10799results about "Chemometrics" patented technology

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.

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 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.

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 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 field of geological exploration, in particular to an intelligent prospecting prediction method and system for an ore deposit three-dimensional geological model, a terminal and a medium. The method comprises the steps of obtaining multi-source geological data of a target area to construct an ore deposit three-dimensional geological model, inputting the ore deposit three-dimensional geological model into a trained intelligent prospecting prediction model for ore-forming potential analysis, and optimizing the model or generating an intelligent prospecting prediction scheme according to a predicted resource quantity confidence degree condition; when the model is constructed, three-dimensional inversion calculation, element anomaly field construction and the like are carried out, the model can be optimized through transfer learning, exploration data can be accessed in real time to realize dynamic updating, and a multi-target optimization model is established to output an exploration scheme; the invention also relates to a corresponding system, a terminal and a storage medium. The method achieves the technical effects of improving the accuracy and efficiency of prospecting prediction, dynamically optimizing the model according to the actual situation, reasonably planning the exploration scheme, and reducing the exploration cost and risk.

The invention provides a river pollutant traceability method and a river pollutant traceability system. The method comprises the following steps: constructing a potential pollution source feature fingerprint database fused with an LSTM time sequence feature extraction model, carrying out abnormal water quality fingerprint identification on a monitored river reach, if identification is abnormal, collecting an upstream water sample, detecting to obtain a water quality fingerprint, comparing the water quality fingerprint with the abnormal water quality fingerprint, determining a target river reach according to a comparison result, and determining the target river reach according to the comparison result. The method comprises the following steps: sampling all enterprises of a target river reach in real time, comparing detection result data with detection result data of an abnormal water sample, determining potential pollution sources according to a comparison result, determining high-matching candidate sources from the potential pollution sources by utilizing an LSTM (Long Short Term Memory) time sequence feature extraction model, carrying out space transition verification on the high-matching candidate sources, and carrying out space transition verification on the high-matching candidate sources. And determining the pollution source according to the verification result. According to the method, the accuracy, efficiency and result reliability of tracing the river pollutants can be effectively improved, and the scenes of multi-source pollution, intermittent emission and the like of complex rivers can be effectively handled.

The invention discloses a concrete strength remote monitoring method and system suitable for a complex environment, and belongs to the technical field of civil engineering structure health monitoring. The remote monitoring method comprises the following steps: step 1, acquiring performance data of a concrete structure and related environmental factor data, and transmitting multi-source heterogeneous data to a data processing center in real time through a preset wireless communication protocol; step 2, constructing a five-dimensional tensor data structure, and realizing accurate mathematical expression of a complex coupling relationship between environmental factors and material characteristics through tensor decomposition; step 3, capturing nonlinear time-varying characteristics of concrete strength evolution; 4, quantifying the age effect through an intensity development rate index; step 5, based on the intensity development rate change trend, adaptively adjusting the data sampling frequency and monitoring the environmental condition fluctuation; and step 6, evaluating the safety state of the concrete structure in real time, and ensuring safe and reliable operation of the concrete structure in a complex environment.

The invention relates to the field of agricultural fertilization, in particular to a soil carbon nitrogen ratio fused straw returning fertilization optimization method, which comprises the following steps of: performing standardization processing on farmland static characteristic data to obtain a farmland standardization characteristic vector and a characteristic standardization threshold value; performing coupling analysis on the straw returning amount and the soil carbon-nitrogen ratio in the farmland standardized feature vector set to obtain risk factors; performing path gradient integration on the risk potential energy field function to obtain a symmetric path cost gain factor; performing path cost gain correction on the Euclidean distance to obtain adaptive distance measurement; and fuzzy C-means clustering is performed on the farmland standardized feature vectors through adaptive distance measurement to obtain differentiated fertilization management partitions, so that the problem of inaccurate fertilization decision caused by incapability of identifying key feature nonlinear coupling risks in existing management partitions based on Euclidean distance is solved.

