3888results about "Chemical processes analysis/design" patented technology

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 carbon dioxide mineralization storage dynamic intelligent regulation and control and permeation enhancement optimization method and system. The optimization method comprises the following steps: collecting field monitoring injection parameters and related data of reaction products in a mineralization storage process in real time; according to injection parameters monitored on site and related data of reaction products, two optimization objective functions of mineralization rate and free CO2 volume are formed; constructing a mineralization sequestration multi-objective optimization model, and screening out an optimal injection parameter set value from the Pareto solution set to obtain an optimal condition parameter; optimal injection parameters in the Pareto optimal solution set are input into the constructed field enhancement regulation and control module, and control variables are adjusted in real time according to real-time changes of reservoir response, mineralization reaction process and injection working conditions; and fracturing transformation is conducted on the target storage rock mass, the seepage enhancement effect of the target storage rock mass is quantitatively evaluated, an injection scheme is dynamically updated based on the transformed reservoir parameters, and the mineralization regulation and control system is enhanced.

The invention provides a method for constructing a thickened flame surface model of hydrogen turbulent combustion, which belongs to the technical field of hydrogen fuel engines, and specifically comprises the following steps: modeling a diffusion enhancement effect and a flame surface wrinkle effect caused by multi-scale turbulent flow by constructing a hydrogen fuel turbulent flame speed scale rate under a wide working condition; and the combustion process of the hydrogen fuel can be described more accurately. In addition, on the basis of a thin reaction zone combustion mode, key physical quantities are extracted, a combustion efficiency function is optimized, multi-scale turbulence and a difference diffusion effect are considered, and the thickened flame surface model is further perfected. The development of the method provides powerful technical support for the forward design of the hydrogen fuel engine based on numerical simulation, promotes the engineering application of the hydrogen fuel engine, and assists the hydrogen fuel engine to play a greater role in efficient, clean and safe energy transformation.

The invention relates to the technical field of die-casting optimization, and discloses a die-casting process parameter optimization method and system based on digital twinning, and the method comprises the steps: arranging a sensor to collect the operation parameters of die-casting equipment and the quality data of a die casting in real time, and forming multi-source die-casting production data; according to multi-source die-casting production data, a multi-physical field simulation model is established, and a digital twinborn model is constructed. And comparing the virtual prediction result with the actually measured quality data, and constructing a virtual-real difference compensation network to correct the parameters of the digital twin model. And performing a multi-target reinforcement learning method based on the compensated digital twin model to generate optimal die-casting process parameters. And applying the optimal die-casting process parameters to die-casting equipment for verification, and updating the virtual-real difference compensation network according to a verification result. Intelligent optimization and continuous self-evolution of the die-casting process parameters are achieved, and the casting forming precision, the energy efficiency utilization rate and the production stability are improved.

Platforms, systems, and methods for genetic generalization in synthetic biology development. According to one aspect, there is provided a method for predicting performance associated with genetic edits, the method comprising: receiving, by a platform, information about a strain of a microorganism, wherein the information about the strain comprises information describing a plurality of genetic edits to a base strain of the microorganism; generating, by the platform, a set of genetic embeddings based on the information about the strain, wherein the generating comprises processing the information about the strain using one or more embedding models, wherein each of the one or more embedding models: receives the information about the strain of the microorganism as input; and applies computational transformations to the input using a corresponding embedding model to generate a multi-dimensional vector representation for each of the plurality of genetic edits.

The invention provides a Bi-BDO fermentation pH dissolved oxygen dynamic optimization method based on an online Raman spectrum, belongs to the technical field of biological fermentation process control, and aims to solve the problems of unstable process and low efficiency caused by fermentation control lag and incapability of sensing the real metabolic state of cells in the prior art. The method comprises the following steps: acquiring the concentrations of a target product BDO and key byproducts such as acetic acid and ethanol in the fermentation liquor in real time through an online Raman spectrum; according to the method, a metabolic stress index is originally proposed and constructed, the index is obtained by performing weighted operation on the instantaneous generation rate of the by-product and the target product, and the index is used for quantitatively characterizing the intrinsic metabolic stress level of the cells in real time. The control strategy of maintaining the metabolic stress index in the preset optimal stable interval is taken as a core control strategy, the conversion from passive response to active prediction in the fermentation process is realized, and the yield, the stability and the batch repeatability of Bi-BDO production are remarkably improved.

