409 results about "Dynamic prediction" patented technology

The invention relates to the technical field of power system dispatching, in particular to an emerging load power supply potential improvement method based on dynamic prediction, which comprises the following steps of: acquiring emerging load power consumption, meteorological environment and power grid schedulable resource data in a target area through an Internet of Things sensing terminal, and performing two-channel modeling to obtain a new load power supply potential improvement model; a deep space-time network is used to predict a load curve, a model is established to quantify resource regulation potential, a scheduling priority list and a capacity allocation strategy are generated by means of a matching rule base according to load fluctuation and resource evaluation results, an actual scheduling effect is fed back to the prediction model, parameters are corrected through error back propagation, a closed-loop optimization link is formed, and the scheduling efficiency is improved. The method improves load prediction accuracy and resource scheduling adaptability, is suitable for emerging load power supply optimization in a novel power system, and guarantees stable and efficient operation of a power grid.

The invention relates to the technical field of drainage basin water scheduling, and discloses a drainage basin water regulation and control optimization method based on ecological element changes. The method comprises the following steps: deploying a drainage basin monitoring system, and collecting ecological element real-time data such as a hydrological parameter sequence and a remote sensing image; after the data is cleaned and converted, hydrological trend features and spatial distribution features are extracted by adopting a feature learning model, and the hydrological trend features and the spatial distribution features are fused into unified ecological representation through a cross-modal alignment mechanism; inputting the unified ecological representation into a physically constrained neural network prediction model, and outputting a water regimen dynamic prediction value; and finally, based on the predicted value, a water resource regulation and control instruction is generated and executed by using a multi-objective decision algorithm so as to optimize the watershed water circulation process. According to the method, feature extraction comprehensiveness is improved through multi-source data fusion and cross-modal analysis, prediction reliability is enhanced in combination with physical constraints, reasonable allocation of water resources is achieved by means of multi-target decision, the ecological condition of a drainage basin can be improved, and the water utilization efficiency is improved.

The invention relates to the technical field of mineral resource management, in particular to a mineral resource dynamic prediction and mining management system which comprises a data perception and fusion layer, a unified digital twinborn model, a dynamic prediction and decision intelligent agent and a visualization and interaction control layer. The data perception and fusion layer collects structured data such as geological exploration and mining environment and market unstructured data, and generates a unified space-time tensor through processing; the unified digital twinborn model generates a dynamic comprehensive mining area situation map containing resource reserve risk economic indicators through a three-dimensional convolutional neural network embedded with an attention mechanism; the dynamic prediction and decision-making agent predicts future reserves and geological risks, and constructs a dual-objective optimization model to generate an optimal mining path equipment scheduling and resource allocation scheme; and the visualization and interaction control layer presents the mining area state and the decision scheme in a three-dimensional manner and provides an interaction interface. According to the invention, the data utilization rate and decision scientificity are improved, the safety risk is reduced, and mine management intellectualization is promoted.

The invention belongs to the technical field of crossing of environmental engineering and ocean dynamics, and particularly relates to a tidal dynamics prediction method and control device for a seawater desulfurization system, and the method comprises the steps: constructing a time-space coupling prediction model fusing multi-source hydrological observation data, and introducing a nonlinear dynamic weight distribution mechanism; the influence of terrain constraint, wind stress disturbance and upstream runoff on tidal propagation is quantified in real time, and a rolling prediction sequence of tide level phase, flow velocity gradient and salinity disturbance in the next three hours is output; the prediction result drives the scheduling of a desulfurization pump set, the adjustment of spraying density and the matching of aeration intensity, and the model weight is corrected on line based on the actually measured feedback of desulfurization efficiency to form closed-loop control. By means of the technical scheme, accurate cooperation of the operation parameters of the desulfurization system and tidal dynamics is achieved, meaningless energy consumption is reduced while the desulfurization efficiency is guaranteed, and the utilization rate of the desulfurization agent and the stability of the system are improved.

