A constant temperature and humidity control method and system for a cell culture box

Abstract

The present invention relates to the field of temperature and humidity control of an incubator. The present invention discloses a method and system for controlling constant temperature and humidity of a cell incubator, comprising obtaining temperature and humidity data sets inside and outside the cell incubator, loss data of a temperature and humidity sensor, calculating a loss coefficient, establishing a temperature and humidity compensation model, and obtaining an accurate temperature and humidity data set; obtaining an incubator door opening and closing event set, a temperature controller state set, and a humidity controller state set, and calculating corresponding influence coefficients; based on the above data, constructing a temperature and humidity prediction model, predicting a temperature and humidity data sequence in a cell incubator for a period of time in the future, and controlling a heater, a cooler, a humidifier, and a dehumidifier according to the predicted temperature and humidity data sequence to achieve precise control. The method and system can achieve precise control of the temperature and humidity inside the cell incubator, and avoid the influence of temperature and humidity fluctuations on cell culture.

Description

Technical Field

[0001] The present invention relates to the field of temperature and humidity control of an incubator, and more specifically, to a constant temperature and humidity control method and system for a cell incubator. Background Art

[0002] Cell culture is an important technology in biomedical research, which has strict requirements on the temperature and humidity of the culture environment. Any fluctuations in temperature and humidity may affect the growth, differentiation and metabolism of cells. Existing cell culture incubators have some shortcomings in temperature and humidity control, which are mainly reflected in the following aspects:

[0003] Temperature and humidity sensors have loss and drift problems and cannot accurately measure the actual temperature and humidity. Related sensor loss compensation technologies include patent document CN116907669A, which discloses a temperature sensor drift compensation method, and patent document CN209416962U, which discloses a humidity sensor drift compensation circuit. These compensation technologies require special hardware circuits or pre-calibration, which are costly and have limited application scenarios.

[0004] It is impossible to predict the temperature and humidity changes in the future, and the control strategy cannot be adjusted in advance. Although there are some prediction models such as the temperature prediction method disclosed in patent document CN114139805A, they fail to take into account all the influencing factors and the prediction accuracy is limited. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a constant temperature and humidity control method and system for a cell culture incubator, which comprehensively considers temperature and humidity data processing, sensor loss, influencing factor analysis, temperature and humidity prediction and other technologies, and applies the predicted data to temperature and humidity control, thereby achieving precise control of the temperature and humidity inside the cell culture incubator, meeting the strict requirements of cell culture on the environment, and providing an ideal environment for the normal growth and metabolism of cells. This method can be widely used in basic biology, drug development, tissue engineering, cell therapy and other biomedical fields that require reliable cell culture.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A constant temperature and humidity control method for a cell culture box, comprising:

[0008] S10: Obtain the initial temperature data set, initial humidity data set, external temperature data set, and external humidity data set of the cell culture incubator in the time period Continuously collect data at fixed time intervals Second-rate, is an integer greater than 1, is the starting time point, is the time interval;

[0009] S20: Acquire temperature sensor loss data and humidity sensor loss data, and calculate the temperature sensor loss coefficient and the humidity sensor loss coefficient based on the loss data;

[0010] S30: inputting the initial temperature data set and the temperature sensor loss coefficient into a pre-built temperature compensation model to obtain a compensated temperature data set; inputting the initial humidity data set and the humidity sensor loss coefficient into a pre-built humidity compensation model to obtain a compensated humidity data set;

[0011] S40: Calculating an accurate temperature dataset and an accurate humidity dataset based on the compensated temperature dataset and the compensated humidity dataset;

[0012] S50: Obtain a door opening and closing event set of the cell culture box, a temperature controller state set and a humidity controller state set in the cell culture box, and calculate a door opening and closing event influence coefficient, a temperature controller influence coefficient and a humidity controller influence coefficient;

[0013] S60: calculating an insulation index of the cell culture incubator based on an accurate temperature data set inside the cell culture incubator and an external temperature data set of the cell culture incubator;

[0014] S70: Based on the internal space volume of the cell culture incubator, the precise temperature data set, the precise humidity data set, the external temperature data set of the cell culture incubator, the external humidity data set, the door opening and closing event influence coefficient, the thermostat influence coefficient, the hygrostat influence coefficient, the incubator insulation index and the pre-built temperature and humidity prediction model, obtain the temperature data sequence and humidity data sequence of the cell culture incubator in the future period of time;

[0015] S80: Based on the temperature data sequence and humidity data sequence of the cell culture incubator for a period of time in the future, the opening and closing and power of the heater, cooler, humidifier and dehumidifier are regulated to keep the temperature data sequence and humidity data sequence inside the cell culture incubator within a set range for a period of time in the future.

[0016] Furthermore, the initial temperature data set ,in All include A data set of temperature data, is the number of temperature sensors, All temperature sensors are included in Data collected at each time point; initial humidity data set ,in All include A dataset of humidity data, is the number of humidity sensors, All humidity sensors are included in Data collected at each time point, and are integers greater than or equal to 1.

[0017] Furthermore, the cell culture incubator external temperature dataset and the external humidity dataset are obtained by one or more sensors. If they are obtained by multiple sensors, the cell culture incubator external temperature dataset and the external humidity dataset are formed by the average value of the multiple sensor data.

