internal and external environmental conditions and generates environmental condition analysis results; based on the environmental condition analysis results and historical heating data, the control algorithm is modeled through adaptive dynamic programming to simulate the temperature recovery process of different heating strategies and generate a multi-dimensional heating strategy model; based on the multi-dimensional heating strategy model, the algorithm searches for the best heating strategy through iterative optimization to ensure that the set temperature is reached as quickly as possible under low energy consumption and to generate the optimal power output value; based on the optimal power output value, the control system determines the specific working parameters of the heating element, such as power output, heating time and heating mode; the control system generates the optimal power output plan to guide subsequent heating control, ensure the specific power output value and working plan of the heating element, and ensure heating efficiency and safety.

[0028] By following the above steps, agricultural laboratories can ensure that the temperature in the constant temperature incubator not only quickly returns to the set range, but also is evenly distributed throughout the heating process, providing an ideal environment for plant seed germination.

[0029] The present application takes into account that the formula is mainly used to evaluate and optimize the temperature control performance and heating strategy of the constant temperature incubator by combining the control algorithm of adaptive dynamic programming with multi-objective optimization, ensuring that the temperature recovers quickly and approaches the set value, while controlling energy consumption, uniform temperature distribution and smooth changes, and generating an efficient and long-term stable temperature control strategy.

[0030] Optionally, based on the environmental condition analysis results and in combination with historical heating data, a control algorithm based on adaptive dynamic programming is used to simulate the temperature recovery process of different heating strategies to generate a multidimensional heating strategy model, including: Perform data cleaning on the environmental condition analysis results to remove noise and fill in missing values ​​to ensure data accuracy and reliability; Using time series analysis to identify the temporal characteristics of environmental condition analysis results, combined with the periodicity and trend of historical heating data, to provide a parameter basis for generating future performance indicators; The future performance indicators are calculated using the following formula: ; in, From time Starting future performance indicators; For time The internal temperature of the incubator at To set the temperature; For time Heating element power output at ; is the weight coefficient of the square term of temperature error; is the weight coefficient of the square term of power output; is the weight coefficient of the absolute value term of temperature error; is the time index, from arrive ; is the current time step; is the time step of prediction; Based on the future performance indicators, the constraints of temperature uniformity and temperature change rate are introduced to construct a multi-objective optimization problem to generate an optimal heating strategy; The optimal heating strategy is calculated using the following formula: ; in For time The optimal heating strategy when From time The future begins Step performance indicators; For time Time The temperature measured by a temperature sensor; For time The average temperature measured by all temperature sensors at the time; is the weight coefficient of the square term of temperature uniformity; For the The weight coefficient of each temperature sensor; is the weight coefficient of the square term of temperature change rate; is the weight coefficient of the absolute value of the temperature change rate; is the time step; The index of the temperature sensor number, from 1 to ; is the total number of temperature sensors; is the time index, from arrive ; is the current time step; is the time step of prediction; Indicates that in all possible Among the values, find the value that minimizes the objective function. value; For time The internal temperature of the incubator at For time The internal temperature of the incubator at Based on the optimal heating strategy, it is applied in the actual control system. Through simulation and actual testing, the optimal heating strategy is verified and optimized, the performance is evaluated and the parameters are dynamically adjusted to ensure the robustness and reliability of the optimal heating strategy and generate a multidimensional heating strategy model.

[0031] This method aims to comprehensively consider environmental conditions, historical heating data and temperature distribution uniformity, use adaptive dynamic programming control algorithm modeling, simulate the temperature recovery process of different heating strategies, and generate a multidimensional heating strategy model to optimize the heating strategy, ensure the temperature in the constant temperature incubator recovers quickly and remains stable for a long time, and improve the overall performance and reliability of the system.

[0032] Performance indicators in the future In the square term of temperature error :Evaluate the square of temperature deviation to ensure that the temperature is close to the set value; power output square term : Evaluate the square of heating power to ensure reasonable energy consumption; absolute value of temperature error :Evaluate the absolute value of temperature deviation and ensure the absolute size of temperature deviation; In the optimal heating strategy Performance indicators : Evaluates basic temperature control performance; temperature uniformity square term :Evaluate the uniformity of temperature distribution and ensure that the temperature is evenly distributed at each point; the square term of temperature change rate : Evaluate the smoothness of temperature change and ensure that the temperature changes smoothly; the absolute value of the temperature change rate : Evaluate the absolute rate of temperature change to ensure that the temperature change rate is within a reasonable range; in, Through experiments and historical data tuning, ensure that the weight coefficients are reasonable and balance the absolute value of temperature error, power output and temperature deviation; Through experiments and historical data tuning, ensure that the weight coefficients are reasonable and balance the temperature uniformity, the square and absolute value of the temperature change rate; Real-time temperature sensors and control systems to obtain current temperature, set temperature and power output of heating elements; Obtain the temperature of each point through multi-point temperature sensors and calculate the average temperature; , Get the current and next moment temperature through real-time temperature sensor; , , , , The current time step, prediction time step, time index, time step, and total number of temperature sensors are set through the control system.

