3938results 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 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 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 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 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 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.

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.

The invention discloses a drinking water disinfection by-product generation prediction method based on a reaction kinetic model, and belongs to the technical field of drinking water treatment and drinking water quality prediction, and the method is realized based on the following steps: 1) constructing a disinfection by-product generation kinetic model of a model compound and determining the reaction rate of each step; 2) constructing and optimizing a disinfection by-product generation kinetic model of the natural organic matter on the basis of the obtained reaction rate to obtain a disinfection by-product generation kinetic model of an actual water body; 3) establishing a prediction method for the concentration of each component in the model; and 4) inputting the measured concentration of each component in the actual water body into the constructed model, and calculating and predicting a disinfection by-product generation kinetic result of the actual water body through the model. The method fully considers the generation kinetics of the disinfection by-products of the actual water body under different space (time) distribution, can quickly predict the generation of various disinfection by-products which are managed and controlled and are not managed and controlled but have high toxicity, provides early warning for the effluent quality of drinking water treatment, and ensures the safety of drinking water.

The invention discloses a chalcogenide phase change storage material potential model construction method, a material characteristic calculation method and electronic equipment, and belongs to the field of semiconductor materials. On the basis of a potential model trained by a first training set collected through molecular dynamics simulation based on a first principle calculation method, a current potential model is fully utilized, molecular dynamics simulation is carried out based on the current potential model, training samples are further collected, representative training samples are screened out, and a second training set is formed; according to the method, the training efficiency is high, the generalization ability is high, and a large amount of training data can be obtained with low cost. Meanwhile, verification of physical properties is introduced into convergence conditions of model training, so that the physical consistency and interpretability of the potential model are improved; on the basis, the training set which is good in diversity and sufficient in data volume can be obtained with low cost, and meanwhile the accuracy of the potential model in an actual application scene is further improved.

The invention relates to the field of white carbon black modifier production, and particularly discloses an activity monitoring and early warning method of a white carbon black modifier based on Internet of Things, which comprises the following steps: acquiring reaction temperature, catalyst dosage and equipment operation parameters in real time through an Internet of Things sensor, and constructing a parameter and efficiency mapping table to form a multi-dimensional process database; encoding the temperature, the stirring rate and the raw material ratio into chromosome genes by utilizing a genetic algorithm, and constructing a multi-target model by taking the bifunctional grafting rate as an optimization target and combining the equipment load rate and the energy consumption constraint; a high-adaptability parameter set is generated by adopting iterative optimization, and a three-level response mechanism of local fine tuning, population resetting and historical optimal parameter loading is realized in combination with a hierarchical early warning system. According to the method, the problems that the synthesis conditions of the bifunctional silane coupling agent cannot be accurately controlled and the continuous production data response is delayed in the traditional process are solved, and the activity dynamic optimization and green and efficient production of the white carbon black modifier are realized.

The embodiment of the invention relates to the technical field of new energy, and provides a multi-physics field coupling optimization method of a co-electrolysis system and a related product, and the method comprises the following steps: obtaining a coupling mathematical model and a fractal porous medium permeability model in the operation process of the co-electrolysis system; based on physical field parameters output by the electrochemical kinetics calculation model, the non-Darcy seepage simulation model and the thermal stress field calculation model, corrected control parameters of the co-electrolysis system are obtained; the optimized permeability is obtained by adjusting fractal parameters of the fractal porous medium permeability model; the optimized permeability is provided for a non-Darcy seepage simulation model to update flow field pressure distribution; based on the physical field parameters updated in real time by the electrochemical kinetics calculation model, the non-Darcy seepage simulation model and the thermal stress field calculation model, performing multi-objective optimization on the coupling mathematical model to obtain an optimized coupling mathematical model. According to the embodiment of the invention, multi-physical field coupling of the co-electrolysis system can be optimized, and the method is suitable for complex working condition regulation.

