1457 results about "Experimental laboratory" patented technology

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 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 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 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 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 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 belongs to the field of laboratory automation equipment, and discloses a control method and system of a task execution device for physical and chemical experiment operation, and the method comprises the steps: initializing and calibrating the device; receiving and analyzing an experiment task instruction; collecting object information through a visual sensor; the upper computer generates an execution scheme according to the strategy library; the electric control rotary clamping jaw is driven to complete operation; force and displacement parameters are monitored in real time, and dynamic adjustment actions are fed back through a closed loop; and judging that the operation is completed according to the threshold. The system comprises a visual sensor, a rotary driving body, an electric control rotary clamping jaw and an upper computer. By integrating visual identification and force sense feedback, fine operations such as clamping, taking and placing, bottle cap screwing and the like can be completed in a self-adaptive mode, the efficiency, precision and reliability of laboratory automatic operation are remarkably improved, manual operation errors are reduced, and good application prospects are achieved.

The invention relates to the technical field of laboratory environment control, and discloses an environment parameter control method and system for a complex laboratory, and the method comprises the steps: obtaining environment index data, and carrying out the time sequence processing, and obtaining a real-time change trend; according to the real-time change trend, predicting a future offset by using a prediction model, and performing data fusion when the offset exceeds a threshold value to obtain an accurate environment state estimated value; according to the accurate environmental state estimation value, quantifying the mutual influence among the environmental indexes to obtain the mutual influence relationship of the environmental indexes; determining an environment index priority sequence according to the mutual influence relationship; generating and executing a control instruction according to the priority sequence; and when continuous deviation exists, the prediction model and the mutual influence relation are updated through feedback circulation. According to the method, the dynamic mutual influence among the environmental indexes can be quantified, and accurate and stable cooperative control of multiple parameters in a complex dynamic environment is realized.

The invention relates to the technical field of industrial data analysis, in particular to a concrete mix proportion optimization system and method based on industrial data analysis, comprising a data acquisition and sensing module, a data storage and processing module, a core analysis module and an application output module; compared with the defects of long period, high cost and difficulty in coping with fluctuation of raw materials due to the fact that the prior art mainly depends on laboratory trial and matching and experiences of engineers, the method has the advantages that the performance prediction model based on machine learning and multi-source real-time industrial data fusion analysis are adopted, and the concrete performance can be rapidly and accurately predicted; the mixing proportion design efficiency and scientificity are remarkably improved, and fundamental conversion from experience driving to data driving is achieved.

The invention relates to the technical field of modularized laboratory project management, and discloses a modularization-based laboratory project management method, which comprises the following steps: S1, system architecture design: adopting a micro-service distributed architecture, and cooperatively working through a plurality of business modules; s2, module management and project creation; the invention also provides a laboratory project management system based on modularization, which comprises a project management engine module, an intelligent key node reminding system, a sample full life cycle tracking management module and a multi-party collaborative feedback management module, the project management engine module comprises three core business modules, namely, a project management module, a sample management module and a contract management module, standardized management and efficient operation of laboratory projects are achieved through a project module engine, an intelligent node reminding mechanism and a full-process tracking algorithm, the project management efficiency can be remarkably improved, and the project management efficiency is improved. And the time cost of a project preparation stage is saved.

The invention provides a grounding grid intelligent operation and maintenance system based on digital twinning and computer equipment. A multi-factor coupled corrosion kinetic equation is generated based on a laboratory grounding grid steel sheet accelerated corrosion test, and the corrosion kinetic equation is used for predicting the instantaneous corrosion rate of a galvanized steel sheet and estimating the mass corrosion loss of the steel sheet within specified time; combining the corrosion kinetic equation with the simulation model to establish a steel sheet corrosion data set, and training a physical information neural network to construct a corrosion prediction physical model; training the corrosion prediction physical model by adopting a neural network regression algorithm; constructing a grounding grid digital twin model by taking the corrosion prediction physical model as a base and combining field environment monitoring data; defining a Markov decision process in the digital twin environment of the grounding grid; and based on the feedback and reward function of the digital twin model of the grounding grid, training by adopting a near-end strategy optimization algorithm to obtain an intelligent decision-making body, and continuously optimizing the decision-making of the intelligent decision-making body through an online fine tuning function.

