4830results about "Computational theoretical chemistry" patented technology

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 provides a new energy battery internal microcrack detection method, which comprises the following steps: acquiring a battery internal three-dimensional structure image by adopting a high-resolution X-ray tomography technology, extracting a pole piece area through an image segmentation algorithm, and carrying out bending deformation simulation on a pole piece to obtain a pole piece internal stress distribution cloud picture; and combining the retrained health state evaluation and residual service life prediction model with battery electrochemical performance characterization data, coupling microcrack evolution and battery performance degradation, predicting a future crack propagation trend and a corresponding performance degradation condition of the battery, and guiding quality control in a battery design and production process.

The invention discloses an asphalt pavement fatigue damage model calibration method based on AI and digital twinning, and relates to the technical field of damage detection. Compared with the prior art, the problems that traditional asphalt pavement design depends on empirical formulas and static parameters, material aging, environmental coupling and load uncertainty are difficult to dynamically reflect, and cross-scale correlation between microscopic interface behaviors and macroscopic structure performance is lacked are solved; the method comprises the following steps: collecting and preprocessing multi-source heterogeneous data in real time through a multi-modal data fusion and dynamic sensing system, simulating and accurately quantifying asphalt-aggregate interface binding energy and adhesion work in combination with molecular dynamics, and constructing a cross-scale damage evolution model; a physical information neural network is used for embedding an improved Paris formula, actually measured data and a physical rule are deeply fused, and the locality hypothesis of a traditional model is effectively corrected.

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 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 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 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 provides a self-adaptive high-temperature damage model construction method and system. Belongs to the technical field of material science and engineering calculation. The method comprises the following steps: acquiring experimental data of a material in a high-temperature environment, preprocessing the collected data through a machine learning algorithm, and extracting key damage features; constructing a basic damage prediction model based on a deep learning framework, training the model by using the preprocessed data, and enabling the basic damage prediction model to adaptively adjust internal parameters according to prediction errors through a reinforcement learning mechanism; and carrying out global search on thermodynamic parameters in the model by utilizing a genetic algorithm, searching an optimal parameter combination, and dynamically adjusting model parameters in combination with real-time temperature monitoring data. Through integration of deep learning, reinforcement learning, a genetic algorithm and a molecular dynamics simulation technology, the damage evolution process of the material in a high-temperature environment can be predicted more accurately and comprehensively.

The invention belongs to the technical field of steel and iron material design, and discloses a stainless steel strength and toughness collaborative optimization method and system based on heterogeneous integration, and the method comprises the steps: constructing a database of stainless steel components, process parameters and mechanical properties; according to the database, establishing a stacked heterogeneous integration model; quantizing contribution weights of input variables of the stacked heterogeneous integrated model to mechanical properties based on an SHAP method, identifying key factors, and setting a multi-target strength and toughness collaborative optimization index to guide an optimization direction; optimal components and processes are screened according to SHAP analysis, a sample is prepared through vacuum melting, hot rolling and heat treatment, the performance is verified according to the ASTM standard, and a final optimization scheme is determined; and a cross-scale digital twinborn verification system is constructed, and collaborative optimization of alloy components, process and macroscopic performance is realized. According to the method, the problems of low efficiency and insufficient generalization ability of a single model of a traditional trial and error method are solved, and an efficient and explainable intelligent optimization scheme is provided for development of high-performance stainless steel.

The invention discloses a textile fabric tearing strength detection method and system, and relates to the technical field of fabric detection.The method comprises the steps that fabric microstructure, macromechanics and component data are synchronously collected through a multi-mode sensing system, and detection modes are dynamically switched based on a real-time recognition result; inputting the space-time correlated defect data into the physical constraint enhanced prediction model to generate a crack propagation trend and a tear strength prediction value; wherein the prediction model is fused with a Griffith crack propagation theory and a hidden Markov model, and double constraints of a material mechanics law and dynamic defect evolution are realized; a non-contact detection module and a flexible contact detection module work cooperatively, and a process optimization scheme is output in combination with an edge and cloud distributed architecture.

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 wastewater treatment, in particular to a generation method for a chemical production wastewater treatment system control model. The method comprises the following steps: collecting chemical wastewater data by using an online monitoring instrument, wherein the chemical wastewater data comprises tin-containing wastewater data and etching liquid waste liquid data; determining a wastewater recovery control stage according to the chemical wastewater data; constructing a wastewater control model by utilizing chemical engineering simulation software based on the wastewater recovery control stage; inputting the chemical wastewater data into a wastewater control model, simulating a wastewater recovery control process by using simulation software to obtain wastewater recovery control data, and performing control abnormality judgment on the wastewater recovery control data to obtain abnormal wastewater data; the high-definition industrial camera is used for collecting images of the waste water sedimentation tank, and a centrifugal pump image is recognized and obtained from the images of the waste water sedimentation tank. According to the method, wastewater recovery control is optimized based on a wastewater treatment technology, and the wastewater recovery efficiency and the anomaly detection rate are remarkably improved.

