1665 results about "Pollutant emissions" patented technology

The invention relates to the field of power equipment control data processing, in particular to a boiler combustion optimization control method based on data driving, which comprises the following steps of: acquiring multi-source data such as temperature field distribution, air and smoke pressure, smoke components and coal quality characteristics, and eliminating noise interference by adopting sliding window mean filtering; generating a standardized feature matrix in combination with principal component analysis and a dynamic time warping algorithm; constructing a dynamic coupling model fusing a gradient boosting decision tree and a long short-term memory network, analyzing a nonlinear relationship between pulverized coal particle size distribution and a wind-coal ratio, and predicting combustion efficiency, pollutant concentration and temperature field uniformity; and model parameter self-correction and weight dynamic adjustment are triggered through actual combustion data feedback, and a closed-loop control link is formed. According to the method, accurate modeling of the multi-physical field coupling characteristic of the combustion system is achieved, the time sequence generalization ability under the dynamic working condition is improved, and the purposes of heat efficiency improvement and pollutant emission reduction are effectively balanced.

A dual-motor mixed-hybrid powertrain system, by performing pulse modulation control, i.e., series-hybrid intelligent start-stop control or parallel-hybrid intelligent power switching control, on the instantaneous power time-varying functions of an engine and a battery pack, can convert the surface working condition of an analog electric control engine into a simpler line working condition of a digital pulse control (DPC) engine, either a pre-determined high-state line working condition in the high-efficiency combustion area or a pre-determined non-combustion low-state line working condition with zero fuel consumption and zero pollutant emissions multiplexed in time. The traditional fixed one-to-one bidirectional mapping between an engine working condition and a vehicle working condition is converted into a dynamically adjustable many-to-many bidirectional mapping to achieve the full coverage of any overall vehicle working condition, achieving decoupling between a DPC engine working condition and the overall vehicle working condition and decoupling between software and hardware of a hybrid powertrain.

A data-driven multi-objective optimization control method for a municipal solid waste incineration (MSWI) process. The method comprises: first, establishing an MSWI process whole-process model for pollutant emission, wherein the model includes controlled object models, i.e. a furnace hearth temperature model, a boiler steam flow model and a flue gas oxygen-content model, and CO, CO2 and NOx pollutant emission index models; then, using single-neuron adaptive PID to establish a multi-input multi-output loop controller for key controlled variables, so as to realize stable control over a combustion process; and finally, in view of expert knowledge in the field, realizing adaptive solving of a key controlled variable optimization set value based on a multi-objective particle swarm optimization algorithm. Multi-objective optimization control over an MSWI process can be realized, existing running factories are guided, the pollutant emission concentration can be reduced, and the efficiency of combustion is improved.

The invention discloses an urban road mobile source intelligent monitoring method and system based on multi-source data coupling, and relates to the technical field of environment monitoring and intelligent traffic. Utilizing a machine learning algorithm to construct a pollutant emission, carbon emission and energy consumption prediction model; acquiring vehicle inventory data in the city, and calculating the pollutant emission, energy consumption and carbon emission of the whole city; the method comprises the following steps of: constructing a dynamic distribution diagram of automobile emission in a city by using real-time position data of vehicles and urban road network information, introducing an atmospheric diffusion model, simulating pollutant migration by combining urban geographic information and environmental data, comparing and verifying a simulated migration result with actually measured data of a national control site, and optimizing parameters of the atmospheric diffusion model; and storing the result into a real-time database, and displaying the pollutant distribution, emission, energy consumption and carbon emission of the urban road network in real time through a visual interface. According to the invention, the temporal-spatial resolution and prediction precision of data are effectively improved.

The invention discloses a pollution research and water quality prediction method based on river segmented water environment capacity analysis. The method comprises the following steps: accurately dividing a drainage basin into water environment capacity accounting units; obtaining a river water body image through satellite remote sensing; identifying and quantifying the positions and the intensities of point source and non-point source pollution sources in the drainage basin by utilizing the water quality parameters of the river obtained by the remote sensing image; establishing a river water quality prediction model; calculating the water environment capacity of each river reach under different surface water environment quality standards; comparing the estimated pollutant discharge amount with the water environment capacity under each water quality standard, and judging whether the pollutant discharge amount exceeds the capacity or not; and predicting future water quality change by using the model. According to the method, remote sensing, GIS and machine learning technologies are utilized, rapid analysis of the river segmented water environment capacity and accurate prediction of the water quality are achieved, dependence on a large amount of field sampling and complex software simulation is reduced, and the working efficiency is greatly improved.