The invention discloses an off-line virtual laboratory and digital intelligent application iteration method and system based on an intelligent agent. The method comprises the following steps: constructing an off-line virtual laboratory consistent with a site in a non-production environment, and keeping the consistency of data and configuration through a synchronization mechanism; performing automatic scheduling on working condition arrangement, simulation deduction and rule checking by utilizing an intelligent agent; carrying out process deduction by adopting a mode of combining data driving and similar working condition retrieval, and completing index calculation and safety verification; and full-life-cycle management is carried out on the digital intelligent application, and cross-site rapid copying is realized through template packaging. According to the system, low-risk and high-efficiency iterative verification and popularization of digital intelligent application can be realized on the premise of not interfering field production, and the safety and engineering level of industrial intelligence landing are remarkably improved.

The invention discloses laboratory centralized full-automatic analysis five-day biochemical oxygen demand monitoring equipment, which relates to the technical field of five-day biochemical oxygen demand monitoring equipment, and comprises a laboratory centralized full-automatic analysis system, a sample temporary storage incubator, an incubator, a sample transport vehicle and a pretreatment system, the pretreatment system is arranged at the side part of the laboratory centralized full-automatic analysis system. According to the laboratory centralized full-automatic analysis five-day biochemical oxygen demand monitoring equipment, the sample temporary storage constant temperature box, the culture box, the sample transport vehicle, the pretreatment system and other structures are arranged, so that the defect of a laboratory centralized full-automatic analysis system in the aspect of five-day biochemical oxygen demand monitoring is overcome, and the overall automation level of a laboratory is improved; tedious consumable preparation of a laboratory is simplified, operation of experimenters can be greatly simplified, personal errors in the experiment process are reduced, and result accuracy is guaranteed.

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.

A method for processing a sample in a container includes introducing the sample to the container. A cap is applied to the container. The cap contains a plurality of beads. The plurality of beads is mixed with the sample. The plurality of beads is separated from the sample. Subsequent to removing the plurality of beads, a further process is initiated.

The invention discloses a laboratory sample detection management method, system, equipment and medium based on automatic scheduling, and belongs to the technical field of laboratory intelligent scheduling, and the method comprises the following steps: collecting task and equipment data in real time, binding and storing, and quantifying an initial priority by calculating task complexity; the priority is dynamically adjusted by combining the fault severity, queuing duration attenuation and environment temperature and humidity overrun correction, the completion time is predicted based on the historical efficiency of the equipment and the current load, the task is preferentially allocated to the automatic equipment for execution according to the corrected priority score, and the progress is updated in real time. The intelligent scheduling capability, the resource utilization efficiency and the operation stability of laboratory detection tasks are improved, and efficient and reliable technical support is provided for a complex and changeable experiment environment.

The invention discloses a proportioning optimization method for efficiently balancing a chemical oil displacement system. The proportioning optimization method comprises the following steps: S1, collecting and sorting experiment and field data of the existing chemical oil displacement system; the oil displacement effect can be effectively improved by continuously optimizing the ratio of the chemical agents through experiments and numerical simulation, so that the recovery efficiency of an oil field is improved, the oil-water interface characteristics can be improved, the fluid viscosity can be reduced, the oil fluidity can be improved through the reasonable ratio of the chemical agents, and by analyzing the cost benefits of different ratios, the oil displacement effect can be effectively improved. The chemical agent combination with higher economical efficiency can be selected, the expenditure in the chemical oil displacement process is reduced, the oil displacement effect and cost are comprehensively considered, the resource utilization efficiency of the chemical oil displacement system is improved by optimizing the proportion, and it can be ensured that the selected proportion is not only theoretically effective but also theoretically effective through laboratory verification and on-site small-scale tests. And the method also has practical significance in practical application.

The invention discloses a system and a method for predicting reaction conditions of a menthane carboxamide cooling agent, and aims to optimize the reaction conditions, improve the experiment efficiency, reduce the production cost and ensure the stable product quality through an intelligent method. According to the system, the design experiment DoE and the response surface method RSM technology are combined, the relation between reaction conditions and products is systematically analyzed, multi-scale modeling is utilized, a molecular reaction mechanism and a macroscopic reactor model are combined, seamless connection from a laboratory to an industrial scale is achieved, and challenges in the amplification process are solved. By means of real-time data monitoring and feedback adjustment, the system can dynamically optimize the reaction process, controllability and stability of the production process are improved, the scientificity and intelligent level of reaction optimization are improved, and an efficient, controllable and environment-friendly solution is provided for large-scale production of the menthane carboxamide cooling agent.