The invention relates to the cross technical field of artificial intelligence and environment monitoring, and discloses an algal community structure change prediction algorithm and system based on multi-source data fusion, and the algorithm comprises the steps: obtaining water quality, weather and plankton multi-source time sequence data; performing time alignment and missing value interpolation; eliminating and screening key environment factors through recursive features; performing dynamic weighted fusion on the multi-modal features by using a space-time attention mechanism; inputting a three-layer stacked LSTM network to output future algae dominant species abundance prediction; and model parameters are corrected on line based on measured data. The system comprises a multi-source data acquisition module, a preprocessing module, a key factor extraction module, a space-time attention fusion module, a dynamic prediction module and an adaptive correction module. According to the method, the prediction accuracy and stability are remarkably improved, and algal bloom early warning and ecological regulation are effectively supported.

The invention discloses a reservoir water regimen analysis method and system based on artificial intelligence, and the method comprises the steps: collecting original signals from multi-source monitoring indexes, such as water level, flow, rainfall and water quality, carrying out the standardized conversion through employing distributed calculation nodes, and forming a unified multi-source data set; based on this, using a feature extraction network to fuse upstream rainfall and reservoir flow, extracting space-time correlation features, and determining a short-term water level change trend; historical water quality abnormal data are integrated through a sequence prediction network, a time sequence is modeled, and potential pollution risks are judged; when the risk exceeds a threshold value, dynamically adjusting the weight of the prediction model, and generating an optimized water regimen simulation scene; finally, resource scheduling logic is fused, multi-scene risks are evaluated, and an optimization management strategy is output. Through deep fusion of spatial-temporal feature extraction and dynamic prediction, accurate water regimen prediction and flood control water supply decision support are realized, and the water resource management efficiency and the pollution prevention and control capability are improved.

The invention discloses a power transmission channel forest fire risk early warning method and system based on spatial-temporal feature fusion, and relates to the technical field of risk early warning. The method comprises the following steps: constructing a power transmission channel risk grid and fusing historical multi-source data to generate a static flammability background map; the method comprises the following steps: collecting real-time visual and micrometeorological data, and after space-time alignment processing, respectively extracting visual risk features and environmental flammability trend features by using a double-branch space-time feature extraction network; inputting the static background and the dynamic characteristics into a space-time fusion module to obtain a comprehensive forest fire risk index; when the index exceeds a threshold, triggering a forest fire spreading deduction model corrected by combining a power transmission corridor effect, predicting fire spreading, evaluating a line tripping probability, and issuing a graded early warning and emergency strategy; according to the method, the defects of serious data islands and inaccurate early warning are overcome, the crossing from pure fire point monitoring to risk situation dynamic prediction is realized, and the accuracy of forest fire early warning and the intelligent level of power grid prevention and control are remarkably improved.

The invention discloses an equipment health state dynamic prediction method fusing digital twinning and multi-scale evaluation, and relates to the technical field of equipment health prediction of an active launching platform, and the method comprises the steps: laying multiple types of sensors on a mechanical structure layer, a hydraulic execution layer, an electric control layer and an environmental action layer of the active launching platform; multi-source operation data such as stress strain, pressure torque, temperature power and environment load are collected in real time, and a standardized input parameter set is formed; and constructing a multi-layer digital twinborn model, forming a multi-scale twinborn mapping matrix, calculating a healthy coupling evolution coefficient, and evaluating the stability of the multi-layer collaborative structure of the platform. Calculating a multi-scale stability coefficient, and updating the multi-scale twin mapping matrix; and calculating a dynamic prediction driving index to realize prediction reliability judgment and digital twin model self-learning updating. And a health prediction closed loop of digital twinning and physical equipment is realized.

