Cell culture synchronous control system based on artificial intelligence

Abstract

The invention relates to the technical field of cell culture, in particular to an artificial intelligence-based cell culture synchronous control system, which comprises a data acquisition module, a data processing module, an intelligent prediction module, a control strategy generation module, an execution mechanism control module, a feedback regulation module and a user interaction module, the data acquisition module acquires environmental parameters in a culture environment in real time, the data processing module preprocesses the acquired environmental parameters, the intelligent prediction module predicts the change trend of the culture environment in future time by using a machine learning algorithm, and the control strategy generation module generates a cell culture control strategy. The execution mechanism control module converts the control strategy into an execution instruction, the feedback adjustment module monitors the effect of the execution instruction and adjusts the control strategy according to feedback information, and the user interaction module provides a user interaction interface. According to the invention, through comprehensive real-time monitoring, intelligent prediction, optimal control and real-time feedback adjustment, the precision, efficiency and safety of cell culture are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cell culture, and in particular to a cell culture synchronization control system based on artificial intelligence. Background Art

[0002] With the rapid development of biomedicine, genetic engineering, and regenerative medicine, cell culture technology has become a key link in research and application. Traditional cell culture methods usually rely on manual operations, involving complex and tedious steps, which are prone to human errors and affect the stability and repeatability of experimental results. In order to improve the efficiency and accuracy of cell culture, automated control systems have been gradually introduced into the cell culture process in recent years. However, simple automated systems often lack sufficient intelligence and flexibility, and are difficult to cope with complex changes in the culture environment and diverse cell needs.

[0003] The existing automated cell culture systems have many functional limitations, which are mainly reflected in the following aspects: First, the system lacks a comprehensive real-time monitoring and feedback adjustment mechanism, making it difficult to achieve precise control and dynamic adjustment of the culture environment. Second, the control strategies are mostly preset static parameters, which cannot be intelligently predicted and optimized according to environmental changes, resulting in insufficient flexibility and adaptability of the culture process. Summary of the invention

[0004] Based on the above objectives, the present invention provides a cell culture synchronization control system based on artificial intelligence.

[0005] The cell culture synchronization control system based on artificial intelligence includes a data acquisition module, a data processing module, an intelligent prediction module, a control strategy generation module, an actuator control module, a feedback regulation module and a user interaction module, wherein; The data acquisition module is used to collect environmental parameters in the culture environment in real time, including temperature, humidity, CO2 concentration, pH value, dissolved oxygen content and light intensity; The data processing module is used to pre-process the collected environmental parameters, including data cleaning and outlier detection, and generate a status report of the culture environment; The intelligent prediction module is used to predict the changing trend of the culture environment in the future period of time based on historical data and current data using a machine learning algorithm; The control strategy generation module is used to generate an optimized cell culture control strategy based on the prediction results; The actuator control module is used to convert the control strategy into execution instructions to control the incubator, heater, humidity controller, CO2 supply system and lighting control system equipment; The feedback adjustment module is used to monitor the effect of executing instructions and adjust the control strategy in real time according to feedback information to ensure the stability and consistency of the cell culture process; The user interaction module is used for the user to view the culture status, set the culture parameters and manually intervene in the culture process.

[0006] Optionally, the data acquisition module includes the following sensors: Temperature sensor: used to collect temperature data in the culture environment, using high-precision thermocouples or thermistors; Humidity sensor: used to collect humidity data in the culture environment, using capacitive or resistive humidity sensors; CO2 concentration sensor: used to collect CO2 concentration data in the culture environment, using non-dispersive infrared sensing technology; pH sensor: used to collect the pH value of the culture solution, using an electrochemical sensor; Dissolved oxygen sensor: used to measure the dissolved oxygen content in the culture medium, using fluorescence method or electrochemical method; Light sensor: used to monitor the light intensity of the culture environment, using a photodiode or photodetector.

[0007] Optionally, the data processing module includes a data storage unit, a data analysis unit and a status report generation unit, wherein; The data storage unit is used to store the collected historical data and real-time data of environmental parameters; The data analysis unit is used to perform data cleaning and outlier detection on the stored data; The status report generating unit generates a status report of the culture environment according to the processed data, which includes the real-time value, mean value, maximum value, minimum value and fluctuation range of each parameter.