[0018] Furthermore, the temperature sensor loss data includes a real-time signal-to-noise ratio of the temperature sensor, a real-time response time of the temperature sensor, a real-time linearity of the temperature sensor, and a real-time sensitivity of the temperature sensor. The method for calculating the temperature sensor loss coefficient based on the temperature sensor loss data includes:

[0019] ;

[0020] In the formula, is the temperature sensor loss coefficient, is the real-time signal-to-noise ratio of the temperature sensor, is the standard signal-to-noise ratio of the temperature sensor, is the real-time response time of the temperature sensor, is the standard response time of the temperature sensor, is the real-time linearity of the temperature sensor, is the standard linearity of the temperature sensor, is the real-time sensitivity of the temperature sensor, is the standard sensitivity of the temperature sensor, is the signal-to-noise ratio of the temperature sensor, is the temperature sensor response time ratio, is the temperature sensor linearity ratio, is the temperature sensor sensitivity ratio, is the weight coefficient of the sum of the temperature sensor signal-to-noise ratio and the response time ratio, is the weight coefficient of the temperature sensor linearity ratio, is the weight coefficient of the temperature sensor sensitivity ratio;

[0021] The humidity sensor loss data includes the real-time signal-to-noise ratio of the humidity sensor, the real-time response time of the humidity sensor, the real-time linearity of the humidity sensor, and the real-time sensitivity of the humidity sensor. The method for calculating the humidity sensor loss coefficient based on the humidity sensor loss data includes:

[0022] ;

[0023] In the formula, is the loss coefficient of the humidity sensor, is the real-time signal-to-noise ratio of the humidity sensor, is the standard signal-to-noise ratio of the humidity sensor, is the real-time response time of the humidity sensor, is the standard response time of the humidity sensor, Real-time linearity of humidity sensor, is the standard linearity of the humidity sensor, is the real-time sensitivity of the humidity sensor, is the standard sensitivity of the humidity sensor, is the humidity sensor signal-to-noise ratio, is the humidity sensor response time ratio, is the humidity sensor linearity ratio, is the humidity sensor sensitivity ratio, is the weight coefficient of the sum of the humidity sensor signal-to-noise ratio and the response time ratio, is the weight coefficient of the humidity sensor linearity ratio, is the weight coefficient of the humidity sensor sensitivity ratio.

[0024] Furthermore, the method for constructing the temperature compensation model includes:

[0025] Acquire a first sample data set, the first sample data set includes historical initial temperature data, historical temperature sensor loss coefficients and historical real temperature data, the first sample data set is divided into a sample training set and a sample test set, a regression network is constructed, the historical initial temperature data and the historical temperature sensor loss coefficient in the sample training set are used as input data of the regression network, the historical real temperature data in the sample training set are used as output data of the regression network, the regression network is trained to obtain an initial regression network for predicting compensation temperature data, the initial regression network is tested using the sample test set, and a regression network that satisfies a preset error value is output as a temperature compensation model, and the regression network is preferably a neural network model;

[0026] The method for constructing the humidity compensation model includes:

[0027] A second sample data set is obtained, wherein the second sample data set includes historical initial humidity data, historical humidity sensor loss coefficients, and historical real humidity data. The second sample data set is divided into a sample training set and a sample test set. A regression network is constructed, and the historical initial humidity data and the historical humidity sensor loss coefficient in the sample training set are used as input data of the regression network. The historical real humidity data in the sample training set is used as output data of the regression network. The regression network is trained to obtain an initial regression network for predicting and compensating humidity data. The initial regression network is tested using the sample test set, and a regression network that satisfies a preset error value is output as a humidity compensation model. The regression network is preferably a neural network model.

[0028] Furthermore, the method for calculating the precise temperature dataset and the precise humidity dataset based on the compensated temperature dataset and the compensated humidity dataset includes:

[0029] Traverse the compensated temperature dataset and compensated humidity dataset at the same time point Temperature data and The average temperature data and the average humidity data at the corresponding time point are calculated, and the average temperature data and the average humidity data at all the collected time points are obtained to generate an accurate temperature data set and an accurate humidity data set.

[0030] Furthermore, the door opening and closing event set Include time period All door opening and closing events that occurred within the time period, each event contains the number of door openings , door open time , precise temperature inside the cell culture incubator , accurate humidity inside the cell culture incubator , cell culture incubator external temperature , cell culture incubator external humidity ;

[0031] Furthermore, the door opening and closing event influence coefficient calculation method includes:

[0032] ;

[0033] In the formula, It is The duration of the door opening, It is The precise temperature inside the cell culture incubator when the door is opened for the first time. It is The temperature outside the cell culture incubator when the door is opened for the first time, It is The precise humidity inside the cell culture incubator when the door is opened for the first time. It is The humidity outside the cell culture incubator when the door is opened for the first time;

[0034] The thermostat state set is a six-dimensional vector ,in For time period Internal heater operation times, is the heater operating power, The operating time of the heater, For time period Intercooler operation times, is the cooler operating power, How long does the cooler run?

[0035] Furthermore, the temperature controller influence coefficient calculation method includes:

[0036] ;

[0037] In the formula, is the thermostat influence coefficient, It is Secondary heater operating power, It is Secondary heater operation time, It is Secondary cooler operating power, It is How long the secondary cooler runs;

[0038] The hygrostat state set is a six-dimensional vector ,in For time period Number of times the internal humidifier runs, The operating power of the humidifier is How long does the humidifier run? For time period Internal dehumidifier operation times, The operating power of the dehumidifier is How long to run the dehumidifier;

[0039] Furthermore, the calculation method of the influence coefficient of the hygrostat includes:

[0040] ;

[0041] In the formula, is the hygrostat influence coefficient, It is Secondary humidifier operating power, It is How long does the humidifier run? It is Secondary dehumidifier operating power, It is The duration of the dehumidifier operation.

[0042] Furthermore, the method for calculating the insulation index of the cell culture box includes:

[0043] ;

[0044] In the formula, is the insulation index of the cell culture incubator, For the incubator Accurate temperature data, For incubator External temperature data, is the average value of the precise temperature data inside the incubator, is the average value of the incubator external temperature data, is the volume of the internal space of the incubator.

[0045] Furthermore, the temperature and humidity prediction model construction method includes:

[0046] Obtain sample data sets: including door opening and closing event influence coefficient, thermostat influence coefficient, hygrostat influence coefficient, incubator insulation index, incubator internal space volume, cell incubator external historical temperature data set, cell incubator external historical humidity data set, internal historical temperature sequence, internal historical humidity sequence, true predicted temperature sequence, true predicted humidity sequence;

[0047] Model training, verification and testing: The sample data set is divided into a sample training set, a sample verification set and a sample test set to construct a temperature and humidity prediction model. The door opening and closing event influence coefficient, thermostat influence coefficient, hygrostat influence coefficient, incubator insulation index, incubator internal space volume, cell incubator external historical temperature data set, cell incubator external historical humidity data set, internal historical temperature sequence and internal historical humidity sequence in the sample training set are used as input data of the temperature and humidity prediction model. The true predicted temperature sequence and true predicted humidity sequence in the sample training set are used as output data of the prediction model. The prediction model is trained to obtain an initial prediction model for predicting the temperature and humidity sequence. The sample verification set is used to adjust the parameters of the prediction model, and the output prediction model that satisfies the preset error value is used as the temperature and humidity prediction model. Finally, the temperature and humidity prediction model is tested using the sample test set.

