3219 results about "Experimental laboratory" patented technology

The invention relates to the technical field of data processing, and discloses an intelligent comprehensive management method and system for laboratories, which are used for improving the intelligent comprehensive management efficiency of the laboratories. The method comprises the following steps: performing experimental stage identification and workflow mapping on a laboratory ecological state data stream to obtain a dynamic laboratory activity hologram; performing experimental key path analysis on the dynamic laboratory activity hologram to obtain a multidimensional health index pedigree of laboratory operation; performing intelligent comparison and abnormal mode recognition on laboratory ecological state data collected in real time and the multi-dimensional health index pedigree to obtain a laboratory sub-health event feature chain; performing traceability analysis and development trend analysis on the laboratory sub-health event feature chain to obtain a laboratory risk evolution path map; and generating a laboratory safety early warning signal based on the laboratory risk evolution path map, and triggering a corresponding emergency response strategy.

The invention relates to the technical field of material physicochemical property monitoring of a multi-source sensing network, in particular to a multi-source sensing storage environment collaborative monitoring and early warning method, which comprises the following steps of: dividing grid units according to material storage types, binding sensor node coordinates, constructing a grid region reference model, and constructing a grid region reference model; integrating the volatilization characteristic parameters analyzed by the laboratory, historical monitoring data and an environment threshold value to generate a substance and environment relation matrix; the method comprises the following steps of: calling model parameters to invert a theoretical gas concentration value, and triggering finite element physical field simulation through residual analysis to generate a three-dimensional simulation field data set containing a temperature gradient and a diffusion path. And the dynamic correlation analysis precision and the early warning reliability of storage environment monitoring are improved.

The invention provides an AI-based laboratory equipment scheduling optimization method and system, and the method comprises the steps: firstly obtaining a state monitoring data set containing the characteristics of equipment operation power consumption, idle time length, environment interference factors and the like in real time, and then carrying out the multi-dimensional analysis of the state monitoring data set; generating an availability evaluation index set containing characteristics of equipment load fluctuation, maintenance period prediction, compatibility matching and the like, and an experiment task priority queue, performing cross decision analysis on the availability evaluation index set and the experiment task priority queue based on a preset dynamic resource allocation model, and obtaining a scheduling strategy set containing a task allocation path, a cooperative operation rule and a conflict resolution mechanism; scheduling strategy parameters are calibrated according to experimental task operation log data, an optimized execution instruction set is generated, instructions are fed back to an equipment control system to adjust the equipment state, a dynamic resource allocation model is iteratively updated according to execution feedback data, and efficient scheduling optimization of laboratory equipment is achieved.

The invention relates to the technical field of laboratory safety management. The experimenter monitoring method based on behavior analysis comprises the steps of performing human body detection and tracking on video information through an RCNN algorithm to obtain experimenter position information in a laboratory, performing face recognition verification on the experimenter information through a DLIB library to obtain experimenter identity information, and performing face recognition verification on the experimenter identity information through a DLIB library. If the identity information of the experimenter is successfully matched, carrying out behavior analysis on the experimenter in the laboratory through a behavior analysis model to obtain behavior state information, and judging whether the operation behavior of the experimenter accords with a preset laboratory safety management rule or not based on the behavior state information of the experimenter; and if the operation behavior of the experimenter does not conform to the preset laboratory safety management rule, safety early warning information is generated, and early warning levels are divided according to the safety early warning information. The method has the effect of improving the safety management of the laboratory.

The invention discloses a deep learning-based intelligent identification method and system for rock and soil mechanical parameters, and aims to accurately identify the elastic modulus, strength parameter and permeability coefficient of rock and soil and support the decision and design of engineering projects under complex geological conditions. And starting from data demand analysis, determining a parameter identification target and planning a field test layout. A basic data set is constructed by comprehensively collecting and integrating multi-source data including laboratory and field test data, remote sensing information and the like. The data is pre-processed and analyzed to remove noise and explore associations between the data. The method comprises the following core steps: constructing a rock-soil mechanical model by using a deep learning algorithm, and realizing high-precision inversion of parameters by defining and optimizing an objective function. And verifying an inversion result and carrying out uncertainty analysis. According to the method, an analysis result is fed back to engineering design adjustment and decision support, and scheme optimization is achieved. The model and strategy are dynamically adjusted through continuous monitoring and real-time data, and safety and high efficiency of engineering implementation are guaranteed.

