Collaborative operation system and method for screening medicinal effect substances of component traditional Chinese medicine

Abstract

The invention provides a collaborative operation system and method for screening drug effect substances of component traditional Chinese medicine, and the system comprises a user interaction module which is used for obtaining an operation instruction of a researcher, and carrying out the distribution and scheduling of screening experiment tasks according to the operation instruction; the sample storage module is used for storing component traditional Chinese medicine samples, experimental samples and experimental consumables; the sample transfer module is used for automatically transferring component traditional Chinese medicine samples, experiment samples and experiment consumables according to the screening experiment tasks; a sample processing module; a data acquisition module; a data analysis and storage module; and a system real-time supervision module. According to the collaborative operation system and method for screening the medicinal effect substances of the component traditional Chinese medicine, through cooperation of multiple robots, the user interaction module and the unmanned automatic system, transmission and butt joint among all the modules are completed by the robots, and continuity and consistency of sample treatment are ensured.

Description

Technical Field

[0001] The present invention belongs to the field of automation technology, and in particular relates to a collaborative operation system and method for screening effective substances of component traditional Chinese medicines. Background Art

[0002] As an important part of traditional Chinese medicine, Chinese medicine has a long history and rich clinical application experience. Component Chinese medicine is an important research discovery in the modernization of Chinese medicine. With the development of modern medicine and pharmacy, the research and development of Chinese medicine faces new challenges. Especially in the screening and development of component Chinese medicine, due to the complexity of components and the difficulty in determining active ingredients, traditional screening methods are inefficient and the results are not accurate and consistent.

[0003] At present, the screening of component Chinese medicines mainly relies on traditional methods, including manual screening, laboratory testing and empirical judgment. These methods are not only labor-intensive and time-consuming, but also difficult to ensure the accuracy and consistency of the screening results. Manual screening methods are easily affected by human factors, resulting in low screening efficiency and large errors. In addition, although laboratory tests can provide more accurate results, due to the complex process and large sample size, they often face problems such as long experimental cycles and high resource consumption. These traditional methods are difficult to meet the needs of modern Chinese medicine research and development for efficiency and accuracy.

[0004] With the rapid development of information technology and artificial intelligence technology, automation and intelligent means have been widely used in various industries. Especially in the pharmaceutical field, the application of software robots and intelligent systems has gradually become a trend. Through automation, standardization and intelligent means, the efficiency and quality of production and R&D processes can be significantly improved. Therefore, in order to solve the problems in the screening process of component traditional Chinese medicine, it is urgent to develop an intelligent screening system that integrates modern information technology. Summary of the invention

[0005] In view of this, the present invention aims to propose a collaborative operation system and method for screening effective substances of component traditional Chinese medicines, so as to solve the problem that the existing traditional screening methods of component traditional Chinese medicines have low automation levels and affect the screening efficiency and quality.

[0006] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0007] In a first aspect, an embodiment of the present invention provides a collaborative operation system for screening effective substances of component traditional Chinese medicines, comprising:

[0008] A user interaction module is used to obtain the operating instructions of researchers and to allocate and schedule screening experiment tasks according to the operating instructions;

[0009] The sample storage module is used to store component Chinese medicine samples, experimental samples, and experimental consumables;

[0010] The sample transfer module is used to automatically transfer component Chinese medicine samples, experimental samples, and experimental consumables according to the screening experiment tasks;

[0011] The sample processing module is used to extract the component Chinese medicine samples into experimental samples according to the screening experiment task, and perform pre-reaction treatment, incubation reaction treatment, and cleaning treatment on the experimental samples to realize the screening experiment of the component Chinese medicine samples;

[0012] Data acquisition module, used to record various experimental parameters and reaction results of experimental samples in screening experiments;

[0013] Data analysis and storage module, used to clean, store, calculate and analyze the collected experimental parameters and reaction result data, and generate and store screening reports and suggestions;

[0014] The system real-time monitoring module is used to monitor the key reaction indicators in the screening experiment and the operating status of each module in real time, and dynamically adjust and alarm the screening experiment tasks according to the monitoring results.

[0015] Furthermore, the user interaction module includes a user terminal and an interactive display connected to the user terminal; the interactive display is used to obtain operating instructions from researchers, and the user terminal is used to allocate and schedule screening experiment tasks according to the operating instructions.

[0016] Furthermore, the sample storage module includes a ring-shaped stacking storage rack and a desktop stacking storage rack.

[0017] Furthermore, the sample transfer module includes a conveyor belt, a flexible robotic arm, an AGV trolley, and a multifunctional manipulator arranged on the AGV trolley, the conveyor belt is used to transfer component Chinese medicine samples, experimental samples, and experimental consumables according to the screening experiment tasks, the flexible robotic arm and the multifunctional manipulator are used to grab and transfer component Chinese medicine samples, experimental samples, and experimental consumables according to the screening experiment tasks, and the AGV trolley is used to drive the multifunctional manipulator to transfer component Chinese medicine samples, experimental samples, and experimental consumables according to the screening experiment tasks.

