54 results about "Lab management" patented technology

The invention discloses a laboratory automatic process management and multi-source data fusion system based on the Internet of Things, and belongs to the technical field of laboratory management. The system comprises a sensing layer, an analysis layer, an intelligent management layer and an application layer. The sensing layer carries out global data acquisition and real-time analysis; the analysis layer integrates data, mines an association relationship and optimizes task matching; the intelligent management layer performs full-process automatic management and control on a laboratory, and comprises an equipment cooperation module, a dynamic detection module, a dynamic configuration module, a real-time simulation module, a preset trigger module and an intelligent auditing module; and the application layer performs multi-terminal collaborative interaction and intelligent assistant assistance processing. According to the system, intelligent collaboration of laboratory equipment, automatic detection of samples, dynamic optimization of equipment parameters and automatic early warning and processing of abnormal conditions are realized through technical means such as knowledge graph construction, a digital twinborn technology and multi-mode biological recognition, and the laboratory management efficiency and the data processing accuracy are effectively improved.

The invention relates to the technical field of laboratory management, in particular to a paperless office-based laboratory whole-process quality control management system, which comprises an abnormity matching unit, an abnormity early warning unit, an experiment monitoring unit, an experiment reexamination unit and a score evaluation unit, and further comprises an identity recognition unit, a data processing unit and a control unit, and the identity recognition unit is used for carrying out face image acquisition on an experimenter by arranging face image acquisition equipment outside a laboratory so as to obtain a face image of the experimenter, and carrying out feature extraction on the face image through a trained feature extraction model so as to obtain a face feature set. The abnormal flow track of the experiment sample is monitored and recognized in real time through the abnormal matching unit and the abnormal early warning unit, and once the abnormity is detected, abnormal information is generated immediately and early warning is performed, so that laboratory management personnel can take measures in time, and potential problems in the experiment process are prevented from expanding.

The invention provides a laboratory detection task intelligent allocation method and system based on big data, and relates to the technical field of laboratory management, and the method comprises the steps: collecting detection task data, carrying out the detachable analysis to construct a collaborative flow chart, calculating a collaborative efficiency index and a task priority, and extracting personnel collaboration features to generate an association weight matrix; and allocating task queues by adopting a double-layer adaptive task allocation algorithm, performing density peak clustering on the first layer to divide task clusters, and optimizing an allocation scheme in combination with personnel ability and cooperative affinity on the second layer. According to the invention, the distribution efficiency and cooperation quality of complex laboratory detection tasks are improved.

The invention provides a laboratory management method and system based on the Internet of Things. The method comprises the following steps: acquiring power, temperature, humidity and use frequency data of laboratory precise instruments and conventional equipment based on an Internet of Things sensor, and generating an equipment energy consumption influence factor set; dynamically associating equipment types, and constructing an equipment energy efficiency rule base in combination with the power sensitivity of a precise instrument and the power stability of conventional equipment; when the real-time power deviates from a preset reference of the rule base, triggering a multi-objective optimization algorithm, and fusing temperature, humidity and use frequency to generate an adjustment scheme set; real-time power is obtained and an adjustment instruction is issued through a bidirectional Internet of Things architecture, a non-linear relation of an adjustment scheme is screened, compensation parameters of a precise instrument and current limitation of conventional equipment are synchronously adjusted, and energy consumption optimization is achieved. According to the technical scheme provided by the invention, classified adjustment of power sensitivity and stability of laboratory equipment is completed, and accurate optimization and closed-loop management of energy consumption are achieved.

