6954results about "Chemical processes analysis/design" patented technology

The invention discloses a municipal road state sensing method and system based on sensor fusion, and the method comprises the steps: collecting multi-modal road information data from a sensing network along a municipal road, and generating a standardized road feature vector; the method comprises the following steps: extracting frequency domain characteristics of pavement bearing capacity through time-frequency analysis, establishing a coupling model of vehicle load and pavement response, and calculating various road state indexes; outputting a structure health index and marking a thermodynamic diagram of a key damage position; real-time traffic flow data and meteorological environment parameters are fused, and an early warning level adaptive adjustment model based on fuzzy reasoning is established; and when a risk entropy value output by the early warning level adaptive adjustment model exceeds a dynamic threshold value, triggering a multi-level collaborative maintenance decision scheme and generating a visual road state digital twinborn body. According to the invention, the problem of insufficient road state sensing precision in a complex environment is solved, and dynamic evaluation and intelligent early warning of the full life cycle health state are realized.

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

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

The invention discloses an energy management and safety protection cooperation method for a liquid cooling industrial and commercial energy storage system, and particularly relates to the technical field of energy storage system management. A battery electrochemical model, a heat distribution diagram, temperature gradient data and electrical parameters are used as input, and a battery temperature change trend curve is output; a liquid cooling control strategy is set according to the prediction result; fusing the temperature gradient abnormal parameters, the temperature trend risk and the multi-modal environment data abnormal parameters, starting a fire risk assessment model, predicting the fire probability and position, calculating a fire risk coefficient, generating a fire risk report and setting safety protection measures; a multi-objective optimization mathematical model is constructed based on the energy efficiency ratio, the full life cycle income and the battery health degree, energy storage operation data and power grid requirements are combined, a Pareto optimal solution set is generated by adopting a non-dominated sorting genetic algorithm, and a charging and discharging strategy and liquid cooling parameters are optimized; the liquid cooling pipeline layout is optimized through reinforcement learning, and the problem that the battery temperature cannot be effectively managed is solved.

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

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

The invention relates to the technical field of water treatment intelligent control, in particular to an AI-based intelligent urban water purification and sewage treatment process optimization system and method, and the method comprises the steps: obtaining related sensor data information, and monitoring and recording the change of key data in real time; establishing a water quality change prediction model by using a multiple linear regression model, wherein the water quality change prediction model predicts the water quality change according to the key data change acquired in real time; the dynamic agent AI model adjusts the dosage, and the water treatment chemical / biological reaction kinetics AI model predicts the pollutant degradation condition; a local lightweight AI model optimizer and a multi-target genetic algorithm are combined with energy consumption, medicament cost, processing efficiency and carbon emission to obtain an optimal control model; the optimal control model optimizes the operation parameters of the sewage treatment process according to the pollutant degradation condition. According to the method, the operation parameters of the sewage treatment process are optimized through the optimal control model, the energy consumption, the agent cost and the carbon emission are reduced, and the treatment efficiency is improved.

The invention relates to the technical field of environmental science, and discloses an atmospheric pollution diffusion simulation system, which comprises a data acquisition module, a data preprocessing module, a dynamic simulation model module, a simulation calculation module, a visualization module and a decision support module, and is characterized in that the data acquisition module is used for acquiring multi-source data; the data preprocessing module is used for cleaning, fusing and optimizing the collected data; the dynamic simulation model module adopts a dynamic mixed diffusion model and performs model parameter optimization in combination with deep learning; the simulation calculation module is used for completing efficient simulation of pollutant diffusion; the visualization module is used for dynamically displaying pollutant concentration distribution and diffusion paths in a three-dimensional manner; and the decision support module generates pollution control suggestions and risk early warning information based on the simulation result. According to the method, intelligent switching between the Gaussian plume model and the CFD model is realized by introducing the dynamic mixed diffusion model, and the method can adapt to various scenes from simple terrains to complex terrains.

The invention provides a river water quality parameter supervision method and system based on deep learning. The method comprises the steps of self-calibration multi-source data acquisition, diffusive water quality prediction, extreme water quality parameter simulation, reverse diffusion pollution positioning and water quality parameter intelligent supervision. The invention relates to the technical field of river water quality supervision, in particular to a river water quality parameter supervision method and system based on deep learning. By introducing a graph convolutional neural network and a physical diffusion constraint model, space-time diffusion trend modeling of pollutants in a river channel is realized; constructing an extreme pollution event simulation and attribution mechanism by combining a generative adversarial network and physical verification; further adopting a multi-modal Bayesian inversion model and a graph deconvolution structure to realize accurate source tracing of the pollution source; the system can dynamically sense hydrological changes, construct an adaptive threshold judgment mechanism, realize prediction, tracking and response to pollution risks, and provide efficient and intelligent technical support for river ecological safety management.

