Automatic platform suitable for laboratory

Abstract

The invention relates to the technical field of laboratory automation, and discloses an automatic platform suitable for a laboratory, which comprises a sample processing module, a detection module, a control module and a software module. According to the automatic platform suitable for the laboratory, the sample processing module optimizes operation of a pneumatic control system, a high-precision pump and a magnetic stirrer through a PID control algorithm, when a sample is loaded, the PID algorithm adjusts the pressure of an air cylinder through the pneumatic control system, once it is detected that the position of the sample deviates from the target position, the PID algorithm will calculate the pressure value needing to be adjusted, and the pressure value needs to be adjusted. The pneumatic control system adjusts the pressure of the air cylinder according to the pressure until the sample is accurately in place, similarly, in the liquid separation process, the PID algorithm adjusts the liquid separation amount by controlling the rotating speed and the stroke distance of the pump, and accurate liquid separation is ensured; in the mixing process, the PID algorithm ensures that samples are uniformly mixed by controlling the speed of the magnetic stirrer, personal errors are reduced through the accurate control, and the consistency and repeatability of experiments are improved.

Description

Technical Field

[0001] The invention relates to the technical field of laboratory automation, and in particular to an automation platform suitable for laboratories. Background Art

[0002] In modern laboratories, the application of automation technology has greatly improved experimental efficiency and accuracy, especially in the fields of biomedicine, chemical analysis, etc. Traditional manual experimental methods are not only time-consuming and labor-intensive, but also easily affected by human factors, resulting in unstable experimental results. In order to overcome these shortcomings, scientists have begun to seek to introduce advanced automation technology into laboratories, such as robotic arms, automated sample processing systems, etc. However, existing automation solutions often have problems such as complex operation, high maintenance costs, and difficulty in integration. Especially for small laboratories, how to build an efficient and economical automation platform is an urgent problem to be solved.

[0003] With the development of computer technology and sensor technology, PID control algorithms and adaptive filtering algorithms have been widely used in industrial production to achieve precise control and signal processing. In laboratory environments, if these mature control theories can be effectively applied to experimental equipment, the accuracy and repeatability of the experiment will be greatly improved. However, most laboratory automation platforms currently have a relatively simple control strategy and lack flexible and changeable control methods to meet different experimental needs. In addition, data exchange and communication between laboratory instruments also face the problem of insufficient standardization, which limits the overall performance of the automation system. Therefore, it is particularly important to develop a laboratory automation platform that integrates multiple detection methods and intelligent control algorithms. It can not only simplify the experimental operation process, but also significantly improve the quality of experimental data. Therefore, we propose an automation platform suitable for laboratories. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides an automation platform suitable for laboratories, which can not only simplify the experimental operation process, but also significantly improve the quality of experimental data. It solves the problem that most current laboratory automation platforms have a relatively single control strategy and lack flexible and changeable control methods to cope with different experimental needs. In addition, data exchange and communication between laboratory instruments also face the problem of insufficient standardization, which limits the overall performance of the automation system.

[0006] (II) Technical solution

[0007] In order to achieve the above-mentioned purpose of not only simplifying the experimental operation process but also significantly improving the quality of experimental data, the present invention provides the following technical solutions: an automation platform suitable for a laboratory, characterized in that it includes a sample processing module, a detection module, a control module and a software module;

[0008] The sample processing module includes sample loading, liquid separation and mixing functions. The sample loading function adopts a pneumatic control system, and the air pressure is controlled by a PID control algorithm to achieve rapid sample loading; the liquid separation function adopts a high-precision pump, and the speed and flow of the pump are controlled by a PID control algorithm to achieve accurate sample separation; the mixing function adopts a magnetic stirrer, and the stirring speed is controlled by a PID control algorithm to achieve uniform mixing of the sample;

[0009] The detection module includes optical detection, electrochemical detection and mass spectrometry detection means, wherein optical detection uses a spectrometer and an adaptive filtering algorithm is used to improve the signal-to-noise ratio of the detection signal; electrochemical detection uses an electrochemical workstation and an adaptive filtering algorithm is used to improve the signal-to-noise ratio of the detection signal; mass spectrometry detection uses a mass spectrometer and an adaptive filtering algorithm is used to improve the signal-to-noise ratio of the detection signal;

[0010] The control module: adopts a PLC controller, realizes real-time control and data transmission of each module through an event-driven model, and realizes communication with a host computer through an Ethernet interface;

[0011] The software module provides a user operation interface, data processing and analysis functions, and adopts a modular design to facilitate functional expansion and upgrading.

