1408results about "Chemical data visualisation" patented technology

The AI-enhanced cellular modeling and simulation platform is a computational system designed to enhance biomedical research and development and personalized medicine and wellness. This platform integrates simulation modeling, machine learning and artificial intelligence, multi-omics data, and sophisticated data fusion and decision-support techniques to create comprehensive models of cellular systems and processes across multiple scales. It enables researchers and clinicians to simulate complex biological interactions, predict disease progression, and design or optimize treatment strategies or medical devices with improved accuracy and efficacy. The system's architecture allows for integration of various components, including real-time data processing, federated learning, and quantum computing enhancements. From personalized drug discovery and cancer therapies to synthetic biology and epidemiological analysis, this platform offers powerful tools for understanding and manipulating cellular systems and bioengineered systems. By bridging the gap between molecular-level interactions between cells and materials and organism-wide effects, it enables significant advancements in healthcare and biological sciences.

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

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

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

The invention relates to the technical field of meteorological data processing, provides an artificial influence weather operation optimization method and system based on multi-scale analysis, and is used for improving the operation efficiency of artificial catalysis operation. The method comprises the steps that multi-source meteorological observation data of a target area are acquired, and the multi-source meteorological observation data comprise cloud layer dynamic distribution data, atmosphere vertical motion data and water vapor flux data; performing multi-scale space-time fusion processing on the multi-source meteorological observation data to generate space-time distribution data corresponding to different meteorological scales; based on the spatial and temporal distribution data, multi-scale meteorological characteristic parameters associated with the artificial catalysis potential tag are extracted, and the multi-scale meteorological characteristic parameters comprise a cloud phase state distribution parameter, a water vapor transmission intensity parameter and a vertical movement rate parameter; and inputting the multi-scale meteorological characteristic parameters into the operation catalysis effect prediction model, and outputting an artificial catalysis operation optimization scheme of the target area.

The invention discloses a drug target activation and inhibition relation prediction method based on a depth map neural network, and aims to improve the modeling precision and prediction performance of an activation or inhibition action mechanism between a drug and a target. According to the method, on the basis of a fine-grained graph interaction modeling mechanism, multi-scale structural characteristics of drug molecules and three-dimensional space structural information of protein residue levels are fused, and a heterogeneous interaction graph between drugs and proteins is constructed. The method comprises the following steps: firstly, acquiring a drug-target sample with an activation / inhibition tag through a public database, predicting a protein structure by utilizing AlphaFold2, and constructing a protein residue map and a drug molecular map; multi-scale structure semantic representation is obtained through sub-graph decomposition, atomic-scale feature extraction and graph neural network coding of drug graph features; protein graph node features are combined with context embedding generated by a pre-training language model, DSSP coding, secondary structure spectrum and atomic structure features are constructed, and edge features are designed based on the geometrical relationship between residues. Then, based on constraints such as spatial distance and biochemical similarity, a fine-grained mapping relation between drug atoms and protein residues is established, an interaction graph is constructed, and coding is carried out through a GraphSAGE network; and finally, fusing the interacted multi-source embedding, and completing the prediction of the activation / suppression relationship through a multi-layer perceptron. A cross entropy loss function, an Adam optimizer and hyper-parameter grid search are adopted in model training; in the evaluation stage, five-fold cross validation and an independent test set are adopted, and indexes such as the accuracy rate, the recall rate, the F1 score, the specificity and the Morse correlation coefficient are used for comprehensively evaluating the performance of the model. Experimental results show that compared with an existing method, the method has the advantages that the prediction accuracy and mechanism interpretability are remarkably improved, and the method has good generalization ability and application prospects and is suitable for multiple fields of drug action mechanism research, new drug discovery and the like.

