484results about How to "Accurate prediction" patented technology

The invention provides a GNSS data quality prediction method and device based on a fisheye camera, and a medium, and belongs to the technical field of data processing, and the method comprises the steps: determining the satellite orbit information of a future time period with a to-be-evaluated observation station as a reference point based on ephemeris prediction; according to the fisheye sky image shot at the to-be-evaluated observation station and the satellite orbit information, determining satellite feature data of a future time period and the occlusion porosity of the future time period; respectively inputting the occlusion porosity of the future time period and at least part of satellite feature data of the future time period into a general prediction model and an observation station prediction model corresponding to the observation station to be evaluated for data quality evaluation, and obtaining a first data quality evaluation index and a second data quality evaluation index; and determining a data quality prediction result based on the first data quality evaluation index and the second data quality evaluation index. A general prediction model and an observation station exclusive prediction model are introduced to carry out double-model evaluation, and accurate prediction of GNSS observation data quality is realized.

The invention relates to the technical field of intelligent monitoring, in particular to an electronic guide vehicle steering system fault prediction method based on time sequence data analysis, which comprises the following steps: mapping a steering system into perception, decision and execution virtual computing nodes to construct a digital twin model; extracting inter-node time lag correlation through multivariable Granger causality operation, and constructing a virtual causality matrix; fault parameters such as current, angle and bias are injected into the model for numerical simulation, and a response sequence is generated through time domain iteration; using a dynamic time warping algorithm to calculate the morphological difference between the time sequence and the reference sequence so as to generate a time sequence feature; and constructing a digital twinning dynamic graph, inputting the dynamic graph attention network, and extracting spatio-temporal evolution characteristics to generate a fault prediction result. According to the method, accurate deduction of the full-life-cycle fault evolution process of the steering system is realized through digital twinning.

The invention provides equipment and a method for testing the emission amount of harmful gas in a tunnel, and relates to the technical field of tunnel construction. Comprising a sleeve, a gas parameter testing unit installed on the sleeve, a water tank communicated with the interior of the sleeve and a gassing module installed on the water tank, and the gassing module is connected with the gas parameter testing unit; one end of the sleeve can be dynamically sealed with the drill rod, and the other end of the sleeve is of an opening structure; the gassing module is used for separating out gas in water and conveying the gas to the gas parameter testing unit; and the gas parameter testing unit is used for detecting the concentration and flow velocity of harmful gas in the sleeve and the concentration of harmful gas separated out from water. A gas parameter testing unit is arranged to monitor the data change of gas gushing out of the drill hole in real time; the gassing module can separate out gas in water in real time and convey the gas to the gas parameter testing unit to measure the concentration of dissolvable gas, so that the calculation of the emission amount is more comprehensive and accurate.

The invention discloses a transformer area distribution transformer reverse heavy overload prediction method and system, and the method comprises the steps: obtaining data which comprises the historical electrical quantity characteristics of a photovoltaic user, the numerical weather forecast characteristics of the geographic position of a transformer area, and a prior graph structure formed according to the topological graph of the transformer area; preprocessing the data to construct a data set; the method comprises the steps of establishing a space-time diagram prediction model, inputting historical electrical quantity characteristics and numerical weather forecast characteristics of N photovoltaic users and a prior diagram structure into the model to obtain a future K-step area distribution transformer load prediction result, performing heavy overload judgment based on a prediction value, and finally obtaining a future K-step area distribution transformer reverse heavy overload prediction result. According to the method, the topological structure of the power distribution network, the electrical characteristic data and the numerical weather forecast characteristics are comprehensively utilized, high-precision prediction of the multi-time-step power of the photovoltaic grid-connected system is achieved through the space-time diagram neural network model, and the method is suitable for application scenes such as operation optimization and early warning management of the power distribution network with distributed photovoltaic access.

The invention relates to the technical field of composite material performance prediction, and discloses a composite material flame retardant property and thermal stability prediction method based on thermogravimetric analysis and infrared spectrum data fusion. The invention aims to solve the problem that it is difficult to effectively fuse multi-modal data to realize quantitative prediction in the prior art. The method comprises the following steps: firstly, collecting thermogravimetric and infrared spectrum combined data, calculating gas transmission lag time, and carrying out forward correction on infrared spectrum data to realize time sequence alignment; then solid-phase thermogravimetric feature vectors and gas-phase infrared feature vectors are extracted, and a fusion feature matrix is generated; extracting solid-phase and gas-phase characteristics respectively by using a dual-channel LSTM network, and performing weighted fusion based on a self-attention mechanism; and finally, synchronously outputting a flame retardant index predicted value and a flame retardant grade classification result through numerical mapping and classification judgment. According to the method, multi-dimensional feature mining of the whole pyrolysis process of the material is realized, and a rapid and accurate digital evaluation means is provided for screening of the flame-retardant material.