[0008] Optionally, the intelligent prediction module uses a long short-term memory network algorithm to predict the culture environment, specifically including the following steps: Data standardization: Receive historical data and real-time data pre-processed by the data processing module, and perform standardization on the historical data and real-time data; Constructing a long short-term memory network model: Building a neural network model that includes an input layer, a long short-term memory network layer, a fully connected layer, and an output layer; Model training: The standardized historical data is divided into training set and validation set according to the time series. The long short-term memory network model is trained using the training set data. The model parameters are adjusted to minimize the prediction error. During the training process, the model's weights and bias items are repeatedly adjusted to optimize the model's prediction ability. After the model training is completed, the model performance is verified using the validation set data to ensure the model's generalization ability. Prediction output: Use the trained long short-term memory network model to input real-time data to predict future environmental parameters.

[0009] Optionally, the control strategy generation module specifically includes: Receive prediction results: The control strategy generation module receives the prediction results of environmental parameters in the future from the intelligent prediction module. The prediction results include temperature, humidity, CO2 concentration, pH value, dissolved oxygen content and light intensity; Define target values: Set target values ​​for each environmental parameter based on the needs of cell culture; Calculate control deviation: Calculate the deviation between the predicted value and the target value for each environmental parameter; Generate preliminary control strategy: Calculate preliminary control strategy based on deviation and adjust corresponding equipment through PID controller; Optimize control strategy: Particle swarm optimization algorithm is used to further optimize the control strategy.

[0010] Optionally, the actuator control module specifically includes: Receiving control strategy: The actuator control module receives the optimized control strategy from the control strategy generation module. The control strategy includes specific adjustment values ​​of parameters such as temperature, humidity, CO2 concentration, and light intensity; Analyze control strategies: decompose control strategies into specific execution instructions; Generate execution instructions: Generate specific equipment operation instructions based on the analyzed control strategy; Send execution instructions: Send the generated execution instructions to the corresponding actuators, such as heaters, humidity controllers, CO2 supply systems and lighting control systems.

[0011] Optionally, the feedback adjustment module specifically includes: Collect feedback data: The feedback adjustment module obtains environmental parameter data from the data acquisition module in real time, including temperature, humidity, CO2 concentration, pH value, dissolved oxygen content and light intensity; Calculate the actual effect: compare the environmental parameters collected in real time with the defined target values ​​and calculate the deviation of each parameter; Analyze feedback data: Compare and analyze actual operation data and target values ​​to evaluate the execution effect of control strategy. If the deviation is large, it means that the control strategy needs to be adjusted; if the deviation is small, it means that the control strategy is well executed. Adjust control strategy: According to feedback data and calculated deviations, adjust control strategy in real time, and adjust control strategy through PID control algorithm to make each parameter closer to the target value; Generate new execution instructions: convert the adjusted control strategy into new execution instructions and send them to the corresponding equipment through the actuator control module to adjust the culture environment in real time; Real-time feedback loop: The feedback regulation module continuously cycles the above steps to form a closed-loop control, ensuring the stability and consistency of the cell culture environment through continuous monitoring, analysis and adjustment.

[0012] Optionally, the user interaction module provides a user interaction interface, including real-time data display and parameter setting, wherein; The real-time data display includes: Real-time data acquisition: The user interaction module obtains real-time environmental parameter data from the data processing module, and the environmental parameter data is updated in real time through the data acquisition module; Graphical display: Display real-time data in a graphical way on the user interface; Historical data query: Users can query historical data through the user interface and analyze the changing trend of the cultivation environment; The parameter setting includes: Parameter input: The user sets the target values ​​of various culture parameters through the user interaction interface, including temperature target value, humidity target value, CO2 concentration target value, pH value target value, dissolved oxygen target value and light intensity target value; Parameter submission: Submit the parameters set by the user to the control strategy generation module to define the target value.

[0013] Optionally, the user interaction module provides a manual control option for the user to manually intervene in the cultivation process when necessary, allowing the user to directly adjust the operating status of the device.

[0014] Beneficial effects of the present invention: The present invention collects environmental parameters in the culture environment in real time through the data acquisition module, and pre-processes the collected data using the data processing module. The intelligent prediction module uses the long short-term memory network algorithm to accurately predict the environmental change trend in the future based on historical data and current data. By combining real-time data and prediction results, the system can accurately adjust the control strategy to ensure the stability and consistency of the culture environment, thereby significantly improving the accuracy and effect of cell culture.