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 a data-mechanism fused airport runway surface water and ice condition prediction method. The method comprises the following steps: establishing a theoretical model of a runway surface temperature field and a water and ice condition; thermal parameter inversion is carried out on the theoretical model based on actually measured data, and a dynamic theoretical model adapted to an actual application scene is obtained through multi-time iteration optimization of model parameters; and utilizing the fused time sequence data and the dynamic theoretical model to predict the water and ice condition on the surface of the airport runway, and providing dynamic evolution information of the icing process. According to the method, the problems of insufficient real-time performance and accuracy of runway icing prediction in the prior art are solved, the icing time, the ice layer thickness and the ice layer range of the runway surface can be accurately predicted, real-time deicing decision support is provided, and the runway operation safety and the airport operation efficiency are effectively improved.

The invention relates to the technical field of information, in particular to a method for detecting and monitoring the cleaning effect of a semiconductor wafer in real time in the technical field of semiconductor wafer manufacturing, which comprises the following steps: introducing a rapid chemical analysis technology, combining an electrochemical sensor and spectral analysis, monitoring metal ion residues on the surface of the wafer in real time, and shortening the detection period; instant feedback on a production line is realized; a real-time feedback control system is constructed, according to the output of the comprehensive evaluation model, the working parameters of the cleaning equipment are automatically adjusted, the cleaning process is optimized, and the usage amount of cleaning liquid and the environmental influence are reduced; an edge computing technology is integrated, data processing and analysis tasks are sunk to a cleaning equipment end, data transmission delay is reduced, and the response speed of real-time monitoring and feedback control is increased; a visual monitoring platform is developed, key parameters, detection results and prediction trends in the cleaning process are displayed in a graphical mode, and operators can conveniently master the cleaning state in real time and quickly respond to abnormal conditions.

The invention discloses a Frechet distance-based abnormal inflow traceability method and system for a sewage treatment plant, and the method comprises the steps: carrying out the unit division of the inflow range of the sewage treatment plant through combining land parcel information, pipe network information, convergence conditions, monitoring stations and the like, and analyzing the correlation between total phosphorus and conductivity; arranging a pipe network water quality monitoring micro station, and carding a factory-network-land parcel-source pointer relation library; a sewage treatment plant big data warehouse is constructed, related data are cleaned and treated, and the data are accessed to a pollutant traceability visualization platform; a sewage plant water pollution traceability model is constructed, a water inlet total phosphorus standard exceeding event is selected to carry out traceability research, the traceability model is embedded into a visual platform, and sewage plant total phosphorus concentration standard exceeding traceability visualization is achieved. Compared with the abnormal inflow tracing method of the sewage treatment plant, the large data resource utilization rate can be greatly improved while a large amount of manpower and material resources are saved, and a basis is provided for locking secretly-discharged enterprises, predicting pollution diffusion and environment emergency.

The invention relates to the technical field of data integration, and particularly discloses a perfluoro / polyfluoroalkyl substance multi-source exposure data integration risk assessment system, which is characterized in that environmental exposure data of perfluoro / polyfluoroalkyl substances is acquired through a multi-source environmental sensor array, and a PFAS multi-mode exposure feature database is established; carrying out pollution source isotope fingerprint analysis, and obtaining source contribution rate distribution maps of three pollution sources of industrial emission, surface runoff and atmospheric settlement through a nonlinear source analysis algorithm; constructing a three-dimensional geographic information dynamic migration model according to the source contribution rate distribution map, and generating a multi-medium dynamic migration flux matrix; a composite risk assessment model is established based on the multi-medium dynamic migration flux matrix, probability risk assessment is executed in combination with an ecological toxicity threshold database, and a space gridding risk grade map is output; the method not only fills the blank of the prior art in the aspects of multi-medium dynamic modeling and nonlinear source analysis, but also provides powerful technical support for environmental pollution control and ecological risk prevention and control.