A dynamic prediction method for the residual gas content of a pre-extraction coal seam comprises the steps that on-site working conditions are considered, parameters such as gas content changes and related geological factors and extraction factors are collected, and a dynamic monitoring data set is constructed; screening main control factors based on grey relational degree, and determining the main control factors influencing the residual gas content after extraction; a main control factor of the residual gas content is used as an independent variable, a pre-pumping dynamic correction factor is combined, a dynamic prediction model is established, and actually measured data is adopted regularly to verify and optimize parameters of the dynamic prediction model; dividing a pre-extraction working face into uniform grid units, and calculating the residual gas content of each grid based on a dynamic prediction model; an extraction blind area is judged according to a quantitative standard and is visually presented; and formulating a differentiated drilling optimization scheme according to the blind area distribution. According to the method, the residual gas content prediction model dynamically responding to mining condition changes is constructed, extraction blind area prediction and drilling optimization are achieved, the coal seam gas extraction efficiency can be improved, and gas disasters are reduced.

The invention discloses a drainage basin water body heavy metal pollution prediction system based on multi-modal attention, and the system comprises a data collection module which is used for collecting multi-modal data related to drainage basin water body heavy metal pollution, and a preprocessing module which is used for carrying out the standardization processing of the obtained multi-modal data. The multi-modal attention fusion module is used for performing feature extraction and cross-modal interaction on the preprocessed multi-modal data to generate fusion features; the dynamic prediction module is used for outputting a spatial-temporal distribution prediction result of the heavy metal pollutant concentration in the drainage basin by constructing a spatial-temporal coupled prediction model; according to the method, the cross-modal interaction accuracy is improved by dynamically focusing the key association information of the multi-modal data through the intra-modal and inter-modal attention mechanism, meanwhile, the data-driven prediction model is constructed, the prediction precision of the high-risk area is optimized through the weighted loss function, the prediction error is effectively reduced, and the prediction efficiency is improved. And high-precision dynamic prediction of heavy metal pollution under the watershed scale is realized.

The invention discloses an intelligent wheat yield prediction method and system, and relates to the technical field of agricultural information. According to the method, basic geography, climate, soil, crop physiology and agricultural management multi-source data are collected, and an input feature set is obtained through preprocessing and feature engineering; constructing an Attention-LSTM-CNN model fused with an improved attention mechanism, extracting local features through CNN, capturing time sequence association through LSTM, highlighting key contribution through an attention layer, and finishing model training in combination with an RMSE loss function and an Adam optimizer; and dynamically updating data and a prediction result in a wheat growth cycle, and outputting and triggering early warning in multiple forms. The system correspondingly comprises a data acquisition module, a data storage module, a data preprocessing module, a model calculation module, a prediction output and early warning module and a communication module. The method solves the problems that a traditional method is single in data, poor in model adaptability and lack of dynamic prediction, high-precision full-period prediction is achieved, and scientific support is provided for agricultural decision making.

The invention relates to the technical field of coast engineering, in particular to an estuary sandy coast erosion and deposition simulation system based on the coupling effect of flood peak runoff, waves and tide, which comprises a training data generation module, an intelligent agent model construction module, a dynamic prediction engine module and a scene evaluation module. The training data generation module is used for outputting a hydrodynamic state field, a wave characteristic field, a bed surface shear stress field and a bed surface elevation variable quantity by utilizing a traditional numerical model to construct a training data set; the intelligent agent model building module is based on a deep learning network and introduces physical constraint loss function training to obtain an intelligent agent model; the dynamic prediction engine module loads the trained deep learning network to realize bed surface elevation variation prediction and update terrain boundary conditions; and the scene evaluation module executes erosion and deposition evolution simulation according to different hydrological boundary condition combinations and extracts terrain evolution data to quantitatively evaluate the coast stability and the channel deposition risk. The invention relates to the field of estuary dynamic landform simulation and coast engineering.

The invention belongs to the technical field of environment regulation and control, and discloses a livestock breeding environment dynamic prediction and regulation and control method based on machine learning. Comprising the following six key steps: firstly, preprocessing multi-source data, and constructing a standardized feature data set; secondly, performing time sequence feature extraction and environment-biological response correlation analysis to obtain a multi-factor interaction network; then, constructing a dynamic coupling model of the environmental parameters and the production efficiency; a multi-target reinforcement learning regulation and control model is constructed based on the animal husbandry production efficiency prediction space; predictive environmental parameter regulation and risk assessment are carried out in combination with real-time monitoring data; and finally, intelligent environment intervention is executed, and a regulation and control strategy is iteratively optimized through production index feedback. According to the method, the breeding environment is converted from passive response to active prediction, complex correlation understanding between environmental factors and biological indexes is established, and multi-target balance optimization of production efficiency, energy consumption and animal welfare is achieved.