[0018] Furthermore, the sample processing module includes an automated workstation, a cell culture box, and a plate washer; the automated workstation is used to absorb the component Chinese medicine samples into the experimental samples according to the screening experiment task, and perform pre-reaction treatment on the experimental samples; the cell culture box is used to perform incubation reaction treatment on the experimental samples according to the screening experiment task; the plate washer is used to perform cleaning treatment on the experimental samples when the experimental reagents are replaced according to the screening experiment task

[0019] Furthermore, the data acquisition module includes a multi-dimensional acquisition terminal and a transmission terminal, the multi-dimensional acquisition terminal is used to collect biological information, chemical information, and image information of the component Chinese medicine during the screening experiment; the transmission terminal is used to transmit the biological information, chemical information, and image information of the component Chinese medicine to the data analysis and storage module.

[0020] Further, the data analysis and storage module includes a data cleaning unit, a data analysis unit, and a data storage unit;

[0021] The data cleaning unit is used to perform missing value processing, duplicate data processing, abnormal value check processing, data consistency check processing, data conversion processing, and standardization processing on the collected experimental parameters and reaction result data to obtain cleaned data;

[0022] The data analysis unit is used to mine and optimize the cleaned data using big data analysis technology and machine learning algorithms, extract key reaction indicators and influencing factors in the screening experiment, and generate a screening report and suggestions based on the key reaction indicators and influencing factors;

[0023] The data storage unit is used to store and manage the collected experimental parameters and reaction result data, the cleaned data, the screening report and the suggestions using distributed database technology.

[0024] Further, the system real-time monitoring module includes a sensor unit, a data interface unit, a feedback control unit, and an integrated alarm unit;

[0025] The sensor unit and the data interface unit are used to obtain key reaction indicators during the screening experiment and the operating status of each module;

[0026] The feedback control unit is used to predict potential problems in the screening process according to the key reaction indicators and the operating status of each module using a machine learning model based on an adaptive algorithm, and dynamically adjust the screening experiment tasks using a rule engine to optimize the screening results;

[0027] The integrated alarm unit is used to detect the key reaction indicators and the operating status of each module. When the key reaction indicators or the operating status of each module are detected to be abnormal, an alarm prompt is given to the researchers according to the preset multi-level alarm rules.

[0028] Furthermore, the preset multi-level alarm rules include a first-level sound and light alarm and a second-level remote push alarm.

[0029] In a second aspect, the present invention also provides a collaborative operation method for screening effective substances of component traditional Chinese medicines, comprising:

[0030] Step S1, obtaining an operation instruction input by a user through a user interaction module, and obtaining a task requirement of a screening experiment according to the operation instruction;

[0031] Step S2, according to the task requirements, the sample transfer module is scheduled to obtain the component Chinese medicine samples, experimental samples, and experimental consumables from the sample storage module, and the component Chinese medicine samples, experimental samples, and experimental consumables are transferred to the sample processing module;

[0032] Step S3, using the sample processing module to extract the component Chinese medicine sample into the experimental sample to achieve administration of the experimental sample, and incubate, wash and stain the experimental sample to obtain the processed experimental sample;

[0033] Step S4, using the sample transfer module to send the processed test sample to the data acquisition module for data acquisition, to obtain various experimental parameters and reaction results of the experimental sample in the screening experiment;

[0034] Step S5, transmitting various experimental parameters and reaction results of the experimental samples in the screening experiment to the data analysis and storage module, and using the data analysis and storage module to clean, store, calculate and analyze the collected experimental parameters and reaction result data, and generate and store screening reports and suggestions;

[0035] In steps S1 to S5, the system real-time monitoring module is used to monitor the key reaction indicators of the screening experiment and the operating status of each module in real time, and the screening experiment tasks in steps S1 to S5 are dynamically adjusted and alarmed according to the monitoring results.

[0036] Compared with the prior art, the collaborative operation system and method for screening effective components of Chinese medicines described in the present invention have the following advantages:

[0037] (1) The collaborative operation system and method for screening effective substances of component traditional Chinese medicine described in the present invention, through multi-robot collaboration, user interaction modules and unmanned automation systems, the transmission and docking between modules are completed by robots, ensuring the continuity and consistency of sample processing, and reducing errors and delays in the handover process of component traditional Chinese medicine samples.

[0038] (2) The collaborative operation system and method for screening effective substances of component traditional Chinese medicines described in the present invention can perform accurate data collection and analysis. The data collection module is equipped with a variety of advanced data collection equipment, which can record multi-dimensional information during the processing of component traditional Chinese medicine samples, such as biological information, chemical information and image information, providing a rich data foundation for subsequent data analysis. The data analysis and storage module uses big data technology and machine learning algorithms to conduct in-depth mining and optimization analysis of the collected data, extract key reaction indicators and influencing factors, and generate high-value screening reports and suggestions.

[0039] (3) The collaborative operation system and method for screening effective substances of component Chinese medicines described in the present invention have efficient experimental processes and processing capabilities. The processing unit in the component Chinese medicine sample processing module can automatically complete various experimental operations and ensure the accuracy and efficiency of the processing process through a feedback control mechanism. In addition, this system also adopts control logic based on real-time data analysis and machine learning, and dynamically adjusts the processing process in combination with specific experimental requirements and sample characteristics, so that the system can intelligently schedule each module according to task requirements, achieve optimal resource allocation and shortest path, and further improve the overall operation efficiency of the system.