The invention discloses a rapid sorting method for retired lithium ion batteries based on an electrochemical impedance spectroscopy and a semi-parameter clustering algorithm, which comprises the following steps of: firstly, performing electrochemical impedance spectroscopy test and relaxation time distribution analysis on the retired lithium ion batteries to obtain electrochemical impedance spectroscopy data and relaxation time distribution peak value data; and then, extracting features from the electrochemical impedance spectroscopy data by using a convolutional auto-encoder, extracting features from relaxation time distribution peak data by using a full-connection neural network, carrying out feature splicing on the two features, and inputting the spliced features into a deep neural network to quickly obtain the estimated capacity of the retired battery. And finally, taking the ohmic internal resistance, the impedance characteristic information and the estimated capacity of the retired lithium ion battery extracted from the electrochemical impedance spectrum data and the relaxation time distribution peak value data as the input of a semi-parameter clustering algorithm, and carrying out rapid sorting on the retired battery. According to the method, the capacity of the decommissioned lithium ion battery can be quickly predicted, outliers of the decommissioned battery are eliminated, and the decommissioned battery is quickly sorted.

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 provides an earth surface net flux calculation method and system based on a frequency domain soil moisture equation. The method comprises the following steps: establishing a frequency domain soil moisture motion model of soil water changing along with depth; performing frequency domain decomposition on the time sequence observation data and the upper flux boundary of the site soil water; calibrating the soil parameters according to the obtained upper flux and the frequency component of the soil water; carrying out discrete Fourier decomposition on the soil moisture time sequence at the next time period and any depth, and extracting soil moisture fluctuation signals at different frequencies; according to the obtained parameters and models, carrying out back calculation on soil moisture at different frequencies to obtain surface net flux signals at different frequencies, and superposing the surface net flux signals in a time domain; and in combination with remote sensing data, obtaining the surface net flux of the regional range according to long-time soil moisture data. According to the method, the problem of the time domain is converted to the frequency domain, so that the long-time-sequence soil water sequence can be processed, and the increase of the calculation amount caused by step-by-step calculation by dividing the long-time sequence is reduced.

The invention discloses a fiber bragg grating strain measurement method and system based on deep learning, designs a double-grating structure and a deep learning analysis module, and separates temperature and strain components in grating wavelengths in real time. The system comprises a fiber grating sensor module, a data acquisition module, a deep learning analysis module and a display module, high-precision separation and real-time compensation of strain and temperature are realized through high-precision data acquisition and deep learning model processing, and the system is widely applied to the field of health monitoring of engineering structures such as bridges and tunnels.

The invention discloses an electronic device parasitic parameter network analysis method based on point cloud deep learning and a related device, and the method comprises the steps: obtaining physical characteristic data in a laser-induced quartz material process, and the physical characteristic data comprises laser wavelength, pulse width, photon energy and material characteristics; and inputting the physical characteristic data into the trained neural network model, and predicting the damage threshold of the laser-induced quartz material. According to the machine learning method fusing physical information, unification of data efficiency and physical consistency is achieved, prediction precision and model expandability are remarkably improved, and the method is used for accurately predicting the damage threshold value of the quartz material under the specific laser condition, so that the laser processing technology is optimized, and the processing precision and efficiency of the quartz material are improved.

The invention relates to a method for predicting river nitrogen metabolism gene abundance and key influence factors thereof based on automatic machine learning. Comprising the following steps: collecting a river water sample, detecting water quality physicochemical indexes, and performing metagenome sequencing to obtain nitrogen metabolism gene abundance; taking conventional water quality physicochemical indexes as explanatory variables, taking nitrogen metabolism gene abundance as a target variable, adopting automatic machine learning to construct a model, evaluating the performance of the model through a mean square error (MSE) and a fitting coefficient (R), and selecting an optimal algorithm for prediction; the model analyzes the relative contribution of each water quality index through an SHAP value graph, and reveals the relationship between each water quality index and gene abundance by using a partial dependency graph (PDP). The method shows high precision and good generalization performance in the aspect of predicting the abundance of the nitrogen metabolism gene, and is suitable for describing the nitrogen dynamic process of a river water body. The model can explain, analyze and quantify the influence of different water quality indexes on the nitrogen metabolism gene afterwards, and a scientific basis is provided for river water quality management and nitrogen metabolism optimization regulation and control.