The invention discloses an intelligent fishery environment sensing and self-adaptive intelligent adjustment method and system, and belongs to the technical field of intelligent fishery and intelligent environment regulation and control. The method comprises the following steps: acquiring multi-dimensional data such as water quality, weather and fish school behaviors through an environment sensing module, processing the multi-dimensional data through a data standardization and treatment module, extracting key features by using an environment-behavior feature engineering module, and performing state assessment and trend prediction through a coupling modeling and risk assessment module; a regulation and control strategy is generated and executed in combination with a multi-objective optimization and execution arrangement module, and finally, through feedback and optimization of a closed-loop evaluation and adaptive learning module, accurate sensing, dynamic prediction and intelligent adjustment of a fishery environment and a fish school state are realized, the stability of a breeding environment and the health level of a fish school are effectively improved, and environmental risks are reduced.

The invention discloses a hydrogen leakage accident holographic perception and disaster situation dynamic prediction system in a tunnel scene, and belongs to the technical field of hydrogen energy traffic safety and intelligent risk management. According to the system, multi-modal information such as environmental parameters, hydrogen concentration, heat source temperature, leakage acoustic characteristics and vehicle states is collected in real time through a multi-modal sensing unit; abnormal detection, time synchronization and space registration are carried out through the information fusion module to generate fusion data in a unified format; the hazard source analysis module locates a leakage source, estimates a leakage rate, locates an ignition source and extracts disaster characteristics based on the fused data; the disaster dynamic prediction module predicts spatio-temporal evolution of diffusion, combustion and explosion by using an agent model and calculates disaster levels; the feedback early warning unit synchronously outputs the prediction information to the tunnel monitoring platform and issues early warning information; and the data storage and self-optimization module uniformly stores historical data and optimizes the prediction model through incremental learning. The system realizes real-time monitoring, rapid evaluation and intelligent disposal of tunnel hydrogen leakage accidents.

The invention belongs to the field of computer application technology and geological disaster prediction and early warning, and particularly discloses a landslide real-time prediction method and early warning method based on multi-modal information fusion, and the prediction method comprises the steps: constructing a landslide event text data set; a BERT-BiLSTM-CRF model is trained after time-space element labeling, and a structured historical landslide knowledge graph is constructed; using the landslide boundary training data set to train a U-Net + + image segmentation model to identify a landslide space boundary; taking time and space information as space-time anchor points, combining with a U-Net + + image segmentation model identification result, and identifying a landslide occurrence range and time on a remote sensing cloud platform; and extracting a multi-dimensional dynamic environment factor by combining a landslide occurrence range and time, constructing a time sequence feature sample set, and training a landslide susceptibility prediction model to realize dynamic prediction of landslide risks. According to the invention, timeliness and space precision are taken into consideration, and prediction accuracy and response capability of landslide disasters can be effectively improved.

The invention relates to the technical field of geological early warning, in particular to a dangerous rock mass instability analysis method, system and equipment based on a space-time diagram neural network, by fusing unmanned aerial vehicle LiDAR, multispectral data, meteorological radar data and the space-time diagram neural network (ST-GNN), the system realizes sub-meter spatial resolution and minute-level time response, and the stability of dangerous rock mass instability analysis is improved. The four-dimensional (time and space) analysis result of the instability probability of the dangerous rock mass is obtained through high-precision space-time modeling, the problems that a traditional geological disaster early warning system is low in resolution ratio, slow in response and high in misinformation are solved, the comprehensiveness, accuracy and reliability of instability prediction of the dangerous rock mass are improved, and the early warning effect is good. And full-chain intelligent closed-loop management of real-time data acquisition-dynamic prediction-early warning push-feedback optimization is supported, the emergency decision time is shortened by real-time rainfall superposition risk thermodynamic diagrams, and the attenuation rate of long-term prediction precision is reduced by dynamically fusing newly added geological data and instability events through incremental learning.