The invention relates to the technical field of fluid control, and discloses a drip-proof self-adaptive fluid dispensing control method and system. The method comprises the following steps: acquiring an image sequence, pressure data and viscosity change data when fluid is disconnected; positioning an abnormal disconnection moment, extracting a corresponding picture, and extracting a necking diameter and a necking length from the picture; calculating the real-time change rate of the necking diameter based on the necking parameters, obtaining the state identification that the fluid is about to be disconnected, and predicting the disconnection opportunity; integrating the cut-off opportunity predicted value and the viscosity data to obtain a comprehensive data set, and calibrating core parameters to generate an adaptive signal waveform; acquiring a real-time flow velocity data correction waveform, and generating an optimization control signal; and extracting a key intervention point, applying pulse intervention, calculating the colloid residual quantity, performing iterative optimization if the colloid residual quantity exceeds a preset threshold value, and performing no intervention if the colloid residual quantity does not exceed the preset threshold value, thereby finally obtaining a non-leakage control sequence. According to the method, the necking characteristic of the fluid can be accurately captured, the working condition is dynamically adapted, non-dripping dispensing is realized, and the control precision and stability are improved.

The invention discloses an intelligent temperature control system for mass concrete, and the system comprises a state collection module which collects initial parameters of the concrete in real time, carries out the time sequence alignment and noise filtering of the state parameters of the concrete, and obtains the temperature state data of the concrete; the intelligent analysis module is used for inverting concrete thermal parameters through a physical information neural network based on the concrete temperature control data, substituting the concrete thermal parameters into a heat conduction finite element control equation to calculate space-time evolution of the whole-field temperature and stress field, and obtaining twin data of the concrete structure; and the prediction optimization module is used for executing multi-step rolling in a multi-input single-output branch mode based on the twin data of the concrete structure to predict temperature and stress evolution in a future time period, and learning dynamic association among the temperature, the stress and the cooling flow to execute minimum energy consumption screening of a temperature control standard and stress constraint to obtain an optimal cooling control instruction.

The invention relates to the field of service life prolonging control, and discloses a reinforcement learning-based self-adaptive service life prolonging control method for a wind turbine generator, which is used for providing a reliable technical means for prolonging the service life of the wind turbine generator. Comprising the steps of collecting a unit operation state and key component health parameters, and generating multi-dimensional state information; establishing a damage evolution model, predicting the residual life and generating a health degree index; designing a multi-objective optimization strategy, and solving to generate a cooperative control instruction; after the instruction is executed, the model and the strategy are dynamically updated based on unit state feedback, and a closed-loop control system is formed. The service life of the wind turbine generator is effectively prolonged.

The invention belongs to the field of soil environment pollution simulation and prediction, and provides a prediction method based on a two-point chemical unbalanced model, which is used for quantitatively evaluating the effect of regulating and controlling the migration behavior of p-nitrophenol in acid red soil by straw stalk biochar. According to the method, a biochar-red soil composite medium system is constructed, a soil column leaching experiment is carried out to obtain penetration curve data, a two-point chemical unbalance model in CXTFIT software is utilized to carry out parameter inversion, and key parameters such as a retardation factor, a two-region distribution coefficient, a mass transfer coefficient and a liquid phase degradation coefficient are accurately identified. Model verification shows that the goodness of fit is high, and a parameter change rule reveals the inherent mechanism of enhancing the pollutant retention capacity, changing the proportion of fast and slow adsorption sites and influencing mass transfer kinetics by the biochar. Based on the verified model, the long-term migration behavior and pollution risk of p-nitrophenol under different environmental conditions can be simulated, and a scientific basis is provided for groundwater pollution control and remediation strategy optimization.

The invention discloses a power distribution network power demand prediction method and device based on big data analysis and a medium, and belongs to the technical field of power demand prediction, and the method comprises the steps: dividing a power distribution network into a plurality of power nodes, collecting the power data of the power nodes, and carrying out the screening to obtain power abnormal nodes; demand information of the power abnormal node is collected, and a power consumption demand event is extracted according to the demand information; acquiring event information of the power consumption demand event, and evaluating according to the event information to obtain a propagation effect condition of the power consumption demand event; acquiring a real-time prediction demand value of a power abnormal node, and determining power change values of other power nodes in combination with a propagation effect condition; and collecting real-time environment data of the power node, predicting according to the real-time environment data to obtain a basic power demand value, and superposing the power change value to obtain a real-time power demand value. The accuracy of power distribution network power demand prediction based on big data analysis is improved.