[0040] (4) The collaborative operation system and method for screening effective components of component traditional Chinese medicines described in the present invention also have efficient data management and sharing capabilities. The data analysis and storage module performs pre-reaction processing on the collected data, such as denoising, deduplication and normalization, and uses distributed database technology for efficient storage and management to ensure the accuracy and integrity of the data. The system supports storing data in the cloud to facilitate remote access and sharing, providing convenience for multi-site collaboration and large-scale screening projects.

[0041] (5) The collaborative operation system and method for screening effective substances of component traditional Chinese medicines described in the present invention reduce human errors and improve screening efficiency and result consistency through standardized operations and intelligent analysis. It is suitable for large-scale component traditional Chinese medicine screening projects and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0043] Figure 1 This is a schematic diagram of the structure of a collaborative operation system for screening effective substances of component traditional Chinese medicines according to the first embodiment of the present invention. DETAILED DESCRIPTION

[0044] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0045] Embodiment 1

[0046] Figure 1 This is a schematic diagram of the structure of a collaborative operation system for screening effective components of Chinese medicine according to the first embodiment of the present invention. Figure 1 , this collaborative operation system includes:

[0047] The user interaction module 101 is used to obtain the operation instructions of the researchers and to allocate and schedule the screening experiment tasks according to the operation instructions;

[0048] The sample storage module 102 is used to store component Chinese medicine samples, experimental samples, and experimental consumables;

[0049] The sample transfer module 103 is used to automatically transfer component Chinese medicine samples, experimental samples, and experimental consumables according to the screening experiment task;

[0050] The sample processing module 104 is used to extract the component Chinese medicine samples into experimental samples according to the screening experiment task, and perform pre-reaction processing, incubation reaction processing, and cleaning processing on the experimental samples to realize the screening experiment on the component Chinese medicine samples;

[0051] The data acquisition module 105 is used to record various experimental parameters and reaction results of the experimental samples in the screening experiment;

[0052] The data analysis and storage module 106 is used to clean, store, calculate and analyze the collected experimental parameters and reaction result data, and generate and store screening reports and suggestions;

[0053] The system real-time monitoring module 107 is used to monitor the key reaction indicators in the screening experiment and the operating status of each module in real time, and dynamically adjust and alarm the screening experiment tasks according to the monitoring results.

[0054] The user interaction module in this embodiment includes a user terminal and an interactive display connected to the user terminal; the interactive display is used to obtain the operating instructions of the researchers, and the user terminal is used to allocate and schedule the screening experiment tasks according to the operating instructions. Among them, the interactive display can be a touch screen, which can be used to obtain and display the operating instructions, and the user terminal can be a computer, which is used to parse the operating instructions, convert the operating instructions into the target state recognized by the software, and allocate and schedule the screening experiment tasks, and pass the screening experiment task requirements to the subsequent modules.

[0055] The sample storage module in this embodiment includes a ring stack storage rack and a desktop stack storage rack. Among them, the ring stack storage rack can be placed in the sample storage space, and its large capacity is used to store batch samples and ensure the safety of samples during storage. The desktop stack storage rack can be placed on the laboratory desktop to facilitate subsequent screening experiments, which is conducive to improving the efficiency of screening experiments. In actual use, different components of traditional Chinese medicine samples, experimental samples and experimental consumables can be stored in the ring stack storage rack in advance, and then connected to the sample transfer module to achieve orderly storage and automatic extraction of samples.

[0056] In actual application, the circular stacking storage rack is used to store the Chinese medicine samples to be screened and the experimental consumables, and is classified and stored according to the sample type and batch. The desktop stacking storage rack is used for sample transfer between the circular stacking storage rack and the sample processing module, and cooperates with the sample transfer module through a mechanical arm or an automatic conveyor device to realize automatic storage and positioning of samples, ensuring orderly and efficient sample management.

[0057] In this embodiment, the sample transfer module includes a conveyor belt, a flexible robotic arm, an AGV cart, and a multifunctional manipulator arranged on the AGV cart. The conveyor belt is used to transfer component Chinese medicine samples, experimental samples, and experimental consumables according to the screening experiment tasks. The flexible robotic arm and the multifunctional manipulator are used to grab and transfer component Chinese medicine samples, experimental samples, and experimental consumables according to the screening experiment tasks. The AGV cart is used to drive the multifunctional manipulator to transfer component Chinese medicine samples, experimental samples, and experimental consumables according to the screening experiment tasks.

[0058] For example, the AGV and multifunctional manipulator can use LanLi composite robots. The flexible manipulator can use Benyao fully automatic film clamps for transferring sample well plates. The conveyor belt is used to transfer samples between different devices, and the flexible manipulator is equipped with flexible grabbing and moving functions, which can accurately transfer samples from one device to another, realizing efficient flow of samples between various devices.