The invention provides a deep geologic body occurrence characteristic quantitative representation modeling method and system, and the model focuses on the multi-physics field occurrence environment of rock mass material composition, structural characteristics, stress distribution and the like under the continuous mining condition of a deep underground metal mine. An information expression framework capable of crossing a'microcosmic-experimental-engineering 'multi-level spatial scale is constructed, microcosmic rock core mineral components and crack information, laboratory mechanical response parameters and structural surface shear characteristics and macroscopic fault system and stress disturbance data reflected by microseismic events are introduced, a heterogeneous data fusion method is comprehensively applied, and a microcosmic-experimental-engineering multi-level spatial scale is constructed. A three-dimensional tensor model with dynamic evolution capability and physical interpretation capability is established, high-precision, dynamic and multi-scale comprehensive modeling of a complex deep geologic body occurrence environment is realized, and the model has the characteristics of clear information hierarchy, high fusion precision, strong physical interpretability and the like. And dynamic perception and scientific research and judgment of geologic mass mechanics and structure states can be realized in a complex deep environment.

The invention discloses a laboratory safety state evaluation method and system based on multi-source data fusion, and relates to the field of laboratory safety management.The method comprises the steps that multi-source sensing data of a laboratory are collected and aligned to a unified time axis, and a laboratory sensing data matrix is constructed; grey correlation analysis is introduced, the risk correlation degree between data of each sensor and a preset risk mode is calculated, and identification of a potential risk mode is achieved; the reliability weight is dynamically determined according to the historical stability, baseline deviation and calibration period of the sensor so as to suppress data distortion caused by aging and drifting; and comprehensively calculating the risk correlation degree, the reliability weight and the risk severity coefficient to form weighted risk energy, and generating laboratory risk field energy through space-time coupling superposition so as to calculate a corresponding laboratory risk state level. Therefore, the dynamic quantification and grading judgment of the overall safety state of the laboratory are realized, and the risk identification capability of the complex laboratory environment is improved.

The invention discloses a multi-machine and multi-task scheduling system for an automatic chemical laboratory, and belongs to the field of automatic chemical experiment control systems. Comprising a task receiving module connected with a multi-machine multi-task scheduling module, a multi-task scheduling module connected with the multi-machine multi-task scheduling module, a multi-robot control module connected with a workstation control module, a multi-robot control module connected with the multi-machine multi-task scheduling module, and a workstation control module connected with the multi-robot control module. And the server is connected with the multi-machine multi-task scheduling module. The system can receive a chemical experiment task input by a user through an interactive graphic task interface, construct a formatted chemical experiment task flow through a rear end, optimize the formatted chemical experiment task flow into a task scheduling schedule through a constraint programming algorithm, and guide a robot and an automatic experiment work station to execute. Therefore, intelligent scheduling and automatic execution of multiple types of chemical experiments in an automatic chemical laboratory scene are realized.

The invention provides an intelligent laboratory man-machine interaction control method and system, and relates to the technical field of control, and the method comprises the steps: collecting experimenter and equipment images through a visual collection device, recognizing equipment operation state parameters based on a deep learning model through a processing server, including a switch state, an indicating lamp state, a display screen state and a fault indication state, and transmitting the state parameters to a server; and comparing the identification result with a preset parameter to generate an evaluation result. And the processing server receives the user voice instruction, extracts the content of the control instruction, and determines the target equipment and the control action. And verifying the legality of the instruction and judging whether an execution condition is met by combining an equipment state evaluation result. And a cooperative control strategy is generated according to the control instruction and the evaluation result, the cooperative control strategy comprises a control time sequence, parameters and a data interaction mode, and the control instruction is sent through the field bus network to realize multi-device cooperative control. According to the invention, intelligent management and control of laboratory equipment are realized, and the experiment efficiency and safety are improved.