The invention relates to the technical field of environmental impact assessment, in particular to an intelligent environmental impact assessment method and system, and the method comprises the following steps: collecting environmental data including pollutant discharge, water resource consumption, air quality change and biodiversity, building an impact factor data set, and segmenting the data set to obtain an impact factor data set; and calling a kernel function type to carry out numerical prediction on pollutant emission. According to the method, by integrating data processing and machine learning technologies, the accuracy and efficiency of environmental impact assessment are improved, numerical prediction is performed on environmental data by using a kernel function and a support vector regression model, the adaptability and precision of a prediction model are enhanced, model parameters are optimized through automatic population generation and crossover mutation operation, and the accuracy and efficiency of environmental impact assessment are improved. The model can better learn and adapt in a complex data environment, sensitivity and response speed to environmental changes are further improved through dynamic clustering analysis and time sequence adjustment, and scientific support is provided for formulating timely and effective environmental protection measures.

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 a coal-fired unit electrostatic dust collection energy-saving optimization method, system and equipment and a storage medium, and the method comprises the steps: obtaining historically collected system operation data of a coal-fired unit electrostatic dust collection system, and constructing an outlet NOx concentration prediction model and an outlet dust concentration dynamic model according to the system operation data; power optimization control strategies of high-voltage power supplies of all levels of electric fields in the dust removal system are obtained, a model is built based on the outlet NOx concentration prediction model, the outlet dust concentration dynamic model and the optimization control strategies, and an electrostatic dust removal energy-saving optimization model is obtained; acquiring a preset environment-friendly standard concentration, and analyzing the environment-friendly standard concentration through the electrostatic dust collection energy-saving optimization model to obtain an optimal dust collection parameter; and adjusting the current or voltage of each level of electric field in the dust removal system according to the optimal dust removal parameters. Through the constructed energy-saving optimization model, the electrostatic dust collection control system can reduce the dust emission concentration and meet the pollutant emission standard, and meanwhile, the energy consumption of the electric dust collector can be greatly reduced.

The invention relates to a ship hybrid power energy management method and device, ship equipment and a medium, and belongs to the technical field of energy management.The ship hybrid power energy management method comprises the steps that operation data of a hybrid power ship are obtained, and a hybrid power ship model is constructed based on the operation data of the hybrid power ship; the operation cost and the pollutant discharge cost of the hybrid power ship are calculated based on the hybrid power ship model, and the comprehensive cost is determined based on the operation cost and the pollutant discharge cost; according to the method, the minimum comprehensive cost is taken as an objective function, constraint conditions are set, and parameters of the hybrid power ship model are optimized by adopting a particle swarm optimization algorithm, so that energy management of the hybrid power ship is realized, and optimal energy utilization is realized.

The invention relates to a waste incineration flue gas pollutant control technology, and aims to provide a waste incineration flue gas multi-pollutant collaborative prediction and intelligent control method and system. On the basis of a deep learning method, a collaborative prediction model for four types of pollutants in waste incineration flue gas is constructed; then, a multi-objective optimization method is combined, and a cost index function considering the absorbent dosage and an environmental protection index function considering the pollutant emission are constructed; and the optimal usage data of the absorbents corresponding to the flue gas pollutants are solved by using a multi-objective optimization algorithm, and finally, the opening degrees of various absorbent feeding valves are adjusted and controlled according to the optimal usage data feedback, so that the intelligent control of the multiple pollutants in the waste incineration flue gas is realized. According to the invention, accurate collaborative prediction of multiple pollutants in waste incineration flue gas can be realized, and the hysteresis problem of traditional flue gas pollutant emission monitoring equipment is effectively overcome; the pollutant control cost of the flue gas purification system is reduced to the maximum extent, and the economical level of the incinerator is improved.

The invention relates to the technical field of energy, in particular to a dynamic self-adaptive control method and system for a gas-steam combined cycle unit, and the method comprises the steps: constructing a multi-modal model library comprising a plurality of operation modes; operating parameters and environment parameters of the unit are collected in real time, and multi-source data fusion and system state evaluation are carried out; carrying out multi-time scale prediction on the load demand, fuel characteristics and environmental parameters of the unit based on a deep learning model; dynamically selecting model precision according to a prediction result, and implementing a multi-mode collaborative optimization decision to generate a control instruction; and executing the control instruction and monitoring an execution effect, and carrying out adaptive adjustment on model parameters and strategies according to feedback. According to the method, the unit can maintain efficient and stable operation under the scenes of power grid peak regulation, rapid load fluctuation, complex environment change and the like, meanwhile, pollutant emission is reduced, and the service life of key equipment is prolonged.