The invention provides a metal material surface difference analysis method which comprises the following steps: calculating Gibbs free energy at different temperatures by adopting a thermodynamic model according to chemical components and an initial microstructure of a material, and drawing a time-temperature-transformation curve of the material through phase diagram analysis software, obtaining a grain growth kinetic parameter and a recrystallization temperature interval; inputting data information of strength, hardness and toughness in different application scenes by utilizing the established multi-objective optimization model, and searching optimal grain size distribution through a genetic algorithm to obtain an optimal microstructure for balancing various performance indexes; according to the optimal microstructure, a heat treatment process route is reversely designed, finite element software is adopted to simulate temperature field and stress field distribution in the heat treatment process, and control parameters of heating, heat preservation and cooling are determined through iterative optimization.

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 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 flavor customization recommendation, in particular to a flavor and flavor customization formula recommendation system and method. The method comprises the following steps: obtaining user sensory preference data and environment scene parameters, and carrying out multi-dimensional demand feature extraction to obtain a user scent demand feature set; performing raw material compatibility analysis based on the user flavor type demand feature set, and generating a basic raw material screening data set; according to the basic raw material screening data set, volatile component detection and odor molecular map construction are carried out on essence and perfume raw materials, and raw material odor molecular characteristic values are extracted; performing note synergistic effect simulation based on the odor molecule characteristic values to generate note synergistic optimization parameters; and constructing a dynamic formula model in combination with the user aroma type demand feature set and the aroma collaborative optimization parameters. According to the invention, through personalized demand analysis, scientific raw material screening, smell synergistic effect simulation and intelligent simulation feedback, the precision and personalized degree of essence and perfume customized formula recommendation are improved.

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.

The invention relates to the technical field of three-dimensional reconstruction and simulation, and discloses a foam material porous structure three-dimensional reconstruction and simulation system, which comprises a scanning imaging module used for carrying out scanning imaging on a foam material sample to obtain pore image data; the pore boundary recognition module is used for performing pore boundary recognition based on the pore image data to obtain a pore boundary recognition result; the three-dimensional reconstruction module is used for executing three-dimensional reconstruction of bubble evolution physical constraints according to the pore boundary recognition result to obtain a three-dimensional reconstruction model; the coupling simulation module is used for performing grid division and coupling simulation on the three-dimensional reconstruction model to obtain mechanical simulation data; and the parameter solving module is used for carrying out density field optimization and parameter solving based on the mechanical simulation data to obtain optimal gap design parameters, so that seamless connection from simulation analysis to optimization design is realized, the structural design period of the foaming material is shortened, and the design precision and consistency are improved.

The invention provides a molecular dynamics quantum variation simulation method and device.The molecular dynamics quantum variation simulation method comprises the steps that an initial quantum state of a target molecular system is measured to obtain a position basis vector set, then the position basis vector set and momentum basis vectors of the initial quantum state are used for constructing a parameterized quantum circuit, and the parameterized quantum circuit is obtained; and iteratively updating the parameters in the parameterized quantum circuit until the target evolution time is reached, thereby obtaining the final quantum state of the target molecular system. According to the method, a large-scale molecular simulation problem can be solved on a shallow quantum circuit, it is guaranteed that the circuit depth is only related to the Hamiltonian of a system and does not increase along with simulation time, and due to the fact that the depth of the quantum circuit is not related to evolution time, the quantum circuit depth can be improved. The method provided by the invention can be applied to simulating the processes of structural evolution, phase change and the like of the material under a long time scale.

The invention discloses a maximum stress intensity prediction method based on a physical information neural network, and the method comprises the steps: collecting stress analysis data of different structures under various load conditions, carrying out the normalization of the data, and mapping all data to a specific interval; a neural network structure is constructed, and the optimal configuration is determined by comparing the performance of different structure models through experiments; determining a control equation related to the maximum stress intensity, and converting the control equation into a residual form; a data fitting item and a physical information constraint item are constructed, a loss function is defined as the sum of the two items, the data fitting item is calculated by adopting a mean square error (MSE), and the physical information constraint item is based on a control equation residual error; and optimizing the loss function by using a mathematical optimization algorithm, and predicting the maximum stress intensity value of the composite material. According to the method, stress analysis data of different structures under various load working conditions are accurately simulated, so that the prediction accuracy and efficiency are improved.