The invention relates to the technical field of environmental science, in particular to a box-type model-based inversion method for methane sulfonic acid particles in marine droplet aerosol, which comprises the following steps of: 1, acquiring satellite remote sensing data, ground observation data and laboratory simulation data, and carrying out data standardization processing; 2, dividing the target sea area into a plurality of sub-areas as independent box bodies; 3, calculating the discharge flux of the marine droplet aerosol based on the dimethyl sulfide concentration and the wind speed, and updating the spatial and temporal distribution of the discharge source; 4, embedding a chemical reaction path for oxidizing dimethyl sulfide on the surface of the aerosol into the box-type model to generate methanesulfonic acid, and introducing the specific surface area of the aerosol to correct the reaction rate; and step 5, outputting spatio-temporal distribution of the concentration of the methane sulfonic acid, and comparing measured data for verification. The accuracy of aerosol source item estimation is improved, the fusion efficiency of multi-source data is improved, and the adaptive capacity of the model in a complex environment is enhanced.

The invention provides a geopolymer preparation and optimization method and system based on machine learning. The method is applied to the technical field of material science and machine learning. The method comprises the following steps: acquiring geopolymer preparation experimental data and preprocessing the data; performing nonlinear regression modeling on the geopolymer performance based on four machine learning regression algorithms, and constructing a geopolymer performance prediction model; calculating and distributing weights according to the mean square error of each machine learning model on the verification set, and performing weighted fusion to obtain a performance prediction result; receiving target performance parameters input by a user and an initial raw material ratio range, performing performance prediction by using the trained geopolymer performance prediction model, and reversely searching an optimal ratio combination meeting target performance constraints through an optimization algorithm; and preparing a geopolymer according to the optimal ratio combination to prepare the coal gangue-slag-fly ash geopolymer grouting material. According to the method, the prediction precision and the model generalization ability are effectively improved, and intelligent recommendation and accurate performance prediction of the raw material ratio are realized.

The invention relates to the technical field of blood chemical analysis, in particular to a parallel task scheduling optimization method and system for a blood detection process, and the method comprises the steps: correcting the attenuation rate of a temperature-sensitive component, a protein component and a light-sensitive component in blood according to real-time environmental parameters, and obtaining the corrected attenuation rate of the blood under the current environmental condition; calculating the concentration change rate of each detection item according to the correction attenuation rate, and establishing a multi-dimensional priority evaluation matrix in combination with the clinical importance coefficient and the detection time demand; identifying component pairs and reaction types of the interactive influence; establishing a coupling reaction kinetic model, and predicting the concentration value of each component at a set time point; sorting the to-be-detected items according to the multi-dimensional priority evaluation matrix; when the detection item combination contains mutual influence components, adjusting a detection sequence according to a coupling attenuation prediction result; and distributing the adjusted detection task to the corresponding parallel detection equipment according to the equipment compatibility, and generating and executing a task scheduling scheme.

The invention is applicable to the technical field of coal bed gas development and geological sequestration, and provides a THMC multi-field coupling simulation method and system for a process of displacing CH4 by coal bed CO2, and the method comprises the following steps: collecting multi-source parameter information of a target coal bed; according to the multi-source parameter information, constructing a coal seam structure model comprising a matrix and a crack system; constructing a control equation system based on the coal seam structure model, and introducing a multi-field coupling mechanism to obtain a fully-coupled mathematical model; performing numerical solution on the fully-coupled mathematical model to obtain a spatio-temporal evolution result of the key field variables; and according to the spatio-temporal evolution result of the key field variable, evaluating the index of the target coal seam. According to the invention, a heat-force-gas-chemical multi-physical field full-coupling simulation system is constructed, so that the CH4 recovery rate and the CO2 storage stability can be accurately predicted. The method is suitable for gas injection-storage coupling optimization of a multi-coal-rank and low-permeability coal seam, and has high precision, high adaptability and remarkable engineering guidance value.