The invention relates to the technical field of environmental pollutant detection, in particular to a straw open-air incineration pollutant emission quantitative method based on multi-source data fusion, which comprises the following steps: based on air pollutant monitoring data, calling a sensor network to obtain PM2.5, CO and NOX concentrations, analyzing fire point heat radiation intensity, collecting meteorological data, and calculating the concentration of PM2.5, CO and NOX; and obtaining a pollution source data set by combining the time sequence change of the fire points and the meteorological parameters. By integrating an environment monitoring sensor, satellite remote sensing and ground meteorological data, the comprehensiveness and accuracy of pollution source monitoring are improved, the combination of fire point intensity and pollutant concentration time sequence data is optimized, the evaluation of fire point and pollution hot spot areas and the comprehensive analysis of wind directions and meteorological conditions are optimized, the accuracy of a pollutant diffusion model is improved, and the pollution source diffusion efficiency is improved. The pollutant release amount is dynamically adjusted, and the diffusion model is corrected by using a geographic information system, so that the scientificity of pollution prediction and emission intensity evaluation is enhanced, and more accurate pollution prevention and control decision support is provided.

The invention relates to the technical field of industrial pollution monitoring, in particular to an industrial pollution source tracing method and system based on machine learning, which can accurately and efficiently determine a pollution source in an industrial park and meet high requirements of environmental protection laws and regulations and the public on environmental protection. The method is used in an industrial park, when an industrial pollution monitoring point monitors that pollution indexes exceed the standard and the emission of each factory in the park meets the emission standard for pollution discharge, pollution is subjected to traceability analysis, and a pollution source with the maximum participation degree in the pollution exceeding event is determined. The method comprises the following steps: acquiring pollution monitoring data information monitored by an industrial pollution monitoring point; carrying out standard-exceeding pollutant feature extraction on the pollution monitoring data information to obtain a park pollutant feature portrait; the park pollutant feature portrait comprises the components of the standard-exceeding pollutants in the park and the real-time concentration of each standard-exceeding pollutant; and matching the park pollutant feature portraits in a pre-established factory pollutant discharge database.

The invention relates to the technical field of boiler combustion control, and discloses a boiler closed-loop combustion dynamic optimization control system based on big data, and the system comprises an intelligent sensing module which is used for obtaining and outputting time-synchronized monitoring data in real time; the edge calculation module is used for processing multi-source sensor data in real time, realizing data credible processing and combustion state feature coding, and executing local caching of working condition perception; the digital twin modeling module is used for constructing a combustion process dynamic model and realizing real-time prediction of a combustion state; the optimization control module is used for dynamically adjusting combustion parameters and achieving collaborative optimization of the air-coal ratio, the opening degree of a secondary air door and the over-fire air ratio; and the closed-loop execution module is used for executing and feeding back the control instruction and realizing transmission and closed-loop verification of the control instruction. The combustion efficiency can be improved, pollutant emission can be reduced, and the stability and safety of boiler operation can be enhanced.

The invention discloses a coal-fired power plant operation parameter adaptive optimization method and system. The method comprises the steps of operation data fusion acquisition, dynamic load response modeling, fire coal load coordination optimization, emission efficiency adaptive balance and operation parameter adaptive optimization. The invention relates to the technical field of intelligent adjustment of parameters of a coal-fired power plant, and aims at realizing future load minute-level rolling prediction by constructing a standardized multi-source data structure and adopting a long-short-term memory network, and realizing dynamic generation of combustion parameters by introducing adaptive dual-stage collaborative scheduling and an improved multi-objective optimization algorithm. On the basis, the dynamic causal regulation and control diagram is used for carrying out rolling weighted balance on the emission efficiency, the energy efficiency, the responsiveness and the emission compliance are comprehensively considered, the power generation efficiency is improved, the pollutant emission is reduced, and the adaptability and the stability of the system are enhanced.

The invention belongs to the field of coal waste gas treatment, and particularly relates to a coal waste gas comprehensive utilization dynamic optimization method and system based on big data, and the dynamic optimization method comprises the following specific steps: S1, collecting and preprocessing multi-source data; s2, big data analysis and model construction: constructing a waste gas generation prediction model, a resource recovery potential evaluation model and a waste gas treatment effect evaluation model; and S3, dynamic optimization decision making and implementation: monitoring key indexes in real time and performing early warning, and through accurate waste gas generation prediction and real-time equipment performance monitoring, waste gas treatment process parameters can be adjusted in time, it is ensured that waste gas treatment equipment is always in an optimal operation state, the waste gas treatment efficiency is effectively improved, the pollutant emission concentration is reduced, and the energy consumption is reduced. The waste gas treatment quality is improved, so that the emission index more stably meets the environmental protection standard.