The invention discloses a dynamic optimization system and method for a compound essential oil ratio, and aims to solve the problems that the traditional technology depends on artificial experience, the optimization efficiency is low and the quality tracing is insufficient. According to the method, raw material molecular fingerprint data are collected in real time through a high-precision spectrum sensor array and a micro-fluidic chip, a production environment field domain is constructed in combination with a computational fluid mechanics model, a formula optimization problem is encoded into an Ising model by using a quantum annealing algorithm, and global search is realized through a quantum tunneling effect. The system integrates digital twin bodies, covers molecular dynamics simulation, phase equilibrium prediction and olfactory receptor activation probability simulation, and accurately maps a physical system state; a multi-modal data fusion network and a reinforcement learning framework are adopted to dynamically adjust formula parameters, and an NSGA-III algorithm is combined to optimize efficacy collaboration degree, cost and stability indexes. And the execution control module realizes nanoscale flow regulation through the piezoelectric micro-injection valve array, and realizes non-tampering evidence storage of formula parameters and quality data based on a block chain technology. Dynamic closed-loop optimization of the compound essential oil ratio is achieved, the production efficiency, the product consistency and the supply chain transparency are remarkably improved, and the method is suitable for the field of intelligent manufacturing of high-end essential oil.

The invention discloses a cross-scale rock elastic mechanical parameter measurement method and system, and relates to the technical field of rock measurement, and the method comprises the following steps: obtaining nano-scale rock elastic mechanical parameters and micro elastic-plastic deformation of rock through a nanoindentation experiment, so as to construct a molecular dynamics model of the rock; according to the molecular dynamics model of the rock, using an inclusion homogenization theory to upgrade the rock elastic mechanics parameters of the nanoscale to the rock elastic mechanics parameters of the centimeter scale, so as to construct a rock elastic mechanics parameter conversion model, and using environmental data in an in-situ environment for correction; and inputting the nano-scale rock elastic mechanical parameters into the corrected rock elastic mechanical parameter conversion model to obtain centimeter-scale rock elastic mechanical parameters. According to the invention, centimeter-scale rock elastic mechanical parameters in a deep stratum in-situ environment can be accurately measured.

The invention discloses a drainage basin nitrogen and phosphorus pollution prediction method based on rainfall runoff migration, and relates to the technical field of water quality pollution prediction, and the method comprises the steps: accurately obtaining land utilization and water body distribution through a remote sensing technology, carrying out the space division of a pollution source through combining a nitrogen and phosphorus load coefficient, and constructing a rainfall runoff model. The runoff volume and time and space distribution under different rainfall events are reflected, and the spatial dynamic change of pollutants is captured through a hydrodynamic model, in combination with a convection diffusion equation and by introducing a conversion model of nitrogen and phosphorus in various forms. By integrating remote sensing data, rainfall runoff simulation, hydrodynamic force and nitrogen and phosphorus form transformation, the space-time migration and transformation process of nitrogen and phosphorus pollution in a drainage basin is described, so that the coupling relation between pollution source distribution and runoff power is revealed, the conveying and diffusion rule of pollutants in a river network is dynamically reflected, and the drainage effect is improved. And refining to transformation evolution of different nitrogen and phosphorus forms, and generating intuitive concentration distribution and thermodynamic diagrams through spatial interpolation.

The invention relates to a charge accumulation characteristic analysis method for a polyimide material, and the method comprises the steps: applying a multi-stress synergistic effect to the polyimide material based on the multi-stress state of the polyimide material in the electric field, temperature, mechanical stress and irradiation in a deep space environment; a dynamic charge migration model of the polyimide material under the multi-stress synergistic effect is constructed, and the charge accumulation characteristic of the polyimide material is analyzed. According to the invention, based on the multi-stress state of electric field, temperature, mechanical stress and irradiation of the polyimide material in a deep space environment, a multi-stress synergistic effect is applied to the polyimide material, so that the problem of distortion of traditional single-stress or pairwise combined stress measurement is solved; through a dynamic charge migration model, charge accumulation characteristics of the polyimide material under the action of an electric field, temperature, mechanical stress and irradiation are analyzed, and accurate prediction, regulation and control for researching the electrical performance of the polyimide material are realized.