56435results about "Climate change adaptation" patented technology

A dynamic evaluation method for an extreme rainstorm waterlogging disaster risk for disaster prevention and reduction. The method comprises: investigating and surveying urban system data and disaster prevention and reduction data, using GIS technology to divide disaster-bearing objects into refined risk units on the scale of urban buildings and road networks, and determining the spatial distribution of the disaster-bearing objects; on the basis of an extreme rainstorm waterlogging scene, simulating the disaster influence of a dynamic change process of a flood ponding depth on the disaster-bearing objects; developing refined dynamic evaluation on a waterlogging risk by combining the two methods of waterlogging process simulation and an indicator system; using a spatial complex network and a shortest path plan to calculate a traffic capacity and emergency service accessibility of a road network system; and on this basis, taking into comprehensive consideration the rational allocation of disaster prevention emergency drainage and emergency rescue services to a high-risk area, and proposing dynamic evaluation technology for a waterlogging risk that integrates a disaster evolution process and a disaster prevention response process, and ultimately realizing the dynamic evaluation of the waterlogging risk of each disaster-bearing unit during the waterlogging disaster evolution.

A vacuum insulating panel may include: a first substrate; a second substrate; a plurality of spacers provided in a gap between at least the first and second substrates, wherein the gap is at pressure less than atmospheric pressure; a pump-out / evacuation tube extending at least partly into an aperture in one of the substrates; and a pump-out / evacuation tube seal. The pump-out / evacuation tube seal may include at least one of: (a) from about 20-80 wt. % tellurium oxide, the tellurium oxide comprising TeO4 and TeO3, wherein the pump-out tube seal comprises more TeO3 than TeO4 by wt. %; and / or (b) tellurium oxide and from about 10-50 wt. % vanadium oxide, wherein the pump-out tube seal by wt. % comprises more tellurium oxide than vanadium oxide, and wherein the vanadium oxide comprises VO2 and V2O5, and wherein more V in the pump-out tube seal is in a form of VO2 than V2O5. A substantially donut-shaped laser beam may be used to heat pump-out tube material in order to form a pump-out tube seal.

The invention relates to a disaster multi-source situation intelligent sensing management and control method and system. According to the method, hydrometeorological and topographic data are collected, and a standardized data set is generated through space-time alignment and anomaly cleaning; constructing a directed topological graph containing node and edge attributes based on the extracted river network topological relation; designing a neural network model, and training through a physical constraint loss function embedded in a water balance principle to obtain a flood dynamic routing prediction model; inputting real-time hydrological data into the model for graph convolution operation, and predicting water level, flow and split ratio changes of each node in a future time period; and finally, carrying out submerging simulation analysis in combination with a digital elevation model, and generating a flood control scheduling scheme and risk early warning information. The deep fusion of a physical mechanism and data driving is realized, the flood propagation rule under the river network topology constraint is effectively captured by using the graph neural network, the calculation efficiency is remarkably improved while the prediction precision is ensured, and real-time and reliable decision support is provided for flood disaster prevention and control in a complex river network region.

The invention provides a flood control project virtual simulation and risk rehearsal method based on digital twinning, and the method comprises the following steps: obtaining drainage basin multi-source data collected by a space-air-ground integrated monitoring network, the multi-source data comprising a satellite remote sensing image, an unmanned aerial vehicle LiDAR point cloud and ground sensor monitoring data; inputting the multi-source data into a pre-constructed digital twin model, wherein the model comprises a spatio-temporal data fusion layer, a physical process modeling layer, a multi-scale simulation deduction layer and a risk assessment decision-making layer which are connected in sequence; the spatio-temporal data fusion layer is used for performing spatio-temporal registration and feature fusion on input multi-source data, and constructing a total element digital backplane; through the space-air-ground integrated monitoring network and the spatio-temporal data fusion technology, high-precision digital mapping of basin total elements is realized. Compared with a traditional single data source, time-space consistency of flood routing simulation is remarkably improved through multi-source data fusion, and prediction errors of key physical processes such as river channel scouring are greatly reduced.

A vacuum insulating panel may include: a first substrate; a second substrate; a plurality of spacers provided in a gap between at least the first and second substrates, wherein the gap is at a pressure less than atmospheric pressure; a seal provided between at least the first and second substrates, the seal including a first seal layer and a second seal layer, wherein, for at least one location of the seal, the first seal layer has a first width and the second seal layer has a second width, wherein the first width of the first seal layer may be from about 2-20 mm, more preferably from about 3-10 mm, and possibly from about 4-8 mm.