The invention relates to the technical field of atmospheric pollutant detection, and discloses a method and a system for detecting excessive emission of atmospheric pollutants. The method comprises the following steps: acquiring pollutant concentration data of multiple monitoring points in a target area to form an original data set; abnormal value detection and correction are carried out on the data set, sensor fault outliers are eliminated, and a preprocessed data set is obtained; extracting concentration change trend characteristics in a preset time window of each monitoring point, and constructing a spatial-temporal characteristic matrix; inputting the matrix into a pollutant diffusion model, calculating a transmission path and strength between monitoring points, and generating a regional transmission network; identifying a potential source region of abnormal fluctuation of pollutant concentration based on a network, and marking the potential source region as a candidate region to be checked; performing multi-scale concentration gradient analysis on the candidate area, and determining a key monitoring area; arranging mobile equipment in the key monitoring area, and collecting high-precision component data; and comparing the data with a standard emission source feature library, matching emission source types of which the similarity exceeds a threshold value, judging whether the emission exceeds the standard or not, and generating a detection report.

The invention provides a multi-source data fusion modeling method and system in an aeration process, and is applied to the field of intelligent aeration control in sewage treatment. The method comprises the steps that multi-source time sequence data such as dissolved oxygen, turbidity, flow, temperature, power and pool bottom pressure pulsation signals are collected, dissolved oxygen response lag is calculated through cross-correlation analysis with power change as the reference, time sequence alignment is carried out, and a dissolved oxygen reference interval is predicted by utilizing calibration data in combination with a physical constraint LSTM model; performing spectral analysis on the pressure pulsation signal to extract a gas-liquid coupling characteristic value, and generating a cooperative regulation instruction of the frequency of the blower and the rotating speed of the stirrer based on the information; by means of the scheme, control oscillation caused by lag of the dissolved oxygen sensor can be effectively overcome, online monitoring of bubble form distribution is achieved, the gas-liquid mass transfer efficiency is improved, invalid aeration is avoided, and system energy consumption is remarkably reduced on the premise that stable effluent quality is guaranteed.

The invention discloses an altered mineral analysis-based prospecting target prediction method and system, and relates to the technical field of prospecting target prediction. An altered mineral analysis-based prospecting target prediction system comprises a data acquisition module, a clustering analysis module, an alteration combination discrimination module, a spatial modeling module, a space-time coupling module and a metallogenic evaluation module. According to the method, altered minerals and symbiotic combinations of the altered minerals are subjected to layered clustering treatment by introducing mineral thermodynamic phase diagram constraints, multi-stage superposed alteration information in a complex structure area is effectively analyzed, and a mineral symbiotic combination structure model with cause difference expression ability is established; by constructing cause period labels and forming a time sequence decoupling model, systematic distinguishing of alteration bodies formed under the mineralization effect of different times is achieved, and a time sequence basis is provided for identification of the multi-stage mineralization process.

The invention relates to the technical field of microbial fermentation control, in particular to an automatic microbial reaction fermentation control system and a control method. The method comprises the following steps: collecting real-time microbial environment parameters and identifying a data multi-axis structure, extracting master control environment interval data, eliminating redundant signals to generate standard microbial environment data, defining a fermentation degree contrast template through fermentation historical records, and constructing a microbial fermentation timeline framework. The method comprises the following steps: acquiring standard environmental data, identifying a fermentation state of the standard environmental data, constructing a simulated microbial environmental field by using the standard microbial environmental data, performing global microbial reaction simulation, determining fermentation reaction structure partitions based on the data, performing difference comparison to generate fermentation difference data, and performing compensation simulation by using the difference data. And automatic fermentation compensation control of microorganisms is realized. According to the invention, the intelligent automatic control and optimization of the microbial fermentation process are realized, the fermentation efficiency and stability are obviously improved, and the manual intervention is reduced.