The invention belongs to the technical field of equipment intelligent control, and discloses a multi-objective optimization intelligent control method for a combustion carbon dioxide production system. The method comprises the following steps: constructing a combustion mechanism model; training a combustion data model; fusing the combustion mechanism model and the combustion data model to obtain a dual-drive model, and determining an optimization target; a dual-drive model is adopted to simulate the garbage combustion process, the control strategy is optimized according to the optimization target, and combustion equipment is adjusted; the advantages of the two models are effectively fused, advantage complementation between the models is achieved, and therefore the physical interpretability and adaptability of the models are improved; in the control strategy optimization process, not only is the combustion efficiency considered, but also carbon emission and pollutant emission are considered, the combustion efficiency is improved, and the carbon emission and the pollutant emission are reduced, so that multi-target optimization of the combustion carbon dioxide production system is achieved, and the control strategy optimization efficiency is improved. And the environment-friendly performance and the economic performance of the garbage combustion carbon dioxide production system are improved.

The invention provides a vehicle-road cooperative control method and system for a double-layer ecological city in a mixed traffic flow environment, and the method comprises the steps: taking a signal lamp at each intersection as an intelligent agent, and obtaining the real-time driving information of a vehicle through infrastructure communication; the intelligent agent calculates the vehicle number and the driving speed of each motorcade according to the headstock distance between the vehicles; the upper perceptron predicts the time required by the motorcade to arrive at an intersection stop line by stacking a full-connection layer and a long-short-term memory network; the predicted time information is input into a strategy network and a value network of the intelligent agent to generate actions, and the strategy network is updated through delayed feedback rewards; the optimized suggested driving speed is sent to the head vehicle of the motorcade so as to optimize the driving track of the motorcade, relieve road network congestion and reduce pollutant emission; according to the invention, cooperative control is carried out through distributed decision making and cooperative learning by the intelligent agent, and the vehicle-road cooperative control capability and the traffic passing efficiency of the urban road network are improved.

The invention discloses a circulating fluidized bed temperature intelligent prediction method based on a physical information neural network, and mainly relates to the technical field of industrial process intelligent control and energy power engineering crossing. Comprising the following steps: S1, acquiring operation data of the circulating fluidized bed, and preprocessing the operation data to obtain a data set; s2, constructing a physical information residual neural network; s3, constructing a loss function of adaptive weight adjustment; s4, training the physical information residual neural network by using the data set, and performing iterative optimization in combination with the constructed physical information residual neural network and the loss function to obtain a bed temperature prediction model; s5, inputting operation data of the circulating fluidized bed into the bed temperature prediction model to obtain a bed temperature prediction value; the method can solve the problem of complexity and nonlinearity of dynamic prediction of the bed temperature of the circulating fluidized bed, can optimize the operation efficiency of the boiler, improves the combustion stability, and reduces the emission of pollutants.

The invention discloses a gasification slag mixed biomass fuel blending combustion control method and system, and relates to the field of energy and environmental engineering. The method comprises the steps of collecting sensor data for data fusion, and preprocessing original data; according to the preprocessed data, an environment combustion synergy index is constructed, and the raw materials are classified; screening an optimal mixing ratio by utilizing dynamic game scores; temperature and flame stability parameters are collected in real time to construct a closed-loop feedback system, and intelligent adjustment of the combustion process is achieved; and a dynamic emission risk index is constructed, and closed-loop optimization control of pollutant emission is realized in combination with variable regression and a dynamic adjustment algorithm. Advanced technologies such as sensor data fusion, environment combustion synergy index, dynamic game scoring and feedback control are combined, on the premise that the combustion efficiency and stability are ensured, the blending combustion strategy of the gasified slag mixed biomass fuel can be optimized, and pollutant emission is effectively reduced.