[0048] Furthermore, the internal historical temperature series and the real predicted temperature series are obtained by: Randomly select the temperature data from the accurate temperature data set of The continuous data is taken as the historical temperature sequence. Continuous data is used as the real predicted temperature series;

[0049] The internal historical humidity series and the real predicted humidity series are obtained by: Randomly select the accurate humidity data set of humidity data The continuous data is taken as the historical humidity sequence. Continuous data is used as the true predicted humidity sequence. and are integers greater than or equal to 1, and ;

[0050] Continuously perform temperature and humidity forecasts, and control heaters, coolers, humidifiers and dehumidifiers based on the latest temperature and humidity forecast sequence to keep the forecast temperature and humidity data within the set range.

[0051] A constant temperature and humidity control system for a cell culture incubator, which is used to implement the above-mentioned constant temperature and humidity control method for a cell culture incubator, comprising:

[0052] Data acquisition module: used to obtain the initial temperature data set, initial humidity data set, external temperature data set and external humidity data set of the cell culture box. Continuously collect data at fixed time intervals Second, based on the initial humidity data set and the initial humidity data set, a compensated temperature data set and a compensated humidity data set are obtained, and then based on the compensated temperature data set and the compensated humidity data set, an accurate temperature data set and an accurate humidity data set are calculated;

[0053] Loss coefficient generation module: used to obtain temperature sensor loss data and humidity sensor loss data, and calculate the temperature sensor loss coefficient and the humidity sensor loss coefficient based on the loss data;

[0054] Temperature and humidity compensation module: used to input the initial temperature data set and the temperature sensor loss coefficient into the pre-built temperature compensation model to obtain a compensated temperature data set; input the initial humidity data set and the humidity sensor loss coefficient into the pre-built humidity compensation model to obtain a compensated humidity data set;

[0055] Precise data generation module: used to obtain a precise temperature data set and a precise humidity data set by compensating the temperature data set and the humidity data set;

[0056] Influence coefficient generation module: used to obtain the door opening and closing event set of the cell culture box, the temperature controller state set and the hygrostat state set in the cell culture box, calculate the door opening and closing event influence coefficient, the temperature controller influence coefficient and the hygrostat influence coefficient, and calculate the insulation index of the cell culture box based on the precise temperature data set inside the cell culture box and the temperature data set outside the cell culture box;

[0057] Temperature and humidity sequence prediction module: used to obtain sample data sets, and based on the sample data sets and the pre-built temperature and humidity prediction model, obtain the temperature data sequence and humidity data sequence inside the cell culture box in the future;

[0058] Temperature and humidity control module: used to control the opening and closing and power of the heater, cooler, humidifier and dehumidifier according to the temperature data sequence and humidity data sequence in the cell culture incubator for a period of time in the future.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] The present invention obtains the loss data of the temperature and humidity sensor, calculates the loss coefficient, and inputs the initial temperature and humidity data and the loss coefficient into the constructed temperature and humidity compensation model, so as to obtain the compensated temperature and humidity data, thereby avoiding the measurement deviation caused by the sensor loss. The present invention establishes a temperature and humidity prediction model, which can predict the temperature and humidity change trend in the future, so that the temperature and humidity controller can adjust the operating state in advance, thereby achieving more accurate temperature and humidity control. At the same time, a variety of influencing factors are comprehensively considered. In addition to the temperature and humidity data in the incubator, multiple influencing factors such as door opening and closing events, temperature and humidity controller status, insulation coefficient, etc. are also included in the prediction model, making the prediction more comprehensive and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A method flow chart of a method for controlling constant temperature and humidity of a cell culture box in the present invention;

[0062] Figure 2 This is a functional module diagram of a constant temperature and humidity control system for a cell culture incubator in the present invention. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] Example 1

[0065] See also Figure 1 As shown, this embodiment provides a method for controlling constant temperature and humidity of a cell culture chamber, comprising:

[0066] S10: Obtain the initial temperature data set, initial humidity data set, external temperature data set, and external humidity data set of the cell culture incubator in the time period Continuously collect data at fixed time intervals Second-rate, is an integer greater than 1, is the starting time point, is the time interval;

[0067] The number of temperature sensors in the cell culture incubator is , initial temperature data set ,in All include A data set of temperature data, Contains the data of all temperature sensors at the first acquisition time point, Contains the data of all temperature sensors at the second acquisition time point, All temperature sensors are included in Data collected at each time point, such as ,in The first time collection point, Temperature data from temperature sensors, initial temperature data set include The number of initial temperature data in the cell culture incubator is , initial humidity data set ,in All include A dataset of humidity data, Contains the data of all humidity sensors at the first collection time point, Contains the data of all humidity sensors at the second collection time point, All humidity sensors are included in Data collected at each time point, such as ,in is the humidity data of the bth humidity sensor at the first collection time point, the initial humidity data set include Initial humidity data, and are all integers greater than or equal to 1;

[0068] The external temperature and humidity data set of the cell culture incubator is obtained by one or more sensors. Preferably, the external temperature data set of the cell culture incubator is obtained by one temperature sensor, and the external humidity data set of the cell culture incubator is obtained by one humidity sensor.

