588 results about "Dynamic prediction" patented technology

The invention relates to a water quality change trend rapid prediction method based on multi-source data fusion and physical constraint, and the method specifically comprises the following steps: 1, synchronously collecting spectral information, DO, COD, temperature, pH and other data at a key monitoring station, constructing a hydrodynamic water quality coupling equation, simulating the spatial-temporal dynamic distribution of water quality parameters, and calculating the water quality change trend; 2, outputting a water quality sensitive area through a hydrodynamic force-water quality model, screening sensor layout point positions in combination with information entropy evaluation and spatial clustering, realizing low-cost water quality sensor network deployment through a multi-objective optimization algorithm, and calculating a water body global water quality distribution diagram by adopting a spatial interpolation method, 3, synchronously collecting spectral information according to key monitoring sites, analyzing main pollution sources, and adopting a principal component analysis and attention mechanism neural network; 4, based on real-time optical characteristic value-DO data, in combination with a spatial topology network, a water quality gradient and a cross-regional covariance, capturing water quality parameter spatial correlation among different sites, and determining the water quality parameter spatial correlation among different sites; an optical characteristic value-DO-COD dynamic prediction model is constructed; a COD predicted value is corrected by combining pollution traceability and spectral characteristics, and multi-source data is assimilated by adopting ensemble Kalman filtering, so that the model precision is improved.

The invention belongs to the technical field of fishery resource informatization management and ecological prediction, and particularly relates to a dynamic prediction method of a resource habitat dynamic prediction system based on multi-source heterogeneous data fusion, and the method comprises the steps: a multi-source heterogeneous data collaborative collection and standardization processing module synchronously collects cross-regional data, and generates a time-space aligned standardized data set; the multi-modal habitat adaptability evaluation and prediction model is used for receiving the standardized data set as input, constructing environmental, biological and social modalities based on multi-source fusion data, and generating a habitat adaptability prediction result; the three-dimensional dynamic visualization and decision support platform is used for receiving the habitat suitability prediction result and generating a habitat thermodynamic diagram, a resource abundance gradient and an environmental parameter dynamic visualization display and decision under multiple spatial and temporal scales; according to the method, an international data collaboration mechanism, an ecological niche model optimization algorithm and a lightweight visualization engine are subjected to system-level integration, and an engineering solution is provided for biological resource protection of a sea area.

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 sewage treatment, and particularly discloses a sewage and wastewater treatment control system and method based on an intelligent optimization algorithm. Water quality data are collected through a data acquisition module, and a water quality characteristic matrix is generated through preprocessing. And the prediction module analyzes the feature matrix by using the trained water quality dynamic prediction model to obtain a water quality prediction result. And processing the prediction result by using a multi-objective optimization algorithm to obtain an initial control parameter. And the parameter optimization module calculates a water load fluctuation ratio, a model confidence coefficient and an equipment state according to the sewage and wastewater treatment data, inputs the water load fluctuation ratio, the model confidence coefficient and the equipment state into the adaptive fuzzy network and generates a multi-target parameter optimization suggestion. And the dynamic optimization module adjusts the multi-objective optimization algorithm parameters according to the parameters, and processes the prediction result again to obtain optimization control parameters. And the control module regulates and controls sewage and wastewater treatment according to the optimized parameters. The system realizes closed-loop management from data acquisition, prediction and optimization to control, can dynamically adapt to water quality change, and operates stably and efficiently.

The invention provides a flood peak evolution path and dam break risk early warning method and system. According to the method, a time-space coupling water conservancy data set is constructed by fusing multi-dimensional water conservancy monitoring data and regional rainfall prediction information, and a basin topology perception model is established. And further performing mode matching on the parameters and a historical dam break event characteristic spectrum, and generating a dynamic response strategy set including a flood storage and detention area capacity allocation scheme by correcting a space weight of a matching result. And finally, based on a watershed topology perception model, matching the strategy set with historical dam break features, realizing accurate identification of a flood peak evolution path and graded early warning of dam break risks, outputting a water conservancy analysis report containing risk grades, and realizing closed-loop management from data fusion and dynamic prediction to risk decision. According to the technical scheme provided by the invention, the efficiency and accuracy of intelligent analysis of the water conservancy data can be improved.