The invention relates to a method, a device and equipment for identifying abnormal pollution discharge based on multi-source data, and a medium, and belongs to the technical field of environment monitoring. The method comprises the following steps: collecting working condition electricity consumption historical data and pollutant discharge historical data; extracting at least two key variables from the working condition electricity consumption historical data and the pollutant emission historical data, taking the key variables as endogenous variables, constructing a vector autoregression model according to the endogenous variables, and determining a lag order of the vector autoregression model by using an information criterion method; processing the working condition electricity consumption historical data and the pollutant emission historical data to obtain a training set, and training a vector autoregression model by using the training set; obtaining input data according to a to-be-predicted moment and the lag order, inputting the input data into the trained vector autoregression model, outputting a predicted value of a key variable at the to-be-predicted moment, and obtaining a corresponding measured value; and judging whether pollution discharge is abnormal or not according to the predicted value and the measured value of the key variable, and analyzing an abnormal reason.

The invention provides a solid waste recycling full life cycle environmental impact evaluation method, which belongs to the technical field of solid waste recycling, and evaluates environmental impact by constructing a system boundary and time dynamic list including solid waste collection, transportation, pretreatment, recycling and product use stages and applying a time weighted characterization model. A time discount rate is introduced to quantify future environmental influence, a multi-scene influence prediction function is adopted to construct a simulation matrix, and an environmental influence time distribution curve is calculated. A four-dimensional index system of resource recovery efficiency, energy substitution efficiency, pollutant emission reduction and environmental impact mitigation is established, a system dynamics and Envi rPred ictor deep learning model is combined to predict a solid waste long-term environmental behavior, an environmental impact comprehensive evaluation matrix is constructed, characterization factors are dynamically adjusted, an iterative optimization mechanism is formed, and the environmental impact mitigation efficiency is improved. Scientific evaluation on the solid waste recycling full life cycle environment influence is realized.

The invention relates to the technical field of data processing, and discloses a flue gas treatment intelligent regulation and control method and system based on artificial intelligence and a medium. The method comprises the following steps: collecting parameter data of a flue gas system through a sensor network and processing to obtain preprocessed data; using unsupervised clustering to identify working condition features and categories; constructing an emission concentration error active switching control model according to the working condition information; different working condition control parameters are optimized by adopting a differential evolution algorithm; optimized parameters are input into a controller, and modes are dynamically switched according to pollutant errors. The control strategy can be automatically switched according to the working condition characteristics of the flue gas treatment system, the complex and changeable industrial production environment can be effectively dealt with, and the system operation cost is optimized while stable and up-to-standard emission of pollutants is guaranteed.

The invention relates to the field of thermal power generation, and discloses a thermal power boiler combustion optimization method based on multi-parameter feedback control, which comprises the following steps: acquiring key parameters such as oxygen concentration, temperature, pulverized coal concentration, air flow, pulverized coal flow and boiler load in boiler operation; establishing a distributed parameter model for describing dynamic changes of oxygen concentration, temperature and pulverized coal concentration in the hearth along with time and space; key characteristic variables representing the combustion state are extracted through multi-scale analysis; dynamically optimizing multiple parameters of different areas in the hearth based on a distributed feedback controller; a global optimization objective function is constructed, and the ratio of the air flow to the pulverized coal flow is dynamically adjusted; and the optimized control instruction is sent to a boiler system, and combustion parameters are adjusted in real time through an execution mechanism. According to the method, through distributed modeling, multi-scale analysis and feedback control, the combustion efficiency is remarkably improved, pollutant emission is effectively reduced, and the method can adapt to coal quality fluctuation and load change.

The invention relates to the technical field of environmental protection, in particular to a garbage incinerator combustion management system which comprises a data acquisition module, a data preprocessing module, a data analysis module, a decision control module and a combustion adjustment module. Wherein the data acquisition module is used for acquiring combustion data in the combustion process of the garbage incinerator; the data preprocessing module is used for preprocessing the collected combustion data; the data analysis module is used for analyzing the preprocessed combustion data in real time and judging the efficiency and the pollutant emission level of the current combustion process; the decision control module is used for generating a combustion adjustment instruction; and the combustion adjusting module is used for adjusting the air flow, the temperature and the fuel input amount in the combustion process. Through real-time data analysis and automatic adjustment and optimization of combustion parameters, efficient control over the combustion process of the garbage incinerator is achieved, the combustion efficiency is remarkably improved, pollutant emission is reduced, and the environmental protection property and economical efficiency are improved.