The invention provides a chemical exposure dynamic simulation evaluation method and system for a wastewater treatment process, and the method comprises the steps: firstly obtaining a chemical adding data set and a real-time monitoring data set of a target wastewater treatment process, and then carrying out the dynamic exposure parameter extraction; generating a dynamic exposure parameter set containing chemical diffusion rate, exposure contact time and environment interaction influence parameters, and then performing multi-stage iterative simulation on the dynamic exposure parameter set based on a preset dynamic simulation strategy network; a chemical exposure dynamic simulation result containing exposure concentration distribution and exposure risk probability of each processing node is obtained, exposure risk assessment is carried out according to the chemical exposure dynamic simulation result, and a chemical exposure optimization strategy set containing dosing concentration adjustment, dosing time sequence optimization and emergency response triggering strategies is generated; and feeding back to a wastewater treatment control system so as to dynamically adjust the chemical adding process and realize more accurate and safer wastewater treatment chemical management.

Provided is a method and a system for evaluating enzymatic reaction feasibility based on multiple tasks and molecular multi-modal features. An enzymatic reaction feasibility dataset is constructed with a public dataset and a bioengineering reaction rule template library; SMILES sequence features and Morgan fingerprint spatial structure features of a product molecule and a substrate molecule of a reaction are used as inputs to a neural network; a dual-branch network is constructed based on an attention mechanism and a convolutional neural network to extract molecular multi-modal features; a product SMILES sequence generation task is taken as a secondary task to strengthen the capability of a model learning sequence feature; richer features are provided for an enzymatic reaction feasibility evaluation task; and the trained model is effectively enabled to accurately determine reaction feasibility by taking the molecular multi-modal features into overall consideration.

The invention relates to the technical field of sewage treatment, in particular to a sewage treatment control process based on dynamic regulation and control of aeration and carbon source addition, which comprises the following steps: acquiring microbial metabolism heat change data in real time; biochemical treatment: monitoring the dielectric constant of a water body and the Reynolds number of sewage flow in real time, and constructing a multi-parameter coupling regulation and control model and dynamically regulating and controlling the aeration rate in combination with microbial metabolism heat change data obtained in the pretreatment step; accurately adding the carbon source according to a calculation result of the carbon source demand prediction model; sewage subjected to biochemical treatment is subjected to ultrafiltration through a nanofiber membrane and then subjected to combined disinfection treatment through ultraviolet rays and ozone, treatment parameters are regulated and controlled in real time according to the membrane flux attenuation trend in the treatment process, and dynamic and accurate control over sewage treatment is achieved. According to the method, the problem of energy consumption waste caused by excessive aeration or low degradation efficiency caused by insufficient aeration in a traditional process is solved, and accurate matching of dissolved oxygen supply and microbial requirements is realized.

The invention belongs to the technical field of pollution tracing, and discloses a distributed water body pollution tracing method and system, and the method comprises the steps: constructing a plurality of directed node pairs according to a topological structure of each partition node in a monitored water area; according to the pollutant concentration data in the monitored water area, determining a time delay interval of a pollutant concentration peak value between each directed node pair; determining an effective node pair of which the time delay interval meets the space-time constraint from the plurality of directed node pairs, and constructing a plurality of backtracking paths according to the effective node pair; performing particle tracking simulation on the pollutant concentration data to obtain a plurality of simulation paths, and determining a particle intersection area of pollutants according to the plurality of simulation paths; and determining a pollution source area according to the plurality of backtracking paths and the space intersection of the particle intersection area, and further determining traceability positioning. According to the method, the backtracking path is constructed through dynamic time-delay analysis, high-precision identification of the pollution source is realized by combining particle tracking and gridding intersection positioning, and the problems of insufficient space-time dynamics and large positioning deviation of a traditional method are solved.

The invention belongs to the technical field of high polymer material design and intelligent manufacturing, and discloses a machine learning driven epoxy resin based composite material thermal-mechanical property aided design method, which comprises the following steps: S1, data acquisition and feature construction; s2, performing feature screening; s3, constructing and training an interpretable prediction model; s4, carrying out reverse design and optimization; and S5, performing closed-loop verification and updating. According to the method, the quantitative relation of structure-process-performance is constructed through an interpretable machine learning model, and the contribution mechanism of each factor is revealed by means of SHAP analysis. And finally, reversely designing an optimal epoxy resin monomer structure and a matched curing process according to the performance target. The limitation of a traditional trial and error method is broken through, collaborative optimization of the material structure and the forming process can be achieved, and the development efficiency of the epoxy resin-based carbon fiber composite material is remarkably improved.