The invention relates to an AI-driven intelligent design and preparation method of an inorganic hydrated salt phase change material, and solves the problem that the traditional technology is mainly based on experience trial and error and single performance optimization and cannot give consideration to multi-performance balance and multi-scene efficient adaptation development requirements of the inorganic hydrated salt phase change material. The method comprises the following steps: acquiring multi-dimensional performance requirements (including phase change temperature, latent heat value and the like) of a material, generating a candidate formula and a prediction result by using a trained Gaussian process regression model, and performing multi-objective optimization to screen out a Pareto optimal formula; and carrying out experimental verification and calculating deviation, retraining the model by complementary data exceeding a threshold value, and determining a final formula after reaching the standard so as to be matched with continuous process large-scale preparation. The method has the advantages that the AI replaces experience trial and error, multi-performance cooperation of materials is achieved, the research and development period is greatly shortened, the cost is reduced, and the method is suitable for multiple energy storage scenes.

The invention discloses a multi-agent cooperation molecular optimization method based on large language model driving. The method comprises the following steps: S1, task initialization and input analysis; s2, constructing an intelligent agent cluster; s3, task decomposition and scheduling execution; s4, performing expert optimization and tool calling; s5, performing evaluation and feedback screening; and S6, multi-round optimization and result output: the scheduling agent adjusts an optimization strategy and redistributes tasks according to a feedback result of the step S5, drives the expert agent to execute a next round of optimization operation, circulates the steps until the evaluation agent judges that an optimization target is met, and outputs a final optimization molecule and related attribute information thereof. And outputting an optimized path and an intermediate result for tracing analysis. According to the method, multi-round optimization and evaluation feedback iteration of a molecular structure are realized by fusing the knowledge reasoning ability of a large language model and an efficient interaction mechanism between intelligent agents.

The invention relates to the technical field of pipeline safety monitoring, and discloses an online lossless real-time monitoring system for micro-strain of an in-service natural gas pipeline. A micro-strain data acquisition unit of the system acquires a micro-strain data set on the surface of the in-service natural gas pipeline in real time. And the three-dimensional strain field reconstruction unit receives the data set and executes three-dimensional strain field reconstruction processing to generate strain distribution characteristics of the pipeline. And the life prediction model analysis unit calls a pre-trained life prediction model to carry out nonlinear analysis processing on the strain distribution characteristics, and outputs a residual life prediction value and a key risk area identifier of the pipeline. The environmental factor compensation unit performs environmental factor compensation correction processing on the residual life prediction value to generate a corrected residual life prediction value. And the maintenance strategy generation unit generates a pipeline maintenance strategy set according to the key risk area identifier. According to the invention, real-time and accurate evaluation and intelligent maintenance decision support of the health condition of the pipeline are realized.

The invention discloses a water quality time sequence prediction method of an SSA-VMD-LSTM-XGBoost hybrid model, and belongs to the technical field of water quality monitoring and prediction. Comprising the following steps: (1) data preparation and preprocessing; (2) optimizing the water quality time sequence decomposition of the VMD based on SSA: optimizing a penalty factor and a modal number of the VMD by adopting a sparrow search algorithm (SSA), and decomposing the water quality time sequence into a plurality of sub-components with high stability and low complexity by utilizing the optimized VMD; (3) construction and training of an LSTM-XGBoost hybrid prediction model: constructing a hybrid prediction model fusing long-short term memory (LSTM) and extreme gradient boost (XGBoost), inputting a high-frequency component into the LSTM model, inputting a low-frequency component into the XGBoost model, and finally performing superposition and integration on prediction results of the models; and (4) multi-component prediction result integration and performance verification. According to the method, adaptive optimization of VMD parameters is realized through SSA, the feature extraction and time sequence modeling capability is improved by combining the advantages of LSTM and XGBoost, and the prediction precision and stability of the water quality time sequence are effectively improved.