The invention discloses a large-range landslide mass displacement early warning method based on GNSS and radar data fusion, and the method comprises the steps: obtaining GNSS monitoring sequence data in a landslide region based on the position information of a reference station and a GNSS monitoring station in a GNSS monitoring system, and further obtaining the displacement related information of the landslide region; collecting data through a satellite InSAR, and generating deformation field data of a landslide area by comparing radar images at different time points; performing space-time alignment on the obtained data, and performing two types of data fusion S by adopting an adaptive Kalman filtering method after alignment; decomposing time series data of the fused landslide mass displacement by using wavelet transform, and extracting multi-scale features of a time domain and a frequency domain; the proposed features are combined with historical data of landslide mass displacement, a landslide mass displacement trend prediction model using an LSTM method added with a memory decline factor is input, and future trend prediction of landslide displacement is carried out; setting a multi-level threshold value, judging the change trend of the landslide mass displacement according to the displacement rate, the acceleration and the prediction result, and generating a multi-level early warning signal.

The invention discloses a bone defect repair material suitability prediction method based on deep learning, and the method comprises the steps: carrying out the overlapping and uniform partitioning of a bone defect region and a host bone region in three-dimensional CT image data, carrying out the position coding and feature extraction of a plurality of sub-blocks through a three-dimensional visual Transform model of multi-scale position coding, and obtaining the anatomical and functional features; calculating a bone metabolism activity score and a systemic inflammation score based on blood detection data, and then constructing pathophysiological features; performing multi-round interaction on the anatomical and functional features, the pathophysiological features, the parameter data and the multidirectional multi-head cross attention layer, in each round, selecting one modal feature as a query vector, and selecting any modal feature in the rest modal features as a key vector and a value vector; and fusing the interaction features of each round, and determining the suitability score and the risk level of the bone repair material by combining the attention weight and the bone metabolism activity score in the multidirectional multi-head cross attention layer.

The application discloses a kind of high-pressure fuel filter prediction model construction method and flow prediction method, including two prediction models, method specifically includes: using computational fluid dynamics method carries out fluid dynamics simulation calculation to high-pressure fuel filter, constructs working condition sample data set;The size of filter hole, quantity, filter working pressure, fuel viscosity and filter pressure drop are used as input variable, and filter flow is used as output variable, two new high-pressure filter general nonlinear flow prediction models are established using artificial neural network and regression prediction method respectively, the flow characteristics of different models of filter in working interval can be accurately predicted using the neural network prediction method proposed in the application at any working condition point, and the error is low;The flow of different models of filter under different conditions can be quickly and accurately obtained, the accuracy meets the engineering needs, while greatly shortening the experimental time and calculation time, saving design cost.

This invention relates to the field of water storage tank operation and management technology, and particularly to an interactive control method and system for urban water storage tank operation and management. The method includes the following steps: acquiring the geometric structural parameters of the water storage tank; setting dynamic boundary parameters based on the geometric structural parameters; wherein the dynamic boundary parameters include a safe water storage upper limit, a recommended emptying water level, and a forced emptying water level; constructing a three-dimensional interactive visual interface containing spatiotemporal constraints based on the dynamic boundary parameters; and performing virtual execution verification in response to the input of interactive operating condition switching commands on the three-dimensional interactive visual interface. Once the virtual execution verification is successful, the equipment linkage pre-simulation sequence is converted into control commands and issued to realize the operation and management of the urban water storage tank. This invention, by constructing a three-dimensional water level dynamic boundary mapping and equipment linkage virtual pre-simulation mechanism, achieves intuitive perception of operating condition switching and water hammer safety verification, thereby solving the problems of difficulty in judging operation timing and the potential for misoperation.

This invention provides a method and system for intelligent load control in ammonia synthesis production based on wind and solar forecasting, relating to the fields of energy and chemical production technology. The method includes collecting wind and solar data and ammonia synthesis process data; using a dual-current variational autoencoder combined with a residual connection mechanism to predict power generation curves and process parameter trends; determining process parameter constraints and safety boundaries; establishing a process parameter safety constraint model; obtaining the load adjustment space; generating optimal load commands based on optimization algorithms; and achieving intelligent load control through cascaded PID control. This invention achieves precise dynamic control of the load on ammonia synthesis units, improving wind and solar energy absorption capacity and production efficiency.