The invention discloses a geotechnical engineering stability early warning system and method combining side slope deformation monitoring and numerical simulation, and relates to the technical field of geotechnical engineering safety monitoring and disaster early warning. Side slope deformation monitoring and numerical simulation are combined, and multi-source real-time monitoring data of side slope deformation and numerical simulation are fused; constructing a dynamically updated slope deformation evolution analysis system; performing dynamic partition identification on the slope deformation evolution process according to the multi-source real-time monitoring data, and identifying a slope deformation threshold value and abnormal evolution characteristics of rock and soil; and setting an automatic triggering grading early warning signal of a geotechnical engineering early warning index, and completing slope risk dynamic assessment and early warning output of the rock and soil. According to the invention, the automatic triggering of the grading early warning signal is realized, the response speed, the judgment accuracy and the risk assessment scientificity of the geotechnical engineering early warning system are obviously improved, and the intelligent, dynamic and systematic technical support is provided for the safety prevention and control of the side slope.

The invention specifically relates to the technical field of big data analysis, and discloses a water conservancy gate multi-parameter cooperative intelligent monitoring system, which comprises a multi-parameter acquisition module, a multi-parameter processing module, a comprehensive analysis module, an intelligent decision module, an operation and maintenance early warning module and a man-machine interaction module, the multi-parameter processing module is used for calculating flood control and discharge indexes, structure safety indexes and equipment health indexes; the comprehensive analysis module is used for judging gate risk levels; the intelligent decision-making module is used for generating an optimal gate scheduling scheme; the operation and maintenance early warning module is used for constructing a multi-level early warning mechanism; according to the method, parameter coverage is comprehensive, a gate digital twinborn model and a gate opening comprehensive evaluation model are constructed, the gate risk level is evaluated, an optimization strategy is dynamically adjusted through an intelligent decision module, the accuracy of gate risk judgment is improved, and the self-adaptive capacity of the system is improved.

The invention discloses a cascade reservoir collaborative flood control scheduling decision-making method of a coupling meteorological-hydrological-hydraulic model. The method comprises the steps of multi-source meteorological data fusion and probability forecast generation, dynamic coupling hydrological-hydraulic simulation, risk entropy driven collaborative optimization decision-making, digital twin platform verification and correction, instruction execution and closed loop feedback. Through multi-technology fusion and an intelligent optimization mechanism, the scientificity, timeliness and safety of flood control scheduling are remarkably improved. In the weather forecast stage, multi-source data are integrated to output ensemble rainfall forecast scene data, rainfall input uncertainty is reduced from the source, it is ensured that initial driving precision of flood simulation is improved, and a reliable input basis is provided for subsequent model coupling; in the hydrological-hydraulic dynamic coupling stage, a coarse-fine grid dynamic division strategy is adopted to reduce redundancy calculation, the asynchronous pipeline technology enables the flood routing calculation efficiency to meet the real-time scheduling requirement, and the simulation speed is greatly accelerated.

The invention relates to the technical field of underwater fish school monitoring, and discloses an underwater fish school monitoring statistical system based on image fusion. According to the system, underwater video streams and sonar reflection intensity data of different spectral bands are acquired through an underwater multi-source image acquisition module, and time-space synchronous multi-modal image data streams are generated through timestamp alignment; a fish school contour reconstruction module is used for segmenting a fish school contour boundary and fusing visible light texture and sonar geometric features to generate an underwater three-dimensional fish school distribution set; the dynamic track mapping module tracks the mass center displacement, calculates the movement rate and the direction deviation angle, and correlates the water area depth to generate a dynamic track topological graph; the behavior anomaly analysis module extracts environment data based on the track mutation node, and detects aggregation density change and direction dispersion to mark an anomaly feature cluster; and the population statistics output module integrates the data, performs classified statistics on population distribution, a quantity threshold value and a migration path overlap ratio, and finally generates a fish school quantity distribution statistics thermodynamic map.