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 provides an agricultural environment monitoring method and system based on the Internet of Things. According to the method, multi-source data weights are dynamically distributed through a soil sensor array connected with Internet of Things nodes, and a global soil parameter set is generated; deploying a micro-electro-mechanical micro-fluidic chip in the coverage area to carry out soil solution selective permeation, converting the target ion concentration into an electric signal, and synchronously transmitting the electric signal and soil parameters; historical time series data are extracted, coherence is established through time correlation analysis, and undeployed area data are filled in combination with a spatial interpolation algorithm to construct a dynamic prediction model; according to the nutrient space change trend output by the model, crop growth requirements are matched to generate a fertilization amount adjustment instruction, and the fertilization amount adjustment instruction is issued to field fertilization equipment through the Internet of Things to execute dynamic regulation. Space-time precise sensing of soil nutrients and self-adaptive fertilization regulation and control are realized, and the utilization efficiency of agricultural resources and crop growth sustainability are improved.

The invention provides a new energy output prediction method and system based on Monte Carlo Dropout, and the method comprises the steps: carrying out the probabilistic prediction of new energy output through a Monte Carlo Dropout technology, generating a dynamic prediction result containing a confidence interval, and quantifying the uncertainty of meteorological sudden change and equipment state; secondly, constructing a multi-stage random dynamic programming model, discretizing a prediction interval into multi-scene input, designing a non-linear objective function based on the discharge depth, and synchronously optimizing the electricity purchase cost and the energy storage aging cost; and finally, realizing rolling optimization of the system in combination with a model prediction control framework, and dynamically adjusting an energy storage aging cost weight by updating prediction data and a scheduling instruction on line and embedding an energy storage health state real-time feedback mechanism. The photovoltaic and wind power consumption rate can be remarkably improved, the full life cycle cost of an energy storage system is reduced, and meanwhile, the robustness of a scheduling strategy in extreme weather is ensured.

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 discloses an agricultural product cold chain carriage temperature control method based on space equalization, and relates to the technical field of intelligent cold chain logistics, and the method comprises the steps: collecting carriage three-dimensional point cloud data and agricultural product electronic tag information, building a carriage loading three-dimensional model, carrying out the space grid division, and generating a partition topological feature vector; collecting temperature, humidity and carbon dioxide concentration data, calculating average temperature and average gas concentration observation values, and generating a breathing heat estimation value in combination with the partition topological feature vector; inputting the breathing heat estimation value into a dynamic prediction model, generating a subarea cold load curve and a priority weight by combining the subarea temperature deviation, and outputting a subarea cold load sequence; and according to the partition cold load sequence, generating a local execution proposal, constructing a global optimization problem taking the space temperature balance degree as an optimization target, and generating a global control plan. According to the method, global temperature equalization and energy consumption collaborative optimization are realized through network reasoning and dynamic optimization of the meta-reinforcement learning strategy.

The invention provides a carbon emission accounting method and system based on an artificial intelligence technology, and belongs to the field of carbon emission. According to the technical scheme, the method comprises the following steps: constructing a sensor network of an equipment operation state, obtaining real-time operation data, and preprocessing the real-time operation data through an edge computing node; establishing a dynamic prediction model of emission factor boundary parameters based on the real-time operation data; according to the dynamic prediction model, constructing a carbon emission propagation map, analyzing the spatial and temporal distribution trend of carbon emission intensity, and identifying the position of an abnormal emission source; according to the abnormal emission source position identification result, carbon emission trend prediction is executed, and emission early warning response is triggered in combination with a preset threshold value. The method has the beneficial effects that dynamic prediction of carbon emission factors, identification of emission abnormal sources and trend early warning response are realized, the real-time modeling and space-time propagation analysis capabilities of the system on carbon emission behaviors are enhanced, and the intelligent level and response efficiency of emission management are effectively improved.

