17207results about "Chemometrics" patented technology

The invention relates to the technical field of meteorological data analysis, and provides an aerosol prediction method and system based on artificial intelligence, which are used for accurately identifying an abnormal aerosol gathering mode in an extreme meteorological event. The method comprises the steps that an original multi-mode meteorological observation data set is acquired, and the original multi-mode meteorological observation data set comprises an earth surface aerosol concentration sequence, three-dimensional atmosphere state variables and boundary layer dynamic parameters; performing space-time heterogeneity correction on the original multi-modal meteorological observation data set to generate a space-time aligned target multi-modal meteorological observation data set; inputting the target multi-modal meteorological observation data set into a spatial-temporal feature fusion network to extract multi-scale spatial distribution features and cross-modal time sequence correlation features, and generating a spatial-temporal feature matrix based on the multi-scale spatial distribution features and the cross-modal time sequence correlation features; and performing multi-stage recursive optimization on the spatial-temporal characteristic matrix through a hierarchical time domain aggregation algorithm to generate an aerosol concentration prediction sequence of the target region.

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

The invention relates to the technical field of environment monitoring, in particular to an underground water pollution source traceability identification method and system, and the method comprises the following steps: S1, collecting the flow velocity, flow direction and water level change data of underground water in a polluted region in real time; s2, constructing a preliminary underground water flow model; s3, simulating a migration path of pollutants in underground water, and predicting a potential pollution source position; s4, processing the collected underground water flow velocity and pollutant diffusion path data, and removing abnormal data interference; s5, generating a spatial distribution map of the potential pollution source in combination with a geographic information system; and S6, verifying pollutant components and concentration distribution thereof, and confirming the accuracy of a traceability result. According to the invention, by combining a multi-pollutant traceability algorithm, an underground water flow model and a geographic information system technology, a pollutant migration path is accurately simulated and a pollution source spatial distribution map is generated, so that the precision and reliability of underground water pollution source traceability are greatly improved.

The invention discloses an energy storage battery pack equalization control method, which belongs to the field of energy storage battery packs, and comprises the following steps: S1, constructing a multi-physics field-aging coupling model; s2, based on a multi-physical field-aging coupling model, space-time-frequency domain joint state estimation and unbalanced pattern recognition are realized; s3, formulating optimal equalization topology and control parameters according to space-time-frequency domain joint state estimation and an unbalanced mode recognition result; s4, establishing an electric-thermal-fluid multi-physics field coupling control model, and optimizing equalization current regulation and control, liquid cooling flow velocity and cooling fan rotating speed based on the optimal equalization topology and control parameters; and S5, optimizing the full life cycle of the digital twin drive. By adopting the equalization control method for the energy storage battery pack, the efficient coordination of the energy storage battery pack from dynamic modeling, accurate state estimation and intelligent equalization decision to full life cycle management is realized, and the equalization efficiency and the system reliability are remarkably improved.

The invention relates to the technical field of pollutant tracing, and discloses a method and a system for tracing new pollutants in various environmental media based on machine learning. The method comprises the following steps: collecting multivariate environment data to construct a pollutant fingerprint database; performing space-time correlation analysis on the fingerprint database and the environment data, and reconstructing a transmission path; converting the data, the fingerprint database and the path diagram into source node fusion to construct an analytical model, and forming a contribution rate matrix; and positioning the pollution source through the three-dimensional features based on the matrix. According to the method and the device, an intelligent decision-making system based on machine learning can be constructed by integrating multi-source heterogeneous data under a complex environment background, automatic, standardized and precise traceability of new pollutants is realized, and the traceability efficiency and accuracy are improved.

The invention discloses a ligand information generation model training method and device and a ligand information generation method and device, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring sample receptor information and sample ligand information, wherein the binding affinity between a ligand described by the sample ligand information and a receptor described by the sample receptor information is not less than a set affinity; denoising the reference noise data based on the sample receptor information through a to-be-trained neural network model to obtain predicted ligand information; determining a first loss for characterizing a difference between the sample ligand information and the predicted ligand information; and training the neural network model based on the first loss to obtain a ligand information generation model. The reference ligand information can be generated based on the reference receptor information through the ligand information generation model, and the binding affinity between the ligand described by the reference ligand information and the receptor described by the reference receptor information is high.

