1349 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.

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 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 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 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 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 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 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 discloses a unit full-load real-time intelligent optimization control system based on a complex coal type mode, which comprises a data acquisition and processing module, a coal quality analysis module, a load prediction and optimization module, a combustion control module, a fault diagnosis and early warning module, a man-machine interaction module and a central control unit, the modules and the units are connected through a high-speed communication network, so that real-time transmission of data and issuing of control instructions are realized; the unit full-load real-time intelligent optimization control system can monitor the running state of a unit in real time, formulate an optimal control strategy according to coal quality changes and load requirements, achieve efficient combustion and reduce pollutant emission, timely discover potential faults and send out early warning information through a fault diagnosis and early warning module, and improve the working efficiency of the unit. And safe and stable operation of the unit is ensured.

According to the incineration pollution cooperative control method based on multi-target reinforcement learning, a multi-modal sensor array is deployed in a hearth, a flue and a fly ash processing unit, multiple parameters such as combustion temperature and flue gas retention time are collected in real time to construct a high-dimensional dynamic characteristic matrix, and data collection and processing are carried out to meet subsequent model requirements. Based on the dynamic characteristic matrix, defining three types of objective functions of pollutant emission minimization, fly ash metal recovery rate maximization and resource cost minimization, adopting a depth deterministic strategy gradient algorithm to construct a multi-objective reinforcement learning joint optimization model, and taking a combustion air ratio and the like as an action space; and strategy exploration and value evaluation are realized through a dual-network structure. Meanwhile, a priority evaluation module based on fuzzy logic is constructed, the weight coefficient of an objective function is dynamically adjusted according to real-time working conditions, and dynamic weight distribution and multi-objective collaborative optimization control are achieved. According to the method, multiple targets in the waste incineration process can be effectively balanced, the incineration efficiency and the resource utilization level are improved, and pollutant emission is reduced.

The invention belongs to the technical field of environmental protection, and particularly relates to a pollution reduction and carbon reduction technology collaborative evaluation method, which comprises the following steps: S1, calculating the collaborative degree of a technical path on multiple pollutants and carbon emission through a collaborative effect coefficient model, the synergistic effect coefficient model calculates a synergistic effect coefficient based on pollutant emission reduction, greenhouse gas emission reduction and a preset weight coefficient; s2, calculating a cooperative control emission reduction equivalent ER-eq based on an equivalent weight coefficient, wherein the equivalent weight coefficient comprises a greenhouse gas weight coefficient and a pollutant equivalent weight coefficient; and S3, on the basis of the cooperative control emission reduction equivalent ER-eq, cooperative emission reduction cost EC-eq is calculated through an economic cost model, and the economic cost model comprises project investment cost, operation cost and energy-saving and efficiency-increasing benefits. According to the method, the collaborative emission reduction effect of the measures can be comprehensively and visually reflected, the collaborative emission reduction cost is accurately estimated, a scientific decision basis is provided for policy formulators and enterprises, and the method is successfully applied to pollution and carbon reduction projects in the automobile industry.

The invention relates to the field of power systems and automation thereof, in particular to a multi-energy system collaborative operation optimization method based on improved particle swarm optimization and a related device. Modeling according to the operation characteristics of the multiple energy systems to obtain a multi-energy system model; optimizing the multi-energy system model based on particle swarm optimization, and calculating and generating an energy scheduling result by taking system operation cost, energy utilization efficiency and pollutant emission as optimization targets; and distributing output priorities of different energies according to an energy scheduling result, and generating an energy storage system operation strategy based on the output priorities. According to the invention, the comprehensive power system model of photovoltaic, wind power, hydroelectric, thermal power and energy storage systems is constructed, and according to the operation characteristics of each energy device, the physical characteristics and dynamic interaction relationships of different energy sources can be accurately simulated. According to the model, equipment constraint conditions and real-time operation parameters are fully considered, high-precision basic data are provided for optimal scheduling, and the practicability and reliability of the model are remarkably enhanced.

The invention provides a thermal power combustion optimization control method and system based on deep learning, and relates to the technical field of thermal power combustion optimization control, and the method comprises the steps: recognizing an abnormal scene based on a maximum average difference criterion and a multi-kernel density estimation method, and carrying out the sampling of operation data according to the abnormal scene. A mutual information maximization criterion is adopted to carry out dynamic quantization coding on a sample, and a recurrent neural network is utilized to extract a working condition feature vector. And evaluating a system state according to the working condition feature vector, screening control actions meeting constraint conditions through an adversarial learning network, and calculating strategy gradient optimization to obtain an optimal control strategy. And finally, decomposing the optimal strategy into sub-strategies, constructing a distributed consistency coordination model based on a fuzzy decision tree, determining an optimal execution time sequence, and correcting a control signal through a self-adaptive dead-zone compensator to realize optimal control of thermal power combustion. The thermal power generating unit operation stability and economy can be effectively improved, and pollutant emission is reduced.