The invention provides a ground source heat pump buried pipe system design method based on building load change, comprising the following steps: S1, acquiring geological survey data, meteorological data and building load data, and presetting buried pipe heat exchange system parameters; s2, establishing a multi-physics field coupling numerical model; s3, transient simulation is carried out through a multi-physics field coupling numerical model, and system short-term thermal response characteristic data corresponding to each candidate length are acquired and stored; s4, constructing and training a long-term dynamic performance prediction model through the long and short-term memory network model; and S5, based on a multi-objective optimization algorithm, solving a multi-objective optimization problem, obtaining a group of Pareto optimal buried pipe total length solution sets, and selecting a final buried pipe optimal total length from the optimal solution sets. According to the method, the design precision of the ground source heat pump buried pipe can be remarkably improved, the long-term dynamic prediction capacity is achieved, the reliability, economical efficiency and environment friendliness of long-term operation of a ground source heat pump system can be guaranteed, and the design efficiency and reliability are improved.

The invention discloses a forest-town junction region fire dynamic early warning method based on multi-source data assimilation, and the method comprises the following steps: inputting collected multi-source observation data into a fire risk prediction model for training, and outputting a fire risk grid map covering the whole forest-town junction region for monitoring a high-risk building group; constructing a fire spreading numerical model covering multi-material, multi-scale and multi-physical mechanism coupling; a fire propagation characteristic quantitative model is constructed, and the building heat influence is evaluated; and constructing a data assimilation and fire dynamic prediction model, and dynamically predicting and outputting fire spreading parameters and a spreading trend chart. According to the method, a forest-building-oriented complex scene is constructed, and propagation behaviors of flames under multiple mechanisms of convection, radiation and conduction are effectively described; through the modes of risk identification, dynamic simulation, data assimilation, visual early warning and the like, the precision, response speed and engineering applicability of forest-town boundary region fire prediction are remarkably improved.

The invention discloses a planting optimization method and system based on crop yield estimation in a saline-alkali land area, and relates to the field of crop yield prediction and planting optimization, and the method comprises the steps: data collection and fusion processing; performing dynamic prediction on soil water and salt migration, and realizing water and salt space-time distribution prediction by adopting a space-time cubic modeling self-adaptive model combined with physical correction; crop salt tolerance modeling: establishing a multi-interval salt tolerance model based on physiological response fitting and threshold segmentation, and introducing covariable correction; carrying out migration salt tolerance coupling modeling, introducing a salinity memory factor and a stage sensitivity regulation factor, and dynamically simulating growth response of crops under salinity stress; and crop yield estimation and planting optimization: generating a regional yield prediction and sowing configuration scheme based on coupling model output, and forming an optimization strategy in combination with measures such as irrigation and fertilization. According to the method, model-driven integrated optimization from yield estimation to planting decision of crops in the saline-alkali region is realized, and the method has high precision and wide applicability.

The invention relates to the technical field of power distribution network dispatching. By providing a power grid load dynamic prediction and optimal scheduling method, device, equipment and medium, the method comprises the following steps: performing multi-source heterogeneous fusion processing on meteorological parameters, historical load curves and new energy output data to generate a dynamic load prediction map; constructing a dynamic network model, and performing power flow distribution simulation processing based on the dynamic network model to obtain a stability margin calculation result and a preset safety threshold boundary; performing stage decomposition processing on the global scheduling target to generate a progressive scheduling stage sequence; performing matching processing on the response characteristics of the power generation equipment to generate a self-adaptive progressive scheduling instruction sequence; and executing an adaptive progressive scheduling instruction sequence, performing feedback processing on the real-time state of the power grid, and generating a dynamic adjustment instruction so as to realize multi-dimensional data association modeling, stability margin quantitative analysis and dynamic instruction optimization, thereby improving the load prediction precision and reducing fault diffusion and transient oscillation.