The invention discloses a sewage treatment plant effluent prediction method based on multi-task learning. The method comprises the following steps: acquiring sewage treatment data; based on the sewage treatment data, establishing an effluent prediction model; the input of the effluent prediction model is inflow water quality data, process data, environmental data and sewage treatment unit data, and the output of the effluent prediction model is predicted effluent index data; predicting the water outlet index data in future time based on the water outlet prediction model and the input of the water outlet prediction model; according to the method, the water outlet prediction model is constructed in combination with multi-task learning, and the model can comprehensively consider the time sequence dependence and mutual influence relationship among the input data to perform prediction, so that the calculation redundancy is reduced, and the prediction demand that an actual process needs to cooperatively consider multiple targets is met; the effluent quality prediction precision and the process regulation and control efficiency are remarkably improved, and a solid foundation is laid for promoting intelligence of operation management of a sewage treatment plant.

Disclosed in the present invention is an artificial-intelligence-based performance prediction processing method for a carbon-fiber carbonization process. The method comprises: preprocessing experimental data under test, so as to obtain said experimental data that has been subjected to data cleaning; then, using a sliding window processing method to slide on time series data, extracting data within a window at each position and using the extracted data as an input sample, and determining an input feature and an output variable feature of each input sample, so as to convert the time series data into a plurality of experimental data samples under test in the format of a target model input; performing random data set division on said plurality of experimental data samples, so as to obtain some training sets and some test sets; and constructing a target model, and inputting said experimental data samples into the target model. The target model can implement a relatively accurate mechanical-performance prediction for a carbon-fiber-precursor carbonization process, and the model has an optimal performance in all aspects and has a relatively good generalization capability.

The invention provides a reinforcement corrosion electrochemical parameter inversion method based on LSTM (Long Short Term Memory) time sequence prediction, which comprises the following steps: S1, acquiring electrochemical time sequence data in a reinforcement corrosion process through an electrochemical workstation to form an original reinforcement corrosion electrochemical time sequence data set; s2, preprocessing is carried out to obtain a training set, a verification set, a test set and normalization coefficients of all parameters; s3, constructing and training an LSTM time sequence prediction model; s4, constructing and calibrating a steel bar corrosion electrochemical parameter forward modeling model; and S5, constructing an inversion framework fusing a particle swarm optimization algorithm, a simulated annealing algorithm and an Adam optimization algorithm, forming closed-loop cooperation by the particle swarm optimization algorithm, the simulated annealing algorithm and the Adam optimization algorithm so as to minimize an error between a target electrochemical response parameter and a theoretical electrochemical response parameter, and outputting an inversion result. According to the method, through organic combination of time sequence prediction and multi-algorithm cooperation, the problems that a traditional inversion method is low in precision and poor in stability are solved, and a reliable technical means is provided for reinforced concrete structure health monitoring.

The invention relates to the technical field of aircraft part processing, testing or inspection and the like, and provides an aircraft aluminum alloy plate aging evaluation method based on an equivalent circuit model, and the method comprises the following steps: collecting electrochemical impedance spectrums of samples with different exposure age limits, and extracting electrochemical impedance spectrum characteristics of the samples; establishing an equivalent circuit model comprising solution resistance, coating resistance, coating capacitance, Warburg impedance, anodic oxide film charge transfer resistance, interface capacitance, aluminum alloy matrix charge transfer resistance, interface electric double-layer capacitance, inductance and corresponding resistance of the inductance based on the characteristics, wherein the equivalent circuit model comprises the solution resistance, the coating resistance, the coating capacitance, the Warburg impedance, the anodic oxide film charge transfer resistance, the interface capacitance, the aluminum alloy matrix charge transfer resistance and the interface electric double-layer capacitance; and analyzing a resistance curve and a capacitance curve obtained by fitting the model to complete the evaluation of the corrosion and aging degree. According to the method, systematicness and accuracy of aging evaluation are improved, an electrochemical mechanism in the corrosion process can be disclosed, time correlation modeling of the aging process can be achieved, and the method is suitable for long-term service performance monitoring and service life prediction of the aviation aluminum alloy structure.