The invention relates to the field of laboratory environment control, and discloses a laboratory environment abnormity self-adaptive linkage control system based on multi-source heterogeneous perception, which comprises the following steps: when any environment parameter exceeds a set safety threshold value, judging whether comprehensive environment abnormity exists in combination with the numerical value change trend of other types of sensors; based on historical monitoring data and real-time sensing data, constructing a multi-dimensional environment characteristic spectrum, and identifying internal association and change paths between abnormal signals; whether linkage control measures are needed or not is evaluated by analyzing the current anomaly type and the influence area; dynamically generating a linkage control path and a priority sequence according to a system topological structure and an equipment operation state, and determining equipment objects needing to be linked; and according to the generated control path and parameters, the operation state of related equipment is automatically adjusted, active response control for abnormity is completed, and an environment feedback result after control is monitored. The method has the advantage of improving the intelligent control level.

The invention discloses a dual-model-driven intelligent reservoir logging evaluation method, which comprises the following steps of: establishing a lithology identification logging data set based on a rock slice identification result and conventional logging data, and constructing a data model-driven lithology identification pre-training model; establishing a porosity prediction data set of different lithology based on a core porosity analysis result, and constructing a dual-model driven porosity prediction model of a data model and a knowledge model under the guidance of a lithology classification result; establishing a permeability prediction data set of different lithology based on a permeability analysis result, and constructing a dual-model driven permeability prediction model of a data model and a knowledge model under the guidance of a lithology classification result; on the basis of different lithologic saturation parameters measured in a laboratory, the water saturation of different lithologies is calculated in combination with physical property prediction results driven by double models of different lithologies and conventional logging data. The method can effectively solve the well logging evaluation problem faced by a complex reservoir.

The invention discloses a system and a method based on laboratory process intelligent management, and relates to the technical field of intelligent management. The system comprises an experiment task classification and sorting module, a historical data analysis and task duration prediction module, an overlapping time period analysis and intelligent scheduling module and a real-time monitoring and task progress adjustment module. The experiment task classification and sorting module is responsible for acquiring an initial experiment task list and carrying out task classification and sorting; the historical data analysis and task duration prediction module predicts the total duration of the experiment task by using historical data and provides accurate task duration prediction; time overlapping between the overlapping time period analysis and intelligent scheduling module processing tasks is carried out, and the task execution sequence and the equipment use time are intelligently adjusted; and the real-time monitoring and task progress adjusting module tracks the task execution condition in real time, analyzes the actual and predicted deviation and carries out necessary progress adjustment.

The invention relates to the technical field of safety management and risk assessment, and discloses a laboratory safety intelligent monitoring and early warning method and system, and the method comprises the following steps: S1, building a three-dimensional digital model of a laboratory; s2, deploying a sensor network covering a laboratory; s3, monitoring an operation behavior in real time through a sensor network, and comparing the operation behavior with the operation logic chain; s4, when the sensor network detects that the physical environment is abnormal; s5, collaborating the logic abnormal state, the position and strength of the risk source and the risk type; and S6, performing evolution prediction based on the risk type and the risk source. According to the method, Bayesian fusion reasoning is performed on context information such as multi-dimensional sensor evidence and real-time operation regulations, so that high-precision and high-confidence identification of risk types is realized, and the problem that the risk types cannot be identified due to lack of cognition on field operation activities is solved. And the risk qualitative is fuzzy, the false alarm rate is high, and the real dangerous case and compliance operation interference cannot be distinguished.

The invention relates to the field of ventilation equipment early warning, in particular to a ventilation equipment early warning method and system for a smart laboratory, and the method comprises the steps: building a window with a target moment as a center, and obtaining a multi-dimensional state parameter sequence of ventilation equipment in the window; calculating the maximum cross correlation coefficient of the target moment, obtaining the optimal lag, weighting the optimal lag to obtain a final lag coefficient, and obtaining the cross correlation difference significance according to the maximum cross correlation coefficient of each sampling moment in the window; and aligning the state parameter sequences of different dimensions in the window based on the final lag coefficient, obtaining the correlation of the state parameter sequences of any two dimensions, calculating a risk value of the target moment, and correcting the risk value to obtain a risk correction value. According to the technical scheme, the early warning precision of the ventilation equipment can be improved, and the influence of hysteresis on early warning is reduced.