The invention discloses a soil fertilizer nutrition customization regulation and control method and system. The system comprises a data acquisition module, an environment compensation module, a soil nutrient dynamic prediction module, a target demand management module, a multi-target optimization module, a safety evaluation module, a monitoring self-adaptive learning module and the like, soil nutrition and safety indexes and external environment factors are acquired, data are corrected by using a compensation function, nutrient changes are predicted, a target range is set according to different growth stages of crops, and a monitoring self-adaptive learning module is established. A multi-objective optimization model is established based on organic fertilizer contribution and missing nutrients, an optimal formula is generated by comprehensively considering crop requirements, cost and environmental risks, and the model is dynamically updated through continuous monitoring and feedback closed loop after fertilization. According to the method, the fertilizer utilization rate and the fertilization precision can be remarkably improved, the soil acidification and heavy metal risks are reduced, and the balance of yield, quality and ecological safety is achieved.

The invention discloses a field crop full-period planting management system and method based on digital twinning. Comprising a multi-source sensing and data acquisition module, a crop-environment digital twinborn modeling module, a full-cycle growth state evaluation module, an intelligent planting decision generation module, a precise execution and process control module, a dynamic growth prediction module and a twinborn model iteration and optimization module. According to the method, a space-time alignment algorithm and a mapping precision function are introduced, dynamic association of physical and virtual environments is constructed, it is ensured that a virtual model can accurately reflect the real state of crops and the environment, and a reliable basis is provided for management decision making; a management scheme adaptive to a growth period is deduced and generated based on twinborn simulation, advanced intervention is realized in combination with dynamic prediction, decision logic is continuously optimized through a model iteration mechanism, and scientificity and adaptability of decision are improved.

The invention discloses an intelligent fishing point dynamic prediction method and system based on multi-source marine environment data fusion. The method comprises the following steps: step 1, access, space-time alignment and pre-screening of multi-source heterogeneous marine environment data; step 2, priori knowledge base construction and suitability modeling based on target fish ecological habits; 3, constructing a fishing point prediction model fusing the multi-time-sequence environmental characteristics and deep learning; step 4, fusing two-channel prediction results under the Bayesian framework and quantifying uncertainty; 5, generating a dynamic mask of a real-time sea condition safety threshold value and fishery regulation space constraint; 6.1, constructing a comprehensive scoring function of the risk perception function. According to the method, multi-source heterogeneous data is constructed, a target fish ecological suitability model and a depth time sequence prediction model are combined, the fishing point posterior probability is generated through a Bayesian fusion mechanism, the prediction uncertainty is quantified, and dynamic fishing point recommendation with risk perception and compliance safety is realized.

The invention provides a penaeus vannamei quality prediction and grade evaluation method and system, and belongs to the field of penaeus vannamei cold-chain transportation process quality prediction, and the method comprises the steps: S1, constructing a penaeus vannamei data set, and carrying out the dynamic time-varying coupling correlation graph generation, convection-diffusion cooperative information propagation, multi-task joint optimization and end-to-end MLP prediction to obtain a penaeus vannamei data set; obtaining a prediction result of the quality of the penaeus vannamei; s2, splicing is performed according to the prediction result and the actual quality data, and a feature matrix is constructed; clustering the feature matrix by using a rule fusion K-means + + clustering method to obtain an optimal clustering center set; and S3, based on the prediction result and the clustering center set, carrying out dynamic prediction and evaluation on the quality of the penaeus vannamei in the refrigeration state in a period of time in the future. According to the invention, an intelligent solution is provided for quality monitoring in cold-chain logistics, and economic loss caused by quality deterioration is significantly reduced.

