69 results about "Regional model" patented technology

The invention discloses a federal learning-based cross-device brain grain model training system. The system comprises a terminal device layer, an edge coordination layer and a central server layer, the terminal equipment layer is responsible for collecting original electroencephalogram data and executing local lightweight training; the edge coordination layer is used for aggregating model updates of a plurality of terminal devices in the jurisdiction to generate a regionalized sub-model; according to the central server layer, a central server performs secondary optimization on each regional model to generate a global unified model; the encrypted global model and strategy configuration are pushed to all edge coordination layers; and the edge coordination layer pushes the optimized model and strategy to terminal equipment. Through a three-layer federated architecture and multi-dimensional optimization, the system efficiency is remarkably improved: the terminal equipment only uploads an encrypted model increment, and source data protection is realized in combination with differential privacy and homomorphic encryption; federal transfer learning shortens the cold start time of new equipment.

The invention discloses a gradient random porous structure design method based on a growth function and RBF (Radial Basis Function) interpolation, which relates to the technical field of geometric modeling and computer graphics, and comprises the following steps: collecting and preprocessing Voronoi seed point coordinates and implicit boundary parameters, extracting density field and gradient parameters, and calculating the density field and gradient parameters; mixing different pore forms based on a beta growth function to generate a transition region model, constructing a scalar field in combination with RBF interpolation, fusing the model and a density field to generate global gradient distribution, optimizing iteration to verify mechanical properties and correcting parameters, mapping the parameters into three-dimensional grid data, rendering a pore density cloud chart and a thermodynamic chart, and marking key parameters. By combining the beta growth function and the RBF interpolation, the problem of strong coupling of gradient control and seed point distribution and the problem of rigid transition of a supporting structure body are solved, flexible design of smooth gradual change and complex gradient of the pore form is achieved, mechanical properties are verified through optimization iteration, a three-dimensional visual model is generated, and the method is suitable for large-scale popularization and application. And the geometric continuity and controllability of the porous structure design are obviously improved.

The invention relates to the technical field of field layout modeling processing, in particular to a prefabricated field layout planning modeling method and system based on digital aided design. The method comprises the following steps: screening out a plurality of construction feature points of each three-dimensional point cloud model; obtaining a plurality of model areas at a real-time moment; according to the number of construction feature points in each model area at the real-time moment, the position distribution of different points and the initial voxel size, the adjustment voxel size of each model area at the real-time moment is obtained, and a three-dimensional adjustment point cloud model at the real-time moment is obtained; obtaining a construction offset direction vector and a construction offset of each unregistered point in the initial three-dimensional point cloud model, and obtaining an unregistered point area model of the initial three-dimensional point cloud model; and splicing the three-dimensional adjustment point cloud model at the real-time moment and the non-registered point region model to obtain a prefabricated field layout model at the next moment. According to the method, the accuracy of planning and modeling is improved by obtaining the accurate deviation trend in the construction process.

The invention discloses an intelligent commanding and testing integrated system for hydropower station debugging, and relates to the technical field of hydropower station equipment debugging and operation testing. Based on the physical connection relation and debugging parameters of the water turbine and the speed regulator, a three-dimensional function sub-region model is constructed, a multi-test-mode working condition database is established, through the effect recognition module, the system can construct and judge key indexes in each test mode, and the test efficiency is improved. Whether the problems of resonance effect, guide vane hysteresis, water attack abnormity and disturbance deviation exist or not is automatically recognized, a corresponding early warning instruction is sent out, and a closed-loop link from'phenomenon recognition 'to'reason tracing' is achieved. According to the method, whether a single test is qualified or not can be judged, the collaborative imbalance risk coefficient in the ith test mode can be further calculated, the risk level between collaborative effects is evaluated according to the collaborative imbalance risk coefficient, and intelligent adjustment of a test strategy is supported.