[0012] Preferably, during the sample loading process of the sample processing module, the PID algorithm adjusts the cylinder pressure through the pneumatic control system to ensure that the sample enters the specified position smoothly and quickly. When it is detected that the sample position deviates from the target position, the PID algorithm calculates the pressure value that needs to be adjusted, and the pneumatic control system adjusts the cylinder pressure accordingly until the sample reaches the correct position. The formula of the PID algorithm is:

[0013]

[0014] Among them, u(t) represents the control signal, e(t) represents the deviation, K p , K i and K d Represents proportional, integral and derivative gains respectively.

[0015] Preferably, during the liquid separation process of the sample processing module, the PID algorithm controls the sample separation volume through the rotation speed and stroke distance of the high-precision pump. When it is detected that the actual separation volume deviates from the set value, the PID algorithm adjusts the pump rotation speed until the separation volume reaches the set value. During the mixing process, the PID algorithm controls the mixing degree of the sample through the speed of the magnetic stirrer. When it is detected that the sample mixing degree does not meet the standard, the PID algorithm adjusts the speed of the stirrer until the mixing is uniform.

[0016] Preferably, in the optical detection means of the detection module, the adaptive filtering algorithm improves the quality of the spectral signal by filtering out the background noise. The adaptive filtering algorithm optimizes the signal quality after filtering by continuously adjusting the filter coefficient. The error function of the adaptive filtering algorithm is:

[0017]

[0018] Among them, y[n] is the expected output, is the filter output.

[0019] Preferably, in the electrochemical detection means of the detection module, the adaptive filtering algorithm improves the quality of the electrochemical signal by filtering out the background noise, and the adaptive filtering algorithm also optimizes the quality of the signal after filtering by adjusting the filter coefficient; in the mass spectrometry detection means, the adaptive filtering algorithm improves the quality of the mass spectrometry signal by filtering out the background noise, and the adaptive filtering algorithm also optimizes the quality of the signal after filtering by adjusting the filter coefficient.

[0020] Preferably, the control module adopts an event-driven model to realize real-time control and data transmission of each module, specifically including:

[0021] An event detection unit, used to detect events that occur in the experiment;

[0022] An event processing unit, which is used to respond to detected events and perform corresponding operations;

[0023] The event queue is used to temporarily store detected events to ensure that events are processed in order;

[0024] The event trigger mechanism is used to detect the occurrence of events and notify the event processing unit through interrupts or other means.

[0025] Preferably, the event detection unit comprises the following sensors:

[0026] Temperature sensor, used to detect temperature changes during the experiment;

[0027] Pressure sensor, used to detect pressure changes during the experiment;

[0028] Flow sensor, used to detect fluid flow during the experiment;

[0029] Position sensor, used to detect position changes of experimental equipment.

[0030] Preferably, the event processing unit includes the following processing logic:

[0031] After the sample loading is completed, the dispensing operation is triggered;

[0032] Trigger sample mixing after dispensing is complete;

[0033] The detection operation is triggered after the mixing is completed.

[0034] Preferably, the event queue organizes events according to the first-in-first-out principle to ensure that events are processed in order; the event triggering mechanism includes the following mechanisms:

[0035] The interrupt mechanism immediately interrupts the current processing and turns to the event processing unit when an event is detected;

[0036] Polling mechanism, regularly checking whether there are new events.

[0037] Preferably, in the software module, the user interface is specifically implemented as follows:

[0038] The user interface adopts a graphical design and provides a friendly operating environment. Users can input experimental parameters through the interface and start the experimental process;

[0039] The data processing module can perform statistical analysis on the experimental data and generate reports. The data processing module uses the SQL query language to process the experimental data stored in the database.