[0069] It is understandable that the installation positions of the temperature sensor and the humidity sensor are crucial for accurately measuring the temperature and humidity. If the sensor is placed in an inappropriate position in the incubator, the temperature and humidity data readings may deviate from the actual temperature and humidity. It is necessary to avoid the sensor being too close to the heater, cooler, humidifier, dehumidifier or other objects that may be generated. By installing multiple sensors, the temperature and humidity at different positions can be monitored to obtain more accurate data; the external temperature data set of the cell culture incubator can be obtained by a calibrated semiconductor temperature sensor, an infrared temperature sensor, etc., and the external humidity data set of the incubator can be obtained by a calibrated resistive humidity sensor, a capacitive humidity sensor, an infrared humidity sensor, etc., which is not specifically limited in this embodiment;

[0070] S20: Acquire temperature sensor loss data and humidity sensor loss data, and calculate the temperature sensor loss coefficient and the humidity sensor loss coefficient based on the loss data;

[0071] In an embodiment of the present invention, the temperature sensor loss data includes the real-time signal-to-noise ratio of the temperature sensor, the real-time response time of the temperature sensor, the real-time linearity of the temperature sensor, and the real-time sensitivity of the temperature sensor. The signal-to-noise ratio refers to the ratio between the signal output by the sensor and the noise contained in the sensor output. The sensor loss causes the signal-to-noise ratio to decrease. The response time refers to the time interval from the sensor being stimulated by the temperature change to the output being stable. The loss causes the response time to be delayed. The linearity refers to the linear relationship between the sensor output signal and the measured temperature. The loss causes the linear relationship between the sensor output signal and the measured temperature to be offset or nonlinear. The sensitivity refers to the sensitivity of the sensor to the temperature change when measuring the temperature. The loss causes the sensitivity to decrease.

[0072] Methods for calculating the temperature sensor loss coefficient based on the temperature sensor loss data include:

[0073] ;

[0074] In the formula, is the temperature sensor loss coefficient, is the real-time signal-to-noise ratio of the temperature sensor, is the standard signal-to-noise ratio of the temperature sensor, is the real-time response time of the temperature sensor, is the standard response time of the temperature sensor, is the real-time linearity of the temperature sensor, is the standard linearity of the temperature sensor, is the real-time sensitivity of the temperature sensor, is the standard sensitivity of the temperature sensor, is the signal-to-noise ratio of the temperature sensor, is the temperature sensor response time ratio, is the temperature sensor linearity ratio, is the temperature sensor sensitivity ratio, is the weight coefficient of the sum of the temperature sensor signal-to-noise ratio and the response time ratio, is the weight coefficient of the temperature sensor linearity ratio, is the weight coefficient of the temperature sensor sensitivity ratio;

[0075] In an embodiment of the present invention, the humidity sensor loss data includes the real-time signal-to-noise ratio of the humidity sensor, the real-time response time of the humidity sensor, the real-time linearity of the humidity sensor, and the real-time sensitivity of the humidity sensor. The method for calculating the humidity sensor loss coefficient based on the humidity sensor loss data includes:

[0076] ;

[0077] In the formula, is the loss coefficient of the humidity sensor, is the real-time signal-to-noise ratio of the humidity sensor, is the standard signal-to-noise ratio of the humidity sensor, is the real-time response time of the humidity sensor, is the standard response time of the humidity sensor, Real-time linearity of humidity sensor, is the standard linearity of the humidity sensor, is the real-time sensitivity of the humidity sensor, is the standard sensitivity of the humidity sensor, is the humidity sensor signal-to-noise ratio, is the humidity sensor response time ratio, is the humidity sensor linearity ratio, is the humidity sensor sensitivity ratio, is the weight coefficient of the sum of the humidity sensor signal-to-noise ratio and the response time ratio, is the weight coefficient of the humidity sensor linearity ratio, is the weight coefficient of the humidity sensor sensitivity ratio;

[0078] It is understood that the real-time signal-to-noise ratio, real-time response time, real-time linearity and real-time sensitivity of the sensor can be obtained by testing with professional testing instruments, and the standard signal-to-noise ratio, standard response time, standard linearity and standard sensitivity of the sensor can be obtained from the performance specification table or corresponding standard specifications provided by the manufacturer. The sensor loss coefficient quantifies the degree of performance degradation of the sensor over time;

[0079] S30: inputting the initial temperature data set and the temperature sensor loss coefficient into a pre-built temperature compensation model to obtain a compensated temperature data set; inputting the initial humidity data set and the humidity sensor loss coefficient into a pre-built humidity compensation model to obtain a compensated humidity data set;

[0080] The method of constructing the temperature compensation model includes:

[0081] Acquire a first sample data set, the first sample data set includes historical initial temperature data, historical temperature sensor loss coefficients and historical real temperature data, the first sample data set is divided into a sample training set and a sample test set, a regression network is constructed, the historical initial temperature data and the historical temperature sensor loss coefficient in the sample training set are used as input data of the regression network, the historical real temperature data in the sample training set are used as output data of the regression network, the regression network is trained to obtain an initial regression network for predicting compensation temperature data, the initial regression network is tested using the sample test set, and a regression network that satisfies a preset error value is output as a temperature compensation model, and the regression network is preferably a neural network model;

[0082] The method for constructing the humidity compensation model includes:

[0083] A second sample data set is obtained, wherein the second sample data set includes historical initial humidity data, historical humidity sensor loss coefficients, and historical real humidity data. The second sample data set is divided into a sample training set and a sample test set. A regression network is constructed, and the historical initial humidity data and the historical humidity sensor loss coefficient in the sample training set are used as input data of the regression network. The historical real humidity data in the sample training set is used as output data of the regression network. The regression network is trained to obtain an initial regression network for predicting and compensating humidity data. The initial regression network is tested using the sample test set, and a regression network that satisfies a preset error value is output as a humidity compensation model. The regression network is preferably a neural network model.

[0084] It is worth noting that the above-mentioned historical real temperature data is obtained through testing of calibrated high-precision temperature sensors, such as semiconductor temperature sensors, infrared temperature sensors, etc.; the historical real humidity data is obtained through testing of calibrated high-precision humidity sensors, such as resistive humidity sensors, capacitive humidity sensors, infrared humidity sensors, etc. This embodiment does not limit the specific models of temperature sensors and humidity sensors; by constructing a temperature and humidity compensation model and utilizing historical data and sensor loss coefficients, the accuracy and reliability of temperature and humidity sensor data are improved, which helps to more accurately control the temperature and humidity in the cell culture incubator.