The invention provides a constructional engineering concrete compressive strength detection method and system.The method comprises the steps that multi-modal information of a to-be-detected concrete member for constructional engineering is collected, and the multi-modal information comprises rebound data, ultrasonic data, resistivity data and temperature data; constructing a hybrid prediction model; and outputting compressive strength data according to the mixed prediction model, and visually displaying the compressive strength data. According to the method and the system for detecting the compressive strength of the constructional engineering concrete, disclosed by the invention, multi-modal information such as ultrasonic, rebound, resistivity and temperature of the to-be-detected concrete member is input into the mixed prediction model for compressive strength prediction, and the model can be used for more accurately processing nonlinear and high-dimensional characteristics in data; a transfer learning mechanism is introduced, so that the model adapts to changes of different regions and materials under limited training data, and the generalization ability and accuracy of prediction are improved. And the compressive strength data is visually displayed, so that the analysis efficiency of engineers is improved.

The invention discloses a mixed surfactant system surface tension prediction method and system based on machine learning. Measuring system surface tension data of the mixed surfactant at different temperatures through experiments; secondly, carrying out theoretical correction and data enhancement on the original data by utilizing a Szyszkowski equation; then constructing a multi-dimensional feature space; a nested K-fold cross validation strategy is adopted; the finally established XGB prediction model is excellent in performance on a test set of an enhanced data set, the determination coefficient R2 reaches 0.9994, and the mean square error MSE is 0.0713. The method is particularly suitable for a binary mixed system containing a nonionic polyether surfactant, and not only can accurately predict the surface tension value, but also can determine key parameters such as critical micelle concentration (CMC) and the like. An efficient and reliable technical means is provided for rapid screening and optimization of a surfactant formula, and the method has wide application prospects in the fields of daily chemicals, petroleum, pharmacy and the like.

The invention provides an energy storage battery state simulation and prediction system and method based on digital twinning, and aims to solve the problems that an existing energy storage system is inaccurate in state modeling, insufficient in sensing granularity, unexplained in prediction, lagged in response and the like. According to the method, an equivalent thermal model, an electrochemical model and an aging mechanism model are adopted for modeling a battery, and a self-adaptive gating parameter fusion mechanism and data characteristics are introduced, so that cross-scale state mapping and characteristic reconstruction from a battery cell to a system level are realized; generating a baseline trajectory by using an electric-thermal-aging coupling relationship, realizing multi-time-domain closed-loop prediction by combining a data-driven residual correction mechanism, and identifying potential faults and risks in advance through a multi-time-domain anomaly judgment system; and constructing a three-dimensional topology model to dynamically display the state parameters, generating a graded alarm signal and providing operation suggestions. The system constructed by the invention has high precision, interpretability and prediction and visualization capabilities, and can significantly improve the intelligent operation and maintenance level of the energy storage battery system.

The invention relates to the technical field of crop monitoring, in particular to a crop phenotypic parameter automatic calculation and extraction method based on a multi-source remote sensing image. Comprising the steps that all images are determined to be in the same coordinate system through geographical registration, point cloud file conversion and ground specific point matching, and image registration is achieved; performing automatic or semi-automatic segmentation on the images determined in the same coordinate system to obtain a multi-source crop remote sensing image cell segmentation map; extracting plant phenotypic parameters, physical parameters and chemical parameters from the multisource crop remote sensing image cell segmentation map by using an algorithm; the plant phenotype parameters comprise a vegetation index, a plant height, a surface area, a volume, a canopy coverage degree and a vegetation projection area; the physical parameters comprise a canopy average temperature value, a canopy temperature standard deviation and a canopy temperature variation coefficient; the chemical parameters comprise chemical elements such as nitrogen, phosphorus, potassium, calcium and magnesium in soil and vegetation. The method has the advantages that large-scale data processing and high-precision area prediction are realized, and the crop growth monitoring capability is enhanced.