[0033] Suppose a pharmaceutical R&D laboratory has a constant temperature incubator for stability testing of active pharmaceutical ingredients; Assume that the current time step , prediction time step , time step Minutes, total number of temperature sensors ; Assuming the current temperature , set temperature , current heating element power ; Assuming weight coefficient , , ; Assume that the future temperature and heating power are ; Future performance indicators ; Assuming weight coefficient , (all sensors have equal weight), , ; Assume that the current temperature sensor reading is ; Average temperature ;Optimal heating strategy

[0034] By following the above steps, the pharmaceutical R&D laboratory can ensure that the temperature in the incubator not only quickly returns to the set range, but also is evenly distributed throughout the incubation process, providing an ideal environment for stability testing of active pharmaceutical ingredients, thereby improving the accuracy and reliability of test results.

[0035] Optionally, based on the optimal power output scheme, the temperature gradient balancing technology is applied to finely control the power output of the heating element to ensure uniform temperature distribution during the heating process and generate temperature gradient balancing parameters, including: Based on the optimal power output scheme, applying the temperature gradient balancing technology, preliminarily allocating the power output of the heating elements, and generating a preliminary power allocation scheme; Based on the preliminary power allocation scheme, temperature distribution data is collected in real time through a multi-point temperature sensor array to generate a real-time temperature distribution map; Based on the real-time temperature distribution graph, analyze the current temperature distribution uniformity, identify the temperature gradient abnormal area, and generate a temperature gradient analysis report; Based on the temperature gradient analysis report, for the abnormal temperature gradient area, the power output of the heating element is adjusted, local temperature correction is performed, and a temperature correction instruction is generated; Based on the temperature correction instruction, the above process is repeated until the temperature distribution reaches a preset uniformity standard, and a temperature gradient balancing parameter is generated.

[0036] Suppose there is a chemical laboratory that needs to use a constant temperature reactor to maintain the ambient temperature of the chemical reaction; Based on the temperature recovery start signal, the control system uses the adaptive dynamic programming control algorithm to analyze the internal and external environmental conditions and historical heating data, calculate the optimal power output value of the heating element, and generate the optimal power output plan; based on the optimal power output plan, the temperature gradient balancing technology is applied to preliminarily allocate the power output of the heating element and generate a preliminary power allocation plan. The power output of the heating element is initially set to 60%; the multi-point temperature sensor array is started to collect the temperature data of each point inside the constant temperature reactor in real time and generate a real-time temperature distribution map. These sensors are distributed at the top, middle and bottom of the reactor to ensure full coverage; based on the real-time temperature distribution map, the uniformity of the current temperature distribution is analyzed, the temperature gradient abnormal area is identified, and a temperature gradient analysis report is generated. It is found that the temperature in the bottom area is 2°C lower than that in other areas; based on the temperature gradient analysis report, the power output of the heating element is adjusted for the temperature gradient abnormal area, local temperature correction is performed, and a temperature correction instruction is generated. The power output of the heating element in the bottom area is increased to 70%; based on the temperature correction instruction, the temperature distribution data is continued to be collected in real time to generate a new real-time temperature distribution map; the process of analyzing the uniformity of the temperature distribution and adjusting the power output of the heating element is repeated until the temperature distribution reaches the preset uniformity standard and the temperature gradient balancing parameters are generated.

[0037] Through the above steps, chemical laboratories can ensure that the temperature in the constant temperature reactor not only quickly returns to the set range, but also has a uniform temperature distribution throughout the heating process, providing an ideal environment for chemical reactions.

[0038] 103. Based on the heating control instruction, a temperature field reconstruction algorithm is used to construct a three-dimensional temperature distribution model, and a recursive neural network is used to predict the temperature change trend, accelerate the temperature recovery process, and generate temperature prediction information; According to the heating control instructions, the temperature data of each internal point is collected; the temperature field reconstruction algorithm is used to generate a three-dimensional temperature distribution map; the recursive neural network is used to predict future temperature change trends by combining real-time and historical data; real-time feedback correction: based on the prediction results, the heating control instructions are corrected to accelerate the temperature recovery process and generate temperature prediction information.