[0040] (III) Beneficial effects

[0041] Compared with the prior art, the present invention provides an automation platform suitable for laboratories, which has the following beneficial effects:

[0042] 1. This automated platform is suitable for laboratories. The sample processing module uses PID control algorithms to optimize the operation of pneumatic control systems, high-precision pumps and magnetic stirrers. When the sample is loaded, the PID algorithm adjusts the cylinder pressure through the pneumatic control system to ensure that the sample enters the specified position smoothly and quickly. Once a deviation between the sample position and the target position is detected, the PID algorithm will calculate the pressure value that needs to be adjusted, and the pneumatic control system will adjust the cylinder pressure accordingly until the sample is accurately in place. Similarly, during the liquid separation process, the PID algorithm adjusts the liquid separation amount by controlling the speed and stroke distance of the pump to ensure accurate liquid separation; during the mixing process, the PID algorithm ensures that the sample is mixed evenly by controlling the speed of the magnetic stirrer. This precise control reduces human errors and improves the consistency and repeatability of the experiment.

[0043] 2. This laboratory-based automation platform uses a detection module with an adaptive filtering algorithm. It can improve signal quality by filtering out background noise in optical detection, electrochemical detection, and mass spectrometry detection. For example, in optical detection, the adaptive filtering algorithm continuously adjusts the filter coefficient to achieve the best signal-to-noise ratio, which means that even under conditions of complex background noise, high-quality spectral signals can be obtained, thereby improving detection accuracy. Similarly, in electrochemical and mass spectrometry detection, the adaptive filtering algorithm also optimizes the filter coefficient to make the detection signal purer and more reliable. This intelligent data processing capability further simplifies the experimental operation process and improves the quality of experimental data, making the platform a powerful tool for scientific researchers to conduct precise analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the embodiments of the present invention and the accompanying drawings 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.

[0046] See also Figure 1 , an automation platform suitable for laboratories, including a sample processing module, a detection module, a control module and a software module;

[0047] Sample processing module: including sample loading, liquid separation and mixing functions. The sample loading function adopts a pneumatic control system, and the air pressure is controlled by the PID control algorithm to achieve rapid sample loading; the liquid separation function adopts a high-precision pump, and the speed and flow of the pump are controlled by the PID control algorithm to achieve accurate sample separation; the mixing function adopts a magnetic stirrer, and the stirring speed is controlled by the PID control algorithm to achieve uniform mixing of the sample;

[0048] Detection module: including optical detection, electrochemical detection and mass spectrometry detection methods. Optical detection uses a spectrometer and uses an adaptive filtering algorithm to improve the signal-to-noise ratio of the detection signal; electrochemical detection uses an electrochemical workstation and uses an adaptive filtering algorithm to improve the signal-to-noise ratio of the detection signal; mass spectrometry detection uses a mass spectrometer and uses an adaptive filtering algorithm to improve the signal-to-noise ratio of the detection signal;

[0049] Control module: adopts PLC controller, realizes real-time control and data transmission of each module through event-driven model, and realizes communication with the host computer through Ethernet interface;

[0050] Software module: provides user operation interface, data processing and analysis functions, and adopts modular design to facilitate function expansion and upgrading.

[0051] Specifically, the PID algorithm allows the automation platform to dynamically adjust the pressure of the cylinder according to the current state (i.e., the sample position), thereby accurately controlling the position of the sample. This ensures that consistent results are obtained in each experiment and reduces errors caused by different sample placement positions. When a deviation is detected between the sample position and the target position, the PID algorithm can calculate the required adjustment amount and adjust the pneumatic control system in real time through the control signal u(t). By using the PID algorithm to optimize the pneumatic control system, the efficiency of sample loading can be improved, the sample can reach the specified position quickly, and the experimental process can be accelerated.

[0052] Specifically, the PID algorithm monitors the dispensing volume and mixing degree in real time, and dynamically adjusts the control signal according to the detected deviation to ensure that the dispensing volume meets the set value and the sample is mixed evenly. This closed-loop control system can effectively reduce errors caused by manual operation and improve the consistency and repeatability of experimental results. By automatically controlling the speed of the pump and the speed of the agitator, the workload of the experimenter is reduced and the experimental efficiency is improved. At the same time, since the automation process reduces the chance of human intervention, it also helps to avoid human errors.

[0053] Specifically, by minimizing the error function:

[0054]

[0055] The adaptive filtering algorithm ensures that the filtered signal is as close to the real signal (i.e. the expected output) as possible. By minimizing the difference between the two, the algorithm can ensure the accuracy of the output signal, thereby improving the accuracy of the entire optical detection process.