The invention provides a bimodal substation foundation settlement deformation dynamic prediction method and system, and relates to the technical field of foundation settlement, and the method comprises the steps: deploying a bimodal sensing monitoring network on a target substation to collect foundation mechanical deformation data and foundation deformation positioning data; building a foundation deformation data processing channel to carry out preprocessing and weighted fusion on the foundation mechanical deformation data and the foundation deformation positioning data to obtain foundation settlement deformation fusion data; and training a foundation settlement deformation prediction network to dynamically predict the foundation settlement deformation fusion data, determining a foundation settlement deformation prediction quantity, and triggering a settlement abnormity response mechanism based on the foundation settlement deformation prediction quantity to carry out graded response early warning. The technical problems that in the prior art, transformer substation foundation settlement risk identification lags behind, the disposal efficiency is low, and safe and stable operation of a transformer substation is difficult to guarantee can be solved, and the technical effect of guaranteeing long-term safe and stable operation of the transformer substation is achieved.

The invention relates to the technical field of power system load prediction, in particular to a multi-modal Bayesian optimization short-term power load dynamic prediction method, which specifically comprises the following steps: collecting multi-source data, and preprocessing the data; decomposing a load trend module, a season module, a special event module and other component modules by using a Neuralprophet model; constructing a CNN-LSTM model, extracting meteorological-load spatial features, and modeling time sequence dependence; fusing multi-model prediction results based on a belief function theory BFT framework; and combining Bayesian optimization to dynamically adjust fusion weight and quality interval parameters, and minimizing a prediction error. According to the method, high-precision and high-robustness load prediction is realized by fusing deep spatial-temporal feature modeling, a probability decomposition framework and dynamic parameter optimization. The method is suitable for real-time scheduling and optimization management of a micro-grid, a power distribution network and an integrated energy system, and helps an electric power company to optimize resource configuration, reduce cost and improve power supply reliability.

The invention provides a coal mine rock burst dynamic early warning method and device based on microseism. According to the method, dynamic prediction and real-time early warning of the rock burst risk can be realized, the adaptability of the early warning model to the mining process and the geological condition change is improved, the prediction hysteresis is effectively reduced, and the early warning accuracy is improved.

The invention discloses a thermal power generating unit coordinated optimization control method based on model prediction, and particularly relates to the field of thermal power generating unit coordinated optimization control, and the method comprises the steps: S1, collecting the real-time operation parameters of a thermal power generating unit through a sensor network, carrying out the preprocessing of the real-time operation parameters, and building a unit operation state database; s2, constructing a machine-furnace dynamic prediction model based on the operation state database, adopting a state-space equation to describe and predict the parameter trend in the next 60s, and verifying the precision; s3, an optimization objective function containing multi-parameter deviation and energy consumption items is constructed according to the prediction trend, the optimal control quantity is solved in a rolling optimization mode every 10 s, and the control quantity is constrained by a rated parameter range; according to the method, the dynamic prediction model of the machine furnace is constructed and combined with the rolling optimization algorithm, the parameter change trend is predicted in advance to realize multi-variable cooperative control, and the advantages of reducing the parameter fluctuation range and improving the unit operation stability under the variable load working condition are achieved.

The invention discloses a biomass gas calorific value dynamic prediction and gas distribution optimization method and system, and relates to the technical field of biomass gas, and the method comprises the steps of data preprocessing and dynamic feature extraction, calorific value dynamic prediction, optimization target setting, gas distribution optimization calculation and output and implementation control. According to the method, the change trend of the calorific value is accurately pre-judged, uncertainty is evaluated and predicted with assistance of a confidence interval, a solid and reliable data basis is provided for optimization decision, the problem of low combustion efficiency caused by fluctuation of gas source components is effectively avoided, multi-target comprehensive optimization is realized, dynamic constraint conditions are set, and an optimal optimization target set is generated; the optimal gas distribution proportion is solved, and closed-loop control is formed through real-time feedback; according to the method, the spanning of biomass gas distribution from passive response to active predictive optimization is realized, and multiple remarkable beneficial effects are brought: firstly, the stability and controllability of a gas heat value are greatly improved;