The invention discloses a laboratory intelligent management and remote review system, and relates to the field of laboratory management, and the system comprises a standard atlas database construction module which is used for generating a standard process atlas of various sample detection tasks; the sample process map generation module is used for collecting actual operation processes of experimenters and generating map representation; the anomaly detection module is used for carrying out structure comparison on the actual process map and the standard process map and identifying an abnormal process path; the abnormal graph learning module is used for classifying the abnormal process graph into an abnormal graph library and carrying out clustering and labeling processing; the abnormal evolution prediction and early warning module is used for predicting a process evolution trend based on a graph neural network model and giving out an early warning when the process evolution trend deviates from a standard; and the remote review intelligent auxiliary module is used for generating a structured review report. According to the method, structured modeling of the laboratory process, inductive precipitation of the abnormal mode and prospective early warning of the process risk are realized.

The invention provides a laboratory operation management intelligent system based on undergraduate scientific research ability cultivation. The laboratory operation management intelligent system based on undergraduate scientific research ability cultivation comprises hardware modules including a laboratory instrument equipment intelligent management device, an access control system, a monitoring camera and a data transmission device; the laboratory instrument and equipment intelligent management device comprises an RFID reader-writer, a sensor and a consumable monitor, and the access control system comprises face recognition equipment, a fingerprint recognition module, an electronic door lock and a software module; equipment state monitoring, consumable use tracking, user authority management and scientific research evaluation data processing are integrated. According to the laboratory operation management intelligent system based on undergraduate scientific research ability cultivation, through the synergistic effect of the hardware module, the software module and the resource management model, the core problem in a traditional laboratory management mode is effectively solved.

The invention discloses a method for predicting frictional wear performance of a DLC (diamond-like carbon) coating based on machine learning, and relates to the cross technical field of surface engineering and machine learning, the method comprises the following steps: collecting feature data and label data of the DLC coating, the feature data comprising image data, spectral data and numerical data; cleaning and integrating the acquired data to obtain a multi-modal data set; according to the condition of the data size, a friction and wear performance prediction model of the DLC coating is constructed; based on the obtained multi-modal data set, training a friction and wear performance prediction model of the DLC coating, evaluating the prediction performance of the model by using label data, analyzing samples with large errors, and continuously optimizing the prediction performance; and deploying the trained model in a laboratory management system or an industrial application platform, and rapidly predicting the friction coefficient and the wear rate of the DLC coating. Therefore, by adopting the method, the depiction capability of the microstructure of the DLC coating can be remarkably improved, the prediction precision is improved, and the method is suitable for various combined working conditions, simple in calculation and high in efficiency.

The invention discloses a laboratory management and control scheduling system and method, and belongs to the field of laboratory management, and the system comprises an Internet of Things equipment monitoring module, a cloud computing resource scheduling module, an experiment task distribution and progress monitoring module, a data analysis and report generation module, an early warning and exception handling module, and a data security and privacy protection module. Through linkage of the cloud computing resource scheduling module and the experiment task allocation module, accurate matching of equipment, personnel and tasks is realized through an intelligent algorithm, the tasks are automatically allocated based on equipment loads and personnel skills, a scheduling plan can be dynamically adjusted according to real-time progress, the problems of resource idleness and task backlog are solved from the source, and the task scheduling efficiency is improved. The cooperative effects of efficient equipment operation, personnel load balance and ordered task promotion are achieved; equipment and environment data are collected in real time through the Internet of Things equipment monitoring module, and the early warning and exception handling module synchronously monitors and responds to exceptions.

A wireless environment parameter monitoring node system based on Bluetooth MESH networking belongs to the technical field of wireless self-organizing sensor network structures, and comprises a laboratory environment monitoring network formed by a plurality of nodes through a Bluetooth MESH self-organizing network; each node is composed of a digital environment parameter sensor module, an electronic paper display module, a lithium battery module, a power management module, a Bluetooth MESH communication module and a master control module, and the master control module is in bidirectional communication connection with the digital environment parameter sensor module, the electronic paper display module, the power management module and the Bluetooth MESH communication module. The lithium battery module is bidirectionally connected with the power management module, and the power management module is connected with the digital environment parameter sensor module. The laboratory environment monitoring system has the characteristics of low cost, small size, low power consumption and the like, can complete large-area multi-laboratory environment monitoring tasks through multi-node networking, is an effective supplement for existing manual reading and single-machine environment monitoring equipment, and conforms to digital scene application of laboratory management system authentication.