The invention provides a microflora abundance trajectory prediction method, device and equipment and a medium, and relates to the technical field of biological information, and the method comprises the steps: obtaining at least one target abundance time sequence corresponding to a plurality of fermentation bodies of a first production team; the target abundance time sequence comprises abundance data of the target microorganism class group in the complete fermentation stage according to fermentation time sorting; dividing each target abundance time sequence to obtain a corresponding first abundance time sequence and a corresponding second abundance time sequence; the first abundance time sequence corresponds to a first fermentation stage, and the second abundance time sequence corresponds to a second fermentation stage after the first fermentation stage; and training the first time sequence prediction model by taking each first abundance time sequence as an input and taking the corresponding second abundance time sequence as a training label to obtain a second time sequence prediction model. The method can accurately predict the abundance change track of the target microorganism class group based on the second time sequence prediction model.

The utility model discloses a portable intelligent substation microstation equipment inspection device, including front end equipment, back end equipment and cloud equipment. Front end equipment is handheld inspection terminal, and integrated main control board, camera, display screen, fingerprint identification, communication interface etc. parts are used for information collection and interaction, and cloud equipment includes equipment information database, fault log record library, solution library, and is connected with front and back end equipment through information interaction module, realizes data storage and scheme calling, and back end equipment includes video processing, state monitoring, fault prediction, personnel scheduling and information transmission etc. module, forms the automation inspection and fault processing process. The device has the advantages of flexible deployment, accurate identification, efficient response, and can improve the intelligent degree of inspection and the substation operation and maintenance efficiency.

The application relates to the technical field of network task allocation, and discloses a heterogeneous computing power network task allocation and path optimization system based on a neural network, which comprises a graph construction module, a node task allocation module and a path optimization module.The graph construction module is used for constructing a traffic dispersion graph, modeling network topology and node data through the traffic dispersion graph, and modeling nodes and edges in combination with graph indexing and graph displacement, and extracting network communication traffic and node features.The node task allocation module is used for inputting graph indexes of all nodes into an improved graph neural network model to intelligently predict and allocate node tasks, and outputting node task allocation prediction results.The path optimization module is used for obtaining path optimization results by minimizing transmission delay and balancing node computing power in combination with node task prediction results.The system optimization module is used for optimizing the overall performance of the system based on the path optimization results and the task allocation prediction results.The application improves the task allocation and path optimization efficiency of the heterogeneous computing power network.

The embodiment of the invention relates to the technical field of oil and gas field development, and discloses a shale oil reservoir carbon dioxide huff and puff recovery ratio prediction method and equipment, and the method comprises the steps: determining a first influence weight for a fracturing fracture morphological parameter in geological parameters and fracturing construction parameters; determining a second influence weight of the geological parameter, the fracturing construction parameter, the fracturing fracture morphological parameter and the carbon dioxide huff and puff mining parameter on the shale oil well productivity; according to the fracturing fracture propagation key parameters, the microseism monitoring fracture morphological parameters, the numerical simulation fracturing fracture morphological parameters and a pre-generated type-2 fuzzy logic model, determining a fracturing fracture morphological propagation key parameter range under different geological parameter, rock mechanics and fracturing construction parameter grade conditions; and predicting the recovery ratio of the target shale work area according to the fracturing fracture form extension key parameter range and different artificial neural networks. According to the method, the shale oil reservoir CO2 huff and puff recovery efficiency can be efficiently, intelligently and accurately predicted.

The invention provides an adjustable load capacity analysis, regulation and control method for a virtual power plant, and relates to the technical field of power system operation and control, and the method comprises the following steps: S1, multi-source data collection and processing: collecting electrical parameters, non-electrical parameters and power grid dispatching instructions of various adjustable loads; s2, classifying adjustable loads, and dividing the adjustable loads into a plurality of grades; s3, constructing a multi-dimensional adjustable load capacity evaluation index system; s4, weighting calculation: carrying out weight calculation on the multi-dimensional adjustable load capacity evaluation index system based on an improved particle algorithm; s5, establishing a dynamic hierarchical collaborative regulation and control strategy, and establishing the dynamic hierarchical collaborative regulation and control strategy according to the power grid dispatching demand type; s6, a prediction model is constructed, and a load response prediction model is constructed; s7, executing an instruction; according to the multi-dimensional characteristic, powerful support is provided for accurate regulation and control, a multi-dimensional evaluation index system is constructed, the load capacity is comprehensively evaluated, and optimal configuration of resources is achieved.