[0085] S40: Calculating an accurate temperature dataset and an accurate humidity dataset based on the compensated temperature dataset and the compensated humidity dataset;

[0086] Traverse the compensated temperature dataset and compensated humidity dataset at the same time point Temperature data and humidity data, calculate the average temperature data and humidity data at the corresponding time point, obtain the average temperature data and humidity data of all collected time points to generate accurate temperature data set and accurate humidity data set;

[0087] It should be noted that there are data, calculate the compensation temperature data set at the same time point The calculated average value is used as the precise temperature data in the cell culture incubator at that time point. The precise temperature data set contains data; the compensated humidity data set has data, calculate the compensated humidity data set at the same time point The calculated average value is used as the accurate humidity data in the cell culture box at that time point. The accurate humidity data set contains The temperature and humidity at different locations in the cell culture incubator may be different. Taking the average value can reduce the error of the data set and obtain more stable and reliable temperature and humidity data.

[0088] S50: Obtain a door opening and closing event set of the cell culture box, a temperature controller state set and a humidity controller state set in the cell culture box, and calculate a door opening and closing event influence coefficient, a temperature controller influence coefficient and a humidity controller influence coefficient;

[0089] Door opening and closing event set For time period The set of all door opening and closing events that occurred within a period, each event contains the number of door openings , door open time , precise temperature inside the cell culture incubator , accurate humidity inside the cell culture incubator , cell culture incubator external temperature , cell culture incubator external humidity ;

[0090] The calculation method of the door opening and closing event impact coefficient includes:

[0091] ;

[0092] in It is The duration of the door opening, It is The precise temperature inside the cell culture incubator when the door is opened for the first time. It is The temperature outside the cell culture incubator when the door is opened for the first time, It is The precise humidity inside the cell culture incubator when the door is opened for the first time. It is The humidity outside the cell culture incubator when the door is opened for the first time;

[0093] It is understandable that when the door of the cell culture incubator is opened, air convection will occur inside and outside the incubator. The air temperature and humidity outside the incubator will affect the air humidity and temperature inside the incubator. The more times the door is opened and the longer the door is opened, the greater the impact.

[0094] It should be noted that the door opening and closing event set of the cell culture incubator can monitor the door opening and closing status in real time through the door magnetic sensor, and the duration of each door opening can be calculated by recording the timestamp of the opening and closing status. The precise temperature and humidity inside the incubator can be obtained through step S40;

[0095] The thermostat state set is a six-dimensional vector ,in For time period Internal heater operation times, is the heater operating power, The operating time of the heater, For time period Intercooler operation times, is the cooler operating power, How long does the cooler run?

[0096] The calculation method of the thermostat influence coefficient includes:

[0097] ;

[0098] Where is the thermostat influence coefficient, It is Secondary heater operating power, It is Secondary heater operation time, It is Secondary cooler operating power, It is How long the secondary cooler runs;

[0099] The hygrostat state set is also a six-dimensional vector ,in For time period Number of times the internal humidifier runs, The operating power of the humidifier is How long does the humidifier run? For time period Internal dehumidifier operation times, The operating power of the dehumidifier is How long does it take for the dehumidifier to run each time?

[0100] The calculation method of the influence coefficient of the hygrostat includes:

[0101] ;

[0102] in is the hygrostat influence coefficient, It is The power of the humidifier operation, It is The duration of the humidifier operation. It is The power of the dehumidifier operation, It is The duration of dehumidifier operation;

[0103] It should be noted that in order to maintain constant temperature and humidity in the cell culture incubator, a thermostat and a hygrostat are usually provided. The thermostat includes a heater and a cooler, and the hygrostat includes a humidifier and a dehumidifier. The more times the heater runs and the longer the running time, the faster the temperature in the cell culture incubator rises. The more times the cooler runs and the longer the running time, the faster the temperature in the cell culture incubator drops. The same is true for humidity. The number of times the thermostat and hygrostat run, the running time and the running power are obtained through the main control program. This step quantifies the impact of door opening and closing events, the operating status of the thermostat and the operating status of the hygrostat on the environment inside the cell culture incubator, and provides data support for the accurate prediction of the temperature and humidity of the incubator in the future.

[0104] S60: calculating an insulation index of the cell culture incubator based on an accurate temperature data set inside the cell culture incubator and an external temperature data set of the cell culture incubator;

[0105] The calculation method of the insulation index of the cell culture incubator includes:

[0106] ;

[0107] In the formula, is the insulation index of the cell culture incubator, For the incubator Accurate temperature data, For incubator External temperature data, is the average value of the precise temperature data inside the incubator, is the average value of the incubator external temperature data, is the volume of the internal space of the incubator;

[0108] It is understandable that poor insulation of the cell culture incubator will make the internal temperature more susceptible to the external temperature. A larger insulation index means that the temperature fluctuation inside and outside the box of the same volume is more different, and the insulation performance of the box is better. Conversely, a smaller insulation index means that the insulation performance of the box is poor. The internal space volume of the cell culture incubator can be obtained by measurement. Adding the spatial volume factor of the incubator can more accurately evaluate the insulation performance of the cell culture incubator. The insulation performance of the cell culture incubator is quantified and input into the prediction model to make the temperature and humidity prediction sequence in the future more accurate.

[0109] S70: Based on the internal space volume of the cell culture incubator, the precise temperature data set, the precise humidity data set, the external temperature data set of the cell culture incubator, the external humidity data set, the door opening and closing event influence coefficient, the thermostat influence coefficient, the hygrostat influence coefficient, the incubator insulation index and the pre-built temperature and humidity prediction model, obtain the temperature data sequence and humidity data sequence of the cell culture incubator in the future period of time;

[0110] The temperature and humidity prediction model construction method includes:

[0111] Obtain sample data sets: including door opening and closing event influence coefficient, thermostat influence coefficient, hygrostat influence coefficient, incubator insulation index, incubator internal space volume, cell incubator external historical temperature data set, cell incubator external historical humidity data set, internal historical temperature sequence, internal historical humidity sequence, true predicted temperature sequence, true predicted humidity sequence;

[0112] Model training, verification and testing: The sample data set is divided into a sample training set, a sample verification set and a sample test set, and a temperature and humidity prediction model is constructed. The door opening and closing event influence coefficient, the thermostat influence coefficient, the hygrostat influence coefficient, the incubator insulation index, the incubator internal space volume, the cell incubator external historical temperature data set, the cell incubator external historical humidity data set, the internal historical temperature sequence and the internal historical humidity sequence in the sample training set are used as the input data of the temperature and humidity prediction model. The real predicted temperature sequence and the real predicted humidity sequence in the sample training set are used as the output data of the prediction model. The prediction model is trained to obtain the initial prediction model for predicting the temperature and humidity sequence. The sample verification set is used to adjust the parameters of the prediction model, and the prediction model that satisfies the preset error value is output as the temperature and humidity prediction model. Finally, the sample test set is used to test the temperature and humidity prediction model.