A method and a system for reconstructing aerosol chemical components based on CNN-BiLSTM-BO, including collecting multi-source environmental observation data through observation equipment, preprocessing and extracting key characteristic variables. The pre-treated multi-source environmental observation data are input into the CNN-BILSTM model for feature analysis, and the CNN-BiLSTM hyperparameters are adjusted by Bayesian optimization algorithm to generate a reconstructed model of aerosol chemical components. After verifying the performance and stability of the reconstructed model, the predicted results of the chemical components of the aerosol are output. On the basis of not relying on traditional chemical analysis technology, the invention can accurately reconstruct various aerosol chemical components, greatly reduce the cost and time of chemical analysis, effectively solve the problems of variable inconsistency, data missing, and spatio-temporal mismatch in multi-source observation data, and automatically adjust hyperparameters through Bayesian optimization algorithm to ensure that the output prediction results are more accurate.

The invention relates to a concrete working performance measurement method and system based on a multi-modal visual large model, and solves the problem that rapid detection of concrete working performance parameters is troublesome, and the method comprises the steps: based on the spatial semantic understanding capability of the multi-modal visual large model, combining a multi-view stereoscopic vision and structured light scanning technology, and calculating the working performance of concrete; reconstructing a three-dimensional geometric structure of the concrete slurry, extracting morphological characteristic parameters, and forming characteristic vectors; inputting the feature vectors into a pre-trained multi-task neural network, fusing the spatial-temporal features and combining a rheological algorithm to identify various working performance parameters; integrating identification results for at least three times by adopting integrated learning, and verifying parameters based on a fluid dynamics basic equation through a fluid simulation platform; and based on the verification result, generating a mix proportion optimization suggestion containing the material components. The method has the advantages that non-contact rapid measurement of concrete working performance parameters is achieved, precision and efficiency are improved, and mix proportion optimization suggestions are provided.

The invention relates to the technical field of data processing, and discloses a pollutant adsorption kinetics analysis method and system based on multi-scale data fusion. The method comprises the steps that macroscopic, mesoscopic and microscopic multi-source data are collected and standardized; feature recognition key parameters are extracted hierarchically; constructing a cross-scale correlation model by using the graph neural network; establishing a multi-scale hierarchical dynamic model; visualizing the adsorption process and constructing a reinforcement learning decision system; and Pareto optimal process parameters are obtained through optimization of a hybrid algorithm. According to the application, a correlation model among macroscopic environment parameters, mesoscopic surface topography and a microstructure is established by a multi-scale data fusion technology, accurate analysis and prediction of adsorption kinetics in the process of treating organic pollutants by coal gangue and persulfate are realized, and a self-adaptive process parameter optimization system is developed based on the analysis and prediction. Therefore, the treatment efficiency is improved, the energy consumption cost is reduced, and efficient removal of pollutants is realized.

The invention provides a river water quality monitoring method based on multi-source remote sensing data, and belongs to the technical field of river water quality monitoring. The method comprises the following steps: establishing an inherent optical characteristic model of a river water body based on a Hybrid water body radiation transmission model, optimizing a calculation path by adopting a shortest path algorithm, and carrying out water body optical component inversion by adopting a quasi-analysis algorithm and a generalized inherent optical characteristic algorithm in combination with a multispectral characteristic enhancement model to obtain absorption coefficients of all components; and constructing a multi-band combination index to realize optical coupling effect decoupling, and applying the fine-tuned water quality parameter inversion basic model to a target river area to output a suspended matter concentration distribution diagram, a chlorophyll concentration distribution diagram and a transparency distribution diagram. The technical problem of low precision of remote sensing inversion of water quality parameters caused by mutual coupling of multiple optical active components in a river water body is solved.

The invention discloses a stationary pollution source data acquisition and calibration method and system, a medium and a program product, and the method comprises the steps: carrying out the segmentation processing of a plurality of concentration change data, and obtaining a plurality of data segments; determining the concentration change trend of various pollutants, and calculating a correlation coefficient matrix; the Euclidean distance value between the correlation coefficient matrix of the current data segment and each correlation coefficient matrix before sorting is calculated; when the Euclidean distance is greater than a preset threshold value, marking the corresponding data segment as an abnormal data segment; determining the pollutant as a target pollutant, and establishing a material balance equation; based on the material balance equation, generating a theoretical concentration change curve of the target pollutant; calculating a deviation value, and when the deviation value exceeds a preset dynamic error limit value, determining a sensor correction coefficient according to the process parameters; and carrying out calibration processing on the acquired data of the target pollutant according to the sensor correction coefficient. According to the invention, the accuracy of data acquisition and calibration of the stationary pollution source is improved, so that the influence on formulation and execution of pollution prevention and control measures is reduced.