The invention relates to a proton exchange membrane fuel cell (PEMFC) high current density performance prediction method, system, equipment and medium. The method comprises the following steps: establishing a multi-physical field coupling model which comprehensively considers complex processes such as electrochemical reaction, proton conduction, gas diffusion and heat transfer, and describing the change of each physical quantity by adopting a partial differential equation based on a basic physical law; performing grid division and numerical discretization on the proton exchange membrane fuel cell model; selecting model parameters, and verifying the model through experimental data of different working conditions; inputting actual working condition parameters into a model to predict performance, and analyzing a simulation result; using a convolutional neural network, a recurrent neural network and an auto-encoder to extract features from different types of data and fuse the features to form a comprehensive feature vector; a deep neural network prediction model is constructed, and a cross entropy loss function and an Adam optimizer are adopted for training; dropout, L1 and L2 regularization, k-fold cross validation and transfer learning are utilized to optimize the model, and the generalization ability is improved; the system, the equipment and the medium realize high current density performance prediction of the proton exchange membrane fuel cell (PEMFC) based on the method; the prediction precision is improved, the experiment cost is reduced, the internal mechanism can be deeply understood, and powerful support is provided for design optimization, operation management and fault diagnosis of the fuel cell.

The invention provides a numerical simulation method suitable for a three-dimensional spherical flame, and belongs to the field of electrical data processing, and the method comprises the steps: building a computational domain model according to the requirements of a computational scene, dividing a grid unit, and obtaining the node information of the required grid unit; establishing a Navier-Stokes equation set of a coupling chemical reaction and diffusion process model; estimating convection and diffusion term variable gradients in the conservation equation; calculating a time step length according to a set Kulang number, and carrying out time advancing; estimating an expected value of a velocity field, and solving a predicted velocity; the divergence of the velocity field and the correction value of the pressure field are calculated; obtaining a corrected velocity field; a self-adaptive grid means is introduced to dynamically adjust the grid resolution; performing iterative correction for multiple times until the velocity field and the pressure field converge to convergence conditions; performing spherical flame numerical simulation, and monitoring the convergence of the solution. According to the invention, the flame dynamics and cellular instability of the premixed spherical expansion flame can be reproduced.

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 present invention relates to a sodium-ion battery material, and specifically relates to a sodium ferric phosphate pyrophosphate composite material, and a preparation process, evaluation method and process optimization method therefor and the use thereof. The sodium ferric phosphate pyrophosphate composite material has a spinel-type NaFePO4 phase; and in an XRD diffraction pattern obtained by subjecting the sodium ferric phosphate pyrophosphate composite material to XRD diffraction using CuKα radiation, there are a characteristic diffraction peak T of sodium ferric phosphate pyrophosphate and a characteristic diffraction peak O of spinel-type NaFePO4 at 2θ of 33.6±0.2° and 32.8±0.2°, respectively, the peak intensities thereof are respectively IT and IO, and the diffraction peak intensity ratio Ir is equal to IT / IO, wherein 6.19≤Ir≤13.

The disclosure relates to an intelligent dosing control method, device and system for softening and removing hardness. Based on calculation by the intelligent dosing control method or device for softening and removing hardness of the disclosure, a softening agent dosing scheme can be intelligently recommended, while allowing the dosage adjustment in real time, thereby realizing the real-time and precise dosing of agents throughout the process. The intelligent dosing control system for softening and removing hardness of the disclosure automatic monitors the inlet water quality, and the dosing device can be controlled to perform precise dosing of agents through the calculation by the intelligent dosing control method or device for softening and removing hardness. Based on the monitoring feedback of outlet water quality and then calculation by the intelligent dosing control method and / or device for softening and removing hardness, the agent dosage can be automatically readjusted.