The invention discloses an inventory prediction and automatic purchase method and system for experimental reagents, and the method comprises the steps: obtaining the consumption of the experimental reagents through a laboratory management system, carrying out the processing of the consumption, carrying out the feature extraction, and constructing an initial graph through the consumption of the experimental reagents after the feature extraction; taking the initial graph as the input of a Transform architecture, introducing the Transform architecture into a graph neural network model, dynamically updating an edge weight value in the initial graph, and predicting the future consumption of the experimental reagent; converting the predicted future consumption of the experimental reagent into an inventory decision in a laboratory management system, and creating an association rule of experimental reagent purchase according to the inventory decision; according to the method, through characterization of the experimental reagent consumption and dynamic graph optimization, the problem that space-time correlation modeling is insufficient in traditional reagent inventory prediction is solved, the method can adapt to reagent consumption prediction in a complex experimental scene, and therefore the accuracy is improved.

The invention relates to the technical field of laboratory management and auditing, and discloses a laboratory auditing method and system based on a block chain and artificial intelligence, and the method comprises the steps: S1, initiating an experiment auditing request through a dynamic reservation system; s2, deploying a camera and a sensor in the experiment area; s3, analyzing the video data through an artificial intelligence behavior recognition module, and generating an audit log with a timestamp and a space identifier; s4, writing the video hash value and the sensor data into a block chain; s5, constructing a three-dimensional space map of the experiment area; and S6, after the auditor passes biological recognition authentication, the access process is recorded and is subjected to uplink evidence storage. According to the method, multi-source heterogeneous data such as video streams, sensor data and auditing logs are uniformly chained through Hash abstracts and encryption processing by utilizing an alliance chain architecture (a laboratory management party, a third-party auditing mechanism, a supervision mechanism and the like), system splitting is broken, and a credible sharing platform for cross-system data intercommunication is formed.

1. The name of the design product: electronic device research and development experiment management graphical user interface. 2. The use of the design product: an electronic device. 3. The design points of the design product: the interface content in the graphical user interface. 4. The picture or photo that best shows the design points: front view. 5. The product carrier is the conventional design, and the rear view, left view, right view, top view, and bottom view are omitted. 6. The use of the graphical user interface: graphical user interface for research and development experiment management system. 7. Other circumstances that need to be explained: the front view is the task list interface of the research and development experiment management system. Clicking "+" experiment design" in the interface enters the change state diagram 1 task addition interface. The interface pops up the task information setting pop-up window. Fill in the relevant information in the pop-up window. Click the "experiment template" addition icon (the icon with a magnifying glass on the right side of the experiment template input box) to enter the change state diagram 2 task template addition interface. The interface pops up the experiment template selection pop-up window. The left side of the pop-up window is the template display area, and the middle is the process node display area. After selecting the required template in the template display area, the corresponding task node is generated in the process node display area. Click any node in the process node display area to enter the change state diagram 3 node information interface. The right side of the interface is the node editing area. Click "add group" in the area to enter the change state diagram 4 group setting interface. The interface pops up the "edit group" pop-up window. After setting the relevant parameters in the pop-up window, click "OK" to enter the change state diagram 5 complete group setting. Click "group design" in the change state diagram 5 under the three small dot icons on the right side of the group design bar to enter the change state diagram 6. The interface pops up the "material calculation" pop-up window. After setting the relevant parameters in the pop-up window, click "generate data" to enter the change state diagram 7. Click "preference settings" in the change state diagram 7 to enter the change state diagram 8. The interface pops up the execution mode setting pop-up window. Click "OK" after selecting "detailed view" in the pop-up window to enter the change state diagram 9 detailed mode experiment execution.