[0039] Optionally, the step 103, based on the heating control instruction, uses a temperature field reconstruction algorithm to construct a three-dimensional temperature distribution model, predicts the temperature change trend through a recursive neural network, accelerates the temperature recovery process, and generates temperature prediction information, including: Based on the heating control instruction, the temperature data acquisition system is started to collect the temperature data of each point inside the constant temperature incubator to generate a real-time temperature data set; Based on the real-time temperature data set, a temperature field reconstruction algorithm is used to construct a three-dimensional temperature distribution model inside the constant temperature incubator to generate a three-dimensional temperature distribution map; Based on the three-dimensional temperature distribution map, combined with the real-time temperature data set and historical heating records, a recursive neural network is used to predict future temperature change trends and generate a temperature change prediction report; Based on the temperature change prediction report, the heating control instruction is corrected through real-time feedback to accelerate the temperature recovery process and generate temperature prediction information.

[0040] Suppose a food processing company has a constant temperature incubator and needs to control the temperature during the fermentation process to ensure the consistency and safety of product quality; Based on the heating control instruction, the multi-point temperature sensor network installed in the constant temperature incubator is started to collect the temperature data of each monitoring point in real time and generate a real-time temperature data set; based on the real-time temperature data set, the temperature field reconstruction algorithm is used to construct a three-dimensional temperature distribution model inside the constant temperature incubator and generate a three-dimensional temperature distribution map. This step can intuitively display the temperature distribution of each point inside the incubator and help identify areas with abnormal temperature gradients; based on the three-dimensional temperature distribution map, combined with the real-time temperature data set and historical heating records, a recursive neural network is used to predict future temperature change trends and generate a temperature change prediction report. By learning historical data, the recursive neural network can accurately predict temperature changes in the future; based on the temperature change prediction report, the heating control instruction is corrected through a real-time feedback mechanism to accelerate the temperature recovery process and generate temperature prediction information; if the prediction report indicates that the temperature in a certain area will drop in the next few hours, the control system will increase the heating power of the area in advance to ensure that the temperature quickly returns to the set range.

[0041] By following the above steps, food processing companies can ensure that the temperature in the incubator not only quickly returns to the set range, but also is evenly distributed throughout the fermentation process, providing an ideal environment for the fermentation process, thereby improving product quality and consistency.

[0042] Optionally, based on the real-time temperature data set, a temperature field reconstruction algorithm is used to construct a three-dimensional temperature distribution model inside the constant temperature incubator to generate a three-dimensional temperature distribution map, including: Based on the real-time temperature data set, data cleaning preprocessing is performed to remove noise and outliers to generate a processed temperature data set; Based on the processed temperature data set, a temperature field reconstruction algorithm is used to perform spatial interpolation and modeling to generate a preliminary temperature distribution model; Based on the preliminary temperature distribution model, it is mapped into a three-dimensional coordinate system, and smoothed by physical simulation to generate a three-dimensional temperature distribution model inside the constant temperature incubator; Based on the three-dimensional temperature distribution model inside the constant temperature incubator, the model is verified and optimized by comparing with the actual measurement results to generate a three-dimensional temperature distribution map.

[0043] Suppose a biological science research institute has a constant temperature incubator and needs to study the growth and metabolism of different biological samples at a specific temperature; Multiple high-precision temperature sensors are arranged in the constant temperature incubator to ensure that temperature data can be collected from different positions and heights. These sensors are connected to a central data acquisition system that regularly records the temperature readings of each sensor to form a real-time temperature data set; data cleaning techniques are used to remove noise and outliers. The sliding average method is used to reduce the impact of random fluctuations, and statistical methods are used to identify and exclude data points that are significantly deviated from the normal range. This ensures the quality of the basic data for subsequent analysis; using the processed temperature data set, a suitable interpolation method is selected to estimate the temperature values ​​of the positions that are not directly measured. In this way, a preliminary temperature distribution model of the entire incubator can be constructed; the preliminary temperature distribution model is converted into a graphical representation in a three-dimensional coordinate system, and the model is further smoothed using computer-aided design software combined with physical simulation technology to eliminate unnatural phenomena caused by interpolation, and finally an accurate three-dimensional temperature distribution model is generated; in order to verify the accuracy of the model, the temperature of some key locations is manually measured at specific time points, and these measured data are compared with the results predicted by the model; if significant differences are found, the model can be optimized by adjusting the parameters of the interpolation algorithm or adding more measurement points until the model can accurately reflect the actual situation.