[0113] It should be noted that the precise temperature data set contains Temperature data, randomly selected from The continuous data is taken as the historical temperature sequence. The continuous data is used as the real predicted temperature series; the accurate humidity data set contains humidity data, randomly selected from The continuous data is taken as the historical humidity sequence. Continuous data is used as the true predicted humidity sequence. and are integers greater than or equal to 1, and The temperature and humidity prediction model is preferably a multivariate prediction model, such as a vector autoregression model (VAR) or a vector autoregression moving average model (VARMA), which can consider the mutual influence and dependency between temperature and humidity to achieve joint prediction and control;

[0114] It is understandable that the current temperature and humidity in the cell culture incubator play a decisive role in the prediction of temperature and humidity in the future. In addition, the opening and closing of the door, the door opening time, the insulation performance of the cell culture incubator, the volume of the incubator, the temperature and humidity outside the incubator, and the working status of the thermostat and hygrostat in the incubator also have an important impact on the temperature and humidity in the incubator in the future. Adding these influencing factors to the prediction model will greatly improve the accuracy of temperature and humidity prediction;

[0115] S80: Based on the temperature data sequence and humidity data sequence of the cell culture box in the future, regulating the opening and closing and power of the heater, cooler, humidifier and dehumidifier;

[0116] According to the difference between the temperature and humidity data sequence and the set temperature and humidity, the existing controllers are used to control the opening and closing and power of the heater, cooler, humidifier and dehumidifier, such as PID (proportional-integral-differential) controller, MPC (model predictive control) controller, etc., to achieve precise control of the temperature and humidity of the cell culture incubator;

[0117] It should be noted that both PID controller and MPC controller are commonly used control strategies with high flexibility and practicality. They can provide stable control performance and can be selected according to the needs of actual applications. Continuously perform temperature and humidity predictions, and control heaters, coolers, humidifiers and dehumidifiers based on the latest temperature and humidity prediction sequence to keep the predicted temperature and humidity data within the set range.

[0118] Example 2

[0119] This embodiment provides a constant temperature and humidity control system for a cell culture chamber based on the embodiment 1. Figure 2 As shown, including:

[0120] Data acquisition module: used to obtain the initial temperature data set, initial humidity data set, external temperature data set and external humidity data set of the cell culture box. Continuously collect data at fixed time intervals Second, based on the initial humidity data set and the initial humidity data set, a compensated temperature data set and a compensated humidity data set are obtained, and then based on the compensated temperature data set and the compensated humidity data set, an accurate temperature data set and an accurate humidity data set are calculated;

[0121] Loss coefficient generation module: used to obtain temperature sensor loss data and humidity sensor loss data, and calculate the temperature sensor loss coefficient and the humidity sensor loss coefficient based on the loss data;

[0122] Temperature and humidity compensation module: input the initial temperature data set and the temperature sensor loss coefficient into the pre-built temperature compensation model to obtain the compensated temperature data set; input the initial humidity data set and the humidity sensor loss coefficient into the pre-built humidity compensation model to obtain the compensated humidity data set;

[0123] Precise data generation module: used to obtain a precise temperature data set and a precise humidity data set by compensating the temperature data set and the humidity data set;

[0124] Influence coefficient generation module: used to obtain the door opening and closing event set of the cell culture box, the temperature controller state set and the hygrostat state set in the cell culture box, calculate the door opening and closing event influence coefficient, the temperature controller influence coefficient and the hygrostat influence coefficient, and calculate the insulation index of the cell culture box based on the precise temperature data set inside the cell culture box and the temperature data set outside the cell culture box;

[0125] Temperature and humidity sequence prediction module: used to obtain sample data sets, and based on the sample data sets and the pre-built temperature and humidity prediction model, obtain the temperature data sequence and humidity data sequence inside the cell culture box in the future;

[0126] Temperature and humidity control module: used to control the opening and closing and power of the heater, cooler, humidifier and dehumidifier according to the temperature data sequence and humidity data sequence in the cell culture box for a period of time in the future;

[0127] Example 3

[0128] This embodiment discloses an electronic device, including a memory, a central processing unit, and a computer program stored in the memory and executable on the central processing unit. When the central processing unit executes the computer program, the above-mentioned method for controlling constant temperature and humidity of a cell culture incubator is implemented.

[0129] Since the electronic device introduced in this embodiment is an electronic device used to implement a cell culture incubator constant temperature and humidity control method in the embodiment of the present application, based on the cell culture incubator constant temperature and humidity control method introduced in the embodiment of the present application, the technical personnel of the field can understand the specific implementation of the electronic device of the present embodiment and its various variations, so how the electronic device implements the method in the embodiment of the present application is not described in detail here. As long as the technical personnel of the field implement the electronic device used in the cell culture incubator constant temperature and humidity control method in the embodiment of the present application, it belongs to the scope of protection of the present application.

[0130] Example 4

[0131] The present embodiment discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed, the above-mentioned method for controlling constant temperature and humidity of a cell culture incubator is implemented.

[0132] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters, weights and thresholds in the formula are set by technicians in this field according to actual conditions.