[0015] In the present invention, the control strategy generation module generates an optimized cell culture control strategy based on the intelligent prediction results, including temperature control, humidity control, CO2 concentration control and light intensity control. The actuator control module converts the control strategy into specific execution instructions, and adjusts the operating status of the incubator, heater, humidity controller, CO2 supply system and light control system in real time. The feedback adjustment module continuously monitors the execution effect and environmental parameters, and uses the PID control algorithm to make real-time adjustments and optimizations to form a closed-loop control system. This dynamic adjustment mechanism ensures the stability and adaptability of the cell culture process, effectively responds to environmental changes, and improves the culture efficiency and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A schematic diagram of a system flow of an embodiment of the present invention; Figure 2 Schematic diagram of the intelligent prediction module flow in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0019] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0020] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0021] like Figure 1-Figure 2 As shown, the cell culture synchronization control system based on artificial intelligence includes a data acquisition module, a data processing module, an intelligent prediction module, a control strategy generation module, an actuator control module, a feedback adjustment module and a user interaction module, wherein; Data acquisition module, used to collect environmental parameters in the culture environment in real time, including temperature, humidity, CO2 concentration, pH value, dissolved oxygen content and light intensity; The data processing module is used to pre-process the collected environmental parameters, including data cleaning and outlier detection, and generate a status report of the culture environment; Intelligent prediction module, which is used to predict the changing trend of the culture environment in the future based on historical data and current data using machine learning algorithms; A control strategy generation module, used to generate an optimized cell culture control strategy based on the prediction results; Actuator control module, used to convert control strategies into execution instructions to control incubators, heaters, humidity controllers, CO2 supply systems and light control system equipment; Feedback regulation module, used to monitor the effect of executing instructions and adjust the control strategy in real time according to feedback information to ensure the stability and consistency of the cell culture process; The user interaction module is used for users to view the cultivation status, set cultivation parameters and manually intervene in the cultivation process.

[0022] The data acquisition module includes the following sensors: Temperature sensor: used to collect temperature data in the culture environment, using high-precision thermocouples or thermistors; Humidity sensor: used to collect humidity data in the culture environment, using capacitive or resistive humidity sensors; CO2 concentration sensor: used to collect CO2 concentration data in the culture environment, using non-dispersive infrared sensing technology; pH sensor: used to collect the pH value of the culture solution, using an electrochemical sensor; Dissolved oxygen sensor: used to measure the dissolved oxygen content in the culture medium, using fluorescence method or electrochemical method; Light sensor: used to monitor the light intensity of the culture environment, using a photodiode or photodetector.

[0023] The data processing module includes a data storage unit, a data analysis unit and a status report generation unit, wherein; The data storage unit is used to store the collected historical data and real-time data of environmental parameters; The data analysis unit is used to perform data cleaning and outlier detection on the stored data; The median filter algorithm is used to smooth the stored data and remove noise. The median filter algorithm is calculated as: ,in, is the smoothed data, is the original data, is the window size; Based on statistical methods The principle of detecting and removing outliers is expressed as: ,in, is the data mean, is the data standard deviation, and values ​​outside this range are considered outliers; The status report generating unit generates a status report of the culture environment according to the processed data, including the real-time value, mean value, maximum value, minimum value and fluctuation range of each parameter.

[0024] The intelligent prediction module uses the long short-term memory network algorithm to predict the cultivation environment, which specifically includes the following steps: Data standardization: Receive historical data and real-time data preprocessed by the data processing module, and standardize the historical data and real-time data. Standardization is to adjust the data to a range of zero mean and one standard deviation. The formula is as follows: ,in, is the original data, is the mean of the data, is the standard deviation of the data, It is the standardized data; Constructing a long short-term memory network model: Building a neural network model that includes an input layer, a long short-term memory network layer, a fully connected layer, and an output layer. The long short-term memory network layer is the core of the model, responsible for processing time series data and capturing long-term and short-term dependencies. The fully connected layer converts the output of the long short-term memory network layer into a prediction result. Model training: Split the standardized historical data into training set and validation set according to the time series, set the appropriate time step (i.e. the length of the data sequence input to the LSTM model each time), use the training set data to train the long short-term memory network model, adjust the model parameters to minimize the prediction error, and optimize the model's prediction ability by repeatedly adjusting the model's weights and bias items during the training process. After the model training is completed, verify the model performance with the validation set data to ensure the model's generalization ability; Prediction output: Use the trained long short-term memory network model to input real-time data to predict future environmental parameters. The specific calculation formula is: ; ; in, Indicates the hidden state, Represents input data, are the weight matrices of the input, hidden and output layers respectively, and is the bias term, is the activation function.