The invention discloses a laboratory intelligent agent construction method and system based on artificial intelligence, and relates to the technical field of intelligent laboratories. In order to solve the problems of scattered laboratory data information, low scientific and technological resource matching efficiency, lack of a unified AI driving platform and the like in the prior art, a core agent is constructed by introducing machine learning or an artificial intelligence architecture, and digital access and resource pooling are carried out on scientific research total factors such as infrastructures, software and hardware, human property and the like. The intelligent agent is based on semantic alignment and inference rules, dynamic intelligent configuration of resources such as field observation, experimental analysis and computational simulation is achieved, and finally a full-life-cycle intelligent management and scientific research environment integrating equipment perception, intelligent control and business application is formed. According to the method, resource islands are effectively broken, the resource utilization efficiency is improved, a novel scientific research normal form of'big data + AI + model 'is promoted, and systematic support is provided for refined and automatic management of laboratories.

The invention discloses a coal bed gas biological yield increase effect dynamic monitoring and evaluation method which comprises the following steps: arranging a multi-parameter sensor in a target coal bed gas well site, and collecting background data before bacterium injection; injecting a microbial bacterial solution and a nutrient solution into the coal seam according to the experimental design, and recording parameter information in the injection process; periodically collecting a gas production sample, and analyzing gas components; analyzing microbial community structure and abundance change in the water sample; combining the gas generation rate, the isotope characteristics and the microflora analysis result to identify a gas production path and calibrate a key functional flora; and constructing a mathematical model containing microbial activity, coal quality conversion and gas production efficiency, forming an evaluation index system, and outputting field process optimization suggestions based on the model. According to the method, a scientific basis is provided for dynamic optimization of the yield increasing process, and compared with a traditional laboratory method, the method can achieve systematic analysis of the in-situ microorganism process and quantitative evaluation of the gas production effect, and has good field adaptability and popularization value.

The invention relates to a chemical experiment instrument target detection method and device, equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing of an obtained image of a chemical laboratory scene, and obtaining a preprocessing result; inputting the preprocessing result into a chemical experiment instrument target detection model based on an improved YOLO network to obtain bounding box coordinates and corresponding instrument category confidence; a chemical experiment instrument target detection model is obtained by improving a neck network of a YOLOv8 network, and a 3Dfusion module and an FPSF module are innovatively introduced into the neck network. According to the method, the technical problem of recognition challenges caused by variable scales, different angles and local shielding of chemical instruments is effectively solved, and accurate perception of complex laboratory scenes is realized.

The invention discloses a safety early warning method and system, and the method comprises the steps: obtaining environment data and equipment data corresponding to each piece of laboratory equipment, the environment data and the equipment data being data collected by a detection sensor corresponding to the laboratory equipment, and deploying at least one piece of laboratory equipment in a laboratory, a single laboratory device is correspondingly provided with at least one detection sensor, and the detection sensor is detachably installed in the communication network of the access server; comparing the environment data and the equipment data with a preset parameter threshold to obtain a comparison result; inputting the equipment data and the environment data into a pre-trained anomaly evaluation model for identification to obtain an anomaly score; determining an abnormal event and an early warning strategy corresponding to the abnormal event according to the comparison result and the abnormal score; and sending the early warning information to the target early warning equipment to indicate the target early warning equipment to execute the early warning strategy. According to the invention, the timeliness and accuracy of the detection and response of the laboratory abnormal event are improved.

The invention relates to the technical field of laboratory safety risk assessment, and discloses a laboratory safety risk dynamic assessment method based on machine learning, and the method comprises the steps: S1, collecting the multi-source real-time data of a laboratory environment and equipment; s2, carrying out preprocessing and time sequence synchronization on the multi-source real-time data; s3, extracting time sequence features from the time sequence data based on a sliding time window; s4, inputting the feature vector into a machine learning model with an online learning capability; s5, distributing according to the risk prediction result and the current data; and S6, when the risk level reaches a preset condition, triggering laboratory safety early warning and linkage control. Incremental updating is carried out on the machine learning model by inputting feature vectors in real time, adaptive iteration adjustment is carried out on a to-be-updated parameter set based on prediction error changes of a continuous time window, risk judgment is corrected in time along with environment changes, and the real-time performance, sensitivity and stability of model risk prediction are improved.