The invention discloses a dynamic prediction method for a deformation joint of an underground comprehensive pipe gallery. The method comprises the following steps: S1, collecting and fusing multi-source monitoring data; s2, data preprocessing and feature engineering; s3, constructing a dynamic prediction model; s4, dynamic prediction and performance evaluation; s5, early warning and decision support are carried out, in the step S1, temperature field data including the air temperature in the pipe gallery, the concrete temperature (inner and outer surfaces) of the pipe gallery structure and the soil temperature outside the pipe gallery are acquired, and the measuring range ranges from-40 DEG C to 80 DEG C; according to the method, a multi-source monitoring data acquisition and dynamic prediction model system is established, continuous monitoring and trend prediction of the deformation joint of the underground comprehensive pipe gallery are achieved, multi-dimensional data such as the temperature field, the soil pressure and the underground water level are integrated, time sequence analysis is combined, and the deformation joint of the underground comprehensive pipe gallery is obtained. The development rule of the deformation joint can be accurately captured, and a prospective evaluation basis is provided for the safety of a pipe gallery structure.

The invention discloses an optimal selection method and system for a water-drive sandstone reservoir development scheme, and belongs to the technical field of reservoir development. Natural languages input by a user are converted into input parameters corresponding to a reservoir dynamic prediction network model through a large language model, and multiple groups of production schemes are obtained; inputting production dynamic data and well trajectory information corresponding to different production schemes into the updated reservoir dynamic prediction network model to obtain yield prediction results of the different production schemes; according to the oil reservoir domain knowledge map, the yield prediction results of the different production schemes are used as attribute information to be associated to a well entity, a horizon entity and a scheme entity in the oil reservoir domain knowledge map for reasoning and searching, and multiple sets of candidate development schemes are generated; and performing multi-target optimization on the multiple groups of candidate development schemes to determine an optimal oil reservoir development scheme. According to the method, the potential relation and influence between the schemes can be mined, and the candidate schemes better conforming to the actual condition and the development target of the oil reservoir are screened out.

The invention discloses an intelligent prediction method for the stirring power consumption of a reaction kettle, and the method comprises the steps: collecting and calibrating the fluid physical property and process operation parameters in the reaction kettle in real time through a high-precision sensor array and an automatic system, carrying out the multi-stage data cleaning, denoising, normalization and time alignment, generating standardized high-quality data, and carrying out the intelligent prediction of the stirring power consumption of the reaction kettle. Based on physical property mapping, dimension reduction and feature fusion, extracting a low-dimensional fluid physical property descriptor, inputting the low-dimensional fluid physical property descriptor into a deep regression network in combination with process parameters, carrying out stirring power consumption modeling and dynamic prediction, and carrying out dynamic prediction by introducing a self-adaptive physical property embedding mechanism and distribution alignment training. The self-adaption and generalization ability of the model to different and unseen fluid physical property distributions is improved, and the accuracy, the stability and the engineering decision support ability of energy consumption prediction under complex working conditions are remarkably enhanced.

The invention discloses a flight path tracking method and device based on a dynamic prediction step length, a medium and a product, and the method comprises the steps: obtaining the real-time operation state information of a current moment through a sensor borne by a platform in the navigation process of a water surface unmanned platform; according to the real-time operation state information and a pre-constructed interference observer, estimating ocean unknown time-varying interference at the current moment; calculating a curvature time function according to a real-time state and a reference path, obtaining a path curvature through filtering, and dynamically adjusting a prediction step length according to the path curvature; on the basis of a model prediction controller, the motion state of multiple moments in the future is predicted by combining the real-time state, the interference estimation and the dynamic prediction step length; solving the current optimal command rudder angle by using the optimization function, and implementing control; and in each control period, the real-time state of the platform is updated, the execution is cyclically performed until the flight path tracking is completed, and the technical scheme of the embodiment of the invention effectively improves the accuracy and the adaptive control capability of the flight path tracking of the water surface unmanned platform under the complex sea condition.