The invention relates to the technical field of intelligent monitoring of the waste incineration process, in particular to an intelligent monitoring method and system for the waste incineration process and electronic equipment. The method comprises the following steps: (1) collecting multi-dimensional historical data, and cleaning and normalizing the multi-dimensional historical data; (2) constructing a combustion process model by using a convolutional neural network (CNN) and a recurrent neural network (RNN), and weighting time sequence features through an attention mechanism; (3) introducing a transfer learning technology, and reducing cross-scene distribution difference based on a domain adaptive algorithm; and (4) fusing reinforcement learning to dynamically adjust equipment parameters, and giving consideration to thermal efficiency and pollution suppression targets by a reward function. According to the system, an edge-cloud collaborative architecture is adopted, the edge nodes complete real-time calculation, and the cloud executes model iteration. The incineration stability is remarkably improved, pollutant emission is reduced, and the method is suitable for complex working conditions.

An AI-connected-electrified (ACE) heavy truck system equipped with an intelligent multi-mode hybrid (iMMH) powertrain system and a vehicle supervision control strategy based on machine learning (ML) or reinforcement learning (RL) paradigm are presented. This system ensures industry-leading power and braking performance of the ACE heavy truck while automatically optimizes both energy saving and emission reduction based on the vehicle's dynamic driving data and 3D electronic map information of roads for any transport event. In this application, the conventional analog electronic control (AEC) method is replaced by a novel digital pulse control (DPC) method on the instantaneous power function of the engine. The DPC method converts the complex surface working conditions of the AEC engine of the hybrid vehicles into simpler pre-defined working condition lines of the DPC engine. Consequently, the multi-variable nonlinear technical problem of simultaneously optimizing real driving environment (RDE) fuel consumption and pollutant emissions of the ACE heavy truck is simplified into two decoupled quasi-liner optimization problems, and ensures the reduction of on-vehicle computing resources for real-time AI inference computation and the improvement of the optimal performance, the convergent rate, and the robustness of the corresponding fuel-saving algorithm for the trained ML model or the learned RL model. Ultimately, the ACE truck achieves in the engineering sense the global minimum RDE fuel consumption and pollutant emissions meeting the standard consistently at high performance to cost ratio for any transport event and the RDE fuel consumption is decoupled from the vehicle configuration parameters and the human driver.

The invention provides a carbon tank aging diagnosis method and device, a vehicle and a storage medium, and the method comprises the steps: determining a first theoretical fuel oil vapor adsorption amount of a carbon tank according to the fuel oil basic evaporation amount and evaporation time of fuel oil; when a preset carbon tank desorption condition is met, the carbon tank is controlled to execute desorption operation; in the process that the carbon tank executes desorption operation, the actual fuel steam desorption amount of the carbon tank is determined according to the desorption flow of the carbon tank, the air flow of a throttle valve, the fuel injection amount and the actual air-fuel ratio; and determining a carbon tank aging diagnosis result according to a comparison result of the first theoretical fuel oil steam adsorption amount and the actual fuel oil steam desorption amount. As whether the carbon tank is aged or not is diagnosed, a user can know the aging condition of the carbon tank in time, so that the aged carbon tank can be maintained or replaced in time, and the problem that the emission of evaporative pollutants of a vehicle exceeds the standard is avoided to a certain extent.

The invention relates to the technical field of water environment management, in particular to a reservoir ecological scheduling method and system considering water quality dynamic response. The method comprises the following steps: acquiring reservoir water quality monitoring data; performing water eutrophication analysis according to the reservoir water quality monitoring data to obtain water eutrophication data; pollution source tracking is carried out based on the water eutrophication data to obtain pollution source data; the pollutant emission intensity is evaluated based on the pollution source data; a high-pollution area is calibrated according to the pollutant emission intensity; carrying out algae density detection according to the high-pollution area to obtain algae density data; performing water algal toxin analysis according to the algae density data to obtain water algal toxin data; and obtaining reservoir hydrological data, and carrying out maximum rainfall feature extraction to obtain maximum rainfall data. The response rate and the pollution risk identification rate of reservoir water quality dynamic scheduling are improved based on the water environment management technology.