The invention discloses a drug molecule generation method capable of explaining a federal large model, and relates to the technical field of artificial intelligence, and the method comprises the steps: S1, through a data feature extraction and model initialization step, extracting multi-modal features from distributed data, constructing and initializing a drug molecule generation model, and obtaining a drug molecule generation model; s2, performing parameter optimization on a federal learning client through the large model, including local training fine tuning based on a distributed client, and integrating fine-tuned parameters into a global model of a server to improve model performance and data utilization efficiency, S3, based on the optimized model, generating drug molecular structure information including a molecular description sequence and a molecular graph; according to the drug molecule generation method capable of explaining the federal large model, on the basis of guaranteeing data privacy, the generation capacity and diversity of new molecules are remarkably improved, meanwhile, the transparency and credibility of the model are enhanced through causal analysis and an attention mechanism, the performance and robustness of the model are improved, and the method has wide application prospects.

The invention discloses a molecular dynamics-macroscopic multi-scale coupling calculation method, which relates to the field of material damage numerical simulation analysis, and comprises the following steps: respectively establishing a microcosmic plate model and a macroscopic plate model; young modulus and Poisson's ratio parameters are calculated through microscale pneumatic simulation in the microcosmic plate model and are transmitted into the macroscopic plate model, a stress value I and a strain value I are obtained through macroscopic pneumatic simulation calculation and are transmitted into the microcosmic plate model, and the stress value I and the strain value I are obtained through microcosmic-macroscopic continuous coupling iteration. And obtaining the mechanical property indexes of the silicon thin plate in each stage from stress starting, crack generation, crack propagation to failure and fracture. The invention provides a molecular dynamics-macroscopic multi-scale coupling calculation method. A new analysis method is provided for researching structural failure and damage in an aerodynamic hot extreme environment through coupling calculation of macroscopic and microscopic scale models.

The invention discloses a method for increasing the phenol yield through catalytic pyrolysis of a decommissioned fan blade, and relates to the technical field of waste composite material resourceful treatment.The method comprises the steps that decommissioned fan blade fragment materials obtained after crushing and magnetic separation are subjected to multispectral imaging, multispectral data are obtained, and pretreatment is conducted; performing spectral feature extraction on the preprocessed multispectral data by using a convolutional neural network; identifying and separating the fragment materials based on spectral characteristics and automatic separation equipment; carrying out activation and carbonization treatment on the separated balsa wood fragments to prepare a carbon-based catalyst; carrying out catalytic pyrolysis on the separated fragment material by utilizing an automatic adjusting reactor and a carbon-based catalyst; collecting a product generated by catalytic pyrolysis, and carrying out qualitative and quantitative analysis on phenols to obtain a qualitative analysis result and a quantitative analysis result. According to the method, through a mode of combining multispectral imaging and the convolutional neural network, different types of composite materials are accurately identified and classified.

The invention relates to the technical field of thermal hydraulic power, in particular to a rapid prediction method for flow heat transfer characteristics of supercritical carbon dioxide of a micro-channel heat exchanger, and the method comprises the steps: dividing a training set and a test set according to a preset working condition parameter space, and generating a full-order flow field database under multiple working conditions through numerical simulation; performing eigenorthogonal decomposition on the full-order flow field database, and extracting a key mode of a dominant flow heat transfer feature to construct a reduced-order solution space; training an optimized neural network model by using the training set, and establishing a mapping relationship between the working condition parameters and the reduced-order solution space; and reconstructing flow field and temperature field information based on the mapping relation, and calculating and outputting a flow heat transfer characteristic prediction result. The method aims at solving the technical problem that the heat transfer characteristic prediction calculation efficiency of the supercritical carbon dioxide heat transfer flow of the micro-channel heat exchanger is low.