The invention discloses a grouting splitting pressure simulation device under high-ground and high-water-pressure conditions, and relates to the technical field of tunnel and underground engineering model tests. A first water permeable unit controlled by the water injection unit and a second water permeable unit controlled by the confining pressure unit are respectively arranged at two ends in the length direction, so that water pressure and stratum confining pressure are independently applied. The integrated grouting unit of the device is used for grouting soil in the box and is provided with a comprehensive detection unit, the comprehensive detection unit monitors various parameters such as grouting pressure, water pressure, confining pressure, flow, soil resistance and wave velocity in real time, and independent loading and accurate dynamic control of the grouting pressure, formation water pressure and formation stress can be achieved; therefore, the grouting process of the deep-buried tunnel under the conditions of different burial depths, high water head differences and complex grout characteristics can be simulated in a laboratory with high precision, and the technical bottleneck that the complex environment of high ground stress and high water pressure coupling is difficult to reproduce in a traditional test method is effectively solved.

The invention relates to the technical field of laboratory environment control, and discloses a laboratory temperature control method and system based on multiple sensors and a storage medium. According to the method, a first temperature difference value is calculated by obtaining a real-time temperature value and a target temperature value, and the total cooling capacity output power is adjusted; temperature sensors and local air ducts are arranged in the multiple experiment areas respectively, heating modules are arranged at air inlets of the local air ducts, refrigerating modules are arranged at air outlets of the local air ducts, and the multiple local air ducts are communicated in series; acquiring a first temperature value of the previous experimental area and a second temperature value of the current experimental area, and calculating a second temperature difference value between the temporary temperature value and the target temperature value; and if the absolute value of the difference value exceeds the preset reference difference value, the output power of the refrigerating or heating module is adjusted according to the size relation between the target temperature value and the temporary temperature value. According to the method, accurate temperature control of the whole laboratory and each experimental area is realized, and the temperature stability and uniformity are improved.

The invention relates to the technical field of underground geotechnical engineering and tunnel construction monitoring, in particular to a shield tunneling rock stratum real-time judgment method. The method comprises the steps that according to signals of neutron radiation characteristics released when different lithologic samples are fractured under a shield tunneling stress path simulated in a laboratory, a knowledge base of the neutron radiation characteristics of a rock stratum is constructed; deploying an on-site multi-source data acquisition system, and acquiring data of neutron radiation characteristics and shield tunneling parameters; jointly forming a fusion feature vector according to the data of the neutron radiation features and the shield tunneling parameters; the method has the advantages that signals of neutron radiation characteristics released in the rock fracture process are directly monitored, the signals are fused with shield tunneling parameters in a multi-source mode, a machine learning model is combined, and the lithology classification probability and the rock mass integrity index are obtained. Real-time and accurate discrimination of lithology and integrity of a front rock stratum is realized, physical characteristics of a rock mass are reflected, and a discrimination effect is continuously optimized.

The invention discloses a full-automatic fluid stirring device performance test system and a method thereof, and relates to the technical field of electrical digital data processing, on one hand, by calculating a flow state deviation degree Lpld and combining an amplification prediction model to output an amplification reliability coefficient Fkxs, the dynamic similarity between laboratory test and industrial working conditions is ensured, and amplification distortion is effectively solved; on the other hand, the dynamic change of fluid attributes is simulated, the energy efficiency attenuation rate Nxsl, the mixed dead zone expansion rate Hqkz and other process indexes are quantified, the dynamic matching coefficient Ddps is calculated on the basis, and the limitation of static testing is overcome; finally, according to the method, Fkxs, Ddps and other multi-dimensional dynamic indexes are subjected to weighted fusion, a comprehensive performance prediction index Zhyc is generated through nonlinear mapping, accurate prediction of the real performance of the stirring device in the whole period is achieved, and the accuracy and reliability of equipment type selection are remarkably improved.

The invention discloses a smart laboratory scene knowledge retrieval method based on an AI large model, and particularly relates to the technical field of laboratory knowledge management.The method comprises the following steps that multi-modal data in an experiment is obtained, semantic vectors of all modals are generated through preprocessing, and the semantic vectors are stored in a database; aiming at each modal semantic vector, performing cosine similarity alignment and fusion to form a unified semantic vector, and constructing a laboratory knowledge graph and a cross-modal large model; creating a knowledge association network in combination with the cross-modal large model and the laboratory knowledge graph; and outputting a knowledge retrieval result adaptive to the current experiment scene based on the knowledge association network, and recording user feedback. According to the scheme, the problems that a traditional retrieval method is insufficient in heterogeneous data processing capacity, insufficient in context understanding and the like are solved, the accuracy, the intelligent level and the scene adaptability of knowledge retrieval are remarkably improved, intelligent applications such as experimental scheme recommendation and exception handling are effectively supported, and the refined knowledge management requirement of an intelligent laboratory is met.