A federated distributed computational system enables secure oncological therapy optimization through multi-expert integration and advanced uncertainty quantification. The system implements a multi-expert integration framework that coordinates domain-specific knowledge through token-space communication for precision oncological treatment, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration. Through a distributed graph architecture, the system enables advanced fluorescence imaging with wavelength-specific targeting, multi-level uncertainty estimation combining epistemic and aleatoric approaches, and multi-scale tensor-based integration with adaptive dimensionality control. The system implements light cone search and planning for adaptive treatment strategy optimization, enabling medical institutions and research organizations to collaborate on complex oncological therapy projects while maintaining strict data privacy controls.

The present invention provides a method and intelligent agent for data collection and drug safety signal mining for PMS, which relates to the field of drug monitoring technology, including standardizing PMS data using a medical text pre-training model, and integrating drug molecular characteristics and clinical medication information to construct a multi-layer drug knowledge graph. A heterogeneous graph neural network with a time series feature extraction function is used to represent the graph, and the drug action mechanism rules are integrated into the training process to obtain a node vector representation of multi-dimensional information fusion. Based on the node vector representation, a multi-task learning framework is constructed to simultaneously complete the drug-adverse reaction association probability prediction and risk level prediction, and combine time series analysis to detect dynamic change characteristics to generate candidate signals. Finally, a hierarchical evaluation system is constructed to verify the candidate signal, and its reliability is confirmed in combination with clinical data, the target signal is output, the warning level is determined, and differentiated monitoring cycles and trigger thresholds are set to perform real-time risk warnings.

The invention provides a methane leakage anomaly detection system and method based on dual-light fusion, and belongs to the field of industrial safety detection. According to the system, a double-light-path holder type laser detector is arranged in a monitoring area, and methane concentration and image data are synchronously acquired through a 1653nm near-infrared laser module and a visible light image module. Time features (mean values, trends and the like) are extracted from the concentration data through time sequence analysis, and spatial features (edges, textures and the like) are extracted from the image data through deep learning; the system fuses time and space features to generate a comprehensive feature vector, reduces the calculation complexity through a dimension reduction technology, and retains core features at the same time. And constructing an anomaly detection model based on an unsupervised learning algorithm, identifying anomaly features in real time, and triggering an alarm. The system realizes real-time and accurate detection of methane leakage, and enhances the reliability and efficiency of industrial safety.

The invention discloses a safety management risk dynamic monitoring system for safety production, and belongs to the technical field of industrial safety management. The system collects monitoring parameter data in an enterprise in real time by integrating toxic gas, temperature, pressure and other sensors and video monitoring equipment, dynamically displays risk distribution of major hazard sources and key areas by combining a three-dimensional map labeling function, identifies high-risk areas and potential hazards through risk grading evaluation and hidden danger correlation analysis, and improves the safety of the enterprise. A response mechanism is dynamically adjusted according to the risk level, an emergency plan and a remote equipment control function are combined, major risks are handled in a rapid linkage mode, the possibility of accident expansion is remarkably reduced, an intelligent inspection module achieves efficient and accurate inspection tasks, inspection data are collected and analyzed in real time, and an inspection report containing the task completion rate and abnormal conditions is generated. Through closed-loop management, hidden danger rectification and continuous optimization are realized, real-time monitoring of safety risks is realized, and the occurrence rate of safety production accidents is effectively reduced.

The invention discloses a digital twinning-based wastewater and dirty salt treatment process optimization method and system, and relates to the technical field of intelligent wastewater and dirty salt treatment. The method comprises the steps that a digital twinborn model synchronously mapped with a processing system is established, and the digital twinborn model integrates the technological parameter incidence relation of an evaporation section, a crystallization section and a filtering section; collecting component data and flow data of the polluted salt wastewater corresponding to each process section in real time; correcting model characteristic parameters of each process section in the digital twin model based on the component data and the flow data collected in real time; according to the corrected digital twin model and the optimization target, generating a process optimization strategy corresponding to each process section; and monitoring the component data of the treated discharge water, reuse water, industrial salt and solid waste in real time, and if the data does not reach the standard, triggering model parameter correction, optimization strategy iteration or cross-section collaborative logic recalculation. According to the invention, intelligent cooperation of wastewater and dirty salt treatment is realized.