The present application relates to a kind of lake reservoir type chlorophyll concentration prediction method, device and medium, method includes the following steps: obtaining the water quality, water dynamics and weather of the online automatic monitoring data of lake reservoir including forecast point and upstream point;The online automatic monitoring data is preprocessed, and the data after processing is obtained;The data after processing is carried out feature extraction to obtain the water quality feature and water dynamics index feature of forecast point and upstream point, and construct cumulative illumination feature;Upstream water quality feature, water dynamics index feature and illumination feature are input into the fusion prediction model pre-trained, to obtain upstream transport chlorophyll prediction result and time series influence chlorophyll prediction result, using error reciprocal method upstream transport chlorophyll prediction result and time series influence chlorophyll prediction result are fused;The chlorophyll prediction result obtained by fusion is output.Compared with prior art, the present application has the advantages of high accuracy, strong stability and the like.

The application provides a three-dimensional anisotropic composite material heat conductivity coefficient prediction method and system, and relates to the technical field of heat analysis, and comprises the following steps: establishing a heat transfer unit cell model of a to-be-detected three-dimensional composite material in different directions based on structure parameters and material parameters of the to-be-detected three-dimensional composite material; the to-be-detected three-dimensional composite material is a three-dimensional orthogonal structure; the material parameters comprise first heat conductivity coefficients of each material; taking solid heat conduction as a current heat transfer form, determining equivalent thermal resistances corresponding to the heat transfer unit cell model of different regions by using an equivalent thermal resistance method and the first heat conductivity coefficients, establishing a heat transfer connection relationship between each equivalent thermal resistance by combining a Fourier equation and a thermal resistance network method, and determining first total thermal resistances along each heat transfer direction of the to-be-detected three-dimensional composite material; the heat transfer connection relationship comprises series connection and parallel connection; converting the first total thermal resistances based on overall size parameters of the heat transfer unit cell model, and determining an equivalent heat conductivity coefficient of a target direction.

This application belongs to the field of functional ceramic material performance optimization technology, specifically disclosing a method and system for predicting the performance of multiphase carbon ceramic resistors based on entropy descriptors. The method includes: determining the multi-source microstructure characteristic parameters of the carbon ceramic resistor to be tested; inputting the multi-source microstructure characteristic parameters of the carbon ceramic resistor to be tested into a carbon ceramic resistor performance prediction model to obtain the comprehensive performance entropy descriptor prediction information of the carbon ceramic resistor to be tested output by the carbon ceramic resistor performance prediction model, thereby determining the energy tolerance performance of the carbon ceramic resistor to be tested; the carbon ceramic resistor performance prediction model is trained based on the multi-source microstructure characteristic parameter samples of the carbon ceramic resistor sample and the label information of their corresponding comprehensive performance entropy descriptors; the comprehensive performance entropy descriptor is used to characterize the degree of energy dissipation order of the carbon ceramic resistor material. Through this application, accurate and efficient prediction of the performance of carbon ceramic resistor materials can be achieved.

This invention relates to the field of navigation and timing technology, specifically to a GNSS timing data prediction method and related equipment. The method involves acquiring Roland timing data when GNSS timing data is invalid and preprocessing it. The preprocessed data is then input into a trained single-hidden-layer extreme learning machine model. This model calculates the hidden-layer output matrix by randomly initializing the input layer weight matrix and bias vector, and solves for the output weights using regularized least squares. The model outputs a normalized GNSS timing data prediction value, which is then denormalized to obtain the final GNSS timing prediction result. This method combines the high stability of the Roland system with the efficient computational power of the extreme learning machine to achieve accurate GNSS timing data prediction. It is suitable for enhancing the resilience of positioning, navigation, and timing systems in critical infrastructure scenarios, ensuring the continuous operation of the system even when GNSS signals are interfered with or fail.

The invention relates to the field of metro station air conditioning system energy consumption prediction, and discloses a self-correcting metro station air conditioning energy consumption simulation system and method.The simulation system comprises a main cause analysis module and a self-correcting module, and the self-correcting module establishes a corresponding energy consumption simulation model and optimizes the energy consumption simulation model; the main cause analysis module comprises a data acquisition module and a data analysis module; the self-correction module comprises a model building module and an optimization module. The invention further discloses a simulation method based on the system, principal component analysis is performed on measured data of the subway station air conditioner system, an optimization algorithm is utilized to automatically correct an energy consumption simulation model, and the corrected energy consumption simulation model is applied to the subway station air conditioner control system. Therefore, the energy efficiency of the air-conditioning system of the subway station is quickly and accurately predicted, and efficient and low-consumption operation of the air-conditioning system is improved.

