4324results 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 relates to the technical field of sewage treatment, in particular to a sewage treatment control process based on dynamic regulation and control of aeration and carbon source addition, which comprises the following steps: acquiring microbial metabolism heat change data in real time; biochemical treatment: monitoring the dielectric constant of a water body and the Reynolds number of sewage flow in real time, and constructing a multi-parameter coupling regulation and control model and dynamically regulating and controlling the aeration rate in combination with microbial metabolism heat change data obtained in the pretreatment step; accurately adding the carbon source according to a calculation result of the carbon source demand prediction model; sewage subjected to biochemical treatment is subjected to ultrafiltration through a nanofiber membrane and then subjected to combined disinfection treatment through ultraviolet rays and ozone, treatment parameters are regulated and controlled in real time according to the membrane flux attenuation trend in the treatment process, and dynamic and accurate control over sewage treatment is achieved. According to the method, the problem of energy consumption waste caused by excessive aeration or low degradation efficiency caused by insufficient aeration in a traditional process is solved, and accurate matching of dissolved oxygen supply and microbial requirements is realized.

The invention 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 discloses a sewage denitrification dosing method and system based on machine learning and a storage medium, and belongs to the technical field of sewage treatment.The method includes the steps that data are collected and preprocessed, and variable data influencing biochemical pool carbon source dosing behaviors are obtained; and lagging influence of carbon source input on the denitrification amount index is analyzed, and the duration time range of the drug effect is determined. And adopting the trained prediction model, and based on the denitrification amount index and the prediction variable of the future t + X period, obtaining the dosage of the (t + 1) th period. Through a correlation analysis method, the correlation rule of nitrogen conversion in the future X period after the carbon source is added is analyzed, the duration time of the drug effect is determined, the lag effect is accurately quantified, and the problem of mismatching of regulation and control opportunities is avoided. The hysteresis effect is captured and subjected to multi-factor coupling analysis based on the prediction model, the carbon source adding amount and time are optimized, system load fluctuation caused by excessive carbon sources or incomplete nitrogen removal caused by insufficient carbon sources are avoided, and the stability of an original sewage ecological system is gradually improved.

The invention belongs to the technical field of iron phosphate preparation, and discloses an energy-saving control system for iron phosphate preparation based on an energy consumption scheduling model. The system is composed of a data acquisition module, an energy consumption sensing module, a preparation process modeling module, an energy consumption prediction module, an energy-saving scheduling module, an intelligent execution module, a feedback correction module, a man-machine interaction module and a remote operation and maintenance module. The energy consumption sensing module intelligently senses an energy consumption state, the preparation process modeling and energy consumption prediction module accurately predicts energy consumption, the energy-saving scheduling module generates an optimal scheduling strategy, the intelligent execution module accurately executes an instruction, and the feedback correction module realizes closed-loop adaptive regulation and control; all the modules cooperatively operate, process parameters are adjusted in real time according to actual working conditions of iron phosphate preparation, energy consumption in the preparation process is remarkably reduced, the energy utilization rate is increased, and energy-saving optimization of iron phosphate preparation is achieved.

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 concrete multi-target ratio design, and discloses a reinforcement learning-based concrete multi-target ratio optimization method and device and a medium, and the optimization method comprises the steps: carrying out candidate gene screening on a data set based on elastic network regression; obtaining a prediction model based on reinforcement learning optimization; and the prediction model outputs a concrete multi-target ratio. According to the method, a concrete original data set containing raw material composition, microstructure characteristics and typical performance indexes is constructed, and material composition, microstructure and typical performance coexist; key genes are screened through an elastic network sparse modeling mechanism, an initial concrete multi-target proportion prediction model is constructed, and the nonlinear mapping and coupling principle among multiple performance indexes is embodied; the feature contribution degree in the prediction model is used for constructing a concrete material knowledge graph, strategy adjustment is counteracted on the basis of the contribution degree, and a data, model and strategy three-in-one performance-driven optimization closed loop is achieved.

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

The invention 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 belongs to the technical field of material life prediction, and particularly relates to an X70 steel gas pipeline multi-working-condition life prediction method based on transfer learning. Aiming at the problem of low prediction precision caused by dependence on single working condition data and large distribution difference between laboratory and actual service environment data in a traditional life prediction method, the invention provides a cross-domain transfer learning framework. The method comprises the following steps: acquiring mechanical property data through slow strain tensile tests of different hydrogen doping ratios in a laboratory environment, and constructing a domain self-adaptive life prediction model in combination with multi-modal feature decoupling, dynamic distribution alignment and physical constraint fusion strategies. The method significantly reduces actual scene data requirements, realizes high-precision prediction of the residual life of the pipeline under multiple working conditions, is suitable for oil and gas pipeline health monitoring and hydrogen embrittlement risk assessment, and has the advantages of high efficiency, low cost and high reliability.

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 reaction site prediction method and device based on chemical and physical prior driving, and the method comprises the steps: extracting set features through the multi-modal input of a fusion molecular map, an SMILES sequence and a three-dimensional conformation; generating atomic embedding by using a message passing neural network, and calculating a mixed feature fusing a topological path and a three-dimensional distance; combining the key type weight to construct a graph position code of chemical environment correction; injecting the mixed distance and the charge difference into a Transform attention mechanism, and explicitly modeling an inter-atomic long-range electron effect; a model is jointly trained through double tasks of comparative learning and mask prediction, the comparative learning adopts a directional negative sample to enhance generalization, and mask prediction synchronously recovers an atom type and a charge transfer matrix; and finally, injecting quantum chemistry priori constraint attention weights such as a Fuzzy well function, outputting an atomic-scale reaction activity probability, generating a thermodynamic diagram, and realizing high-precision and interpretable active site labeling. According to the method, the drug design and reaction mechanism analysis efficiency can be remarkably improved.