The invention discloses an operation state monitoring method and system for AEM water electrolysis hydrogen production equipment, particularly relates to the technical field of electrolytic bath operation monitoring control, and aims to solve the problem of efficiency nonlinear fluctuation caused by polarization mode alternating dominance and microstructure degradation under a low current density working condition in the prior art. Extracting polarization fluctuation characteristics by generating a dynamic response sequence and calculating a dynamic coupling degree to identify a polarization alternating dominant mode; analyzing a local blocking probability based on frequency characteristics, positioning an active site sparse region in combination with temperature gradient spatial distribution characteristics, and generating an efficiency fluctuation evaluation coefficient; an active site sparse region and a dynamic compensation factor matrix of a cathode flow field pressure gradient are constructed to cooperatively adjust an anode current density distribution weight and a cathode pressure gradient parameter, so that an adjusting variable is accurately matched with a local blocking probability and a dynamic coupling degree; the multi-scale correlation analysis of the microcosmic degradation state and the macroscopic efficiency fluctuation of the electrolytic cell is realized, and the operation stability under the low current density is remarkably improved.

The invention relates to the technical field of water quality monitoring, in particular to a mining area groundwater environment monitoring system and method, and the system comprises a mining area groundwater partition recognition module, a groundwater pollution risk assessment module, a groundwater monitoring point optimization module, a groundwater pollution path analysis module and a groundwater dynamic monitoring planning module. According to the invention, through data-driven zoning and risk assessment processes, fine and dynamic management is realized in underground water environment monitoring, functional zone zoning boundaries are accurately calibrated, understanding of an underground water flowing mode is enhanced, pollution source control is more targeted, diffusion risk intensity is assessed in combination with flow velocity and pollutant release intensity, and the risk assessment accuracy is improved. The risk management strategy is optimized, the pollution diffusion range is predicted and identified more efficiently, the emergency response accuracy is improved, monitoring point arrangement is optimized through risk data, reasonable monitoring resource allocation is ensured, the sampling task weight and the time interval are dynamically adjusted, and resource optimization and monitoring efficiency improvement are achieved.

The invention provides a switch cabinet solid insulating material performance detection method and system based on temperature detection, and relates to the technical field of temperature detection, and the method comprises the steps: constructing a grid detection matrix on the surface of a solid insulating material, collecting real-time temperature data through a passive radio frequency identification temperature sensor, and building an initial state model; adopting a deep learning algorithm to calculate a temperature gradient value and thermal stress distribution, and constructing a performance degradation index; and the recurrent neural network is combined to predict a performance evolution trajectory, and dynamic early warning and diagnosis are realized. The performance degradation state of the solid insulating material can be accurately evaluated, the detection precision and the prediction accuracy are improved, and intelligent monitoring and early warning are achieved.

The invention discloses a traditional Chinese medicinal material authenticity identification platform, and relates to the technical field of traditional Chinese medicinal material authenticity identification, the traditional Chinese medicinal material authenticity identification platform comprises a data acquisition module, the data acquisition module is connected with a data analysis module, and the data analysis module is connected with a result display module; and the data acquisition module is used for acquiring appearance images, spectral data and background information of the traditional Chinese medicinal materials. According to the traditional Chinese medicine authenticity identification platform, traditional Chinese medicines are comprehensively analyzed from multiple angles, limitation of a single method is avoided, and identification accuracy is improved; the characteristics of the traditional Chinese medicinal materials are deeply analyzed to accurately judge authenticity and quality; the quality of the traditional Chinese medicinal materials is accurately evaluated by establishing a quantitative analysis model; the model is verified by adopting a cross validation method, so that the accuracy and stability are ensured, the identification accuracy is improved, and the problem that the traditional identification method is difficult to meet the requirement of quickly and accurately identifying the authenticity of the traditional Chinese medicinal materials is solved.