The invention provides an equipment corrosion thinning life prediction method and system, and relates to the technical field of life prediction, and the method comprises the steps: deploying edge computing nodes at a plurality of equipment sites to collect corrosion data, carrying out the encryption through employing a differential privacy algorithm, and constructing a federated learning network for model training; and the central server extracts prediction experience through knowledge distillation to form knowledge priori, the edge nodes predict the corrosion rate and the residual life based on real-time data, and the server generates a comprehensive evaluation report and continuously optimizes the model. According to the method, safe sharing and collaborative prediction of the equipment corrosion data are realized, the prediction precision is improved, and the data privacy is protected.

The invention discloses an atmospheric environmental pollution event emergency monitoring and early warning method and system, and belongs to the technical field of environmental management. The method comprises the following steps: acquiring multi-scale meteorological field data and chlorine concentration real-time data of at least five monitoring stations, extracting a wind field direction and a concentration change rate, and determining a position and a wind direction of a potential pollution hot spot area. The information is sent to the unmanned aerial vehicle, and the unmanned aerial vehicle automatically plans a path and collects supplementary concentration data from three angles. And fusing the monitoring data and the supplementary sampling data, and combining with a meteorological field input pollutant three-dimensional concentration distribution model to generate three-dimensional distribution data. And inputting the CALPUFF reverse model, and carrying out inversion to obtain the specific position and the emission intensity of the pollution source. According to the scheme, the accuracy and timeliness of source tracing of the pollution source are improved, high spatial resolution and flexible response capability are achieved, and efficient and intelligent technical support is provided for intelligent early warning, rapid source tracing and scientific response of sudden toxic gas leakage events.

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

The invention relates to the technical field of sewage plant-oriented carbon neutralization regulation and control scheme design, in particular to a sewage plant-oriented carbon neutralization regulation and control method and a sewage plant-oriented carbon neutralization regulation and control system. The method comprises the steps of collecting water quality, energy consumption and gas emission data in real time through a multi-source sensor, inputting the data into a space-time diagram convolutional network to predict a carbon emission trend after edge computing node cleaning, and dynamically optimizing an aeration rate and a sludge reflux ratio based on a fuzzy PID controller. A whole-process carbon footprint is tracked by adopting a life cycle evaluation method, a multi-target compensation strategy is generated in combination with an improved NSGA-III algorithm, and photovoltaic, biogas and green power resources are scheduled preferentially. A digital twin platform and a block chain evidence storage module are innovatively integrated, the former calibrates model parameters in real time, and the latter generates a non-tampering carbon neutralization voucher. According to the method, the problems of one-sided data, regulation and control lag and insufficient credibility of a traditional method are solved, and minute-level sensing, prediction and offset closed-loop control of carbon emission in the sewage treatment process is realized.

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 boiler engineering and artificial intelligence modeling, in particular to a coal-fired boiler biomass blending combustion working condition intelligent optimization method, which comprises the following steps: constructing a three-dimensional mathematical model of coal-fired boiler biomass blending combustion based on experimental test data; performing multi-working-condition numerical simulation according to boiler combustion working condition fluctuation in the three-dimensional mathematical model; according to the numerical simulation data, constructing a time-space coupling model for co-combustion of biomass in the coal-fired boiler; the modeling accuracy is improved through a numerical simulation technology, the boiler operation condition is dynamically optimized in combination with space-time modeling, and theoretical support is provided for energy conservation and emission reduction and blending combustion proportion optimization; the method is suitable for environment-friendly transformation and operation optimization of the coal-fired boiler, and carbon emission and pollutant emission can be effectively reduced.

The invention relates to the technical field of ecological restoration prevention and control, in particular to a sea-entering river pollution source targeted blocking and ecological restoration integrated prevention and control system which comprises a pollution source recognition unit, a targeted blocking unit, an ecological restoration unit and a cooperative control platform. The recognition unit combines a multispectral unmanned aerial vehicle with a water quality sensor and positions a pollution source through artificial intelligence traceability; the targeted blocking unit implements physical-chemical combined blocking according to pollution types; the ecological restoration unit purifies water and reconstructs ecology through a modular constructed wetland and the like; the cooperative control platform optimizes each unit strategy based on hydrological data; each unit correlation formula accurately calculates parameters, such as a recognition algorithm, a gate dam opening control formula and the like. According to the invention, multi-technology fusion and system collaboration are realized, pollution events can be quickly responded, measures are accurately applied, the sea-entering river pollution prevention and control efficiency is effectively improved, the treatment cost is reduced, and drainage basin water environment ecological restoration and sustainable development are promoted.