The invention discloses a sandstone reservoir deep acidification long-acting permeation enhancement effect numerical simulation method, and relates to the technical field of rock reservoir acidification transformation processes, and the method comprises the steps: collecting real-time sensor data and geological exploration data of a sandstone reservoir, and making a preliminary acidification operation plan; according to the preliminary acidification operation plan, in combination with real-time data feedback, the preliminary acid liquor concentration, the preliminary injection rate and the preliminary acid liquor injection temperature are optimized, and acidification parameters are output; based on real-time acidification operation monitoring data and sampling feedback data, a multi-field coupling numerical model is constructed and calibrated, the long-acting evolution track of the permeability of the acidified reservoir is simulated, the effective permeation increasing period and the final permeation increasing amount are evaluated, and an acidification long-acting permeation increasing effect simulation report is output. According to the method, the real-time regulation and control capability of acidification operation is improved, a scientific basis is provided for evaluating the economic validity period of acidification transformation, and an innovative solution is provided for efficient development of a deep sandstone reservoir.

The invention relates to the technical field of purity monitoring and anomaly detection in the production process of ultra-pure zinc oxide, and discloses a purity anomaly detection method for production of ultra-pure zinc oxide. A disturbance direction coding vector is generated through time sequence segmentation and disturbance sensitivity mapping, and a collaborative structure embedding matrix is constructed based on channel weighting and time window fusion and used for representing a collaborative disturbance relation between monitoring channels; by taking the collaborative structure embedding matrix as input, carrying out structure frequency spectrum transformation and attributive suppression operation to obtain a diffusion disturbance structure response value; the diffusion disturbance structure response value serves as input, the disturbance direction gradient is calculated, a disturbance total variation mapping value is formed, and a disturbance anomaly probability value is generated in combination with local neighborhood average and deviation degree measurement; finally, a power logarithmic equilibrium loss function is adopted for optimization, and stable convergence and high-precision detection under multi-channel disturbance are achieved. The anomaly detection model provided by the invention can effectively perform purity anomaly detection on the ultra-pure zinc oxide production process.

The invention provides a lithium ion battery core temperature prediction method based on a finite element model, and the method comprises the following steps: 1, designing an electrochemical test experiment, and constructing a one-dimensional electrochemical model containing a lithium ion battery electrochemical heat production mechanism; 2, designing a lithium ion battery temperature test experiment, obtaining electrochemical parameters, heat transfer characteristic parameters, internal thermophysical parameters and environmental parameters of the battery under different working conditions, and obtaining a temperature change curve of the center position and the surface of the battery; and step 3, based on the heat balance equation, establishing a three-dimensional heat transfer model having the same geometric characteristics as the battery used in the experiment. According to the experimental result in the step 2, the three-dimensional heat transfer model is subjected to non-uniform region division, each region corresponds to one one-dimensional electrochemical model, and the heat production rate per unit volume is calculated by the one-dimensional electrochemical model in the step 1; introducing the heat production rate of each area into a three-dimensional heat transfer model to calculate the temperature distribution of the battery, and designing an experiment to correct a one-dimensional electrochemical model; 4, modifying the operation conditions and heat dissipation conditions of the battery, and calculating and analyzing the temperature difference delta T between the center position and the surface position of the battery under different operation conditions and heat dissipation conditions by using the three-dimensional heat transfer model in the step 3; and 5, measuring the surface temperature of the battery, and calculating the temperature of the center of the battery according to the working condition of the battery in the step 4 and the delta T corresponding to the environment temperature, thereby realizing monitoring of the temperature of the center position of the battery. According to the method, a high-precision algorithm model is constructed and verified by utilizing data accumulated in an earlier-stage experiment, the central point temperature which is difficult to directly measure in the battery is predicted only through real-time and easily-acquired battery surface temperature information, operation condition parameters and heat dissipation conditions, key thermal state information is provided for a battery management system, and the battery management efficiency is improved. The method is used for real-time safety monitoring and thermal management optimization.