The invention relates to the technical field of data analysis, and discloses a laboratory data analysis method and system based on a machine learning technology. The method comprises the following steps: firstly, introducing an adaptive threshold, and identifying and deleting abnormal data in original laboratory data by using an improved isolated forest algorithm to obtain a processed laboratory data set; secondly, improving the generative adversarial network to obtain an improved generative adversarial network filling model, and performing data filling on the processed laboratory data set for multiple times to obtain a processed laboratory data set; according to errors of experimental result data, an improved black-wing optimization algorithm is used for searching for the minimum error, and optimization of laboratory data is achieved; and finally, establishing a laboratory database, performing data storage on the processed laboratory data set, and completing laboratory data analysis. By analyzing and processing the laboratory data, the purpose of analyzing the laboratory data is achieved, and the method is accurate and objective.

The present invention describes the development of a laboratory system for testing rock samples with fractures subjected to single-phase flow, using visible fluid and equipment made of material not sensitive to a microtomograph, allowing (i) the quantification of parameters that represent the flow in a fractured medium in the flow simulator and (ii) images that portray the distribution of fluids in the medium, in different injection steps.

The invention relates to the technical field of data processing, in particular to a laboratory safety monitoring method and system based on multi-sensor data. The method comprises the following steps: acquiring standardized data of dimension data of multi-source sensors deployed at different positions in a laboratory at a plurality of sampling moments; obtaining a fused data stream at each sampling moment; constructing a dynamic heterogeneous graph in a laboratory; updating the node state of the dynamic heterogeneous graph in real time by using the fusion data stream, and processing the dynamic heterogeneous graph by using a graph neural network model to extract a graph feature vector sequence representing the overall state of the laboratory scene; and inputting the graph feature vector sequence into a time sequence prediction model, and outputting a risk vector so as to monitor the safety risk of the laboratory environment. According to the scheme of the invention, the safety monitoring of the laboratory environment can be safely and accurately carried out.

The invention discloses a parallel bonding model mesoscopic parameter calibration method, system, equipment and medium, and relates to the technical field of rock mechanics simulation. Comprising the following steps: acquiring a mesomechanics parameter range of a rock test piece; carrying out random interpolation processing on the mesomechanics parameter range to obtain a plurality of random mesomechanics parameters; establishing a parallel bonding model, and performing a uniaxial compression test on the parallel bonding model to obtain a plurality of simulated macro-mechanical parameters corresponding to the random micro-mechanical parameters; training a BP neural network through the input layer training sample and the output layer training sample to obtain a trained BP neural network; and inputting the actual macro-mechanical parameters into the trained BP neural network to obtain the calibrated actual micro-mechanical parameters of the rock test piece to be tested. According to the method, the process of micro-parameter calibration is simplified, the time required for micro-parameter calibration is shortened, and the defects of a traditional trial and error method in the aspect of quantitatively evaluating the matching degree of simulated cracks and laboratory test cracks are overcome.

Provided are a digital twin-based chemical experiment monitoring and optimization method and apparatus. The method comprises: partitioning a laboratory into a plurality of operational unit areas, and creating a corresponding management shell for each operational unit area for experiment control and data acquisition; converting a current experiment process into an experiment task and an experiment operation, and mapping collected data to a corresponding experiment object; creating and training a prediction model, and inputting into the prediction model the data mapped to the experiment object, so as to monitor a current experiment state; and optimizing the current experiment process according to a result of the current experiment process and the data mapped to the experiment object. A closed-loop from data acquisition to experiment monitoring and optimization is established for laboratories so as to implement data acquisition, prediction, monitoring, index analysis, experiment optimization and simulation display for experiment processes.

The invention discloses a laboratory resource efficient allocation method based on big data analysis, and relates to the technical field of laboratory resource management.The method comprises the following specific steps that microenvironment parameters are monitored and collected, comprising a temperature and humidity sensor, an air quality sensor, an illumination intensity sensor and an electromagnetic interference intensity sensor, a dynamic association model of external environment and laboratory resource demands is established by using a big data analysis technology, so that a complex relationship between external environment change and laboratory resource demands is accurately analyzed; a powerful basis is provided for scientific and reasonable resource allocation, so that a laboratory can more accurately predict and judge the demand degree of each resource and the allocation priority under different external environment conditions, unreasonable allocation and waste of the resources are avoided, and the resource allocation efficiency is remarkably improved.