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 discloses a multi-constraint blending combustion heat value optimization regulation and control method and system, and relates to the technical field of thermal power generating unit multi-coal blending combustion regulation and control, and the method comprises the steps: building a load prediction model based on the day-ahead power transaction plan, historical load output data and unit operation state information of a thermal power generating unit; and performing cycle-by-cycle rolling prediction by using a sliding window mechanism to generate a future unit load prediction curve. And according to the load prediction curve and the coal type calorific value data, a blending combustion calorific value optimization model with the minimum unit power generation fuel cost as the target is constructed, meanwhile, constraints are set, and an optimal blending combustion proportion combination is obtained through solving. And dynamically adjusting the blending combustion proportion by combining real-time load data based on the as-fired coal calorific value soft measurement result and the predicted calorific value deviation in the actual operation process, and setting an error threshold to trigger a correction mechanism to realize feedback control. According to the method, through three-step linkage of prediction, optimization and feedback regulation, accurate control over the blending combustion heat value is achieved, the fuel cost is minimized, and the operation stability is improved.

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 discloses a laboratory whole-process intelligent management and control system based on AI and Internet of Things fusion, which relates to the technical field of laboratory management and comprises an equipment access unit, a central decision unit and a business application unit. According to the system, a risk model library and a real-time risk map can be constructed through the risk assessment module, accident risks caused by human errors are reduced through the real-time risk map, automatic emergency disposal and personnel behavior compliance monitoring, and when the equipment operation and maintenance module detects that the equipment is abnormal, predictive maintenance can be automatically carried out, so that the downtime is shortened, and the maintenance efficiency is improved. The resource scheduling module can be used for optimizing the inventory and scheduling the equipment, and carrying out global allocation based on an inventory sharing model, so that resource idleness is reduced through cross-courtyard inventory sharing, resource sharing is facilitated, cross-courtyard collaboration is completed, and the situation that data cannot be synchronized due to independent inventory management of each courtyard is avoided; and further, global optimization can be realized.

The invention relates to the technical field of sponge city sewage control, and discloses a sewage treatment control system of a sponge city. A rainwater collection module of the system obtains rainfall data and initial water quality parameters of rainwater collection points in real time; the pipe network monitoring module monitors water level and flow velocity data of key nodes of the drainage pipe network in real time; the reservoir state module monitors current water level and water quality change parameters of each reservoir in real time; the environment evaluation module receives the data, judges the environment state and generates an environment abnormal signal; the pipe network analysis module analyzes the pipe network sedimentation risk according to the signal and generates a sedimentation evaluation value; the reservoir analysis module analyzes the reservoir load according to the signal and generates a load evaluation value; and the decision control module performs sewage treatment strategy analysis and controls opening or closing of a tail end gate of the drainage pipe network in the target area according to an analysis result. The system can improve scientificity and efficiency of sewage treatment and rainwater regulation and storage of the sponge city.

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 relates to the technical field of mathematical modeling, in particular to a mathematical model modeling method based on data analysis operation processing. The method comprises the following steps: collecting metal pollution data of a target water area by using a multi-parameter water quality sensor array; performing time sequence alignment on the metal pollution data to generate a standardized pollution data set; constructing a metal migration model based on fluid dynamics and chemical adsorption coupling; inputting the standardized pollution data set into the metal migration model for three-dimensional space interpolation calculation, and generating a metal pollutant diffusion cloud picture; pollution source reverse analysis is carried out on the metal pollutant diffusion cloud picture, and a potential pollution source coordinate set and pollution contribution degree sorting are generated. According to the method, by integrating multi-parameter water quality sensor data, fluid dynamics and chemical models and multispectral analysis, the modeling precision of the water pollution diffusion mathematical model is improved.

The invention provides an industrial park-oriented multi-pollution source collaborative treatment and emission optimization regulation and control method, which relates to the technical field of environmental pollution abatement, and comprises the following steps: obtaining real-time monitoring data and meteorological data, generating emission characteristic fingerprint data and a pollutant distribution weight coefficient, and calculating the emission characteristic fingerprint data and the pollutant distribution weight coefficient according to the emission characteristic fingerprint data and the pollutant distribution weight coefficient; a bidirectional fluid-solid coupling calculation method is adopted to generate pollutant transmission path data, management and control partitions are divided, a pollutant migration and diffusion model is constructed to calculate a pollution source correlation intensity matrix, and therefore the optimal emission reduction proportion and dynamic emission regulation and control parameters are determined, and a multi-pollution-source linkage control strategy is established. According to the invention, accurate identification and cooperative treatment of multiple pollution sources are realized, and the pollution treatment efficiency is improved.

The invention relates to the technical field of air quality detection, in particular to an air quality risk early warning and treatment integrated method and system. The method comprises the following steps: acquiring regional air collection sample data, analyzing the evolution difference of air components among regions, and determining the dynamic response characteristics of air dominant factors; air multi-period pollution superposition conditions are detected according to the dynamic response characteristics of the dominant factors, the air quality pollution risk growth trend is identified, and the ecological influence gradient growth trend is estimated; performing air quality risk assessment on the corrosion and aging trend of the peripheral equipment to obtain air quality abnormal state assessment data; abnormal conditions of air pollution sources are recognized, and pollution source grade classification is formed; formulating air quality treatment strategy data according to the abnormal risk data and the pollution source identification condition, and executing response treatment measures; through air quality risk early warning, air quality treatment is more efficient.