The invention discloses an underground water pollution traceability analysis and ecological restoration decision support system, and relates to the technical field of computer models, the system comprises a data monitoring and graph network construction part, a pollution diffusion simulation analysis part and an ecological restoration part; the data monitoring and graph network construction part is used for setting a plurality of monitoring points in a target underground water area, and completing the construction of a graph network by taking the reciprocal of the distance between nodes as an edge weight; the pollution diffusion simulation analysis part is used for performing feature extraction on the graph network to obtain graph network features and pollutant concentration distribution of each position in the target underground water area; and the ecological restoration part is used for setting a target pollutant concentration distribution, and screening out an optimal ecological restoration operation by utilizing the knowledge graph according to the difference. According to the invention, the monitoring and repairing capabilities of underground water pollution are obviously improved, and the repairing efficiency is improved.

The invention relates to the technical field of model construction, and discloses a neural network-based frozen soil unfrozen water content detection model construction method and system. The method comprises the following steps: carrying out segmented sampling test on a clay sample in a temperature interval of 0 DEG C to-20 DEG C to obtain a multi-dimensional original data set, and carrying out standardization and principal component analysis on the data set to obtain a standardized feature vector; and performing three-stage classification marking on the feature vectors to obtain a training data matrix. A deep neural network is adopted to carry out five-fold cross validation training, and a prediction model of three hidden layers is constructed. And performing correction calculation on model output according to soil mineral composition and porosity parameters, and finally identifying an abnormal value through root-mean-square error analysis and a three-time standard deviation principle to obtain a standardized prediction result. According to the method, the mapping relation between the thermal parameters and the unfrozen water content is established, correction is carried out in combination with the soil body characteristic parameters, and the technical problem that the prediction precision of the unfrozen water content is not high in the prior art can be solved.

The invention discloses a machine learning-based soil pollution risk assessment method and a machine learning-based soil pollution risk assessment device, and belongs to the technical field of soil pollution risk assessment. The split biological effect and chemical indexes in traditional evaluation are converted into unified risk driving force measurement. A nonlinear interaction entropy calculation mechanism can adaptively capture an antagonism-collaborative balance relationship between microbial communities and pollutants in a soil environment, so that the degradation potential of functional flora on specific pollutants is reflected, and the dynamic regulation effect of environmental factor fluctuation on bioavailability is quantified. According to the cross-scale feature fusion technology, the evaluation model not only can identify the current pollution space distribution, but also can pre-judge the morphological transformation trend of pollutants in biogeochemical circulation, and provides a quantitative basis with ecological interpretation for risk early warning.

The invention relates to the technical field of water quality monitoring, in particular to an automatic water quality monitoring method and system.The method comprises the steps that multiple pieces of collected water quality monitoring data are combined pairwise, dynamic coupling strength is calculated, a topological network atlas is generated, and automatic extraction and structural characterization of the dynamic coupling relation among complex water quality parameters are achieved; the limitation of dependence on manual feature recognition traditionally is overcome; secondly, matching the topological network atlas with a preset pollution mode feature library, dynamically determining a newly added abnormal mode, and outputting an abnormal feature code set, thereby solving the key defect that a static model cannot recognize an unknown pollution mode; and finally, a water quality monitoring and early warning signal is output by fusing the pollution diffusion prediction result and the abnormal feature code set, so that bidirectional verification of data driving and a mechanism model is realized, and the early warning accuracy of a water quality abnormal phenomenon is remarkably improved.

The invention relates to the technical field of quartz sand detection, in particular to a quartz sand impurity detection method which comprises the following steps: acquiring spectral data of a quartz sand sample, and obtaining spectral line interference degrees of characteristic peaks according to energy distribution conditions of the characteristic peaks of the spectral data; based on the peak shape variation condition of each characteristic peak and the offset condition of each characteristic peak and a matrix characteristic peak, the matrix interference degree of each characteristic peak is obtained, and the interference intensity of each characteristic peak is obtained by combining the anti-interference degree of each characteristic peak so as to optimize the chaos regulation factor when the chaos mapping population is initialized; and finally, combining with an optimization algorithm to obtain a characteristic wave band with minimum interference, and detecting the purity and impurity content of the quartz sand based on the characteristic wave band. The quartz sand impurity detection precision can be improved.