[0044] Through the above process, the Institute of Biological Sciences can obtain detailed information about the temperature distribution inside the constant temperature incubator, helping to study the growth and metabolism of different biological samples at specific temperatures.

[0045] This application considers that the formula is mainly used to evaluate and optimize the temperature distribution model of the constant temperature incubator through the temperature field reconstruction algorithm and nonlinear adjustment method, ensure the accuracy and stability of the temperature distribution, and generate an efficient temperature control strategy. In this way, potential temperature fluctuations can be identified in advance, timely intervention can be made, and the overall performance and stability of the system can be improved.

[0046] Optionally, based on the processed temperature data set, a temperature field reconstruction algorithm is used to perform spatial interpolation and modeling to generate a preliminary temperature distribution model, including: Based on the processed temperature data set, the temperature response characteristics under different environmental conditions are evaluated through dynamic response analysis, revealing the dynamic behavior and response mode, and providing a dynamic basis for the calculation of the initial spatial temperature value: The initial space temperature value is calculated using the following formula: ; in, is the initial space temperature value; For point To a known temperature measurement point The distance weight of For point To a known temperature measurement point The Euclidean distance of is a small positive number to prevent the denominator from being zero; is the exponent of the distance weight; For the The actual temperature value of a known temperature measurement point; is the amplitude factor; is the frequency factor; The timestamp for the temperature measurement; is the attenuation coefficient of temperature difference; is the average temperature of all known temperature measurement points; is the index of the known temperature measurement point, from 1 to ; is the number of known temperature measurement points; Based on the initial spatial temperature value, error analysis is performed to evaluate the difference with the actual measured value, identify the main error source, introduce nonlinear adjustment items to ensure flexible response to environmental changes and temperature fluctuations, and generate optimized temperature error values; The optimized temperature error value is calculated by the following formula: ; in, is the optimized temperature error value; is the initial space temperature value; is the nonlinear adjustment coefficient; is the attenuation coefficient; is the adjustment factor; is the power index; is the exponent of the total nonlinear term; To set the adjustment coefficient of temperature deviation; For the The actual temperature value of a known temperature measurement point; To set the temperature; To set the adjustment coefficient of temperature deviation; is the power exponent of setting temperature deviation; is the adjustment factor for the mean temperature deviation; is the average temperature of all known temperature measurement points; is the adjustment factor for the mean temperature deviation; is the power exponent of the mean temperature deviation; is the index of the known temperature measurement point, from 1 to ; is the number of known temperature measurement points; Based on the optimized temperature error value, it is applied to the initial spatial temperature value to perform error correction, and a preliminary temperature distribution model is generated by using a heat conduction simulation method and comprehensively considering the spatial propagation characteristics of temperature.

[0047] This method aims to evaluate and optimize the temperature distribution model of the constant temperature incubator by comprehensively considering the processed temperature data set, using the temperature field reconstruction algorithm and the nonlinear adjustment method, ensuring the accuracy and stability of the temperature distribution, generating an efficient temperature control strategy, and improving the overall performance and stability of the system.

[0048] At the initial space temperature In the distance weight and dynamic response term : By distance weight and Euclidean distance Comprehensively consider the influence of known temperature measurement points and introduce temperature dynamic response , ensuring the accuracy and robustness of the initial spatial temperature value; the attenuation coefficient of the temperature difference : Reduce the impact of temperature differences on the results and improve the robustness of the calculation.

[0049] The temperature error value after optimization The initial space temperature value is :As the basis of the optimized temperature error value, ensure the starting point of the error calculation is accurate and reflect the actual state of the current space temperature; nonlinear adjustment item :Through the nonlinear adjustment coefficient, attenuation coefficient, adjustment coefficient, power exponent and other parameters, the nonlinear characteristics of temperature distribution are comprehensively considered to ensure the accuracy of the optimized temperature error value; set the temperature deviation item :By setting the adjustment coefficient of temperature deviation, the temperature error value is further optimized to ensure the stability of the temperature distribution model; the average temperature deviation term : Through the adjustment coefficient of the average temperature deviation, the temperature error value is further optimized to ensure the stability of the temperature distribution model.