[0059] In actual application, the AGV can transfer samples between the sample storage space and the laboratory. Specifically, the multifunctional manipulator on the AGV can grab samples on the stacking storage rack, then use the AGV to move the samples, and finally use the multifunctional manipulator to place the grabbed samples on the desktop stacking storage rack for subsequent flexible manipulators to take samples for experiments. In actual use, the sample transfer module can be used to automatically transfer samples between modules to ensure seamless connection of samples during processing, and can automatically plan the transfer path according to the task requirements of the screening experiment task to reduce waiting time.

[0060] In this embodiment, the sample processing module includes an automated workstation, a cell culture box, and a plate washer; the automated workstation is used to absorb the component Chinese medicine samples into the experimental samples according to the screening experiment task, and perform pre-reaction treatment on the experimental samples; the cell culture box is used to perform incubation reaction treatment on the experimental samples according to the screening experiment task; the plate washer is used to perform cleaning treatment on the experimental samples when the experimental reagents are replaced according to the screening experiment task

[0061] Specifically, the sample processing module can include the Biomek i5 automated workstation for automated liquid preparation and feeding; the Thermo cell culture chamber for cell incubation; and the MD AquaMax2000 plate washer for sample well plate cleaning. In actual use, the sample processing module can process experimental samples, including sample pre-reaction processing, incubation reaction processing, and cleaning processing. All of the above equipment has automated control and feedback functions, which is conducive to ensuring the accuracy and efficiency of the processing process.

[0062] For example, the automated workstation can be used to aspirate component Chinese medicine samples into experimental samples according to the screening experiment task, and perform pre-reaction treatment on the experiment; the cell culture incubator can be used to perform incubation reaction treatment on the experimental samples during the experiment according to the screening experiment task; and the plate washer can be used to clean the experimental samples when replacing experimental reagents according to the screening experiment task.

[0063] In actual application, the sample processing module may also include a control unit. The above-mentioned devices can process samples according to the experimental process preset in the screening experiment task. Specific operations include drug administration, staining, plate washing, incubation and other steps. The control unit is responsible for coordinating and controlling the operation of the above-mentioned devices according to the screening experiment task to ensure the accuracy and consistency of the processing process.

[0064] In this embodiment, the data acquisition module includes a multi-dimensional acquisition terminal and a transmission terminal. The multi-dimensional acquisition terminal is used to collect biological information, chemical information, and image information of the component Chinese medicine during the screening experiment; the transmission terminal is used to transmit the biological information, chemical information, and image information of the component Chinese medicine to the data analysis and storage module.

[0065] Exemplarily, the multi-dimensional acquisition terminal may include the MD ImageXpress high-content imaging system for visualization of fluorescent signals in screening experiments; the MD iD5 multifunctional microplate reader for detecting chemical, light absorption and other signals in screening experiments; and the FLIPR Penta high-throughput real-time fluorescence detection and analysis system for real-time detection of fluorescent signals in screening experiments. The transmission terminal may use the industrial Ethernet communication protocol to collect information from the above-mentioned instruments and transmit it to the data analysis and storage module.

[0066] In actual application, each device in the multi-dimensional acquisition terminal can collect and record various experimental parameters and reaction results of the component Chinese medicine samples in the screening experiment, realize the information changes of various dimensions such as biological information, chemical information, image information, etc. of the component Chinese medicine during the experiment, and can transmit the collected data to the data analysis and storage module through the transmission terminal for subsequent analysis and storage.

[0067] In this embodiment, the data analysis and storage module includes a data cleaning unit, a data storage unit, and a data analysis unit.

[0068] The data cleaning unit is used to perform missing value processing, duplicate data processing, abnormal value check processing, data consistency check processing, data conversion processing, and standardization processing on the collected experimental parameters and reaction result data to obtain cleaned data.

[0069] Specifically, the data can be cleaned in the data cleaning unit. Data cleaning (or data cleaning) refers to identifying and correcting (or deleting) inaccurate, incomplete or inconsistent data in the data set before data analysis.

[0070] Data cleaning functions: 1. Improve data quality: ensure the accuracy, completeness and consistency of data to make the analysis results more reliable. 2. Reduce noise and errors: remove or correct erroneous data and noise to avoid their negative impact on the analysis results. 3. Improve analysis efficiency: cleaned data is neater, reducing the waste of computing resources and improving analysis efficiency. 4. Enhance data usability: ensure that every record in the data set is useful and actionable.

[0071] Data cleaning implementation methods: 1. Missing value processing: Deletion: If there are few missing values, you can delete the records containing missing values. Filling: Use the mean, median, mode, nearest neighbor method or interpolation method to fill missing values. 2. Duplicate data processing: Deduplication: Detect and delete duplicate records in the data set. 3. Outlier detection and processing: Identify outliers: Identify outliers in the data through statistical methods (such as standard deviation) or visualization methods (such as box plots). Process outliers: You can delete, correct or perform special processing during analysis. 4. Data consistency check: Standardization: Ensure that the data format is consistent, such as date format, unit consistency, etc. Correction: Correct spelling errors, inconsistent formats, etc. 5. Data conversion and standardization: Data type conversion: Convert data to appropriate data types (such as strings, integers, floating point numbers, etc.). Standardization and normalization: Standardize or normalize the data according to analysis requirements.