The invention discloses a data analyzer for intelligent laboratory data analysis, and relates to the technical field of intelligent laboratory analysis, the data analyzer comprises an experiment box body, an experiment data acquisition module is arranged in the experiment box body, an experiment platform is arranged at the front end of the experiment data acquisition module, and a data interaction platform is arranged outside the experiment box body; a data processing system is arranged between the experimental data acquisition module and the data interaction platform; according to the data analyzer for intelligent laboratory data analysis, a desktop six-axis AI visual robot is used for autonomously recognizing an original instrument display window and preprocessing collected image data, and the robot can obtain feedback information in real time and adjust and optimize the feedback information according to needs until variable data become static data; data information is read, data interconnection with a laboratory management system can be carried out through a modem, collected data are uploaded in real time, and data are archived and analyzed.

The invention discloses a shipborne laboratory information management method and system supporting marine geological scientific investigation, and relates to the field of ship digital design. The shipborne experimental method supporting marine geological scientific investigation comprises the steps of core and sample management, voyage number and experimental business process management, experimental resource and environment management, experimental data acquisition and management, and comprehensive data and file management visualization. Scientific and intelligent management and application of shipborne core library and laboratory management are realized by using a modern informatization technology. According to the shipborne experimental method supporting marine geological scientific investigation, management and application such as warehousing, ex-warehouse, borrowing, observation, sampling and gathering of physical samples are more scientific and normative, and integration of laboratory analysis business process, resource and personnel management is realized; the problems of confusion of rock core samples, separation of samples and data, disordered rock core detection progress, incapability of tracking working rock core positions and difficulty in searching archived rock cores which are easily caused by adopting a manual recording mode are solved.

1. Name of the product in this design: Graphical User Interface for Laboratory Intelligent Management Platform of Electronic Equipment. 2. Purpose of this design: An electronic device. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design's key points: Design 2 main view. 5. Design 2 is designated as the basic design. 6. Purpose of the graphical user interface: It is used by users to view and operate equipment, environment, and safety management in the laboratory management platform. 7. Human-computer interaction method of graphical user interface: The main view of Design 1 is the main interface of the equipment management platform. Clicking "VAV" (variable air volume) (control cabinet) in the main view of Design 1 will enter the change status diagram of the Design 1 interface; The main view of Design 2 is the main interface of the environmental management platform. Clicking any air pressure value in the middle of the interface in the main view of Design 2 will enter the change status diagram of the Design 2 interface; The main view of Design 3 is the main interface of the safety management platform. Different floors can be selected to view equipment, and the equipment name can be entered to click to query the equipment. 8. Other situations requiring explanation: Other explanation: "X" in the interface represents text.

The invention discloses an intelligent laboratory management system for fusion graph machine learning, and the system comprises a data layer which is configured to store data related to laboratory management and comprises a business database, a process database, an instrument state database and a knowledge graph database; the AI middle table layer is connected with the data layer and comprises a graph machine learning engine and a reinforcement learning engine; the application layer is connected with the AI middle table layer and is used for calling the service of the AI middle table layer to realize a laboratory service function; and the user layer is connected with the application layer and is used for providing an interactive interface for users of different roles. According to the invention, the AI capability is embedded into the laboratory management process by constructing the machine learning and reinforcement learning of the intelligent platform fusion graph, so that the multi-source data integration and mining are realized, and the information isolated island is effectively broken; resource configuration is optimized through dynamic scheduling, and the operation efficiency is improved; by means of active risk prediction and propagation analysis, early risk identification and intelligent management and control are realized, and automation, intelligence and optimization levels of laboratory management are further improved.