The invention discloses an oil product deterioration intelligent analysis and operation and maintenance evaluation system of power plant host equipment, and relates to the technical field of state monitoring and fault prediction of power plant key equipment. Comprising a multi-mode sensing module, an edge preprocessing unit, a dynamic reference model building module, a residual analysis engine, a mirror image diagnosis engine, a fault tracing module and a self-evolution digital twin platform, the device is used for collecting physical characteristic parameters, pollution state parameters, electrochemical characteristic parameters and wear information parameters of oil in real time. According to the oil degradation intelligent analysis and operation and maintenance evaluation system of the power plant host equipment, high-precision real-time monitoring of multiple parameters of the oil state is achieved, environment interference data are effectively recognized and filtered, the accuracy and reliability of fault diagnosis are ensured, the recognition time of the abnormal state of the equipment is advanced, and the false alarm condition is effectively controlled.

The invention discloses a low-temperature sewage deep denitrification treatment method and system based on an AOA process, and relates to the technical field of sewage treatment.The method comprises the steps that multiple types of sensing units are arranged in an anoxic zone and an aerobic zone to obtain multi-zone characteristics, and a low-temperature denitrification collaborative state tensor is constructed through space-time decoupling and correlation analysis; and inputting a denitrification task target, a state tensor and an equipment state into a collaborative decision model, outputting a combined control instruction of gas-liquid distribution, a reflux ratio, aeration intensity and the like through attention fusion, and issuing the combined control instruction to a related unit so as to realize deep denitrification control under a low-temperature condition. The method solves the technical problems of low denitrification efficiency and poor stability caused by the fact that the existing AOA process cannot effectively identify and regulate the coupling denitrification dynamic relationship of multiple reaction intervals under the low-temperature condition, realizes accurate regulation and control of multi-zone denitrification power under the low-temperature condition through multi-zone cooperative control and refined parameter optimization, and improves the denitrification efficiency. The denitrification efficiency and the stability are improved.

The invention discloses an intelligent marinating regulation and control method for leisure old marinated claw snacks based on multi-source sensing, which comprises the following steps: acquiring environmental parameters and material parameters in a marinating process through a multi-source sensor, and analyzing texture change of old marinated claws by utilizing a collagen triple-helix structure denaturation kinetic model; a dynamic time warping smell recognition algorithm is adopted to recognize a smell change stage, a color change rule is analyzed through a Maillard reaction kinetics coupling model, multiple parameters are fused to establish an incidence matrix, and the heating power of marinating equipment, the marinating liquid circulation rate and the ventilation quantity are regulated and controlled according to the matrix. According to the method, multi-parameter collaborative monitoring and deep analysis are realized, the regulation and control accuracy and the automation level are improved, the defects that a traditional method is insufficient in key component change analysis and poor in smell recognition and multi-parameter fusion collaboration are overcome, the product quality stability is guaranteed, and the standardized production requirement is met.

The invention provides a pollutant traceability method, system and device based on multiple media and multiple links and a medium, and belongs to the technical field of target area pollution abatement, and the method comprises the steps: preprocessing pollutant monitoring data to obtain a standardized data set; the pollutant monitoring data comprises data of pollutants in a plurality of links and a plurality of media in a target area, and the standardized data set comprises a plurality of standardized data; based on the standardized data set, main characteristic components representing pollutant distribution and evolution rules are extracted; constructing a transfer coupling relation model based on the main characteristic components, and obtaining a transfer path and a coupling strength coefficient of the pollutants according to the transfer coupling relation model; based on the transmission path and the coupling strength coefficient, constructing a causal model and carrying out joint verification; and when the verification is passed, outputting a pollution traceability result. According to the invention, the scientificity, the accuracy and the decision support reliability of mine pollution traceability are obviously improved.