The invention relates to the technical field of cross-modal data fusion analysis of soil component detection and improvement agent preparation, in particular to a preparation method of a soil improvement agent based on component detection, which comprises the following steps: acquiring soil component data through a near infrared spectrometer, a high performance liquid chromatograph and an inductively coupled plasma mass spectrometer; an incremental principal component analysis algorithm is combined with an oscillation suppression function to update a covariance matrix in real time, a dynamic defect factor priority list is generated, a multi-objective optimization model improved based on NSGA-II is guided to integrate the soil pH value, humidity and organic matter content to construct a dose response curved surface, and a Pareto optimal solution set is solved. A sensor array collects environment feedback data, drives a transfer learning algorithm to calibrate parameters, a dynamic incidence matrix attenuation rate and optimal weight adaptive adjustment, iteratively outputs a modifier synergistic effect solution set through a closed-loop control mechanism, and solves the problems of difficult data fusion, principal component weight offset and component synergistic deviation. And the proportioning accuracy and stability are improved.

The invention belongs to the field of bioinformatics, and relates to a molecular property prediction method based on multi-modal gating and comparative learning, which comprises the technologies of comparative learning, graph neural network, cross-modal alignment, gating attention and the like. Firstly, data standardization and graph construction are carried out, and molecular fingerprint embedding is extracted; secondly, a heterogeneous dual-channel graph coding architecture is adopted, one path captures atom short-range interaction through an attention mechanism, the other path integrates a molecular global structure and long-range dependence, and complementary molecular representation is generated; then, a cross-modal attention mechanism is introduced, bidirectional association of graph and fingerprint features is achieved, and modal weights are adaptively and dynamically distributed through a gating fusion module; and finally, a comparison pre-training strategy is adopted, a molecular graph and fingerprints are utilized to construct a sample pair, and discriminative molecular representation is learned on unlabeled data. According to the method, the accuracy of molecular property prediction is remarkably improved, and an efficient and reliable calculation tool is provided for virtual drug screening and lead compound optimization.

The invention discloses a field soil heavy metal pollution health risk dynamic assessment and intelligent early warning system, and relates to the technical field of soil environment monitoring. The system comprises a multi-source data acquisition unit, a data preprocessing unit, a risk calculation engine and a visual interaction terminal. The key technical point is that a dynamic field evolution analysis module and an adaptive grid rendering control module are introduced; the dynamic field evolution analysis module constructs a pollution potential energy field matrix representing a pollutant migration trend based on soil heavy metal concentration and hydrogeological parameters, and calculates a space-time gradient change vector of the pollution potential energy field matrix; and the latter dynamically adjusts the grid local density according to the gradient vector module value, and automatically encrypts the computational nodes in the region with severe risk change. In cooperation with a time sequence prediction deduction and feedback correction mechanism, the method can simulate the dynamic evolution of the pollution plume in the porous medium in real time, solves the problems that a migration rule is difficult to capture and the calculation efficiency of a uniform grid is low in traditional static evaluation, and achieves three-dimensional dynamic risk early warning with high precision and low calculation power consumption.

The invention relates to the technical field of computer-aided drug design, in particular to a drug molecule screening and optimizing method based on artificial intelligence prediction, which comprises the following steps: S1, obtaining a dynamic protein conformation set and molecular multi-dimensional characterization: obtaining a dynamic conformation set of a target protein and a physicochemical property spatial distribution diagram of a binding pocket of the dynamic conformation set, a two-dimensional molecular map topological structure and three-dimensional conformation coordinates of the drug molecules are obtained; s2, multi-modal fusion prediction is carried out; s3, generating interpretable optimization guidance; and S4, automatic iterative optimization: performing batch prediction and screening on the new candidate molecular structure, taking the screened optimal molecule as a new starting point, repeatedly executing the interpretability optimization guidance generation step and the step until an iteration termination condition is met, and outputting a final optimized molecule list. Through the multi-modal fusion deep learning model, the interaction strength of the drug molecules and the target protein can be quickly and accurately predicted, and the screening efficiency of the drug molecules is greatly improved.