[0072] The data analysis unit is used to utilize big data analysis technology and machine learning algorithms to mine and optimize the cleaned data, extract key reaction indicators and influencing factors in the screening experiment, and generate screening reports and recommendations based on the key reaction indicators and influencing factors.

[0073] Specifically, the following methods can be used to mine and optimize the cleaned data:

[0074] First, the cleaned data is processed in sequence for data pre-reaction processing, feature extraction, model training, model verification, data mining and optimization analysis. The data pre-reaction processing is refined to standardize and normalize the cleaned data. Through the above processing, problems such as inconsistent data formats and large differences in data ranges can be solved.

[0075] Then, use machine learning algorithms (such as principal component analysis PCA and factor analysis FA) to extract important features from the data, use machine learning algorithms (such as regression analysis, support vector machine SVM, random forest, etc.) to train the data, build a prediction model, and verify the accuracy and reliability of the model through cross-validation and independent data sets.

[0076] Finally, the trained model is used to predict new data, identify key reaction indicators and influencing factors, and perform parameter optimization.

[0077] By using big data analysis technology and machine learning algorithms to mine and optimize the cleaned data, extract key reaction indicators and influencing factors in the screening experiment, it is possible to identify representative features from massive data and reduce data dimensions. By establishing a model that can accurately predict key reaction indicators and influencing factors, and evaluating the generalization ability and prediction accuracy of the model, it is helpful to ensure the consistency and comparability of data, improve the efficiency and accuracy of data analysis, realize the prediction and optimization of experimental results, ensure the stable performance of the model on different data sets, improve the success rate and efficiency of the experiment, and reduce the cost of the experiment.

[0078] In actual application, the key reaction index refers to the negative / positive experimental results. The parameters with significant changes in the experimental results are identified by statistical analysis and feature extraction methods (such as PCA and FA) of the experimental results. The influencing factors refer to the types / concentrations of compounds that make the experimental results negative / positive. Through machine learning models and data mining technology, the factors that have a significant impact on the experimental results are identified from the experimental data. The experimental data can be analyzed using models such as regression analysis and random forests to determine the factors that have a significant impact on the experimental results.

[0079] In addition, the following methods can be used to generate screening reports and recommendations:

[0080] First, the experimental data are summarized, the data of key reaction indicators and influencing factors are summarized, and data reports are generated to ensure the integrity and accuracy of the data and provide comprehensive data support.

[0081] Secondly, the results are analyzed, key reaction indicators and influencing factors are analyzed in detail, charts and statistical results are generated, and intuitive data presentation is provided to help users quickly understand the experimental results.

[0082] Afterwards, optimization suggestions are made based on the analysis results to optimize the experimental conditions, such as adjusting the concentration of reaction reagents, etc., to provide feasible optimization plans, improve the success rate of the experiment, optimize the experimental process, and save experimental time and costs.

[0083] Finally, a report is generated, which integrates data summary, analysis results and optimization suggestions into a complete screening report, providing users with detailed experimental guidance and decision support.

[0084] Through big data analysis and machine learning technology, massive experimental data can be automatically processed and analyzed, valuable information can be extracted from it, and experimental optimization suggestions can be provided. This improves the efficiency and accuracy of experimental data processing, reduces human intervention, significantly saves experimental time, and improves the experimental success rate and operational efficiency.

[0085] The data storage unit is used to store and manage the collected experimental parameters and reaction result data, the cleaned data, the screening report and suggestions using distributed database technology. Specifically, the data storage unit can store the above data to a local hardware device or the cloud to facilitate remote access and sharing.

[0086] In this embodiment, the system real-time monitoring module includes a sensor unit, a data interface unit, a feedback control unit, and an integrated alarm unit;

[0087] The above sensor unit and data interface unit are used to obtain the key reaction indicators and the operating status of each module during the screening experiment. Specifically, the component Chinese medicine data can be collected in real time through sensors, database interfaces or APIs. Use stream processing frameworks such as Apache Kafka, Apache Flink or Apache Spark Streaming to process real-time data streams to ensure the timeliness of data. Use dashboard tools such as Grafana, Tableau or Power BI to display key indicators and the status of the screening process in real time.

[0088] The feedback control unit is used to predict potential problems in the screening process based on the key reaction indicators and the operating status of each module using a machine learning model based on an adaptive algorithm, and to dynamically adjust the screening experiment tasks using a rule engine to optimize the screening results.

[0089] In actual application, a feedback control unit can be used to automatically adjust the screening parameters in the screening experiment task according to real-time monitoring data. For example, a PID controller or adaptive control algorithm is used, and then a machine learning model is trained to predict potential problems in the screening process and automatically adjust the screening task or screening parameters to optimize the screening results. Finally, a rule engine (such as Drools) is used to define and execute a dynamic adjustment strategy for the screening experiment task.

[0090] The integrated alarm unit is used to detect the key reaction indicators and the operating status of each module. When the key reaction indicators or the operating status of each module are detected to be abnormal, an alarm is given to the researchers according to the preset multi-level alarm rules. The preset multi-level alarm rules include the first-level sound and light alarm and the second-level remote push alarm.