The invention relates to the technical field of boiler control, and discloses a gas-fired boiler starting control method and system. The method comprises the following steps: acquiring initial operation parameters such as hearth pressure, water inlet temperature, flue gas oxygen content and fuel valve opening of the gas-fired boiler, establishing a thermodynamic state model in a starting stage according to the initial operation parameters, and dividing dynamic response thresholds of different load intervals; and calculating a theoretical fuel supply rate of the combustor based on the model, generating and dynamically updating a fuel supply correction coefficient in combination with a threshold value, synchronously marking key nodes according to abnormal distribution of hearth temperature, and adjusting a spatial air distribution strategy of the combustor. And after the correction coefficient is subjected to multi-stage buffer smoothing, a fuel control instruction is output to an executing mechanism. And switching to steady state control after the boiler reaches the preset load. The method adapts to the characteristics of a power plant unit, so that the starting process is more stable, the damage to a heated surface and pollutant emission are reduced, and rapid grid connection is assisted.

The invention relates to the technical field of solid waste treatment, and discloses a waste incineration process modeling and optimizing method based on digital twinning, which comprises the following steps: S1, constructing a physical model of a waste incineration process, the physical model comprises geometric structures and physical parameters of equipment such as a garbage incinerator, a waste heat boiler, a flue gas purification system and the like, and a connection relationship among the equipment; s2, collecting real-time data in the waste incineration process, including waste components, feeding amount, combustion temperature, flue gas flow, pollutant emission concentration and the like, and transmitting the data to the digital twin platform by using a data acquisition system; by constructing a highly accurate physical model and combining with a real-time data acquisition system, the operation state of the waste incineration process can be reflected in real time on a digital twin platform. The digital twin model is optimized by using an intelligent optimization algorithm, and optimal operation parameters and control strategies can be automatically searched.

The invention relates to the technical field of boiler combustion control, in particular to a boiler combustion automatic detection control system based on a big data model. The system comprises a data acquisition module used for acquiring various parameter data in a boiler combustion process; the data processing module is used for preprocessing and fusing the collected data; the big data model module constructs a model based on the processed data, learns a combustion process rule and predicts the combustion process rule; the intelligent control module is used for generating a control instruction according to a model prediction result and performing optimization control on boiler combustion equipment; and the monitoring and alarming module is used for monitoring the combustion state and the equipment operation condition in real time, and giving an alarm and taking emergency measures when abnormity occurs. The system overcomes the limitation of traditional boiler combustion control means, realizes comprehensive and accurate monitoring, intelligent and efficient control and advanced fault prediction and treatment of the combustion process, effectively improves the combustion efficiency, reduces the energy consumption and pollutant emission, enhances the operation safety and reliability of the boiler, and is suitable for various industrial boiler application scenes.

The invention relates to the technical field of oil nozzles, in particular to an efficient atomization injection system for a direct injection oil injector in a cylinder. According to the technical scheme, an injection execution unit adopts a laser-cladded asymmetric injection hole array, fuel oil is evenly distributed in a combustion chamber through aperture gradient distribution and logarithmic spiral arrangement, and the oil-gas mixing effect is enhanced. The dynamic pressure adjusting unit is integrated with an MEMS film sensor array and a fuzzy neural network controller, a sensor can accurately monitor fuel oil pressure in real time, and the controller is based on a 7-12-3 type BP network and can rapidly and accurately adjust fuel injection pressure according to the working condition of an engine through mass data training. The fuel oil pretreatment unit comprises an ultrasonic cavitation device and a graded heating system, the ultrasonic cavitation device reduces the viscosity of fuel oil, and the graded heating system accurately controls the temperature by means of a PTC heater, a semiconductor laser and a vortex generator. The three units work cooperatively, the fuel oil atomization and combustion efficiency is greatly improved, pollutant emission is reduced, and the engine performance is remarkably improved.

The invention provides a non-point source pollutant emission flux prediction and coastal risk identification method and system, and belongs to the technical field of water environment pollution big data estimation and risk early warning. The method comprises the following steps: acquiring water quality pollution data and space-time auxiliary characteristic data of a research area, and constructing a database; analyzing a causal relationship of the candidate planar driving elements to water quality pollution by using a CCM causal analysis model; extracting features of the key planar driving elements, and converting and generating feature vectors by utilizing supervised learning; performing layer-by-layer calculation on the feature vectors based on a Transs-GRU coupling model, outputting Transform branch features and GRU branch features, performing weighted coupling, and outputting a non-point source pollution flux prediction value through a prediction layer; and obtaining the emission intensity and the emission trend of the pollutants according to the predicted value, and further dividing risk grades to realize coastal risk identification and early warning. The method effectively improves the prediction precision of the emission flux of non-point source pollutants and the precise recognition of the position and time period of a coastal risk region.