[0056] The adaptive filtering algorithm can adapt to various complex background noise environments. In the actual experimental process, the noise source may be changeable, and the adaptive filter can automatically adjust its parameters according to the current noise situation, thereby providing the best filtering performance at any given moment.

[0057] Specifically, the adaptive filtering algorithm improves the quality of electrochemical and mass spectrometry signals by filtering out background noise. During the experiment, due to external interference or the noise of the instrument itself, the original signal is often mixed with unnecessary noise components, which will affect the authenticity and reliability of the signal. The adaptive filtering algorithm can effectively identify and remove these noises, making the signal purer.

[0058] By continuously adjusting the filter coefficients, the quality of the filtered signal is optimized. The adaptive filtering algorithm can dynamically adjust its parameters according to the signal characteristics, so as to achieve the best filtering effect under different experimental conditions. This method not only improves the signal-to-noise ratio of the signal, but also enhances the adaptability and robustness of the automation platform.

[0059] The high-quality signal obtained after filtering can more accurately reflect the true information of the sample, which is crucial for electrochemical detection and mass spectrometry detection. Higher signal quality means higher detection accuracy, which is very important for scientific research and data analysis.

[0060] Specifically, the event-driven model can ensure that the automation platform responds to various events in a timely manner. When any event that needs to be processed occurs in the automation platform, such as temperature changes or pressure changes detected by sensors, the event detection unit will immediately record the event in the event queue, and then notify the event processing unit through the event trigger mechanism. In this way, the automation platform can respond to and process these events in real time, improving the response speed of the automation platform.

[0061] The event queue organizes events according to the first-in-first-out principle, ensuring that events can be processed in order. This means that even if multiple events occur almost simultaneously, the automation platform can process them one by one in the order in which they are detected, avoiding confusion or omissions in processing and ensuring the orderly operation of the automation platform.

[0062] The event-driven model activates the corresponding processing logic only when an event occurs, rather than continuously polling all possible events. This design reduces the usage of CPU and other system resources and improves the efficiency of the automation platform, especially when dealing with multiple tasks or multiple events concurrently.

[0063] Specifically, the processing logic contained in the event processing unit is as follows:

[0064] Triggering the dispensing operation after sample loading is completed: When the sample is successfully loaded, the automation platform will automatically trigger the next step - the dispensing operation; this logic ensures the continuity and automation of sample processing, reduces the need for human intervention, and improves processing efficiency;

[0065] Trigger sample mixing after dispensing: Once the dispensing operation is completed, the automation platform will automatically start the sample mixing process; this sequence arrangement ensures the consistency between the experimental steps, and each stage immediately enters the next stage after the end, avoiding unnecessary waiting time;

[0066] Trigger the detection operation after mixing is completed: After the sample mixing reaches the predetermined standard, the detection operation will be started immediately; this design not only simplifies the operation process, but also ensures the consistency of experimental conditions, thereby improving the accuracy and repeatability of experimental results;

[0067] The main advantages and effects of these processing logics include:

[0068] High degree of automation: Through pre-set processing logic, the entire sample processing process is automated, without the need for manual supervision of each step, reducing human errors during the experiment;

[0069] Process coordination: The processing logic ensures the coordination and consistency between the various steps in the sample processing process. The completion of each link immediately triggers the next step, forming a seamless automated pipeline;

[0070] Experimental efficiency: Automated processing logic can significantly improve experimental efficiency, reduce waiting time, and make the entire process from sample loading to final detection more compact and efficient;

[0071] Data quality: Automated and standardized processes help improve the quality of experimental data, ensuring that each experiment is conducted under the same conditions, thereby improving the consistency and reliability of experimental results.

[0072] Specifically, the event queue organizes events according to the first-in-first-out (FIFO) principle to ensure that events are processed in order, and the event triggering mechanism includes an interrupt mechanism and a polling mechanism. Together, these two ensure that the automation platform handles events efficiently. The specific advantages and functions are as follows:

[0073] FI FO principle of event queue:

[0074] Orderly processing: The FIFO principle ensures that events are processed in the order in which they enter the queue, avoiding confusion or omissions in event processing and ensuring consistency and predictability of the automation platform's behavior;

[0075] Fairness: Each event has a chance to be processed, and previous events will not be ignored due to subsequent events, thus ensuring fair treatment of all events;

[0076] Traceability: The FIFO organization of the event queue makes it easy to track the history of event processing, facilitating debugging and troubleshooting;

[0077] Event triggering mechanism:

[0078] Interrupt mechanism:

[0079] Immediate response: When an event is detected, the current processing is immediately interrupted and transferred to the event processing unit. This mechanism ensures that the automation platform can respond to important events quickly and reduce latency.