The invention belongs to the technical field of metallurgical industry process intelligent control and prediction, and discloses a self-adaptive mixed intelligent prediction method and system for smelting endpoint parameters of an electric arc furnace. Acquiring smelting process data of the electric arc furnace; constructing a dual-drive prediction system comprising a mechanism model and a data drive model; calculating a decision coefficient of a prediction value and an actual measurement value of the theoretical model, and counting an effective historical data volume; constructing a machine learning prediction model, and selecting a modeling algorithm according to the effective historical data volume; selecting a hybrid prediction strategy based on the decision coefficient and the effective historical data volume; predicting and outputting an end point carbon content predicted value and an end point temperature predicted value according to a hybrid prediction strategy; predictive deviation threshold value judgment and execution control are conducted, and the electric arc power, the oxygen blowing flow, the feeding speed or the cooling water flow are adjusted. Accurate prediction and dynamic optimization control of the electric arc furnace end point parameters are achieved, and the smelting quality, the energy utilization rate and the production stability are remarkably improved.

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 fastener heat treatment-oriented multi-source data fusion processing method, which relates to the field of data fusion and comprises five steps of multi-source data acquisition, data preprocessing, feature extraction, fusion modeling and result optimization. The multi-source data acquisition terminal acquires process data, material data, quality detection data and environment data; data cleaning and standardization are carried out in data preprocessing; the feature extraction terminal extracts a time sequence feature parameter, a component feature parameter, a quality feature parameter and an interference feature parameter; fusion modeling is combined with mechanism prior and data driving to realize fusion of a feature layer and a decision layer; and result optimization: smoothly correcting the fusion result, and outputting quality evaluation and process adjustment suggestions. According to the method, through deep fusion of multi-source data, the accuracy and stability of fastener heat treatment quality evaluation are improved, adaptive optimization of process parameters is realized, and the method is suitable for heat treatment whole-process management and control in a structured industrial scene.

The invention discloses a numerical simulation method and system for corrosion expansion cracking of reinforced concrete based on near-field dynamics, and relates to the technical field of numerical simulation of civil engineering materials. Discretizing a concrete calculation area into a near-field dynamic material point model to form a discrete model; applying boundary conditions to the discrete model; establishing a mapping relation between the material point damage degree and the erosion coefficient; acquiring chloride ion concentration distribution and oxygen concentration distribution of the discrete model at the current time step, and judging whether the chloride ion concentration of the surface of the steel bar reaches a blunt removal threshold value or not; if the reinforcing steel bar is blunt, obtaining a displacement field of the model at the current time step and the damage degree of each material point, and updating the erosion coefficient of each material point in the model; according to the method, a crack path does not need to be preset, initiation and expansion of complex cracks can be naturally described, the problem of singularity of a crack tip and the problem of convergence do not exist, damage behaviors such as brittle fracture and peeling of concrete can be simulated, and extra constitutive adjustment and model adjustment are not needed.

The invention belongs to the technical field of energy storage system control, and particularly relates to an SOC offset optimization control method and system for a 5G base station energy storage system, and the method comprises the steps: obtaining the operation data of an energy storage battery; updating an SOC offset optimization interval through a dynamic threshold adjustment algorithm, performing prediction in combination with a base station load prediction model to obtain a prediction result, and dynamically selecting a target strategy in a charging mode and a discharging mode according to the prediction result and a battery state to perform charging or discharging; based on the target strategy, optimizing the output power of the bidirectional converter by adopting a model prediction control algorithm, and correcting the SOC offset in real time in combination with a dynamic loss model; and the corrected SOC offset, temperature and load fluctuation parameters are transmitted to a cloud monitoring platform, multi-level data fusion analysis is carried out, meanwhile, self-inspection is carried out on the health state of the battery regularly, and three-dimensional alarm information containing a fault position probability cloud picture is fed back. Therefore, the problems of poor adaptability, insufficient estimation precision and the like in the prior art are solved.