The present disclosure provides in some embodiments, a multi -objective binder design framework by aligning autoregressive molecular foundation models (e.g., protein language models (pLMs)) to different objectives, such as binding and developability considerations. In some embodiments, direct preference optimization (DPO) can be utilized in the methods and systems described herein to encode multiple design objectives in the language model through direct optimization on expert curated preference sequence datasets comprising preferred and dispreferred distributions. In some embodiments, utilizing the described framework can enable molecular foundation models (e.g., protein language models), such as ProtGPT2, to effectively design binders conditioned on specified receptors and one or more drug developability criteria. Besides being multi -objective, in some embodiments, the methods and systems provided herein conceive online lab-in-the-loop design pipelines by actively incorporating experimental feedback.

The invention discloses a monitoring and early warning method and system based on laboratory operation, and the method comprises the steps: obtaining and processing laboratory equipment state data and personnel operation records, and constructing a dynamic asymmetric Bayesian network which reflects the personnel operation intention, the equipment state and the interference relation; outputting probability distribution from the equipment state to the personnel operation intention, monitoring whether the equipment state is abnormal or not, and updating the probability distribution; performing judgment and early warning on the updated probability distribution based on an ROC curve, and defining a loss function to dynamically adjust the dynamic asymmetric Bayesian network; according to the method, the dynamic asymmetric Bayesian network is constructed, and parameters are dynamically adjusted according to the real-time change of the equipment state, so that the model shows higher robustness in a dynamically changing laboratory environment; by establishing a backward inference mechanism of the dynamic asymmetric Bayesian network, the operation intention of the personnel is inferred from the equipment state, the perception level of potential risks is improved, and the defects of a traditional system in deep inference are overcome.

The invention discloses a method and a system for adjusting the internal environment of a mobile laboratory cabin, particularly relates to the technical field of environment control, and aims to solve the problem that the existing environment adjusting method adopts a fixed control target and cannot adapt to differentiated environment requirements in a multi-task scene of a mobile laboratory. The method comprises the following steps: acquiring a current task identifier of a mobile laboratory, identifying a corresponding experiment task execution stage, and dynamically generating an inter-parameter collaborative constraint relation matrix based on a fuzzy inference rule base; a regulation and control demand vector is generated by collecting a multi-dimensional environmental parameter measurement value and combining a pre-stored environmental parameter expected value; calculating a regulation and control conflict risk entropy value based on the matrix and the vector, and performing risk judgment; when the risk tolerance is exceeded, a roundabout regulation and control strategy is generated as an optimization guide parameter; finally, an environment control instruction is generated in a coordinated mode according to the optimized guiding parameters and sent to an executing mechanism; the dynamic adaptation of the environmental parameter regulation and control strategy and the experiment task is realized, and the precision and reliability of multi-parameter cooperative control are improved.

The invention provides a micro-nano processing laboratory equipment information control method and device. The method comprises the following steps: acquiring user login information; calling item information to which a user belongs, user experiment reservation information, user associated equipment information and user historical query information according to the user login information; predicting the attention weight of the user to each piece of laboratory equipment management information in combination with the user identity permission, the item information to which the user belongs, the user reservation experiment information, the user associated equipment information and the user historical query information; adjusting the display sequence and the detailed degree of each piece of laboratory equipment management information in combination with the attention weight; and the data board is used for sending laboratory equipment management information to the user according to the display sequence and the detailed degree.