[0050] in, , , , , , , , , , , Tuning through experiments and historical data; Get the temperature of each point through multi-point temperature sensors; Set by control system; Obtain the temperature of each point through multi-point temperature sensors and calculate the average temperature; , Obtained through geometric calculations; , , , , Tuning through experiments and historical data; Calculated by the initial space temperature value calculation formula; Calculate through the optimized temperature error value calculation formula; is the spatial coordinate; Obtained by the number of known temperature measurement points; Suppose there is a green food processing base that needs a constant temperature incubator for temperature control during the food fermentation process; Assume that the temperature measurement point is currently known , each point temperature , average temperature , set temperature , assuming the weight coefficient ; Assuming distance weight , Euclidean distance , small positive number , the exponential of the distance weight , amplitude factor , frequency factor , the attenuation coefficient of temperature difference ; Initial space temperature value ; Optimized temperature error value .

[0051] 104. Based on the temperature prediction information, the system automatically switches to fine temperature control after reaching the set temperature, performs an environmental impact analysis on the external environment, takes measures to offset adverse factors, ensures the long-term stability of the temperature in the constant temperature incubator, and generates temperature control results.

[0052] After reaching the set temperature, a precise temperature control signal is generated; external environmental data is acquired in real time; the impact of the external environment on the internal temperature is analyzed and an environmental impact report is generated; based on the report, the power of the heating element is adjusted or the cooling system is started to offset adverse factors; temperature and environmental data are continuously monitored, and the control strategy is adjusted to ensure long-term temperature stability and generate the final temperature control result.

[0053] Optionally, in step 104, based on the temperature prediction information, the system automatically switches to fine temperature control after reaching the set temperature, performs environmental impact analysis on the external environment, takes measures to offset adverse factors, ensures long-term stability of the temperature in the constant temperature incubator, and generates a temperature control result, including: Based on the temperature prediction information, when the internal temperature reaches the set temperature, the fine temperature control mode is automatically triggered to generate a fine temperature control signal; Based on the precise temperature control signal, a comprehensive environment monitoring system is started to obtain the external environment conditions of the constant temperature incubator in real time and generate real-time external environment data; Based on the real-time external environmental data, perform environmental impact analysis, comprehensively evaluate the impact of external environmental changes, and generate an environmental impact report; Based on the environmental impact report, corresponding targeted measures are taken to offset potential adverse factors in the external environment, ensure the long-term stability of the temperature in the constant temperature incubator, and generate temperature control results.

[0054] Assume that an environmental monitoring station has a constant temperature incubator for culturing microbial samples to monitor water and air quality. Based on the temperature prediction information, when the internal temperature of the constant temperature incubator reaches the set temperature, the control system automatically triggers the fine temperature control mode and generates a fine temperature control signal. The fine temperature control mode is designed to ensure that small temperature fluctuations are within an acceptable range to maintain the stability of microbial culture. Based on the fine temperature control signal, the comprehensive environmental monitoring system is started to obtain the environmental conditions outside the constant temperature incubator in real time and generate real-time external environmental data. Based on the real-time external environmental data, the central control system comprehensively evaluates the impact of external environmental changes on the internal temperature of the constant temperature incubator through data analysis software. The real-time external environmental data is preprocessed to remove noise and outliers to ensure the accuracy and reliability of the data. The changing trend of external environmental conditions is identified through time series analysis. Combined with historical data and current environmental conditions, the degree of impact of external environmental changes on the internal temperature of the constant temperature incubator is evaluated to generate an environmental impact report. Based on the environmental impact report, corresponding targeted measures are taken to offset potential adverse factors in the external environment, ensure the long-term stability of the temperature in the constant temperature incubator, and generate temperature control results to maintain the stability of the internal temperature.

[0055] Through the above steps, the environmental monitoring station can ensure that the temperature in the constant temperature incubator not only quickly returns to the set range, but also is evenly distributed throughout the incubation process, providing an ideal environment for the cultivation of microbial samples, thereby improving the accuracy and reliability of the monitoring results.

[0056] Figure 2 A schematic diagram of a control system for a constant temperature incubator is provided for the present application embodiment. Figure 2 As shown, the device comprises: The monitoring module 21 is used to monitor the real-time temperature in the constant temperature incubator using a temperature sensor, compare and analyze the temperature with a preset temperature threshold in combination with external environmental data, and generate a temperature recovery start signal; A control module 22, for automatically adjusting the power output of the heating element based on the temperature recovery start signal, using a control algorithm based on adaptive dynamic programming, applying a temperature gradient balancing technology to ensure uniform temperature distribution during the heating process, and generating a heating control instruction; A prediction module 23 is used to construct a three-dimensional temperature distribution model based on the heating control instruction using a temperature field reconstruction algorithm, predict the temperature change trend through a recursive neural network, accelerate the temperature recovery process, and generate temperature prediction information; The analysis module 24 is used to automatically switch to fine temperature control after reaching the set temperature based on the temperature prediction information, perform environmental impact analysis on the external environment, take measures to offset adverse factors, ensure long-term stability of the temperature in the constant temperature incubator, and generate temperature control results.