The invention discloses a pollution space analysis method and system based on atmospheric pollution concentration data, and particularly relates to the technical field of data analysis. A high-precision pollutant concentration spatial distribution model is constructed by combining pollutant diffusion characteristics and geographic information, and regional pollution differences are revealed; identifying a pollution source emission mode and analyzing the evolution trends of pollutants in different time and space ranges by matching spatial and temporal change analysis with a pollution source emission list; the influence of real-time meteorological data on pollution diffusion is analyzed, and a pollutant spatial distribution model is adjusted, so that the prediction precision is improved; pollution concentration distribution, pollution source emission and meteorological influence are combined, pollution hot spot areas are accurately identified, future pollution evolution conditions are analyzed and predicted based on historical trends, scientific and reasonable pollution treatment suggestions are provided, the limitation of traditional interpolation and statistical regression models is broken through, dynamic and accurate modeling of pollutant concentration is achieved, and the method is suitable for large-scale popularization and application. And the scientificity of pollution source tracing and treatment decision making is improved.

The invention provides a seawater parameter calibration system, and belongs to the technical field of seawater parameter measurement. A thermohaline depth device is used for constructing a three-dimensional structure model to identify a spring layer position, a multi-parameter water quality sensing device is used for collecting data to establish a correlation model, and a dissolved oxygen analysis device is combined with thermohaline data to calculate a correction coefficient; and the spectral analysis device extracts feature recognition chemical components. The method comprises the key steps that a parameter interaction compensation function and an OceanParamNet model are adopted to construct a seawater comprehensive parameter calibration model, original data are corrected, and abnormal values are detected. The system realizes layered storage and wireless transmission of data, dynamically adjusts sampling frequency and power consumption, periodically executes a self-calibration program to compensate aging drift of a sensor, realizes high-precision self-adaptive calibration of seawater parameters, and solves the technical problems of low precision and poor environmental adaptability of seawater multi-parameter collaborative calibration in the prior art.

The invention discloses a multi-scale multi-deep neural network driven metal material fatigue life prediction method. The method comprises the following steps: constructing a metal material fatigue test data set; adding the maximum normal stress of the critical plane into a data set by considering the additional hardening effect of the metal material under a non-proportional load, and preprocessing the data set; establishing a multi-scale multi-depth neural network framework; considering physical constraint to guide neural network training so as to increase prediction precision, and establishing a physical loss function with soft regulation capability; building a metal material fatigue life prediction model driven by the multi-scale multi-depth neural network based on the multi-scale multi-depth neural network framework and the physical loss function; continuously iteratively optimizing the model to obtain an optimal prediction model; and predicting the fatigue life of the metal material by using the optimal prediction model. The method not only has the advantage of high prediction precision, but also can enhance the stability and generalization ability of the prediction precision.

The invention discloses a dynamic leakage prediction method and system for an old oil and gas pipeline, and the method comprises the steps: collecting pressure, flow and temperature parameters in real time through a multi-source sensor, and recognizing abnormal fluctuation through the combination of time sequence analysis and frequency domain feature extraction; calculating a pipeline state evaluation result based on the material degradation model; establishing a leakage prediction model fusing a wall thickness degradation kinetic equation and an LSTM neural network, calculating a leakage probability by adopting a Monte Carlo method, and generating a diffusion velocity and a concentration gradient through CFD numerical simulation; when the diffusion prediction exceeds a safety threshold value, a control strategy is optimized through fuzzy logic and a genetic algorithm; the verification model is fed back after real-time adjustment, and online learning is carried out through Bayesian optimization; and finally, calibrating the model by using experimental data, and deploying and generating risk early warning. The system comprises a multi-source sensor array, a data processing platform and other modules, and full-chain closed-loop control from sensing to early warning is achieved.

The invention discloses a method and a system for determining the reaction activity of material molecules based on quantum computing, and relates to the technical field of material performance analysis based on quantum computing, and the method comprises the following steps: generating UCCSD (Unified Conduction Cathode Spectroscopy) simulation according to an electronic structure of molecules of a target material; obtaining at least one effective excitation operator based on parameters in the coefficient of each excitation operator in UCCSD simulation and by combining all the excitation operators; constructing a quantum circuit according to all the effective excitation operators; determining energy level distribution of molecules of the target material based on the quantum circuit; and determining the reaction activity of the molecules of the target material according to the energy level distribution of the molecules of the target material. According to the method, in the process of obtaining the effective excitation operator, an ADAPT-VQE algorithm does not need to be executed, so that the analysis cost of the physical and chemical properties of the material can be reduced, and the analysis efficiency of the physical and chemical properties of the material can also be improved.