The present invention discloses a laboratory safety anomaly prediction system based on big data, comprising a raw data acquisition module, a data optimization module, an abnormal sample compensation module, a safety anomaly prediction model construction module, and a laboratory safety anomaly prediction module. The present invention relates to the field of laboratory management data processing technology, and specifically to a laboratory safety anomaly prediction system based on big data. This solution innovatively designs an abnormal sample compensation module, achieving structured and conditional supplementation of scarce abnormal samples; innovatively introduces laboratory identity feature vectors, achieving personalized risk identification and accurate prediction at the laboratory personnel level; innovatively designs a conditional Wasserstein distance adversarial objective function based on laboratory identity feature vectors, significantly improving the authenticity and diversity of synthetic abnormal samples; and innovatively designs an identity safety risk prediction loss function, improving the model's prediction accuracy in complex dynamic laboratory environments.

The invention relates to the field of artificial intelligence engineering, in particular to a laboratory equipment management method based on artificial intelligence, and the method comprises the steps: obtaining course plan data and equipment historical task record data through collection, and generating an equipment use feature vector table according to the course plan data and the equipment historical task record data; according to the method, course planning data are collected to extract course features, equipment state indexes are analyzed in combination with equipment historical records, and a multi-dimensional feature vector table is constructed to realize accurate equipment scheduling; a dynamic scheduling graph is constructed based on equipment performance, time concurrency and cost risk constraints, and a genetic algorithm is adopted to optimize and generate an optimal scheduling strategy; the equipment state is monitored through real-time sensor data, the strategy is dynamically adjusted according to the abnormal index, efficient utilization and risk management and control of equipment resources are achieved, laboratory management is promoted to be converted from a traditional manual mode to intelligent and digital modes, the equipment utilization rate and management efficiency are remarkably improved, and the operation cost is reduced.

The invention relates to the technical field of equipment management, in particular to an intelligent laboratory management method and system, electronic equipment and a storage medium, and the method comprises the steps: carrying out the data preprocessing of historical experiment data, generating an experiment feature feedback table, and carrying out the analysis of current experiment data, determining instrument setting parameters based on the instrument application parameters, respectively inputting the experimental environment parameters, the experimental material attributes and the instrument setting parameters into an experimental feature feedback table for multi-dimensional matching to obtain an experimental data set, and performing time sequence analysis on the experimental data set to obtain a time sequence analysis result; the method comprises the steps of obtaining an instrument setting relation, verifying and analyzing instrument setting parameters according to the instrument setting relation to obtain effective setting parameters, dynamically correcting the instrument setting parameters according to the effective setting parameters, and outputting the corrected instrument setting parameters. According to the invention, the accuracy and reliability of the experiment are improved.

The invention discloses an intelligent filling laboratory management method and system, electronic equipment and a medium, and aims to quantitatively evaluate the pollution risk between filling tasks through a preset filling equipment pollution risk evaluation model, so as to provide a reliable reference for the sorting of the filling tasks and the selection of a cleaning strategy during the switching of the filling tasks. For a multi-task situation, dynamic prediction of a long-term pollution risk and influence of chain transmission of pollution are realized, improvement of the operation efficiency of a filling laboratory is facilitated, a scheduling target is expanded from a single time dimension to a double dimension of time efficiency and pollution risk control, and the scheduling efficiency of the filling laboratory is improved. And a comprehensive technical solution is provided for intelligent management of the liquid food filling laboratory.

This invention discloses a laboratory anomaly identification and autonomous response system based on machine vision, belonging to the field of intelligent laboratory monitoring technology. The system includes a data acquisition layer, a feature extraction layer, an intelligent analysis layer, a decision execution layer, and a digital twin layer. The data acquisition layer collects laboratory video streams and physical sensor data; the feature extraction layer uses a multi-task learning framework to extract multimodal features; the intelligent analysis layer identifies anomalies based on multimodal features and generates handling strategies, incorporating a causal structure learning mechanism and an anomaly hierarchical handling mechanism; the decision execution layer executes corresponding remedial operations; and the digital twin layer constructs a digital twin model corresponding to the physical laboratory and synchronizes status information in real time. This invention achieves accurate identification and autonomous response to laboratory anomalies, improving the intelligence level and operational safety of laboratory management.