The invention belongs to the technical field of urban planning, and provides a digital twinborn-based urban update space intelligent planning and evaluation system, which comprises a data acquisition module for fusing multi-source heterogeneous data in real time, and constructing and updating an urban digital twinborn body; the intelligent planning module generates candidate schemes by combining targets and constraints based on the digital twins and utilizing artificial intelligence; the simulation deduction module performs multi-dimensional dynamic simulation on the candidate scheme, and feeds back and adjusts parameter optimization to generate a planning scheme; the intelligent evaluation module evaluates a planning scheme based on a simulation result through a quantitative index; the monitoring feedback module continuously monitors the actual operation state of the to-be-updated area; according to the invention, through the Internet of Things and the digital twinborn model, the actual condition of a city is dynamically reflected, the integrity and timeliness of data are ensured, and a city updating scheme continuously adapts to city changes.

The invention discloses an urban inland inundation risk multi-level prediction method and device based on time-space diagram learning, a storage medium and a computer program product, and relates to the technical field of natural disaster risk prediction, and the method comprises the steps: collecting multi-modal urban hydrological data; performing hierarchical time modeling on the multi-modal urban hydrological data, and extracting a time embedding vector; constructing a heterogeneous graph based on the time embedding vector, and performing spatial feature aggregation calculation on the heterogeneous graph to obtain spatial embedding representation; and performing multi-level prediction according to the spatial embedding representation to obtain a multi-granularity waterlogging risk index. Through multi-modal data acquisition and preprocessing, layered time modeling, heterogeneous graph construction, spatial feature aggregation calculation and multi-level prediction, multi-modal urban hydrological data are effectively fused, and comprehensive and accurate urban inland inundation risk prediction is realized.

A double-path ultra-short-term wind power prediction method based on numerical weather forecast and multi-order time sequence dynamic gating fusion comprises the following steps: acquiring wind power generation historical data and numerical weather forecast data of a wind power plant, and screening weather factors highly related to wind power by using an MIC; the CEEMDAN is adopted to decompose the power sequence into a plurality of intrinsic mode functions (IMF); a dual-path prediction architecture is constructed, one path adopts xLSTM to predict an intrinsic mode function (IMF), all subsequences are superposed, and a prediction result is obtained; in the other path, the XGBoost is combined with key meteorological characteristics of an intrinsic mode function (IMF) and a numerical weather forecast (NWP) for prediction, and all the subsequences are superposed to obtain a prediction result; the method comprises the following steps: designing an MT-DGFusion module through an enhanced attention and dynamic gating network; and fusing the dual-path prediction results through an MT-DGFusion module to obtain a final prediction result. According to the method, double breakthrough of prediction precision and stability is realized, and a new technical path is provided for a complex time sequence prediction task.

The invention provides a control node locating protecting method of undercutting channel segment water level lowering amplitude. The method comprises the steps that a lateral erosion zone and a deep erosion zone are divided; a target channel section is subjected to meshing, a plane two-dimension water flow mathematic model is established, parameter calibration and verification are carried out; a protection project is generalized to the plane two-dimension water flow mathematic model, the arranging zone and the width of the protecting project are determined; local gradient amplification and upstream water level amplification corresponding to each working condition are obtained, mesh sequences are analyzed respectively; key nodes or other nodes which have obvious influence are selected, for lateral erosion nodes, a hidden closure dam and a beach protection part are arranged on a river branch or a bottomland, and for deep erosion nodes, a bottom protecting band or a hidden closure dam are placed in a main groove. According to the method, nodes are divided into the lateral erosion nodes and the deep erosion nodes, protection part difference of the nodes in different types is provided, by comparing the efficiency of a local channel segment arranging protecting project, the positions of the nodes are accurately located, and the optimum efficiency of the protecting project is guaranteed powerfully.

A method and device for optimizing comprehensive production of an oil reservoir. The method comprises: on the basis of historical production data, performing regional level recognition, single-well level recognition, and production optimization model recognition, and updating oil reservoir dynamic recognition; on the basis of the historical production data, using the following steps to perform cyclic historical fitting on model parameters of a plurality of production optimization models: performing historical fitting on the model parameters, and performing well-to-well communication relationship calibration on the plurality of production optimization models; determining, on the basis of the updated oil reservoir dynamic recognition, a constraint condition corresponding to each production optimization model, solving an objective function corresponding to each production optimization model, obtaining an optimal decision variable corresponding to each production optimization model, and obtaining a development regulation scheme corresponding to each production optimization model; obtaining a comprehensive development regulation scheme of an oil reservoir; and updating the oil reservoir dynamic recognition on the basis of a regulation effect obtained by real-time monitoring of a development operation.