The invention relates to the technical field of coal mine telemetering, in particular to a mining laser methane telemetering system based on multispectral fusion, which comprises a multispectral laser emission module, an optical receiving and signal conversion module, a multispectral fusion processing module, an intelligent algorithm compensation module, a data transmission and display module and a power supply module. According to the method, accurate and real-time monitoring of the methane concentration in the coal mine environment is achieved, reliable guarantee is provided for coal mine safety production, compared with a traditional neural network compensation model, the optimized neural network compensation model has the advantages that the methane concentration compensation precision is greatly improved, the generalization ability of the model to different environment conditions is obviously enhanced, and the method is suitable for popularization and application. Accurate measurement of methane concentration can be realized in a more complex and changeable coal mine environment. Meanwhile, due to the application of a dynamic weight compensation mechanism and an uncertainty quantification and compensation adjustment method, the reliability and the stability of a measurement result are further improved, and a more powerful guarantee is provided for safe production of a coal mine.

The invention belongs to the technical field of environmental water quality pollution monitoring, and particularly relates to a drainage basin water environment monitoring and emergency pollution rapid tracing method and system based on water quality pollution detection, and the method comprises the following steps: constructing a multi-parameter cooperative monitoring model, and carrying out the training and deployment; through distributed sampling points and sampling stations, real-time data with verification marks and time-sharing data of watershed water environment water quality pollution detection are collected; mLP and LSTM / GRU models are adopted, EEM spectrum data and environment characteristics are fused, and multi-source heterogeneous data are fused and analyzed; whether an emergency pollution event occurs or not is automatically identified according to a preset condition, the pollution types and traceability results of water environment monitoring and emergency pollution are automatically output, manual further checking is carried out, and environmental law enforcement checking is carried out. According to the invention, drainage basin water environment pollution condition monitoring and emergency pollution rapid tracking and tracing can be completed in a large-range, low-cost and high-efficiency manner so as to support environment law enforcement.

The invention discloses an energy storage system state evolution trend prediction method based on multi-source data fusion. The method comprises the steps of terminal voltage, current and temperature time sequence data acquisition, time sequence segmentation normalization, multi-physics field coupling feature construction, trend prediction model construction and training and energy storage system state evolution trend prediction. According to the method, the distinguishing capacity of the model for charging and discharging physical characteristics is improved, meanwhile, the voltage change rate, the multi-dimensional feature vector of the differential internal resistance and the thermal-electric coupling effect and the explicit encoding electric-thermal-resistance coupling relation are constructed, the transient response and the temperature hysteresis effect can be effectively captured, and then the model can be used for analyzing the charging and discharging physical characteristics. A degradation-aware cross-cycle feature extraction and gating mechanism is adopted, short-term fluctuation and long-term trend are adaptively balanced in multi-scale prediction, the prediction conflict problem is relieved, finally, physical constraints based on the electrochemical law and the internal resistance temperature characteristic are embedded in a loss function, it is ensured that the prediction result is accurate in numerical value and conforms to the physical law, and the prediction accuracy is improved. And generation of physically impossible solutions is avoided.

A coal mine risk early warning system based on big data analytics, the coal mine risk early warning system comprising: a data collection module, used for collecting data in real time during coal mine operation; a data storage module, configured to store historical data records collected by the data collection module; a data processing module, which uses big data analytics technology to process the stored data and identify potential risk factors; a risk assessment module, which assesses the risk level of coal mine operation on the basis of analysis results of the data processing module, there being three risk levels: low, medium, and high; and an early warning module, which sends an early warning signal to relevant personnel when the risk level reaches a preset threshold.

The invention discloses a concrete mix proportion optimization method and system based on machine learning, and particularly relates to the technical field of intelligent proportioning of building materials, and the method comprises the following steps: constructing a mapping model of target performance indexes by using raw material performance parameters and historical trial matching data, cross-station mix proportion optimization is carried out based on real-time sensing data of a plurality of mixing stations, the resonance relation between the moisture content of raw materials and the iteration frequency of a model is monitored in the dynamic construction process, and a parameter smoothing and disturbance suppression mechanism is triggered to correct a mix proportion scheme. The corrected mix proportion adjustment scheme is applied to raw material controlled feeding of multiple mixing stations; according to the method, the quality of training samples is improved through unified data processing, a multi-dimensional mapping model is constructed to realize cross-mixing-station performance consistency optimization, and a resonance detection and disturbance suppression mechanism is introduced to ensure the stability and anti-interference capability of mix proportion adjustment under a dynamic construction condition; therefore, high-performance, low-cost and high-robustness concrete intelligent optimization control is realized.