The invention discloses an adaptability improving system and method based on a new energy aluminum alloy part manufacturing and machining environment, and relates to the technical field of new energy aluminum alloy part manufacturing. The system comprises the following components: a data acquisition module, a digital twin model construction module, a data transmission and processing module, a quality tracing and analysis module and a decision and optimization module. According to the new energy aluminum alloy part manufacturing and processing full-process digital twinborn model, the processing equipment operation parameters, the part processing process data and the processing environment data are comprehensively collected, and the new energy aluminum alloy part manufacturing and processing full-process digital twinborn model is constructed, so that the processing process is accurately simulated and monitored in real time; the system can quickly position environmental factors and processing links which cause quality problems, and generates a processing technology optimization scheme in combination with a decision and optimization module, and the adaptive capacity of new energy aluminum alloy part manufacturing and processing to environmental changes is remarkably improved through a series of measures.

The invention provides a marine ecological environment influence tracking and monitoring method based on an offshore wind power project, and relates to the technical field of marine ecological environment, and the method comprises the steps: carrying out the ecological risk index calculation based on an obtained multi-modal feature vector, and obtaining ecological risk data, and configuring a space-time sequence based on the preprocessed comprehensive data source and the ecological risk data, and predicting a pollution diffusion path and migration blocking based on the preprocessed comprehensive data source. By introducing a dynamic evaluation model of an ecological risk index and combining time sequence modeling and spatial distribution modeling, dynamic analysis and future trend prediction of the ecological risk are realized, and by generating a risk thermodynamic diagram in real time, identifying a high-risk area and simulating a pollutant diffusion path and a biological migration blocking condition, the risk of the ecological risk can be predicted. According to the invention, the monitoring strategy can be dynamically adjusted according to the real-time monitoring data, a scientific basis is provided for construction optimization and ecological restoration, and the accuracy and timeliness of monitoring and evaluation are improved.

The invention relates to an inorganic sodium ion solid electrolyte material screening method, system, equipment and medium, sodium-based inorganic compound data is extracted from an open source material database, and a candidate library is formed through preliminary screening; constructing a regression model by using training data of the dynamic solid electrolyte database, and optimizing and selecting an optimal machine learning model; inputting the preliminary candidate library into the model, and predicting and screening out a preliminary screening material of which the ionic conductivity is higher than a first threshold value; performing simulation calculation on the primarily screened material based on a first principle, simulating sodium ion diffusion tracks at different temperatures by using de novo molecular dynamics, calculating a diffusion coefficient, and extrapolating the diffusion coefficient to ionic conductivity at room temperature; and comparing the electrical conductivity predicted by machine learning and the electrical conductivity simulated by the first principle, and selecting a material of which the two electrical conductivity are both higher than a second threshold value and the migration energy barrier is relatively low as the optimal sodium ion solid electrolyte. Compared with the prior art, the method can efficiently and accurately screen out the high-performance sodium ion solid electrolyte material.

The invention discloses a multi-modal data fused karst forest soil carbon cycle feature extraction method. The method comprises the following steps: acquiring soil physical and chemical protection component carbon data, bioactive component carbon data, isotopic tracing data and the like of different succession stages of a karst forest to establish multi-source heterogeneous data; integrating the obtained multi-source heterogeneous data into a'physical-chemical-biological-isotope 'four-dimensional feature space through feature tensor decomposition; constructing a carbon saturation capacity prediction hybrid optimization model, and outputting lt; the maximum carbon sequestration capacity and component carbon proportion of 53 [mu] m microaggregates; the method comprises the following steps: performing mineralization culture on soil in different succession stages of the karst forest, analyzing a dynamic proportion of exogenous carbon to original soil carbon and an input / output path of soil carbon in combination with a 13C / 15N double-standard-sample time-varying calibration curve, and calculating a carbon stability disturbance threshold. According to the method, collaborative analysis of multi-source data is realized, and the temporal-spatial resolution and the model precision of carbon cycle feature extraction are remarkably improved.

The invention discloses an intelligent industrial wastewater treatment method based on edge calculation and multi-modal data fusion, which comprises the following steps: synchronously acquiring various data of an industrial wastewater treatment site through edge nodes, automatically adjusting and aligning the generation time of each data, and ensuring that the time error of different data is less than a set threshold value; organically fusing the image features, the chemical parameters and the component analysis report to generate pollutant description features; constructing a simplified simulation model on the edge equipment, and predicting the diffusion trend of the wastewater pollutants by using the simulation model based on the pollutant description characteristics; and when the network is interrupted, the edge equipment is automatically switched to a local mode, and the operation is continued by using the stored model and the wastewater data acquired in real time. According to the method, the simulation model is deployed through the edge equipment, so that the calculation amount can be greatly reduced, the diffusion trend of wastewater pollutants can be accurately predicted, effective treatment measures can be taken in time, and the stability and reliability of an industrial wastewater treatment system are improved.