The invention discloses an urban flood toughness evaluation method, apparatus and device, a medium and a computer program product. The method comprises the steps of obtaining hydro meteorological data of a to-be-evaluated region and geographic information data of an urban spatial form element layer; setting boundary conditions of a plurality of flood scenes according to the hydrometeorological data, constructing a multi-scene urban flood disaster simulation model in combination with geographic information data, and respectively generating flood inundation depth data of urban spatial form element layers of the plurality of flood scenes; and calculating a first toughness index, a second toughness index and a third toughness index of each flood scene so as to calculate a flood toughness comprehensive evaluation result of each flood scene of the to-be-evaluated region. According to the method, an evaluation system of multiple composite flood disaster scenes is established, different disaster factors and interaction thereof are comprehensively considered, and the effect of key space parameters in urban flood toughness is fully considered, so that the capability of coping with flood disasters in cities can be more comprehensively and accurately evaluated.

The invention discloses a wetland ecological restoration dynamic monitoring method based on deep learning, and relates to the technical field of ecological restoration, and the method comprises the following steps: obtaining multi-source wetland ecological sensor data and remote sensing image flow in real time, constructing a space-time fusion data cube, and extracting an ecological feature tensor; performing degradation mode analysis on the ecological characteristic tensor, generating an ecological state dynamic topological graph, and calculating an ecological connectivity index; carrying out restoration demand identification based on the ecological connectivity index, positioning a degradation hot spot region through a multi-modal graph convolutional network, and generating a restoration priority region coordinate set; through multi-source data space-time fusion and deep crossing of deep learning and landscape ecology, a whole-process technical system from ecological state dynamic perception to restoration scheme intelligent optimization is constructed. The problems that in traditional wetland restoration, data scales are not matched, degradation area positioning is fuzzy, restoration path ecological adaptability is poor, and multi-target cooperation is difficult are effectively solved.

The invention relates to the technical field of cables, and discloses a multi-layer composite wrapped high-temperature-resistant anti-aging cable which sequentially comprises a conductor, an insulating cable core, a shielding layer, an outer insulating layer, a fireproof layer, a heat insulation layer, a buffer cushion layer, an armor layer, an anti-aging layer and an outer sheath layer from inside to outside. The fireproof layer is formed by alternately laminating synthetic mica tapes and ceramic boron silicone rubber tapes, a reliable fireproof barrier is formed, and the heat insulation layer is arranged to achieve efficient heat insulation and stable structure through cooperation of gradient pore nano aerogel, ceramic silicone rubber round rods and phase change capsules. The buffering cushion layer is combined with the composite layer, the aluminum oxide hollow ball array and the composite capsule, so that the mechanical buffering and auxiliary heat insulation functions are achieved; the armor layer is woven by stainless steel fibers, the mechanical protection capability is improved, and the anti-aging layer is of a double-layer structure composed of montmorillonite composite fluororubber and an anti-oxidation layer, so that thermal oxidation aging is delayed.

Disclosed is a reservoir ecological operation method integrating ecological flow process and water temperature process requirements of fish species, including: establishing a habitat model for a target fish species and deducing an ecological flow process of a lift history of the target fish species; calculating a critical spawning water temperature threshold and a critical gonadal development accumulated temperature threshold; establishing a multi-objective reservoir operation model to obtain flow process and water level after optimized reservoir operation; establishing a multi-objective reservoir ecological operation model integrating both the ecological flow process and the water temperature process, and calculating reservoir outflow water temperatures before and after the optimized reservoir operation; and calculating an improvement difference between the critical water temperature threshold and the accumulated temperature threshold of the target fish species on arrival dates after the reservoir ecological operation and those before the optimized reservoir operation.

The invention relates to the field of power system supply and demand interaction, and discloses an intelligent greening irrigation system and a water-saving control method, the system comprises a multi-source data acquisition module, a space-time tensor modeling module, a dynamic decision generation module, an edge calculation acceleration module, a distributed execution control module and a drip irrigation module; the method comprises the following steps: collecting multispectral, meteorological and soil moisture data of a plant canopy in real time to generate an original data set; a fusion data set is obtained through space-time alignment and normalization preprocessing; the water demand is predicted based on an LSTM-Transform model, and an irrigation safety interval is screened; the irrigation uniformity and water pressure distribution are simulated and evaluated through multi-physics field coupling; a double-delay DDPG algorithm is used for optimizing valve opening parameters; the model and the execution error are corrected through Bayesian modeling, and an NDVI feedback optimization strategy is combined. According to the invention, a double-delay depth deterministic strategy gradient algorithm is introduced for dynamic optimization of an irrigation strategy, so that the capability of quickly adapting to environmental changes is achieved.