The invention discloses an intelligent laboratory management system based on a PLC, and particularly relates to the technical field of laboratory management, the system takes the PLC as a core, and an RFID personnel management system, an RFID instrument management system, an online examination system, an environment adjusting system, a key system, a human-computer interface, a background recording module and an execution module are integrated; the RFID technology realizes personnel identity verification and intelligent access of instruments, the online examination system prevents cheating through dynamic IP and encryption communication, and the environment adjusting system automatically regulates and controls temperature, humidity and carbon dioxide concentration based on a sensor and a PID algorithm; the key system supports emergency power-off and help seeking functions, the human-computer interface provides a graphical operation interface, and the background recording module adopts a block chain technology to ensure data credibility. The system solves the problems of low efficiency, resource waste, potential safety hazards and the like of traditional laboratory management, remarkably improves the management efficiency, the safety and the energy utilization rate, and is suitable for colleges and scientific research laboratories.

The invention provides an intelligent control and decision-making method based on an intelligent laboratory data cockpit. The intelligent control and decision-making method comprises the following sub-steps: S1, constructing a visual three-dimensional laboratory model based on digital twinning; s2, collection, storage, preprocessing and semantic modeling of laboratory full-dimension data; s3, deploying and training a large language model LLM adaptive to a laboratory scene, generating a decision and replying by using a natural language; on the basis of technologies such as digital twinning, a large language model and AI-agent, the efficiency and the intelligent level of laboratory management are greatly improved through an intelligent control and decision-making method; seamless butt joint of the subsystems is realized by constructing a unified data middle station; information in the laboratory can be collected in real time and concentrated to the data platform, so that the operation of the laboratory is more efficient and transparent; based on introduction of a large language model LLM and an AI-agent technology, interaction with a user is carried out, and an intelligent decision is generated according to real-time data.

The invention provides an experimental result automatic generation management system comprising an MES system module used for managing the whole experimental process and automatically generating an experimental result; the file management module is used for selecting a file storage path before an experiment and storing the physical property data in a specified path for subsequent experiments; the experimental information input module is used for inputting experimental information into a computer after the tensile test is finished and storing the experimental information into a database; the experiment result generation module is used for automatically generating an experiment result according to the experiment data and the input experiment information; and the experiment result uploading module is used for uploading the experiment result to the laboratory management board in real time to ensure the timeliness and accuracy of the data. The invention provides an experiment result automatic generation management system, and solves the problems that the process of selecting a file storage path and inputting experiment information before an experiment is tedious, the file storage path needs to be manually stored in a folder, and data management and updating are not facilitated.

The application provides a kind of wisdom campus management method and system, belong to wisdom campus technical field, method includes: through acquisition module, cumulative boot duration and single continuous running duration are obtained from laboratory equipment, and project switching frequency is obtained from registration terminal, time period is divided according to equipment start-stop time node, and equipment running cycle list is obtained;For the equipment number belonging to the abnormal high value cluster, obtain the historical experiment time period record from the registration terminal, rearrange the record using the time period reorganization method, and determine the optimized experiment time period arrangement table;Update the dispatch table of laboratory management system through the experiment time period arrangement table, obtain the updated dispatch table content, evaluate the improvement of effective experiment time, and obtain the final evaluation result of the all-day available duration of the equipment. Significantly improve the all-day available duration of the equipment, reduce unnecessary loss.

A system for sharing consumable inventory in a laboratory management system includes a middleware software component controlled by a processor, a plurality of instruments operatively coupled to the middleware software component by at least one communications network, and a consumable item configured to be removably installed in a first selected instrument of the plurality of instruments. The consumable item is used by the first selected instrument to perform tests specified by the laboratory management system where the first selected instrument partially depletes the consumable item. The first selected instrument is configured to update status and usage information regarding the consumable item. A consumables database is operatively coupled to the middleware software component and the processor is configured to store the updated status and usage information in the consumables database corresponding to the consumable item. Because the consumables database is accessible by the plurality of instruments, a second selected instrument of the plurality of instruments is able to perform tests using the consumable item, based on the corresponding updated status and usage information.