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 relates to a multi-mode-based laboratory environment monitoring method and system, and relates to the field of environment monitoring, and the method comprises the steps: obtaining environment information of each animal cabin in a laboratory in a preset historical time period and a monitoring video of each animal cabin, the environment information comprises a temperature change curve, a humidity change curve and an oxygen content change curve, determining an activeness change curve of each animal cabin according to the monitoring video, determining an environment quality change curve of each animal cabin according to the environment information, determining a target cabin of which the activeness reaches a preset activeness threshold according to the activeness change curve, and determining a target cabin of which the activeness reaches a preset activeness threshold according to the target cabin. And judging whether an abnormal cabin exists in the target cabin or not according to the environment quality change curve and the activity change curve, and if yes, controlling a ventilation device to ventilate the abnormal cabin. The method has the effects that the abnormal cabin can be conveniently found in time, and the environment of the abnormal cabin is improved.

The invention relates to the technical field of task management, and discloses a laboratory automatic data processing and management method, system, equipment and medium, and the method comprises the steps: obtaining the equipment information and resource information of a laboratory; and establishing an equipment simulation model of the laboratory. A task instruction is collected, and the task instruction is disassembled into atomic tasks. And arranging the execution sequence of the atomic tasks according to the inherent sequence constraint of the task instruction, the maximum resource utilization rate, the minimum scheduling frequency and the comprehensive analysis result of the optimal working time of the equipment. And performing task execution by using the execution sequence of the atomic tasks. Idle and faults of equipment are effectively avoided, resource waste is reduced, and automation and accuracy of experimental operation are improved. Besides, by analyzing the working time of the equipment and the validity period of the consumables, the energy consumption and the operation cost can be reduced while the smooth proceeding of the experiment is ensured, and the automatic management level of the laboratory is further improved.

The invention provides a laboratory safety monitoring method and system based on multi-data fusion. The method comprises the steps that S1, a data acquisition module monitors and acquires environmental data in a laboratory in real time; environment data in a laboratory are preprocessed; performing weight distribution on the laboratory internal environment data and the manual alarm function triggering state, and introducing a comprehensive threat index; s2, calculating a comprehensive threat index according to the collected laboratory internal environment data, the manual alarm function trigger state and the weight; s3, judging a security threat level based on the comprehensive threat index; judging a security threat type based on the security threat level, the laboratory internal environment data and the manual alarm function triggering state; and S4, taking security countermeasures according to the security threat type. The comprehensive threat index can comprehensively reflect the safety condition in the laboratory; and safety countermeasures are taken for the safety threat type, so that the accuracy of safety condition identification and the pertinence and effectiveness of the countermeasures are improved, and personnel and property loss is avoided.

The invention discloses an electric energy meter and environment automatic test management method and system, and the method comprises the steps: collecting electric energy meter data and laboratory environment data, dividing the standardized data into a plurality of layers for processing, carrying out the multi-layer weight fusion, and obtaining a final fusion result through an adaptive weighting and cross validation mechanism; a multi-layer hybrid neural network optimization algorithm is utilized to map the final fusion result and the environmental data to a high-dimensional space, spatial-temporal features are extracted through a spatial-temporal convolution layer, feature integration and final prediction are carried out through a dynamic feedback loop layer and an adaptive weight control full-connection layer, and an electric energy meter performance evaluation result is generated; based on a multi-target adaptive scheduling algorithm, experimental requirements, environmental parameters and an electric energy meter performance evaluation result, a scheduling strategy is optimized through a gradient descent method, the updated scheduling strategy is applied to a test process and experimental conditions, closed-loop control is formed, and continuous optimization and performance improvement of the system are ensured.

The invention discloses a liquid storage type micro-fluidic chip and a working method thereof. The chip comprises a chip body, a sealing film and gum, the sealing film and the back adhesive are respectively bonded on the front surface and the back surface of the chip body; a plurality of solution storage cavities are formed in the chip body, outflow runners at the bottoms of the solution storage cavities are connected with corresponding first runners, sealing partition pieces are arranged on the first runners, the outflow runners and the corresponding first runners are blocked through the sealing partition pieces, and the first runners are communicated with second runners through corresponding gum runners. The whole experiment process does not need to be externally connected with a reagent bottle, the purposes of instant detection, preparation and sample separation of the micro-fluidic chip can be achieved, the detection time is greatly shortened, and the experiment steps are simplified. Waste liquid generated in the experiment process flows into the waste liquid cavity, and cross contamination of a laboratory is avoided. The chip provided by the invention can be used for identifying the species of pathogenic microorganisms.