[0057] Figure 2 The control system for the constant temperature incubator can be implemented Figure 1 The implementation principle and technical effect of the control method for the temperature return of a constant temperature incubator described in the embodiment shown will not be described in detail. The specific manner in which each module and unit performs operations in the control system for the temperature return of a constant temperature incubator in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0058] In one possible design, Figure 2 A control system for a constant temperature incubator temperature return in the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0059] The processing component 32 is used to: monitor the real-time temperature in the constant temperature incubator, compare and analyze it with the preset threshold value in combination with the external environmental data, and generate a temperature recovery start signal; based on the temperature recovery start signal, adopt a control algorithm based on adaptive dynamic programming, apply temperature gradient balancing technology, and generate a heating control instruction; based on the heating control instruction, use a temperature field reconstruction algorithm to construct a three-dimensional temperature distribution model, predict the temperature change trend through a recursive neural network, and generate temperature prediction information; based on the temperature prediction information, automatically switch to fine temperature control after reaching the set temperature, conduct environmental impact analysis, take measures to offset adverse factors, and generate temperature control results.

[0060] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0061] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0062] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0063] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0064] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0065] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0066] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is a control method for returning the temperature of a constant temperature incubator.

[0067] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0068] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0069] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A control method for returning the temperature of a constant temperature incubator, characterized in that: include: Use the temperature sensor to monitor the real-time temperature in the constant temperature incubator, combine it with the external environment data, compare and analyze it with the preset temperature threshold, and generate a temperature recovery start signal; Based on the temperature recovery start signal, a control algorithm based on adaptive dynamic programming is used to automatically adjust the power output of the heating element, and a temperature gradient balancing technology is applied to ensure uniform temperature distribution during the heating process, and a heating control instruction is generated; Based on the heating control instruction, a temperature field reconstruction algorithm is used to construct a three-dimensional temperature distribution model, and a recursive neural network is used to predict the temperature change trend, accelerate the temperature recovery process, and generate temperature prediction information; Based on the temperature prediction information, the system automatically switches to fine temperature control after reaching the set temperature, performs an environmental impact analysis on the external environment, takes measures to offset adverse factors, ensures long-term stability of the temperature in the constant temperature incubator, and generates temperature control results.

2. The method according to claim 1, characterized in that Based on the temperature recovery start signal, a control algorithm based on adaptive dynamic programming is adopted to automatically adjust the power output of the heating element, apply the temperature gradient balancing technology to ensure uniform temperature distribution during the heating process, and generate a heating control instruction, including: Based on the temperature recovery start signal, a control algorithm based on adaptive dynamic programming is used to analyze internal and external environmental conditions and historical heating data, calculate the optimal power output value of the heating element, and generate an optimal power output plan; Based on the optimal power output scheme, the temperature gradient balancing technology is applied to finely control the power output of the heating element to ensure uniform temperature distribution during the heating process and generate temperature gradient balancing parameters; Based on the temperature gradient balancing parameters, further optimizing the power output of the heating element to ensure that the temperature gradient is minimized during the heating process, and generating an optimized heating strategy; Based on the optimized heating strategy, the specific value of the power output of the heating element is determined, a working plan for the heating element is formulated, heating efficiency and safety are ensured, and a heating control instruction is generated.

3. The method according to claim 2, characterized in that Based on the temperature recovery start signal, a control algorithm based on adaptive dynamic programming is used to analyze internal and external environmental conditions and historical heating data, calculate the optimal heating element power output value, and generate an optimal power output solution, including: Based on the temperature recovery start signal, a control algorithm based on adaptive dynamic programming is used to read and analyze the current internal and external environmental conditions to generate an environmental condition analysis result; Based on the environmental condition analysis results and in combination with historical heating data, a control algorithm based on adaptive dynamic programming is used to simulate the temperature recovery process of different heating strategies and generate a multi-dimensional heating strategy model; Based on the multi-dimensional heating strategy model, iterative optimization is performed to find the best heating strategy to ensure that the set temperature is reached as quickly as possible with low energy consumption and to generate the optimal power output value; Based on the optimal power output value, the specific working parameters of the heating element are determined to guide subsequent heating control and generate an optimal power output solution.