[0025] The control strategy generation module specifically includes: Receive prediction results: The control strategy generation module receives the prediction results of environmental parameters in the future from the intelligent prediction module. The prediction results include temperature, humidity, CO2 concentration, pH value, dissolved oxygen content and light intensity; Define target values: Set target values ​​for various environmental parameters based on cell culture requirements, such as: Temperature: 37°C; Humidity: 95%; CO2 concentration: 5%; pH: 7.4; Dissolved oxygen: 20%; Light intensity: 500 lux; Set constraints, such as the physical limits of the equipment and safe operating range, for example: Temperature control range: 35°C to 39°C; Humidity control range: 90% to 100%; CO2 concentration control range: 4% to 6%; Light intensity control range: 400 lux to 600 lux; Calculate the control deviation: Calculate the deviation between the predicted value and the target value of each environmental parameter, the formula is as follows: ; in, Indicates The deviation of environmental parameters, is the target value, is the predicted value; Generate preliminary control strategy: Calculate preliminary control strategy based on deviation, and adjust corresponding equipment through PID controller. The output formula of PID controller is as follows: ; in, is the controller output, is the deviation, , and are the proportional, integral and differential coefficients respectively; Adjust the initial control strategy to ensure that each parameter is within the operating range. If the initial control strategy exceeds the limit conditions, limit it to the safe operating range; Optimize control strategy: Particle swarm optimization algorithm is used to further optimize the control strategy to ensure that the minimum deviation of the target value is achieved under the premise of meeting all constraints. The main steps of particle swarm optimization algorithm are as follows: (1) Initialize the particle swarm and define the position and velocity of each particle; (2) Calculate the fitness value of each particle and update the particle's historical best position and global best position based on the fitness value; (3) Update the particle's position and velocity according to the velocity update formula. The formula is as follows: ; ; in, is the velocity of particle i in time, is the inertia weight, and is the acceleration constant, and is a random number, is the best historical position of particle i, is the global best position, is the position of particle i at time t; The control strategy generation module can generate optimized cell culture control strategies based on the prediction results of the intelligent prediction module, combined with the target values ​​and constraints of cell culture. These strategies are calculated, adjusted and optimized to ensure that each environmental parameter remains within the optimal range and ensure the stability and consistency of the cell culture process.

[0026] The actuator control module specifically includes: Receiving control strategy: The actuator control module receives the optimized control strategy from the control strategy generation module. The control strategy includes specific adjustment values ​​of parameters such as temperature, humidity, CO2 concentration, and light intensity; Analyze the control strategy: Decompose the control strategy into specific execution instructions. Each parameter corresponds to the operation instructions of one or more devices, for example: Temperature control command: adjust the power of the heater; Humidity control command: adjust the working status of the humidifier or dehumidifier; CO2 concentration control command: adjust the flow rate of CO2 supply system; Lighting control instructions: adjust the light source intensity of the lighting system; Generate execution instructions: Generate specific device operation instructions based on the analyzed control strategy. The steps for generating each device instruction include: Temperature control command generation: According to the temperature setting value in the control strategy, the power output required by the heater is calculated, expressed as: ; in, is the power output of the heater, is the target temperature, is the current temperature, and are the proportional, integral and differential coefficients respectively; Humidity control command generation: According to the humidity set value in the control strategy, adjust the working state of the humidifier or dehumidifier, expressed as: ; in, is the humidity control output, is the target humidity, is the current humidity, and are the proportional, integral and differential coefficients respectively; CO2 concentration control command generation: according to the control strategy Concentration setting value, adjustment The flow rate of the supply system is expressed as: ; in, yes The flow rate of the supply system, is the target concentration, is current concentration, , and are the proportional, integral and differential coefficients respectively; Lighting control command generation: According to the light intensity setting value in the control strategy, adjust the light source intensity of the lighting system, expressed as: ; in, is the light source intensity of the lighting system, is the target light intensity, is the current light intensity, , and are the proportional, integral and differential coefficients respectively; Send execution instructions: Send the generated execution instructions to the corresponding actuators, such as heaters, humidity controllers, CO2 supply systems and lighting control systems.