The invention provides an intelligent campus management method and system, and belongs to the technical field of intelligent campuses, and the method comprises the steps: obtaining an accumulated starting time length and a single continuous operation time length from laboratory equipment through an acquisition module, obtaining the number of project switching times from a registration terminal, carrying out the time division according to the starting and stopping time nodes of the equipment, and carrying out the time division; obtaining an equipment operation cycle list; acquiring historical experiment time period records from a registration terminal according to the device number to which the abnormal high-value cluster belongs, rearranging the records by adopting a time period recombination mode, and determining an optimized experiment time period arrangement table; and updating the scheduling table of the laboratory management system through the experiment period arrangement table, obtaining the updated scheduling table content, evaluating the improvement amount of the effective experiment time, and obtaining a final evaluation result of the all-day available duration of the equipment. And the all-day available time of equipment is obviously prolonged, and unnecessary loss is reduced.

This invention discloses a control method for an intelligent management cabinet for laboratory instruments and equipment in universities. The steps include: acquiring the current user's height information; determining the corresponding target grid layer from a preset height-grid mapping relationship based on the height information, where users with larger height values ​​correspond to higher-level grids, and users with smaller height values ​​correspond to lower-level grids; within the target grid layer, selecting and opening a target grid from the currently available grids. Preferably, a random selection method is used to achieve grid load balancing within the layer; when there are no available grids in the target layer, the search is automatically expanded to adjacent layers, and the user is prompted before allocation. At least one layer of the cabinet is designated as a dedicated layer for management personnel and is not involved in allocation for ordinary users. When all available layers are full, the system prompts the user to contact laboratory management personnel to borrow backup equipment, supplemented by manual management procedures. This invention simultaneously solves the operational inconvenience and safety hazards caused by neglecting user height differences in existing laboratory equipment management cabinets, as well as the problem of premature aging of some grids due to neglecting grid wear balance. It is particularly suitable for the borrowing and returning management of instruments and equipment in shared scenarios such as university laboratories, research institutions, and vocational training institutions.

The invention provides an AI artificial intelligence training room intelligent safety management method and system, and relates to the technical field of training room safety management, and the method comprises the steps: building a three-dimensional safety perception matrix which comprises a data flow dimension, an energy flow dimension and a personnel flow dimension, performing entropy weight method weighting on elements of the three-dimensional security perception matrix to calculate an initial risk value, inputting the three-dimensional security perception matrix into a space-time causal inference engine, and correcting the initial risk value to obtain a risk entropy value; when the risk entropy value breaks through the dynamic threshold value, an alarm signal is sent out, and prevention and control measures are started. The intelligent level of the training room management process can be improved.

The invention discloses a medical intelligent laboratory management system, which belongs to the technical field of laboratory management and comprises a sample receiving unit, a sample information registration unit, a sample inspection registration unit, a sample inspection prompt unit, a server, a sample inspection judgment unit, an operation information analysis unit and a message queue unit. The sample receiving unit collects sample information by using an optical identification module; the sample information registration unit registers the sample information and feeds back a result whether the sample information is qualified or not; the sample inspection registration unit receives information through the message queue unit according to a registration result, and the sample inspection prompt unit generates different types of prompt signals according to receipt information of the medical terminal; the sample inspection judgment unit decides sample reissuing according to the judgment result; the operation information analysis unit calculates a plurality of indexes and generates a control signal. According to the system, the sample inspection prompting unit distinguishes two abnormal types, a server root cause analysis engine is combined, a sample abnormal root is quickly positioned, and differentiated reissuing suggestions are given, so that the sample quality is effectively guaranteed.