The invention proposes a flood disaster dynamic prediction method based on multi-source remote sensing data and a knowledge graph, and relates to the field of data prediction, and the method comprises the specific steps: firstly, building a space-time disaster dynamic model through a physical drive text generation module, and describing the evolution process of a flood disaster; generating personalized flood disaster description in combination with the static characteristics and the dynamic remote sensing data; then, a multi-scale residual diffusion enhancement module improves sensitivity to dynamic change of disasters through multi-scale trend extraction and residual calculation, noise is removed, and useful information is reserved; then, the multi-modal fusion module enhances interdependence and information sharing among different modals by using adaptive modal mapping, a cross attention mechanism and a weighted fusion strategy; and finally, training through a regression model, dynamically adjusting the feature weight by using an adaptive feature weighting mechanism and an incremental learning mechanism, and finally obtaining a flood disaster prediction value through the prediction set.

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 relates to the technical field of soil monitoring and data analysis, and discloses a soil carbon and nitrogen content dynamic prediction method based on deep learning. The method comprises the following steps: acquiring soil monitoring data from an environment monitoring platform, performing dimension reduction by using a multi-layer perceptron model to obtain core features, and dividing a dynamic monitoring data set according to the core features; taking the data set as input, and constructing an initial prediction model by using a time convolutional network; and constructing a meteorological factor library and an influence map, replacing an initial model time node, and obtaining a climatic factor node prediction model through cross validation. And performing regression fitting and cross validation verification by using a Gaussian process, and constructing a soil dynamic prediction model. According to the method, through multi-step data processing and model construction, the influence of soil data characteristics and meteorological factors is effectively mined, the dynamic change of the soil carbon and nitrogen content can be accurately predicted, and powerful support is provided for the fields of precision agriculture, environmental protection and the like.

The invention discloses a balance scheduling system for energy management and a balance scheduling method thereof, and relates to the technical field of energy management scheduling, and the balance scheduling system comprises an energy data acquisition module, an energy demand trend analysis module, an energy supply and demand dynamic prediction module, an energy balance scheduling decision module, a scheduling execution module and a performance monitoring module. After the energy supply and demand dynamic prediction module integrates real-time external factor data of the energy data acquisition module and a long-term historical rule of the energy demand trend analysis module, a reference prediction baseline based on a long-term trend is generated, and then a dynamic correction value is superposed to respond to short-term disturbance. And meanwhile, through an error traceability mechanism for separating a trend baseline from external correction, the module performance can be optimized in a targeted manner, if an error is derived from long-term trend deviation, a trend analysis model is calibrated, and if external data is lagged, priority adjustment of a data acquisition module is triggered, so that system self-diagnosis and prediction precision closed-loop improvement are realized.

The invention discloses a logging-while-drilling reservoir parameter dynamic prediction method based on double attention convolution LSTM, and the method comprises the steps: obtaining logging-while-drilling data and drilling data in real time, and carrying out the preprocessing of the data; establishing a DTW dynamic time window model based on a gradual transition strategy, and adaptively adjusting the length of an input sequence according to the stratum change speed; a double attention convolution LSTM model is combined with the DTW dynamic time window model to establish a feature-time double attention mechanism, and two dimensions of features and time are adaptively fused based on the convolution LSTM; setting a buffer area, storing data and controlling data quality; setting an online increment fine tuning strategy; and performing instance training, and performing multi-angle evaluation on the performance of the double attention convolution LSTM model by adopting multiple indexes. According to the scheme, the influence of features and time on the prediction accuracy is fully considered, and the prediction accuracy is 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 relates to the field of environmental monitoring, and discloses a plateau region carbon emission monitoring management method and system, electronic equipment and a medium, and the method comprises the following steps: constructing an atmospheric boundary layer dynamic model for a plateau low-pressure and strong-turbulence environment, and achieving environmental adaptability modeling by correcting a turbulence diffusion coefficient and localized combustion efficiency; based on unmanned aerial vehicle group dynamic path planning and a ground station collaborative sensing network, flight parameters are adjusted in real time in combination with the concentration gradient, and time-space continuous multi-source fusion monitoring data are generated; a high-resolution carbon emission field is output through embedding a combustion efficiency correction factor and a space correlation graph structure by adopting a data assimilation algorithm with mixed ensemble Kalman filtering and a graph convolutional network; and constructing frozen soil degradation index parameters, and driving a transfer learning early warning model to realize methane flux dynamic prediction and multi-stage early warning triggering. According to the invention, the problems of insufficient carbon emission monitoring precision and lack of frozen soil degradation correlation evaluation in a plateau complex environment are solved.