[0091] In actual application, integrated alarm devices can be used to provide instant alarms in physical spaces through hardware buzzers and LED warning lights, and alarm signals can be sent in a timely manner through SMS, email, push notifications, etc. to achieve human-computer interaction. In addition, redundant systems and failover mechanisms can be established to ensure that the system can automatically switch to the backup system in abnormal situations to ensure continuous operation.

[0092] By setting up a real-time system monitoring module, the system is equipped with abnormality detection and alarm functions, which can detect abnormal situations in real time and send out alarm signals in time to ensure the safety and reliability of system operation.

[0093] The specific detection alarm and dynamic adjustment means can refer to the following design:

[0094] 1. Anomaly detection: Detection equipment and technology: Sensors: used to monitor environmental parameters (pressure, etc.) to ensure that the samples are placed in the correct step during the experiment. Data monitoring tools: Use monitoring tools (such as Prometheus, Nagios) to collect and analyze system performance data (CPU, memory, network traffic, etc.) in real time. Log analysis: Use the ELK stack (Elasticsearch, Logstash, Kibana) to analyze system logs in real time and detect abnormal behavior.

[0095] 2. Alarm function: Alarm equipment and system: Alarm tools: such as PagerDuty, OpsGenie, Zabbix, for centralized management and sending alarms. Hardware alarms: such as buzzers and LED warning lights, for instant warnings in physical spaces. Software alarms: SMS, email, and operator interface notifications send alarm signals to achieve human-computer interaction. Alarm strategy: Multi-level alarm: Different types of alarms (such as SMS, email, and push notifications) are sent in different levels according to the severity of the anomaly. Redundant alarms: Install multiple sets of independent alarm devices at key locations to ensure that there are backup devices when a certain device fails.

[0096] 3. Modular detection and alarm: Modular monitoring: Deploy monitoring and detection equipment for different modules of the system (such as data acquisition module, sample processing module, data analysis and storage module). For example, the data acquisition module can deploy sensors to monitor environmental parameters, the sample processing module can deploy performance monitoring tools, and the data analysis and storage module can deploy log analysis systems. Independent alarm: Each module has an independent alarm mechanism, which can independently send out an alarm signal when an abnormality is detected in the module.

[0097] 4. Exception handling that does not affect system operation: Automatic switching: The system can monitor the operating status of each module in real time. When an abnormality is detected in a module, the switching mechanism is automatically triggered to switch the task to the backup module or take predefined degradation measures. Microservice architecture: The system can adopt a microservice architecture to make each service module relatively independent. Each service module runs independently and does not depend on each other. Through the service registration and discovery mechanism, it is ensured that each service module can be dynamically called.

[0098] Specific predefined degradation measures:

[0099] First, it is service degradation. When a module is abnormal, the system can automatically reduce the service level of the module to reduce the impact on other modules. For example, if the data analysis and storage module is abnormal, the system can temporarily stop the advanced analysis function and only provide basic data storage and processing functions. If the sample delivery module or sample processing module is abnormal, the system can temporarily stop the automated operation and switch to semi-automatic mode to allow manual intervention.

[0100] Secondly, it is load balancing. When an abnormality occurs in a module, the system can redistribute the task to other available modules through the load balancer to ensure that the task continues to be executed. If a robot fails, the system can redistribute the robot's task to other normally operating robots to ensure that the experimental process is not interrupted. For example, when the conveyor belt fails, a multifunctional manipulator can be used to replace the conveyor belt to transport samples.

[0101] For example, in the case of data cache degradation measures, when a module fails, the system can temporarily store the unprocessed data in the cache and process it after the module returns to normal. If a data transmission terminal fails, the system can cache the data to local storage and transfer the data to the target location after the data transmission terminal recovers.

[0102] For example, automatic restart downgrade measures: when a module is detected to be abnormal, the system can automatically restart the module to try to restore normal functions. If the experiment control module is abnormal, the system can automatically restart the module to try to restore the experiment control function.

[0103] Service switching and downgrade measures can also be adopted: when a service module fails, the system can switch to the backup service module to ensure service continuity. If the main database service fails, the system can switch to the backup database service to ensure uninterrupted data access.

[0104] In summary, those skilled in the art can adopt an exception handling method and its specific implementation steps that do not affect the work of the system. By combining predefined degradation measures such as automatic switching, service degradation, load balancing, data caching, automatic restart and service switching, and combining the microservice architecture to achieve dynamic adjustment of the screening experiment tasks, the reliability and stability of the system can be effectively improved, ensuring that the system can still operate normally when an abnormality occurs in a certain module, which will not be repeated here.

[0105] The collaborative operation system for screening effective substances of component Chinese medicines described in this embodiment is an efficient multi-robot collaborative operation system for the field of component Chinese medicines. Through multi-robot collaboration, the automation and precise operation of component Chinese medicine samples in storage, delivery, processing, etc. are realized. The system can automatically complete the entire process from sample storage to processing, so that the screening process of component Chinese medicines can be fully automated, significantly shorten the screening time, improve operational efficiency, and greatly reduce the errors caused by human intervention.