[0133] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired network or a wireless network. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD) or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0134] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0135] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0136] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic, for example, the division of the units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0137] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0138] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0139] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0140] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for controlling constant temperature and humidity of a cell culture chamber, characterized in that: include: S10: Obtain the initial temperature data set, initial humidity data set, external temperature data set, and external humidity data set of the cell culture incubator in the time period Continuously collect data at fixed time intervals Second-rate, is an integer greater than 1, is the starting time point, is the time interval; S20: Acquire temperature sensor loss data and humidity sensor loss data, and calculate the temperature sensor loss coefficient and the humidity sensor loss coefficient based on the loss data; S30: inputting the initial temperature data set and the temperature sensor loss coefficient into a pre-built temperature compensation model to obtain a compensated temperature data set; inputting the initial humidity data set and the humidity sensor loss coefficient into a pre-built humidity compensation model to obtain a compensated humidity data set; S40: Calculating an accurate temperature dataset and an accurate humidity dataset based on the compensated temperature dataset and the compensated humidity dataset; S50: Obtain a door opening and closing event set of the cell culture box, a temperature controller state set and a humidity controller state set in the cell culture box, and calculate a door opening and closing event influence coefficient, a temperature controller influence coefficient and a humidity controller influence coefficient; S60: calculating an insulation index of the cell culture incubator based on an accurate temperature data set inside the cell culture incubator and an external temperature data set of the cell culture incubator; S70: Based on the internal space volume of the cell culture incubator, the precise temperature data set, the precise humidity data set, the external temperature data set of the cell culture incubator, the external humidity data set, the door opening and closing event influence coefficient, the thermostat influence coefficient, the hygrostat influence coefficient, the incubator insulation index and the pre-built temperature and humidity prediction model, obtain the temperature data sequence and humidity data sequence of the cell culture incubator in the future period of time; S80: Based on the temperature data sequence and humidity data sequence of the cell culture incubator for a period of time in the future, regulating the on / off and power of the heater, cooler, humidifier and dehumidifier, so that the temperature data sequence and humidity data sequence inside the cell culture incubator for a period of time in the future remain within a set range; The temperature sensor loss data includes the real-time signal-to-noise ratio of the temperature sensor, the real-time response time of the temperature sensor, the real-time linearity of the temperature sensor, and the real-time sensitivity of the temperature sensor. The method for calculating the temperature sensor loss coefficient based on the temperature sensor loss data includes: ； In the formula, is the temperature sensor loss coefficient, is the real-time signal-to-noise ratio of the temperature sensor, is the standard signal-to-noise ratio of the temperature sensor, is the real-time response time of the temperature sensor, is the standard response time of the temperature sensor, is the real-time linearity of the temperature sensor, is the standard linearity of the temperature sensor, is the real-time sensitivity of the temperature sensor, is the standard sensitivity of the temperature sensor, is the signal-to-noise ratio of the temperature sensor, is the temperature sensor response time ratio, is the temperature sensor linearity ratio, is the temperature sensor sensitivity ratio, is the weight coefficient of the sum of the signal-to-noise ratio and the response time ratio of the temperature sensor, is the weight coefficient of the temperature sensor linearity ratio, is the weight coefficient of the temperature sensor sensitivity ratio; The humidity sensor loss data includes the real-time signal-to-noise ratio of the humidity sensor, the real-time response time of the humidity sensor, the real-time linearity of the humidity sensor, and the real-time sensitivity of the humidity sensor. The method for calculating the humidity sensor loss coefficient based on the humidity sensor loss data includes: ； In the formula, is the loss coefficient of the humidity sensor, is the real-time signal-to-noise ratio of the humidity sensor, is the standard signal-to-noise ratio of the humidity sensor, is the real-time response time of the humidity sensor, is the standard response time of the humidity sensor, Real-time linearity of humidity sensor, is the standard linearity of the humidity sensor, is the real-time sensitivity of the humidity sensor, is the standard sensitivity of the humidity sensor, is the humidity sensor signal-to-noise ratio, is the humidity sensor response time ratio, is the humidity sensor linearity ratio, is the humidity sensor sensitivity ratio, is the weight coefficient of the sum of the humidity sensor signal-to-noise ratio and the response time ratio, is the weight coefficient of the humidity sensor linearity ratio, is the weight coefficient of the humidity sensor sensitivity ratio; The method for constructing the temperature compensation model includes: Acquire a first sample data set, the first sample data set includes historical initial temperature data, historical temperature sensor loss coefficients and historical real temperature data, the first sample data set is divided into a sample training set and a sample test set, a regression network is constructed, the historical initial temperature data and the historical temperature sensor loss coefficient in the sample training set are used as input data of the regression network, the historical real temperature data in the sample training set is used as output data of the regression network, the regression network is trained to obtain an initial regression network for predicting compensation temperature data, the initial regression network is tested using the sample test set, and the regression network that satisfies a preset error value is output as a temperature compensation model; The method for constructing the humidity compensation model includes: Acquire a second sample data set, the second sample data set includes historical initial humidity data, historical humidity sensor loss coefficients and historical real humidity data, the second sample data set is divided into a sample training set and a sample test set, a regression network is constructed, the historical initial humidity data and the historical humidity sensor loss coefficient in the sample training set are used as input data of the regression network, the historical real humidity data in the sample training set is used as output data of the regression network, the regression network is trained to obtain an initial regression network for predicting and compensating humidity data, the initial regression network is tested using the sample test set, and the regression network that satisfies a preset error value is output as a humidity compensation model; The temperature and humidity prediction model construction method comprises: Obtain sample data sets: including door opening and closing event influence coefficient, thermostat influence coefficient, hygrostat influence coefficient, incubator insulation index, incubator internal space volume, cell incubator external historical temperature data set, cell incubator external historical humidity data set, internal historical temperature sequence, internal historical humidity sequence, true predicted temperature sequence, true predicted humidity sequence; Model training, verification and testing: The sample data set is divided into a sample training set, a sample verification set and a sample test set to construct a temperature and humidity prediction model. The door opening and closing event influence coefficient, thermostat influence coefficient, hygrostat influence coefficient, incubator insulation index, incubator internal space volume, cell incubator external historical temperature data set, cell incubator external historical humidity data set, internal historical temperature sequence and internal historical humidity sequence in the sample training set are used as input data of the temperature and humidity prediction model. The true predicted temperature sequence and true predicted humidity sequence in the sample training set are used as output data of the prediction model. The prediction model is trained to obtain an initial prediction model for predicting the temperature and humidity sequence. The sample verification set is used to adjust the parameters of the prediction model, and the output prediction model that satisfies the preset error value is used as the temperature and humidity prediction model. Finally, the temperature and humidity prediction model is tested using the sample test set.