[0080] Priority processing: For events that need to be processed immediately, the interrupt mechanism allows the automation platform to skip the currently executing task and directly handle the emergency event, thus improving the real-time performance of the automation platform;

[0081] Polling mechanism:

[0082] Periodic inspection: Regularly check whether there are new events. It is suitable for those events that are not urgent but still need to be checked regularly, ensuring the periodic inspection requirements of the automation platform;

[0083] Resource saving: Compared with continuously monitoring all possible events, the polling mechanism does not consume additional resources when no events occur, thus saving automation platform resources.

[0084] In summary, the sample processing module of this automated platform suitable for laboratories uses the PID control algorithm to optimize the operation of the pneumatic control system, high-precision pump and magnetic stirrer. When the sample is loaded, the PID algorithm adjusts the cylinder pressure through the pneumatic control system to ensure that the sample enters the specified position smoothly and quickly. Once a deviation between the sample position and the target position is detected, the PID algorithm will calculate the pressure value that needs to be adjusted, and the pneumatic control system will adjust the cylinder pressure accordingly until the sample is accurately in place. Similarly, during the liquid separation process, the PID algorithm adjusts the liquid separation amount by controlling the speed and stroke distance of the pump to ensure accurate liquid separation; during the mixing process, the PID algorithm ensures that the sample is mixed evenly by controlling the speed of the magnetic stirrer. This precise control reduces human errors and improves the consistency and repeatability of the experiment.

[0085] This laboratory-suitable automation platform, through the detection module adopting the adaptive filtering algorithm, can improve the signal quality by filtering out the background noise in optical detection, electrochemical detection and mass spectrometry detection. For example, in optical detection, the adaptive filtering algorithm continuously adjusts the filter coefficient to achieve the best signal-to-noise ratio, which means that even under the condition of complex background noise, high-quality spectral signals can be obtained, thereby improving the detection accuracy. Similarly, in electrochemical and mass spectrometry detection, the adaptive filtering algorithm also optimizes the filter coefficient to make the detection signal purer and more reliable. This intelligent data processing capability further simplifies the experimental operation process and improves the quality of experimental data, making the platform a powerful tool for scientific researchers to conduct precise analysis. It solves the problem that most current laboratory automation platforms are relatively single in control strategy and lack flexible and changeable control methods to cope with different experimental needs. In addition, data exchange and communication between laboratory instruments also face the problem of insufficient standardization, which limits the overall performance of the automation system.

[0086] The relevant modules involved in this system are all hardware system modules or functional modules that combine computer software programs or protocols with hardware in the prior art. The computer software programs or protocols involved in the functional modules are themselves technologies that are well known to those skilled in the art and are not improvements of this system. The improvements of this system are the interaction or connection relationships between the modules, that is, improvements to the overall structure of the system to solve the corresponding technical problems to be solved by this system.

[0087] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An automated platform suitable for a laboratory, characterized in that: It includes a sample processing module, a detection module, a control module and a software module; The sample processing module includes sample loading, liquid separation and mixing functions. The sample loading function adopts a pneumatic control system, and the air pressure is controlled by a PID control algorithm to achieve rapid sample loading; the liquid separation function adopts a high-precision pump, and the speed and flow of the pump are controlled by a PID control algorithm to achieve accurate sample separation; the mixing function adopts a magnetic stirrer, and the stirring speed is controlled by a PID control algorithm to achieve uniform mixing of the sample; The detection module includes optical detection, electrochemical detection and mass spectrometry detection means, wherein optical detection uses a spectrometer and an adaptive filtering algorithm is used to improve the signal-to-noise ratio of the detection signal; electrochemical detection uses an electrochemical workstation and an adaptive filtering algorithm is used to improve the signal-to-noise ratio of the detection signal; mass spectrometry detection uses a mass spectrometer and an adaptive filtering algorithm is used to improve the signal-to-noise ratio of the detection signal; The control module: adopts a PLC controller, realizes real-time control and data transmission of each module through an event-driven model, and realizes communication with a host computer through an Ethernet interface; The software module provides a user operation interface, data processing and analysis functions, and adopts a modular design to facilitate functional expansion and upgrading.