[0106] The collaborative operation system for screening effective substances of component traditional Chinese medicine described in this embodiment uses multi-robot collaboration, user interaction modules and unmanned automation systems. The transmission and docking between modules are completed by robots, ensuring the continuity and consistency of sample processing and reducing errors and delays in the handover process of component traditional Chinese medicine samples.

[0107] The collaborative operation system for screening effective substances of component traditional Chinese medicines described in this embodiment can perform accurate data collection and analysis. The data collection module is equipped with a variety of advanced data collection equipment, which can record multi-dimensional information during the processing of component traditional Chinese medicine samples, such as biological information, chemical information and image information, providing a rich data foundation for subsequent data analysis. The data analysis and storage module uses big data technology and machine learning algorithms to conduct in-depth mining and optimization analysis of the collected data, extract key reaction indicators and influencing factors, and generate high-value screening reports and suggestions.

[0108] The collaborative operation system for screening effective substances of component Chinese medicines described in this embodiment has efficient experimental procedures and processing capabilities. The processing unit in the component Chinese medicine sample processing module can automatically complete various experimental operations and ensure the accuracy and efficiency of the processing process through a feedback control mechanism. In addition, this system also adopts control logic based on real-time data analysis and machine learning, and dynamically adjusts the processing process in combination with specific experimental requirements and sample characteristics, so that the system can intelligently schedule each module according to task requirements, achieve optimal resource allocation and shortest path, and further improve the overall operation efficiency of the system.

[0109] The collaborative operation system for screening of effective substances of component traditional Chinese medicines described in this embodiment also has efficient data management and sharing capabilities. The data analysis and storage module performs pre-reaction processing on the collected data, such as denoising, deduplication and normalization, and uses distributed database technology for efficient storage and management to ensure the accuracy and integrity of the data. The system supports storing data in the cloud to facilitate remote access and sharing, providing convenience for multi-site collaboration and large-scale screening projects.

[0110] The collaborative operation system for screening effective substances of component traditional Chinese medicines described in this embodiment reduces human errors, improves screening efficiency and result consistency through standardized operations and intelligent analysis, is suitable for large-scale component traditional Chinese medicine screening projects, and has broad application prospects.

[0111] Embodiment 2

[0112] This embodiment provides a collaborative operation method based on the above embodiment, which specifically includes the following steps:

[0113] Step S1: obtaining an operation instruction input by a user through a user interaction module, and obtaining a task requirement of a screening experiment according to the operation instruction.

[0114] Step S2: According to the task requirements, the sample transfer module is scheduled to obtain the component Chinese medicine samples, experimental samples, and experimental consumables from the sample storage module, and transfer the component Chinese medicine samples, experimental samples, and experimental consumables to the sample processing module.

[0115] Step S3: using the sample processing module to extract the component Chinese medicine sample into the experimental sample to achieve administration of the experimental sample, and incubating, washing, and staining the experimental sample to obtain the processed experimental sample.

[0116] Step S4: using the sample transfer module to send the processed test sample to the data acquisition module for data acquisition, so as to obtain various experimental parameters and reaction results of the experimental sample in the screening experiment.

[0117] Step S5: transmitting various experimental parameters and reaction results of the experimental samples in the screening experiment to the data analysis and storage module, and using the data analysis and storage module to clean, store, calculate and analyze the collected experimental parameters and reaction result data, and generate and store screening reports and suggestions.

[0118] In steps S1 to S5, the system real-time monitoring module is used to monitor the key reaction indicators of the screening experiment and the operating status of each module in real time, and the screening experiment tasks in steps S1 to S5 are dynamically adjusted and alarmed according to the monitoring results.

[0119] The collaborative operation method for screening effective substances in component traditional Chinese medicines described in this embodiment realizes intelligent and efficient screening of component traditional Chinese medicines by utilizing the collaborative operation of a user interaction module, a sample storage module, a sample transfer module, a sample processing module, a data acquisition module, a data analysis and storage module, and a system real-time monitoring module, thereby ensuring the consistency and reliability of the screening results.

[0120] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A collaborative operation system for screening effective substances of component traditional Chinese medicine, characterized in that: include: A user interaction module is used to obtain the operating instructions of researchers and to allocate and schedule screening experiment tasks according to the operating instructions; The sample storage module is used to store component Chinese medicine samples, experimental samples, and experimental consumables; The sample transfer module is used to automatically transfer component Chinese medicine samples, experimental samples, and experimental consumables according to the screening experiment tasks; The sample processing module is used to extract the component Chinese medicine samples into experimental samples according to the screening experiment task, and perform pre-reaction treatment, incubation reaction treatment, and cleaning treatment on the experimental samples to realize the screening experiment of the component Chinese medicine samples; Data acquisition module, used to record various experimental parameters and reaction results of experimental samples in screening experiments; Data analysis and storage module, used to clean, store, calculate and analyze the collected experimental parameters and reaction result data, and generate and store screening reports and suggestions; The system real-time monitoring module is used to monitor the key reaction indicators in the screening experiment and the operating status of each module in real time, and dynamically adjust and alarm the screening experiment tasks according to the monitoring results.