The invention discloses a circuit breaker service life prediction system and method based on multi-source heterogeneous data fusion and dynamic weight correction, and the method comprises the steps: collecting the current, pressure, displacement, main loop current, voltage, contact temperature, environment temperature and partial discharge of a circuit breaker, and carrying out the multi-source heterogeneous data fusion algorithm, thereby achieving the prediction of the service life of the circuit breaker. Carrying out fusion analysis on the collected data; a dynamic weight correction algorithm is executed according to an analysis result, and different types of fault weights are corrected and adjusted, so that the life prediction model is more accurate; and executing a fault identification algorithm after each action of the circuit breaker, and calculating the service life of the circuit breaker according to the weight and the health index corresponding to each fault. According to the invention, the prediction accuracy of the service life of the short-circuiter is greatly improved, and the use safety of power equipment is improved.

The invention relates to the technical field of deep learning, in particular to a stress distribution monitoring and crack risk prediction method for a silicon carbide part, which realizes comprehensive sensing of the stress state of the silicon carbide part, accurate positioning of a risk area and advanced early warning of a crack fault. The method comprises the following steps: synchronously acquiring multi-modal data through multiple types of sensors, and realizing cross-modal time sequence synchronization through feature alignment; designing a crack risk multi-branch feature extraction module, and respectively extracting general depth features and risk features oriented to thermal stress mismatch, microcrack evolution and structural instability through a shared backbone network and a special branch network; constructing a stress nephogram generation and risk area positioning module based on a graph neural network, and realizing visual reasoning and risk area marking from discrete features to full-field stress distribution; and designing a crack risk comprehensive prediction module based on multi-dimensional risk feature fusion, fusing an instantaneous state and an evolution trend, outputting a multi-risk confidence vector and triggering graded early warning.

The invention belongs to the technical field of water treatment, and particularly relates to a whole-process three-stage linkage control intelligent flocculation dosing method, which comprises the following steps: collection: collecting water quality and water quantity parameters of inlet water and outlet water in real time in the water treatment process; collecting a floc image in the reaction process, analyzing the particle size distribution and the breakage rate of the floc image, and collecting a torque parameter of a stirring device at the same time; according to the invention, multiple types of sensors are deployed in the water inlet area of the flocculation tank, the reaction area and the water outlet area of the sedimentation tank, water quality, water quantity and reaction process parameters are collected in real time, and edge calculation and cloud optimization technologies are combined, so that full-flow intelligent control from agent pre-feeding, reaction process regulation and control to water outlet effect refinement is realized; the method is suitable for scenes such as municipal sewage treatment plants and industrial wastewater treatment stations requiring precise control of flocculant addition, and is especially suitable for water treatment processes with frequent water quality fluctuation and sensitive agent cost, so that the treatment efficiency can be remarkably improved, and the operation cost can be reduced.

The invention discloses a drug resistance prediction method and system based on comparative learning and multi-modal fusion, and the method comprises the steps: firstly generating a molecular map and a molecular fingerprint based on the SMILES of a target drug, and extracting the molecular features of the drug through a comparative learning model constructed through combining a map attention network and a map convolution network; and then, acquiring protein expression, gene expression and metabolic expression data from the target tissue cells, extracting modal features through a deep convolutional network, a Transform encoder and a multi-dimensional attention network, and realizing adaptive fusion of the multi-modal features through a heterogeneous interactive attention mechanism. And finally, jointly inputting the fused multi-modal features and drug molecular features into a multi-layer sensor to realize high-precision prediction of the drug resistance of cells to drugs. By introducing a contrast learning and multi-modal feature fusion mechanism, the characterization capability and prediction precision of the model are effectively improved, and efficient and reliable support can be provided for drug screening and clinical decision making.