[0027] The feedback adjustment module specifically includes: Collect feedback data: The feedback adjustment module obtains environmental parameter data from the data acquisition module in real time, including temperature, humidity, CO2 concentration, pH value, dissolved oxygen content and light intensity; Calculate the actual effect: Compare the environmental parameters collected in real time with the defined target values, and calculate the deviation of each parameter. The deviation calculation formula is as follows: ; in, Indicates The deviation of environmental parameters, is the actual measured value, is the target value in the expected control strategy; Analyze feedback data: Compare and analyze actual operation data and target values ​​to evaluate the execution effect of control strategy. If the deviation is large, it means that the control strategy needs to be adjusted; if the deviation is small, it means that the control strategy is well executed. Adjust control strategy: According to feedback data and calculated deviations, adjust control strategy in real time, and adjust control strategy through PID control algorithm to make each parameter closer to the target value; Generate new execution instructions: convert the adjusted control strategy into new execution instructions and send them to the corresponding equipment through the actuator control module to adjust the culture environment in real time; Real-time feedback loop: The feedback regulation module continuously cycles the above steps to form a closed-loop control, ensuring the stability and consistency of the cell culture environment through continuous monitoring, analysis and adjustment; The feedback regulation module can effectively monitor the effect of executing instructions and dynamically adjust the control strategy according to real-time feedback information. A closed-loop control system is used to ensure that all parameters of the cell culture environment remain within the optimal range, thereby improving the efficiency and quality of cell culture.

[0028] The user interaction module provides a user interaction interface, including real-time data display and parameter setting, among which; Real-time data displays include: Real-time data acquisition: The user interaction module obtains real-time environmental parameter data from the data processing module, and the environmental parameter data is updated in real time through the data acquisition module; Graphical display: Display real-time data in a graphical way on the user interface, for example: Line chart: displays the real-time changing trend of environmental parameters; Dashboard: intuitively displays current temperature, humidity and other key parameters; Table: lists the specific values ​​of all real-time data; Historical data query: Users can query historical data through the user interface and analyze the changing trend of the cultivation environment; Parameter settings include: Parameter input: The user sets the target values ​​of various culture parameters through the user interaction interface, including temperature target value, humidity target value, CO2 concentration target value, pH value target value, dissolved oxygen target value and light intensity target value; Parameter submission: Submit the parameters set by the user to the control strategy generation module to define the target value.

[0029] The user interaction module provides manual control options for users to manually intervene in the cultivation process when necessary, allowing users to directly adjust the operating status of the equipment, such as: Manually adjust the heater power; Manually adjust the working status of the humidity controller; Manually adjust the flow rate of the CO2 supply system; Manually adjust the light intensity of the lighting system; The user interface module provides users with the functions of viewing culture status, setting culture parameters and manually intervening in the culture process through a graphical and intuitive interface. Through real-time data display, parameter setting and manual control functions, users can easily manage and control the cell culture process, thereby improving the operability and flexibility of the system.

[0030] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0031] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A cell culture synchronization control system based on artificial intelligence, characterized in that: It includes data acquisition module, data processing module, intelligent prediction module, control strategy generation module, actuator control module, feedback adjustment module and user interaction module, among which; The data acquisition module is used to collect environmental parameters in the culture environment in real time, including temperature, humidity, CO2 concentration, pH value, dissolved oxygen content and light intensity; The data processing module is used to pre-process the collected environmental parameters, including data cleaning and outlier detection, and generate a status report of the culture environment; The intelligent prediction module is used to predict the changing trend of the culture environment in the future based on historical data and current data using a machine learning algorithm; The control strategy generation module is used to generate an optimized cell culture control strategy based on the prediction results; The actuator control module is used to convert the control strategy into execution instructions to control the incubator, heater, humidity controller, CO2 supply system and lighting control system equipment; The feedback adjustment module is used to monitor the effect of executing instructions and adjust the control strategy in real time according to feedback information; The user interaction module is used for the user to view the culture status, set the culture parameters and manually intervene in the culture process.