4. The method according to claim 3, characterized in that Based on the environmental condition analysis results and combined with historical heating data, the temperature recovery process of different heating strategies is simulated through control algorithm modeling based on adaptive dynamic programming, generating a multi-dimensional heating strategy model, including: Perform data cleaning on the environmental condition analysis results to remove noise and fill in missing values ​​to ensure data accuracy and reliability; Using time series analysis to identify the temporal characteristics of environmental condition analysis results, combined with the periodicity and trend of historical heating data, to provide a parameter basis for generating future performance indicators; The future performance indicators are calculated using the following formula: ； in, From time Starting future performance indicators; For time The internal temperature of the incubator at To set the temperature; For time Heating element power output at ; is the weight coefficient of the square term of temperature error; is the weight coefficient of the square term of power output; is the weight coefficient of the absolute value term of temperature error; is the time index, from arrive ; is the current time step; is the time step of prediction; Based on the future performance indicators, the constraints of temperature uniformity and temperature change rate are introduced to construct a multi-objective optimization problem to generate an optimal heating strategy; The optimal heating strategy is calculated using the following formula: ； in For time The optimal heating strategy when From time The future begins Step performance indicators; For time Time The temperature measured by a temperature sensor; For time The average temperature measured by all temperature sensors at the time; is the weight coefficient of the square term of temperature uniformity; For the The weight coefficient of each temperature sensor; is the weight coefficient of the square term of temperature change rate; is the weight coefficient of the absolute value of the temperature change rate; is the time step; The index of the temperature sensor number, from 1 to ; is the total number of temperature sensors; is the time index, from arrive ; is the current time step; is the time step of prediction; Indicates that in all possible Among the values, find the value that minimizes the objective function. value; For time The internal temperature of the incubator at For time The internal temperature of the incubator at Based on the optimal heating strategy, it is applied in the actual control system. Through simulation and actual testing, the optimal heating strategy is verified and optimized, the performance is evaluated and the parameters are dynamically adjusted to ensure the robustness and reliability of the optimal heating strategy and generate a multidimensional heating strategy model.

5. The method according to claim 2, characterized in that: Based on the optimal power output scheme, the temperature gradient balancing technology is applied to finely control the power output of the heating element to ensure uniform temperature distribution during the heating process and generate temperature gradient balancing parameters, including: Based on the optimal power output scheme, applying the temperature gradient balancing technology, preliminarily allocating the power output of the heating elements, and generating a preliminary power allocation scheme; Based on the preliminary power allocation scheme, temperature distribution data is collected in real time through a multi-point temperature sensor array to generate a real-time temperature distribution map; Based on the real-time temperature distribution graph, analyze the current temperature distribution uniformity, identify the temperature gradient abnormal area, and generate a temperature gradient analysis report; Based on the temperature gradient analysis report, for the abnormal temperature gradient area, the power output of the heating element is adjusted, local temperature correction is performed, and a temperature correction instruction is generated; Based on the temperature correction instruction, the above process is repeated until the temperature distribution reaches a preset uniformity standard, and a temperature gradient balancing parameter is generated.

6. The method according to claim 1, characterized in that Based on the heating control instruction, a temperature field reconstruction algorithm is used to construct a three-dimensional temperature distribution model, a recursive neural network is used to predict the temperature change trend, the temperature recovery process is accelerated, and temperature prediction information is generated, including: Based on the heating control instruction, the temperature data acquisition system is started to collect the temperature data of each point inside the constant temperature incubator to generate a real-time temperature data set; Based on the real-time temperature data set, a temperature field reconstruction algorithm is used to construct a three-dimensional temperature distribution model inside the constant temperature incubator to generate a three-dimensional temperature distribution map; Based on the three-dimensional temperature distribution map, combined with the real-time temperature data set and historical heating records, a recursive neural network is used to predict future temperature change trends and generate a temperature change prediction report; Based on the temperature change prediction report, the heating control instruction is corrected through real-time feedback to accelerate the temperature recovery process and generate temperature prediction information.

7. The method according to claim 6, characterized in that Based on the real-time temperature data set, a temperature field reconstruction algorithm is used to construct a three-dimensional temperature distribution model inside the constant temperature incubator to generate a three-dimensional temperature distribution map, including: Based on the real-time temperature data set, data cleaning preprocessing is performed to remove noise and outliers to generate a processed temperature data set; Based on the processed temperature data set, a temperature field reconstruction algorithm is used to perform spatial interpolation and modeling to generate a preliminary temperature distribution model; Based on the preliminary temperature distribution model, it is mapped into a three-dimensional coordinate system, and smoothed by physical simulation to generate a three-dimensional temperature distribution model inside the constant temperature incubator; Based on the three-dimensional temperature distribution model inside the constant temperature incubator, the model is verified and optimized by comparing with the actual measurement results to generate a three-dimensional temperature distribution map.