2. A method for controlling constant temperature and humidity of a cell culture chamber according to claim 1, characterized in that: Each subset of the initial temperature data set contains A data set of temperature data, is the number of temperature sensors; each subset in the initial humidity data set contains A dataset of humidity data, is the number of humidity sensors, and are integers greater than or equal to 1.

3. A method for controlling constant temperature and humidity of a cell culture chamber according to claim 1, characterized in that: The method for calculating the accurate temperature dataset and the accurate humidity dataset based on the compensated temperature dataset and the compensated humidity dataset comprises: Traverse the compensated temperature dataset and compensated humidity dataset at the same time point Temperature data and The average temperature data and the average humidity data at the corresponding time point are calculated, and the average temperature data and the average humidity data at all the collected time points are obtained to generate an accurate temperature data set and an accurate humidity data set.

4. A method for controlling constant temperature and humidity of a cell culture chamber according to claim 1, characterized in that: The door opening and closing event set is a six-dimensional vector containing the time period All door opening and closing events that occur inside; The mathematical expression of the door opening and closing event set is: ； In the formula, is the door opening and closing event set, is the number of times the door is opened, is the length of time the door is open, is the precise temperature inside the cell culture incubator. It is the precise humidity inside the cell culture chamber. is the external temperature of the cell culture incubator, is the external humidity of the cell culture incubator; The door opening and closing event influence coefficient calculation method includes: ； In the formula, It is The duration of the door opening, It is The precise temperature inside the cell culture incubator when the door is opened for the first time. It is The temperature outside the cell culture incubator when the door is opened for the first time. It is The precise humidity inside the cell culture incubator when the door is opened for the first time. It is The humidity outside the cell culture incubator when the door is opened for the first time; The thermostat state set is a six-dimensional vector containing the time period Operating status of internal thermostat; The mathematical expression of the thermostat state set is: ； in is the thermostat state set, is the number of heater operations, is the heater operating power, is the operating time of the heater, is the number of cooler operations, is the cooler operating power, How long does the cooler run? The calculation method of the thermostat influence coefficient includes: ； In the formula, is the thermostat influence coefficient, It is Secondary heater operating power, It is Secondary heater operation time, It is Secondary cooler operating power, It is How long the secondary cooler runs; The hygrostat state set is a six-dimensional vector containing the time period Operating status of the internal hygrostat; The mathematical expression of the hygrostat state set is: ； in is the hygrostat status set, is the number of humidifier operation times, The operating power of the humidifier is How long does the humidifier run? is the number of dehumidifier operation times, The operating power of the dehumidifier is How long to run the dehumidifier; The method for calculating the influence coefficient of the humidity controller includes: ； In the formula, is the hygrostat influence coefficient, It is Secondary humidifier operating power, It is How long does the humidifier run? It is Secondary dehumidifier operating power, It is The duration of the dehumidifier operation.

5. A method for controlling constant temperature and humidity of a cell culture chamber according to claim 1, characterized in that: The method for calculating the insulation index of the cell culture box includes: ； In the formula, is the insulation index of the cell culture incubator, For the incubator Accurate temperature data, For incubator External temperature data, is the average value of the precise temperature data inside the incubator, is the average value of the incubator external temperature data, is the volume of the internal space of the incubator.

6. A method for controlling constant temperature and humidity of a cell culture chamber according to claim 1, characterized in that: The internal historical temperature series and the real predicted temperature series are obtained by: Randomly select the temperature data from the accurate temperature data set of The continuous data is taken as the historical temperature sequence. Continuous data is used as the real predicted temperature series; The internal historical humidity sequence and the real predicted humidity sequence are obtained by: Randomly select the accurate humidity data set of humidity data The continuous data is taken as the historical humidity sequence. Continuous data is used as the true predicted humidity sequence. and are integers greater than or equal to 1, and .

7. A constant temperature and humidity control system for a cell culture incubator, which is used to implement a constant temperature and humidity control method for a cell culture incubator according to any one of claims 1 to 6, characterized in that: include: Data acquisition module: used to obtain the initial temperature data set, initial humidity data set, external temperature data set and external humidity data set of the cell culture box. Continuously collect data at fixed time intervals Second, based on the initial humidity data set and the initial humidity data set, a compensated temperature data set and a compensated humidity data set are obtained, and then based on the compensated temperature data set and the compensated humidity data set, an accurate temperature data set and an accurate humidity data set are calculated; Loss coefficient generation module: used to obtain temperature sensor loss data and humidity sensor loss data, and calculate the temperature sensor loss coefficient and the humidity sensor loss coefficient based on the loss data; Temperature and humidity compensation module: used to input the initial temperature data set and the temperature sensor loss coefficient into the pre-built temperature compensation model to obtain a compensated temperature data set; input the initial humidity data set and the humidity sensor loss coefficient into the pre-built humidity compensation model to obtain a compensated humidity data set; Precise data generation module: used to obtain a precise temperature data set and a precise humidity data set by compensating the temperature data set and the humidity data set; Influence coefficient generation module: used to obtain the door opening and closing event set of the cell culture box, the temperature controller state set and the hygrostat state set in the cell culture box, calculate the door opening and closing event influence coefficient, the temperature controller influence coefficient and the hygrostat influence coefficient, and calculate the insulation index of the cell culture box based on the precise temperature data set inside the cell culture box and the temperature data set outside the cell culture box; Temperature and humidity sequence prediction module: used to obtain sample data sets, and based on the sample data sets and the pre-built temperature and humidity prediction model, obtain the temperature data sequence and humidity data sequence inside the cell culture box in the future; Temperature and humidity control module: used to control the opening and closing and power of the heater, cooler, humidifier and dehumidifier according to the temperature data sequence and humidity data sequence in the cell culture incubator for a period of time in the future.

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