The invention discloses a method for simulating and forecasting flood in a cold highland area based on hydrological and hydrodynamic coupling, and belongs to the technical field of disaster forecasting. The method specifically comprises the following steps: S1, multi-source basic data collection and preprocessing: collecting multi-type and multi-scale basic data for a target cold and cold mountainous area drainage basin; and S2, deep learning correction and fusion of the satellite rainfall data: aiming at the local overestimation and underestimation problems of the satellite rainfall data, a deep learning algorithm is adopted to carry out hour scale correction and fusion. Four types of core data of satellite remote sensing, reanalysis, ground observation and geographic space are collected, total factors of'rainfall-runoff-terrain-underlying surface 'required by flood simulation in the cold and cold mountainous area are covered, simulation one-sidedness caused by lack of data types in traditional modeling is avoided, rainfall and runoff abnormal values are eliminated by adopting a 3-sigma criterion, data formats and spatial-temporal scales are unified, and the modeling efficiency is improved. A standardized data set is formed, and interference of abnormal values, format incompatibility and space-time mismatching on subsequent model input is avoided.

The invention relates to the field of intelligent agriculture, and discloses an intelligent drip irrigation control method, system and device and a storage medium, and the method comprises the following steps: data collection: obtaining soil humidity data, environmental meteorological data and crop growth state data of a target area through a plurality of sensors, and transmitting the data to a data processing module; the system comprises a data acquisition module used for acquiring soil humidity data, environmental meteorological data and crop growth state data and transmitting the acquired data to a data processing and storage module; the device comprises a machine case shell, and an intelligent control chip, a wireless communication module, an electromagnetic valve and a water flow meter are installed in the machine case shell. By integrating a sensor network, intelligent analysis and a real-time feedback mechanism, the water demand of crops is accurately calculated, irrigation is automatically adjusted, and water resource utilization is optimized; a personalized irrigation plan is made by combining a deep neural network and time sequence analysis, and the water requirements of crops in different growth stages are ensured.

A vacuum insulating panel includes first and second substrates (e.g., glass substrates), a hermetic edge seal, a pump-out port, and spacers sandwiched between at least the two substrates. The gap between the substrates may be at a pressure less than atmospheric pressure to provide insulating properties. The vacuum insulating panel may include a multi-layer edge seal structure, including at least one layer including boron oxide (e.g., B2O3 or any other stoichiometry).

The invention discloses a large-model-driven intelligent calculation method and system for a water conservancy mechanism model. According to the method, natural language input, structured conversion and intelligent optimization calculation of a scheduling target are realized by integrating a field-enhanced large language model and a water conservancy professional mechanism model. The method comprises the steps of receiving a calculation target expressed by a user in a natural language, and analyzing and converting the calculation target into a constraint condition and a target function which can be recognized by a water conservancy mechanism model; a hydrological model, a hydraulic model, a hydrodynamic model and other models are called based on a workflow engine, and reverse calculation is carried out by adopting a hybrid optimization strategy of'coarse adjustment-fine adjustment-verification '; synchronously and visually displaying the parameter change and the result convergence state in the calculation process; and outputting a calculation result including parameter adjustment logic, standard conformity analysis and multi-scheme comparison. The system comprises a natural language interaction module, a target conversion module, an intelligent calculation engine module, a visualization module and a result generation module, and supports multiple application scenes such as multi-target scheduling, emergency decision making and ecological guarantee. Compared with a traditional scheme, the method has the advantages that the model use threshold is lowered, the dispatching efficiency and calculation transparency are improved, and the method is suitable for complex hydraulic engineering calculation tasks such as reservoir dispatching, cross-basin water transfer and flood control emergency.

The embodiment of the invention provides a new energy optical power correction method and system based on weather abrupt change prediction. The method comprises the following steps: identifying a cloud layer type contained in a target area, and obtaining a cloud layer type coverage rate; if the cumulonimbus coverage rate exceeds a first threshold value, predicting a cloud layer movement path in combination with the cloud layer movement vector field to obtain a first cloud layer coverage change sequence; if the layer cloud coverage rate exceeds a second threshold value, an irradiance attenuation coefficient is predicted based on the cloud layer thickness in the meteorological data and the solar altitude angle, and a second cloud layer coverage change sequence is obtained in combination with the ground aerosol optical thickness and the corrected irradiance attenuation coefficient; and monitoring real-time operation data of the power station, combining the first / second cloud layer coverage change sequence, predicting a matching relationship between a cloud layer moving direction and a power station power reduction area by adopting a double-flow Transform architecture, calculating a power fluctuation range corresponding to each power station power reduction area, dynamically adjusting power grid operation parameters of a target area, and determining the power grid operation parameters of the target area. The power grid stability is ensured.