2. An automated platform suitable for a laboratory according to claim 1, characterized in that: During the sample loading process of the sample processing module, the PID algorithm adjusts the cylinder pressure through the pneumatic control system to ensure that the sample enters the specified position smoothly and quickly. When it is detected that the sample position deviates from the target position, the PID algorithm calculates the pressure value that needs to be adjusted, and the pneumatic control system adjusts the cylinder pressure accordingly until the sample reaches the correct position. The formula of the PID algorithm is: Among them, u(t) represents the control signal, e(t) represents the deviation, K p , K i and K d Represents proportional, integral and derivative gains respectively.

3. An automated platform suitable for a laboratory according to claim 1, characterized in that: During the liquid separation process of the sample processing module, the PID algorithm controls the sample separation volume through the rotation speed and stroke distance of the high-precision pump. When it is detected that the actual separation volume deviates from the set value, the PID algorithm adjusts the pump rotation speed until the separation volume reaches the set value. During the mixing process, the PID algorithm controls the mixing degree of the sample through the speed of the magnetic stirrer. When it is detected that the sample mixing degree does not meet the standard, the PID algorithm adjusts the speed of the stirrer until the mixing is uniform.

4. The automated platform suitable for a laboratory according to claim 1, characterized in that: In the optical detection means of the detection module, the adaptive filtering algorithm improves the quality of the spectral signal by filtering out the background noise. The adaptive filtering algorithm optimizes the signal quality after filtering by continuously adjusting the filter coefficient. The error function of the adaptive filtering algorithm is: Among them, y[n] is the expected output, is the filter output.

5. The automated platform suitable for a laboratory according to claim 1, characterized in that: In the electrochemical detection means of the detection module, the adaptive filtering algorithm improves the quality of the electrochemical signal by filtering out the background noise, and the adaptive filtering algorithm also optimizes the quality of the signal after filtering by adjusting the filter coefficient; in the mass spectrometry detection means, the adaptive filtering algorithm improves the quality of the mass spectrometry signal by filtering out the background noise, and the adaptive filtering algorithm also optimizes the quality of the signal after filtering by adjusting the filter coefficient.

6. The automated platform suitable for a laboratory according to claim 1, characterized in that: The control module adopts an event-driven model to realize real-time control and data transmission of each module, specifically including: An event detection unit, used to detect events that occur in the experiment; An event processing unit, which is used to respond to detected events and perform corresponding operations; The event queue is used to temporarily store detected events to ensure that events are processed in order; The event trigger mechanism is used to detect the occurrence of events and notify the event processing unit through interrupts or other means.

7. An automated platform suitable for a laboratory according to claim 6, characterized in that: The event detection unit includes the following sensors: Temperature sensor, used to detect temperature changes during the experiment; Pressure sensor, used to detect pressure changes during the experiment; Flow sensor, used to detect fluid flow during the experiment; Position sensor, used to detect position changes of experimental equipment.

8. An automated platform suitable for a laboratory according to claim 6, characterized in that: The event processing unit includes the following processing logic: After the sample loading is completed, the dispensing operation is triggered; Trigger sample mixing after dispensing is complete; The detection operation is triggered after the mixing is completed.

9. The automated platform suitable for a laboratory according to claim 6, characterized in that: The event queue organizes events according to the first-in-first-out principle to ensure that events are processed in order; the event triggering mechanism includes the following mechanisms: The interrupt mechanism immediately interrupts the current processing and turns to the event processing unit when an event is detected; Polling mechanism, regularly checking whether there are new events.

10. An automated platform suitable for a laboratory according to claim 6, characterized in that: In the software module, the specific implementation of the user interface is: The user interface adopts a graphical design and provides a friendly operating environment. Users can input experimental parameters through the interface and start the experimental process; The data processing module can perform statistical analysis on the experimental data and generate reports. The data processing module uses the SQL query language to process the experimental data stored in the database.

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