2. The artificial intelligence-based cell culture synchronization control system according to claim 1, characterized in that: The data acquisition module includes the following sensors: Temperature sensor: used to collect temperature data in the culture environment; Humidity sensor: used to collect humidity data in the culture environment; CO2 concentration sensor: used to collect CO2 concentration data in the culture environment; pH sensor: used to collect the pH value of the culture solution; Dissolved oxygen sensor: used to measure the dissolved oxygen content in the culture solution; Light sensor: used to monitor the light intensity of the culture environment.

3. The artificial intelligence-based cell culture synchronization control system according to claim 2, characterized in that: The data processing module includes a data storage unit, a data analysis unit and a status report generation unit, wherein; The data storage unit is used to store the collected historical data and real-time data of environmental parameters; The data analysis unit is used to perform data cleaning and outlier detection on the stored data; The status report generating unit generates a status report of the culture environment according to the processed data, which includes the real-time value, mean value, maximum value, minimum value and fluctuation range of each parameter.

4. The artificial intelligence-based cell culture synchronization control system according to claim 3, characterized in that: The intelligent prediction module uses a long short-term memory network algorithm to predict the culture environment, specifically including the following steps: Data standardization: Receive historical data and real-time data pre-processed by the data processing module, and perform standardization on the historical data and real-time data; Constructing a long short-term memory network model: Building a neural network model that includes an input layer, a long short-term memory network layer, a fully connected layer, and an output layer; Model training: Split the standardized historical data into training set and validation set, use the training set data long short-term memory network model for training, adjust the model parameters to minimize the prediction error, and optimize the model's prediction ability by repeatedly adjusting the model's weights and bias items during the training process. After the model training is completed, verify the model performance with the validation set data; Prediction output: Use the trained long short-term memory network model to input real-time data to predict future environmental parameters.

5. The artificial intelligence-based cell culture synchronization control system according to claim 4, characterized in that: The control strategy generation module specifically includes: Receiving prediction results: The control strategy generation module receives the environmental parameter prediction results from the intelligent prediction module. The prediction results include temperature, humidity, CO2 concentration, pH value, dissolved oxygen content and light intensity; Define target values: Set target values ​​for environmental parameters based on cell culture requirements; Calculate control deviation: Calculate the deviation between the predicted value and the target value for each environmental parameter; Generate preliminary control strategy: Calculate preliminary control strategy based on deviation and adjust corresponding equipment through PID controller; Optimize control strategy: Particle swarm optimization algorithm is used to further optimize the control strategy.

6. The artificial intelligence-based cell culture synchronization control system according to claim 5, characterized in that: The actuator control module specifically includes: Receiving control strategy: the actuator control module receives the optimized control strategy from the control strategy generation module; Analyze control strategies: decompose control strategies into specific execution instructions; Generate execution instructions: Generate specific equipment operation instructions based on the analyzed control strategy; Send execution instructions: Send the generated execution instructions to the corresponding execution agency.

7. The artificial intelligence-based cell culture synchronization control system according to claim 6, characterized in that: The feedback adjustment module specifically includes: Collect feedback data: The feedback adjustment module obtains environmental parameter data from the data acquisition module in real time; Calculate the actual effect: compare the environmental parameters collected in real time with the defined target values ​​and calculate the deviation of each parameter; Analyze feedback data: compare and analyze actual operation data and target values ​​to evaluate the execution effect of control strategies; Adjust control strategy: adjust control strategy in real time according to feedback data and calculated deviations; Generate new execution instructions: convert the adjusted control strategy into new execution instructions and send them to the corresponding devices through the actuator control module; Real-time feedback loop: The feedback regulation module continuously cycles the above steps to form a closed-loop control.

8. The artificial intelligence-based cell culture synchronization control system according to claim 7, characterized in that: The user interaction module provides a user interaction interface, including real-time data display and parameter setting, wherein; The real-time data display includes: Real-time data acquisition: The user interaction module obtains real-time environmental parameter data from the data processing module; Graphical display: Display real-time data in a graphical way on the user interface; Historical data query: Users can query historical data through the user interface and analyze the changing trend of the cultivation environment; The parameter setting includes: Parameter input: The user sets the target value of each training parameter through the user interface; Parameter submission: Submit the parameters set by the user to the control strategy generation module to define the target value.

9. The artificial intelligence-based cell culture synchronization control system according to claim 8, characterized in that: The user interaction module provides a manual control option for the user to manually intervene in the cultivation process when necessary, allowing the user to directly adjust the operating status of the device.

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