The application provides a self-adaptive flow control method based on printing content feature analysis, relates to the field of data flow control of a thermal printer, and comprises the following steps: segmenting a to-be-printed data stream and extracting energy density values of each data segment; calculating a virtual heat value in an iterative mode, calculating a physical printing time consumption; calculating a transmission time consumption according to the number of bytes of the data segment and the effective transmission rate of a communication interface; obtaining an actual time consumption of the data segment from sending completion to printing completion, obtaining an error ratio based on the ratio of the estimated time consumption to the actual time consumption, updating an error ratio queue by using a sliding window and calculating the standard deviation thereof, determining a self-adaptive grading threshold value according to the standard deviation, updating a reference coefficient according to the grade to which the error ratio belongs, and dynamically adjusting a heat dissipation attenuation coefficient based on actual time consumption feedback and a virtual heat state. The application realizes accurate prediction and control of the risk of buffer overflow.

The application discloses a kind of shotcrete and surrounding rock interface bonding performance multiscale analysis method and system, it is related to supporting interface mechanics analysis technical field, construct the multiscale through analysis method from nanometer scale to macro scale, by macroscopic test, microtest, molecular dynamics simulation and discrete element calculation are combined, establish the quantitative mapping relationship of wet heat environment parameter and interface bonding performance;The application is based on energy equivalence and strength matching principle, realizes the cross-scale parameter mapping of physical mechanism driving, reveals the micro-mechanism of interface bonding energy evolution and bonding degradation under wet heat environment.The application alleviates the technical problems that the accurate prediction and degradation evaluation of interface bonding performance under wet heat coupling environment cannot be realized in the prior art.

The application discloses a landslide deformation prediction method and device, and relates to the field of landslide deformation detection, wherein the method comprises the following steps: acquiring InSAR along-line-of-sight landslide deformation data of a to-be-predicted region and at least one adjacent region thereof; inputting the deformation data of the at least one adjacent region into a first layer LSTM of a double-layer LSTM landslide deformation prediction model to extract feature vectors of the at least one adjacent region; multiplying the deformation data of the to-be-predicted region by a to-be-predicted region influence coefficient matrix to obtain a first matrix, and multiplying the feature vectors of the at least one adjacent region by at least one adjacent region influence coefficient matrix to obtain a second matrix; splicing the first matrix and the second matrix to obtain a spliced matrix; inputting the spliced matrix into a second layer LSTM of the double-layer LSTM model to obtain a final landslide deformation prediction result. The application can accurately predict landslide deformation by combining the deformation correlation between the to-be-predicted region and the at least one adjacent region.

This invention relates to a method for predicting sandbar response under the combined effects of water level and waves, considering hysteresis, during storms. The method includes: starting with initial sandbar morphology data of a target shoreline during a specific storm event, using a sandbar evolution model considering hysteresis to predict sandbar response, and outputting a predicted curve of sandbar position change throughout the storm process. The sandbar evolution model calculates storm dynamic forcing based on historical storm hydrodynamic data, establishes a model relating relaxation timescales to storm dynamic forcing, obtains a relaxation timescale characterizing the hysteresis characteristics of the sandbar response, and establishes a model relating the sandbar equilibrium position to hydrodynamic forcing based on historical hydrodynamic forcing data and sandbar position changes, obtaining... t The sandbar equilibrium position at any given moment. This invention can more accurately simulate sandbar behavior throughout the entire storm process, especially during the attenuation phase, significantly improving the practicality and reliability of sandbar response prediction throughout the entire storm process (including the aftereffect period).

The invention belongs to the field of artificial intelligence networks, and particularly relates to a method for performing visual questions and answers by using an attention mechanism from words to regions. Comprising the following steps: (1) extracting features; and (2) generating candidate answers: firstly positioning related image areas and keywords in questions by adopting a collaborative attention mechanism, then obtaining fine-grained image features and question features, and finally fusing the two features to generate the candidate answers. According to the method, in order to generate candidate answers with higher quality, two question and answer stages are cascaded, a traditional single-stage visual model is expanded into a double-stage model, semantic information contained in the answers is fully mined, and accurate prediction of the final answers is promoted. According to the invention, image areas and keywords related to questions can be extracted and utilized, so that more accurate candidate answers can be generated.