The invention relates to the technical field of road engineering, in particular to a DSR-based method for rapidly predicting low-temperature performance of asphalt, which comprises the following steps: preparing an asphalt cylinder sample; performing a strain amplitude scanning test to obtain a linear viscoelasticity interval; performing frequency scanning test to obtain asphalt dynamic modulus and phase angle data at different frequencies at low temperature; according to the prediction model, converting the dynamic modulus and the phase angle into a creep stiffness modulus and a creep stiffness modulus change rate; and finally, according to a low-temperature performance grade evaluation standard, obtaining the low-temperature grade of the asphalt sample. According to the method, the low-temperature performance of the asphalt can be rapidly predicted, a tedious bending beam rheometer test is not needed, and the test efficiency is greatly improved. The method also has the characteristics of low sample demand, simple operation, accurate result and the like, can be used for rapidly predicting the low-temperature performance of the laboratory asphalt, and also can be applied to asphalt extracted from a pavement core sample, so that the purpose of rapidly obtaining the low-temperature performance of the asphalt is achieved.

The embodiment of the invention relates to the technical field of sludge treatment, and discloses an AGI-based sludge deep dehydration conditioning method, which comprises the following steps: acquiring time sequence sensor parameters detected by a plurality of sensors at a dehydration conditioning system; acquiring an image, acquired by image acquisition equipment, of sludge at the corresponding capillary water absorption detection system; extracting an image feature vector from the at least one image according to the convolutional neural network, and encoding a corresponding time sequence sensor parameter by adopting a Transform module to generate a time sequence feature vector; fusing the image feature vector and the time sequence feature vector into an input state representation; the input state representation is input into an AGI multi-mode model for recognition processing, and output state information is generated through a multi-task decoder; and transmitting the optimal control parameters to a corresponding programmable logic controller so as to adjust the operation state of the dehydration conditioning system. By means of the method, real-time sensing, intelligent decision making and dynamic optimization of the sludge deep dehydration conditioning process are achieved.

The invention discloses a method for determining the critical safety diameter of an emulsion drop of a liquid energetic material synthesis reaction conveying pipeline, which comprises the following steps of: curing and heating a sampled emulsion, determining pyrolysis kinetic parameters of the detected emulsion, constructing a heat production and heat transfer heat insulation system model of a mixed solution of an acid ester emulsion and the emulsion drop by utilizing Comsol, and determining the critical safety diameter of the emulsion drop of the liquid energetic material synthesis reaction conveying pipeline. Calculating a temperature rise change rule of the mixed liquid heat insulation system under different emulsion droplet sizes, determining a function relationship between the emulsion droplet particle size and the temperature rise of the mixed liquid heat insulation system, and deriving to obtain the critical safety diameter of the emulsion droplet; the method comprises the following steps: adsorbing emulsified liquid by using a porous particle material, testing whether the emulsified liquid explodes due to collision and friction of emulsified liquid with different pore diameters at different rates in an impact tank-piston device to obtain the relationship among the movement rate of emulsified liquid drops, the particle size of the emulsified liquid drops and the explosion risk, and determining the explosion risk of the emulsified liquid drops under a specific conveying speed condition according to the relationship. The liquid energetic material is used for synthesizing the optimal size of emulsified liquid drops in a reaction mixed solution.

The invention discloses an ecological environment big data processing method based on mining area pollutant migration behavior modeling, and relates to the technical field of big data processing. Comprising the steps that protection points in a target range and geodetic coordinate values of a mining area are obtained, the protection points comprise a mass residential area, an ecological protection area and a water source protection area, and the geodetic coordinate values are used for representing latitude and longitude coordinates and the altitude; and according to the first group of geodetic coordinate values and the geodetic coordinate values of the mining area, the maximum diffusion range interval of the mining area pollutants is determined, and the first group of geodetic coordinate values specifically refer to the geodetic coordinate values taken by any protection point type. According to the method, the three-dimensional geodetic coordinates of the protection point and the mining area are obtained, the pollutant diffusion range is scientifically defined, it is ensured that the space coverage of pollution monitoring is comprehensive and effective, potential threats of mining area pollution to surrounding people residential areas, ecological protection areas and water source protection areas can be accurately recognized, and the risk of mining area pollution to the surrounding people residential areas, ecological protection areas and water source protection areas is reduced. And the accuracy and pertinence of environmental risk assessment are improved.