2. A collaborative operation system for screening effective components of Chinese medicine according to claim 1, characterized in that: The user interaction module includes a user terminal and an interactive display connected to the user terminal; the interactive display is used to obtain the operating instructions of the researchers, and the user terminal is used to allocate and schedule the screening experiment tasks according to the operating instructions.

3. A collaborative operation system for screening effective components of Chinese medicine according to claim 1, characterized in that: The sample storage module includes a ring-shaped stacking storage rack and a desktop stacking storage rack.

4. A collaborative operation system for screening effective components of Chinese medicine according to claim 1, characterized in that: The sample transfer module includes a conveyor belt, a flexible robotic arm, an AGV trolley, and a multifunctional manipulator arranged on the AGV trolley. The conveyor belt is used to transfer component Chinese medicine samples, experimental samples, and experimental consumables according to the screening experiment tasks. The flexible robotic arm and the multifunctional manipulator are used to grab and transfer component Chinese medicine samples, experimental samples, and experimental consumables according to the screening experiment tasks. The AGV trolley is used to drive the multifunctional manipulator to transfer component Chinese medicine samples, experimental samples, and experimental consumables according to the screening experiment tasks.

5. The collaborative operation system for screening active substances of component traditional Chinese medicines according to claim 1, characterized in that: The sample processing module includes an automated workstation, a cell culture incubator, and a plate washer; the automated workstation is used to absorb component Chinese medicine samples into experimental samples according to the screening experiment task, and perform pre-reaction treatment on the experimental samples; the cell culture incubator is used to perform incubation reaction treatment on the experimental samples according to the screening experiment task; the plate washer is used to perform cleaning treatment on the experimental samples when replacing experimental reagents according to the screening experiment task.

6. The collaborative operation system for screening effective substances of component traditional Chinese medicines according to claim 1, characterized in that: The data acquisition module includes a multi-dimensional acquisition terminal and a transmission terminal. The multi-dimensional acquisition terminal is used to collect biological information, chemical information, and image information of component Chinese medicines during the screening experiment; the transmission terminal is used to transmit the biological information, chemical information, and image information of the component Chinese medicines to the data analysis and storage module.

7. The collaborative operation system for screening effective substances of component traditional Chinese medicines according to claim 1, characterized in that: The data analysis and storage module includes a data cleaning unit, a data analysis unit, and a data storage unit; The data cleaning unit is used to perform missing value processing, duplicate data processing, abnormal value check processing, data consistency check processing, data conversion processing, and standardization processing on the collected experimental parameters and reaction result data to obtain cleaned data; The data analysis unit is used to mine and optimize the cleaned data using big data analysis technology and machine learning algorithms, extract key reaction indicators and influencing factors in the screening experiment, and generate a screening report and suggestions based on the key reaction indicators and influencing factors; The data storage unit is used to store and manage the collected experimental parameters and reaction result data, the cleaned data, the screening report and the suggestions using distributed database technology.

8. The collaborative operation system for screening effective components of Chinese medicine according to claim 1, characterized in that: The system real-time monitoring module includes a sensor unit, a data interface unit, a feedback control unit, and an integrated alarm unit; The sensor unit and the data interface unit are used to obtain key reaction indicators during the screening experiment and the operating status of each module; The feedback control unit is used to predict potential problems in the screening process according to the key reaction indicators and the operating status of each module using a machine learning model based on an adaptive algorithm, and dynamically adjust the screening experiment tasks using a rule engine to optimize the screening results; The integrated alarm unit is used to detect the key reaction indicators and the operating status of each module. When the key reaction indicators or the operating status of each module are detected to be abnormal, an alarm prompt is given to the researchers according to the preset multi-level alarm rules.

9. A collaborative operation system for screening effective components of Chinese medicine according to claim 8, characterized in that: The preset multi-level alarm rules include a first-level sound and light alarm and a second-level remote push alarm.

10. A method for using a collaborative operation system for screening effective substances of component traditional Chinese medicines as claimed in any one of claims 1 to 9, characterized in that: include: Step S1, obtaining an operation instruction input by a user through a user interaction module, and obtaining a task requirement of a screening experiment according to the operation instruction; Step S2, according to the task requirements, the sample transfer module is scheduled to obtain the component Chinese medicine samples, experimental samples, and experimental consumables from the sample storage module, and the component Chinese medicine samples, experimental samples, and experimental consumables are transferred to the sample processing module; Step S3, using the sample processing module to extract the component Chinese medicine sample into the experimental sample to achieve administration of the experimental sample, and incubate, wash and stain the experimental sample to obtain the processed experimental sample; Step S4, using the sample transfer module to send the processed test sample to the data acquisition module for data acquisition, to obtain various experimental parameters and reaction results of the experimental sample in the screening experiment; Step S5, transmitting various experimental parameters and reaction results of the experimental samples in the screening experiment to the data analysis and storage module, and using the data analysis and storage module to clean, store, calculate and analyze the collected experimental parameters and reaction result data, and generate and store screening reports and suggestions; In steps S1 to S5, the system real-time monitoring module is used to monitor the key reaction indicators of the screening experiment and the operating status of each module in real time, and the screening experiment tasks in steps S1 to S5 are dynamically adjusted and alarmed according to the monitoring results.

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