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

The invention provides an AI-enhanced large mine water-doped concrete material performance prediction and mix proportion design integrated system, aiming at the key problem of large mine water-doped concrete material temperature control, the importance of different influence factors such as raw material-structure-environment and the like is comprehensively and quantitatively evaluated; aiming at a complex raw material system of the large mine water-doped industrial concrete, main features of a performance prediction model such as feature dimension reduction and weight calculation are optimized, and meanwhile, a machine learning algorithm combined with a self-attention mechanism is constructed so as to reduce interference of features weakly related to target performance, and the performance regulation and control capability of the large mine water-doped industrial concrete is enhanced. Furthermore, a multi / super multi-target optimization algorithm containing a constraint system is developed and integrated aiming at targets such as material key performance, carbon emission and cost in the preparation process, and a comprehensive and flexible data-driven solution is provided for temperature control, influence factor evaluation, performance regulation and control and proportion optimization of the large-mine water-doped concrete material. Therefore, the overall decision-making and design process is optimized.

The invention provides a method for evaluating the stability of an offshore wind power pile foundation under the coupling action of submarine landslide and wind wave impact load, and develops a finite element model of pile-soil interaction under the action of complex lateral load. Based on an established seabed landslide impact load quantification formula, the lateral load of the single pile under the coupling action of the seabed landslide, wind, wave and flow in the complex environment is given. And finally, carrying out single pile stability analysis under the action of complex lateral load, and discussing the stability of the single pile foundation by considering the sediment strength of different sea areas at the same time. Through the technical scheme of the invention, the method can judge the stability evaluation of the single pile foundation under the action of the wind wave current load and the potential landslide load in the research area, provides reference for site selection of offshore wind power, establishment of the offshore wind power and the like, and reduces the economic loss caused by the instability of the offshore wind power.

The invention discloses a method and system for predicting the service life of a fire valve, and relates to the field of prediction maintenance, and the method comprises the steps: obtaining a multi-dimensional monitoring data matrix which comprises the sealing performance data, operation torque data and corrosion degree data of the fire valve continuously monitored in a preset time period; determining a parameter incidence matrix according to the multi-dimensional monitoring data matrix; determining a plurality of principal component vectors and corresponding characteristic values according to the parameter incidence matrix through a principal component analysis algorithm, and determining a current health state score of the fire valve according to the characteristic values corresponding to the plurality of principal component vectors; determining a degradation law function of the fire-fighting valve according to the historical operation record of the fire-fighting valve; and predicting the service life of the fire valve according to the current health state score and the degradation law function. According to the scheme, the accuracy of life prediction of the fire valve is improved, and the problem that the accuracy of life prediction of the fire valve is low is solved.

The invention discloses a numerical calculation method for simulating combustion of iron powder group particles, belongs to the field of numerical simulation calculation of iron powder combustion, can perform numerical simulation on single-particle and iron powder group combustion under a microgravity condition, quantitatively analyzes the influence of differential diffusion on single-particle iron powder and iron powder group combustion flame temperature, and improves the calculation accuracy of the single-particle and iron powder group combustion flame temperature. And meanwhile, the influence of iron powder combustion characteristics and flame propagation speeds under different iron powder and oxygen concentration conditions is considered. The method has a certain guiding significance in research on low-propagation-speed lean-burn iron powder flames and subsequent application of iron powder fuel. The microgravity environment can eliminate the influence of convection and buoyancy, so that the combustion process is more stable and predictable, and the basic mechanisms of iron powder combustion, including combustion rate, oxidation process, phase change and the like, can be deeply researched.

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 data analysis, and provides a spatial monopropellant engine ignition state analysis method and system, and the method comprises the steps: obtaining an initial temperature parameter, an initial pressure parameter and a propellant flow parameter of a combustion chamber of a spatial monopropellant engine in an ignition state; generating a flow domain grid model containing porous medium effect characteristics and chemical reaction effect characteristics based on the initial temperature parameter, the initial pressure parameter and the propellant flow parameter of the combustion chamber; setting a dynamic boundary condition corresponding to an ignition transient process in the flow domain grid model, and performing full-period numerical simulation on internal temperature field distribution of the spatial single-component engine to generate a temperature field dynamic change sequence in an ignition state; and according to the temperature gradient distribution characteristics at different moments in the temperature field dynamic change sequence, extracting thermal stress evolution data of key parts of the spatial single-component engine, and outputting a performance stability evaluation result in an ignition state in combination with the thermal stress evolution data and a preset overheating failure threshold value.

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.