8. The method according to claim 6, characterized in that Based on the processed temperature data set, a temperature field reconstruction algorithm is used to perform spatial interpolation and modeling to generate a preliminary temperature distribution model, including: Based on the processed temperature data set, the temperature response characteristics under different environmental conditions are evaluated through dynamic response analysis, revealing the dynamic behavior and response mode, and providing a dynamic basis for the calculation of the initial spatial temperature value: The initial space temperature value is calculated using the following formula: ； in, is the initial space temperature value; For point To a known temperature measurement point The distance weight of For point To a known temperature measurement point The Euclidean distance of is a small positive number to prevent the denominator from being zero; is the exponent of the distance weight; For the The actual temperature value of a known temperature measurement point; is the amplitude factor; is the frequency factor; The timestamp for the temperature measurement; is the attenuation coefficient of temperature difference; is the average temperature of all known temperature measurement points; is the index of the known temperature measurement point, from 1 to ; is the number of known temperature measurement points; Based on the initial spatial temperature value, error analysis is performed to evaluate the difference with the actual measured value, identify the main error source, introduce nonlinear adjustment items to ensure flexible response to environmental changes and temperature fluctuations, and generate optimized temperature error values; The optimized temperature error value is calculated by the following formula: ； in, is the optimized temperature error value; is the initial space temperature value; is the nonlinear adjustment coefficient; is the attenuation coefficient; is the adjustment factor; is the power index; is the exponent of the total nonlinear term; To set the adjustment coefficient of temperature deviation; For the The actual temperature value of a known temperature measurement point; To set the temperature; To set the adjustment coefficient of temperature deviation; is the power exponent of setting temperature deviation; is the adjustment factor for the mean temperature deviation; is the average temperature of all known temperature measurement points; is the adjustment factor for the mean temperature deviation; is the power exponent of the mean temperature deviation; is the index of the known temperature measurement point, from 1 to ; is the number of known temperature measurement points; Based on the optimized temperature error value, it is applied to the initial spatial temperature value to perform error correction, and a preliminary temperature distribution model is generated by using a heat conduction simulation method and comprehensively considering the spatial propagation characteristics of temperature.

9. The method according to claim 1, characterized in that: Based on the temperature prediction information, the system automatically switches to fine temperature control after reaching the set temperature, performs environmental impact analysis on the external environment, takes measures to offset adverse factors, ensures the long-term stability of the temperature in the constant temperature incubator, and generates temperature control results, including: Based on the temperature prediction information, when the internal temperature reaches the set temperature, the fine temperature control mode is automatically triggered to generate a fine temperature control signal; Based on the precise temperature control signal, a comprehensive environment monitoring system is started to obtain the external environment conditions of the constant temperature incubator in real time and generate real-time external environment data; Based on the real-time external environmental data, perform environmental impact analysis, comprehensively evaluate the impact of external environmental changes, and generate an environmental impact report; Based on the environmental impact report, corresponding targeted measures are taken to offset potential adverse factors in the external environment, ensure the long-term stability of the temperature in the constant temperature incubator, and generate temperature control results.

10. A control system for returning the temperature of a constant temperature incubator, characterized in that: include: The monitoring module is used to monitor the real-time temperature in the constant temperature incubator using a temperature sensor, compare and analyze the temperature with the preset temperature threshold in combination with external environmental data, and generate a temperature recovery start signal; A control module, for automatically adjusting the power output of the heating element based on the temperature recovery start signal, using a control algorithm based on adaptive dynamic programming, applying a temperature gradient balancing technology to ensure uniform temperature distribution during the heating process, and generating a heating control instruction; A prediction module is used to construct a three-dimensional temperature distribution model based on the heating control instruction using a temperature field reconstruction algorithm, predict the temperature change trend through a recursive neural network, accelerate the temperature recovery process, and generate temperature prediction information; The analysis module is used to automatically switch to fine temperature control after reaching the set temperature based on the temperature prediction information, conduct environmental impact analysis on the external environment, take measures to offset adverse factors, ensure the long-term stability of the temperature in the constant temperature incubator, and generate temperature control results.

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