The invention relates to the technical field of urban drainage and waterlogging prevention, in particular to an urban real-time drainage scheduling method and system based on deep reinforcement learning. The method comprises the steps of constructing a dual-target reward function and constraint conditions for minimizing the total amount of surface waterlogging and the overflow frequency of a combined system discharge port, performing rainfall runoff simulation by adopting a hydrodynamic mechanism model and combining basic data of a target area, and establishing a multi-agent model based on deep reinforcement learning. The method comprises the following steps: constructing interactive coupling of decision actions and simulated environment states, obtaining updated environment states and drainage scheduling reward values through the coupling, driving a multi-agent model to generate a strategy network, predicting and generating drainage waterlogging prevention engineering actions based on real-time rainstorm information, and dynamically updating the actions by utilizing the periodically generated strategy network. According to the method, it is ensured that multi-objective optimization decision is rapidly achieved within response time, the waterlogging relieving effect is achieved, urban rainfall flood toughness is enhanced, and the real-time requirement is met.

The invention relates to a quick river flood forecasting method based on a physical information neural network, and belongs to the field of river flood forecasting. The method comprises the following steps: on the basis of a traditional physical information neural network (PINN), introducing a boundary condition parameter as an input variable, and enabling the PINN to learn a flood wave propagation rule under different boundary conditions. Furthermore, on this basis, a physical information neural network flood fast forecasting framework (RFF-PINN) integrated with a hydrodynamic method is provided, a numerical solution based on grid discretization is reconstructed into a continuous function in a time-space domain through a piecewise polynomial interpolation method, residual error loss between network output and a hydrodynamic model simulation value is constructed, and therefore, a flood fast forecasting result is obtained. The network parameters are optimized in cooperation with the PDE loss, and the problem that the network parameter optimization effect is reduced due to the fact that the PDE loss of the complex flow state area is difficult to converge is solved. The method has the beneficial effect that the water depth change process of each section of the river channel under any boundary condition can be accurately and quickly predicted.

The invention relates to the technical field of intelligent breeding monitoring, in particular to a breeding early warning method and system based on artificial intelligence image recognition. The method comprises the following steps of: 1, dynamically acquiring and adaptively preprocessing a multi-modal image; step 2, constructing a multi-branch convolutional neural network based on the preprocessed image data, respectively extracting space apparent characteristics and time behavior characteristics of a target object, and dynamically fusing characteristic weights by combining real-time environment parameters to generate an abnormal probability value; and step 3, performing multi-level dynamic early warning decision and feedback optimization, dynamically generating a multi-level early warning threshold according to environmental parameters and historical abnormal probability value distribution, triggering differential response operation, and updating parameters of the multi-branch convolutional neural network and the multi-level early warning threshold based on an artificial recheck result and equipment feedback data. The method can automatically adjust the early warning mechanism according to the change of the real-time environment, thereby effectively preventing the potential risk in the breeding process, improving the breeding management efficiency, and guaranteeing the sustainable development of the breeding industry.

The invention relates to the technical field of automatic control, in particular to an intelligent aquaculture automatic control system which comprises a feeding feedback control module, a feeding rhythm allocation module, a water body disturbance partition module, a control unit recognition module and a control path construction module. According to the method, the bait residue density is quantified through image recognition, a feeding state dynamic judgment mechanism is constructed, the accuracy and timeliness of feeding response are improved, the feeding speed is calculated through linkage of time difference and density change, a rhythm allocation strategy based on behavior feedback is formulated, the matching degree of the feeding rhythm to the actual feeding ability is enhanced, and the feeding speed is increased. A main flow path continuous block is constructed by combining flow velocity and flow direction, disturbance levels are marked in a grading manner, a key equipment action area is judged according to cross of fish school active density and dissolved oxygen trend, and an equipment linkage path is constructed by connectivity of a physical channel and an inhabitation area. And an intelligent control scheme of cooperative linkage of ingestion behavior driving, disturbance partition recognition and space layout optimization is realized.