The invention belongs to the technical field of steel smelting, and particularly discloses a comprehensive utilization method of vanadium titano-magnetite, which comprises the following steps: S1, preparing the vanadium titano-magnetite into pellets, and carrying out reduction reaction to obtain direct reduced iron; s2, a carbonaceous reducing agent and the direct reduced iron obtained in the step S1 are mixed and smelted in a melt separation electric furnace, and slag and molten iron are obtained; a slag former is not added in the smelting process, the purity of the titanium slag is improved through optimal design of the furnace profile of the melting electric furnace and combination of various smelting process control means, the problems that refractory materials of the melting electric furnace are excessively eroded, the service life is short, slag-metal separation is difficult, smelting is difficult to control and the like are solved, and the purposes of high-grade titanium slag and long service life of the melting electric furnace are achieved.

The invention discloses a water electrolysis hydrogen production electric pile performance evaluation method, and relates to the field of electric pile performance evaluation, and the method comprises the steps: S1, collecting operation parameters through a sensor system, carrying out preprocessing and time sequence alignment, and constructing a real-time data set; s2, according to the real-time data set and historical data, a stack health index is calculated through multi-layer feature hierarchical fusion and a machine learning model; s3, on the basis of the real-time data set, calculating a composite evaluation index parameter containing a safety margin factor and a performance degradation rate; s4, on the basis of the stack health index, analyzing in combination with the composite evaluation index parameters, and determining the stack performance state grade; and S5, extracting a maintenance suggestion in a preset level and maintenance strategy mapping relation based on the pile performance state level, and transmitting the maintenance suggestion to a visual interface for display through the cloud platform. Through multi-layer feature fusion and machine learning, the method achieves the precise evaluation of the health index of the galvanic pile, dynamically monitors the fault early warning, automatically generates an intelligent maintenance strategy, optimizes the performance, and prolongs the service life of equipment.

The invention relates to the technical field of silver paste preparation, in particular to a silver paste conductivity data modeling and formula optimization system based on machine learning, which comprises a data acquisition and storage module, a preprocessing module, a characteristic influence analysis module, a formula optimization module, a simulation verification module and the like. The method comprises the following steps: acquiring original data of a silver paste formula, preprocessing, calculating influence coefficients of all components on target performance by utilizing a machine learning model, and identifying high and low influence components; the formula optimization module is combined with component content constraints and adopts a multi-objective optimization algorithm to generate candidate formulas; and the simulation verification module verifies the performance of the formula through sintering simulation and process adaptation, and feeds back optimization. According to the invention, the full-process intelligentization of the silver paste formula from data processing to optimization verification is realized, the conductivity and other performances of the silver paste are accurately improved, the research and development cost is reduced, the period is shortened, the suitability of the formula process is enhanced, and the research and development and industrial upgrading of the silver paste are promoted.

The embodiment of the invention provides a battery simulation model method, electronic equipment and a computer readable storage medium. The method relates to the technical field of lithium battery modeling, and comprises the following steps: determining a target mapping mode according to parameter types of parameters included in measured data; thermodynamic parameters of the semi-electrode are determined in a target mapping mode; generating an initial model of the target battery based on the thermodynamic parameters; generating an initial population according to the initial value of each undetermined parameter, carrying out first iteration processing by taking the initial population as a start, and determining a target individual which enables the target function to be minimum; determining an iteration direction of the target function based on the second derivative information; performing second iteration processing based on the iteration direction, and determining a target value of each to-be-determined parameter when the target function meets a predetermined termination condition; and setting the initial model based on the target value of each undetermined parameter to obtain a target model. The technical problem that the simulation accuracy of the battery model is not ideal due to the limitation of experimental data in the prior art is solved.