The invention relates to the technical field of process data analysis and processing, and discloses a coal-based solid waste recycling full-process intelligent management and control platform and a digital twin system. The platform is characterized in that an identification module identifies a coal-based solid waste mineral phase through a ray diffractometer to obtain mineral composition classification data; the construction module constructs a digital twinborn model based on the classification data, simulates a dissolution reaction, and obtains weathering soil formation process data; the establishment module establishes a pollutant migration prediction model according to the process data to obtain environmental risk data; the optimization module optimizes the risk data through an improved particle swarm-genetic hybrid algorithm to obtain a resource path scheme; and the adjusting module adaptively adjusts the process parameters based on the path scheme to obtain a whole-process management and control instruction. The problems that accurate mineral phase recognition and intelligent classification are lacked in the coal-based solid waste recycling process, and a dynamic prediction model cannot be constructed and a weathering soil forming process cannot be simulated in real time based on mineral composition data are solved.

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.

The invention provides a coal-bed gas well production dynamic analysis method and system based on AI time sequence prediction, and the method comprises the steps: firstly collecting a production dynamic data sequence of a coal-bed gas well, a corresponding well position geological condition data sequence and a surrounding environment data sequence, and constructing a ternary time sequence dynamic association network comprising node dynamic time sequence segments and a connection line real-time association relationship; and then calling a pre-trained AI time sequence coupling deviation evolution mining model to process the ternary time sequence dynamic association network to obtain a ternary time sequence coupling deviation result. And a deviation conduction closed-loop path is determined according to a ternary time sequence coupling deviation result in combination with forward conduction positioning and reverse tracing verification, and a deviation conduction analysis report is generated. And finally, generating a dynamic regulation strategy based on the deviation conduction analysis report in combination with a production dynamic prediction threshold value, outputting the dynamic regulation strategy to a production control terminal, and meanwhile, feeding back regulation data to update a network node association relationship, thereby realizing accurate analysis and regulation of the production dynamic state of the coal-bed gas well.

The invention provides an irrigation water demand prediction method based on meteorological soil crop multi-source feature fusion, and relates to the field of data prediction, and the method specifically comprises the steps: firstly collecting farmland region multi-source feature data, and constructing an original data set; then, a nonlinear disturbance relation between farmland characteristics is extracted through a high-order disturbance encoder module, and a structure energy tensor is introduced to describe high-order structure relevance; thirdly, a dynamic gating characteristic generator module is introduced, a disturbance gain factor and a time coupling factor are combined to generate a gating signal, and dynamic screening of multi-source characteristics is achieved; then constructing a cross-variable interaction modeling module, and generating a feature embedding vector to model a complex coupling relationship among weather, soil and crops; and finally, outputting an irrigation water demand predicted value by adopting a multi-layer sensing network in combination with channel mapping and a cross memory mechanism, so that efficient dynamic prediction of the farmland water demand is realized, and the water resource utilization efficiency and the accurate regulation and control capability are improved.

The invention provides a rice growth model construction method and system based on salt stress, and relates to the technical field of growth model.The rice growth model construction method comprises the steps that firstly, monitoring points are arranged in a planting sample area, and soil conductivity data of different depths are obtained through a multi-depth soil conductivity sensor and a Kriging interpolation method; collecting root system density, a multispectral remote sensing image and a leaf area index, and calculating a canopy salt stress index; measuring the concentration of related ions to obtain a sodium adsorption ratio, and establishing a salt stress coefficient by combining the soil conductivity and the irrigation volume; fusing canopy and soil stress indexes to generate a stress comprehensive index, and introducing a salinity feedback item to represent the crop compensation capability; and finally, constructing a hybrid machine learning model by adopting a convolutional neural network and a long-short-term memory network to realize rice growth dynamic prediction. According to the method, multi-dimensional salinity stress quantification of a soil-crop system is realized, and decision support is provided for salinization treatment in precision agriculture.

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.