The invention relates to the technical field of data processing, in particular to a software design method and system based on cloud service, and the method comprises the steps: obtaining home space information and an element database; obtaining a style attribute selected by a user and a function priority set for each hard decoration element, generating a basic component according to the style attribute, a layout relationship of each hard decoration element and a node tree where the hard decoration element is located, and sequentially combining the basic components corresponding to each hard decoration element according to the function priority to generate a component tree; all nodes in the component tree are traversed in sequence, behavior scores fed back by a user to the traversed nodes are obtained, node matching is carried out according to the behavior scores, the matched nodes are combined and rendered, and a region model is generated; generating a global model based on the root nodes in each node tree, and splicing the region models selected by the user based on the global model to obtain a soft installation design model; according to the invention, the intelligent level of soft installation design can be improved, and accurate matching of a design scheme and a user demand is realized.

The invention relates to the technical field of population prediction, and discloses a population prediction method and device based on combination of an ARIMA model and a queue element method. The method comprises the steps of obtaining historical migration population data of a to-be-predicted region, determining a difference order through ADF test, preliminarily determining the range of an autoregression order and a moving average order by using an autocorrelation function and a partial autocorrelation function, and constructing an ARIMA model; the optimized ARIMA model is adopted to fit historical migration population data, a migration population prediction result is obtained, and migration population data of each age group in the target prediction time period is output in combination with age structure data of floating population of the reference year; taking the migration population data of each age group as input parameters of migration population in a queue element method; according to a preset sum growth rate, selecting a western mode of a numeral-demann region model life table as a death model; and setting the average life expectancy according to a preset birth ratio, and realizing permanent resident population prediction of the to-be-predicted region.

This invention relates to the field of airspace management technology, specifically disclosing a method and system for dynamically updating airport airspace obstacle data. The method includes: acquiring regional benchmark data based on a first acquisition precision under standard operating conditions to construct a benchmark regional model; acquiring real-time regional data periodically based on a second acquisition precision under real-time operating conditions to construct a real-time regional model with time labels; identifying the real-time regional model based on an adaptive detection parameter set, locating abnormal candidate areas, and simultaneously locating abnormal obstacles; marking the location results, and visually displaying the real-time regional model. This invention acquires relevant airport information through an acquisition device with data transmission capabilities, performs 3D modeling of the airport, and, combined with a pre-acquired standard model, quickly determines abnormal areas and objects at different times, thus providing intuitive feedback to management personnel. The method boasts extremely high identification efficiency and readability.

The embodiment of the invention provides an optimal scheduling method and system for a virtual power plant based on a cloud and fog edge architecture, a computer readable storage medium and a computer program product. The method comprises the following steps: edge nodes realize local optimization scheduling of end side equipment, fog nodes realize coordinated aggregation of a plurality of edge nodes in a jurisdiction area, and a cloud platform realizes cross-area global optimization and knowledge sharing. According to the scheme, a cloud platform, fog node and edge node three-level collaborative optimal scheduling architecture is constructed, and local optimization capability, fog node area coordination capability and global optimization capability are given to the edge nodes, so that the inherent single-point fault risk of traditional centralized scheduling is overcome, and when the communication of the cloud platform is interrupted or invalid, the cloud platform can be effectively optimized. Each fog node can guide autonomous operation in the jurisdiction based on the latest regional model; and when the fog node hierarchy fails, the edge node can still independently carry out local optimization, so that the robustness and reliability of the system under the abnormal working condition are enhanced.

This invention discloses a method and network model for reconstructing missing soil moisture data based on cross-attention, belonging to the fields of remote sensing image processing and artificial intelligence. It involves collecting SMAP, ERA5-Land soil moisture products, and various auxiliary prediction factor data. The auxiliary data is resampled to 9km and preprocessed using multi-source data. A CASTNet model is then constructed. After inputting the preprocessed data into the model, feature extraction, refinement, and enhancement are sequentially performed through modules for spatiotemporal automatic completion, multi-factor feature refinement, and multi-scale attention enhancement. Finally, the model is trained based on a combined loss function composed of global and local losses, and the trained model is used to fill in the missing areas of soil moisture remote sensing data. The CASTNet model contains three cascaded core modules, each implementing preliminary spatiotemporal feature completion, multi-factor feature fusion refinement, and multi-scale feature enhancement, respectively. This invention fully leverages the complementary information of multi-source data, improving the accuracy of soil moisture data reconstruction and ensuring the spatiotemporal continuity and integrity of the reconstructed data.

The embodiment of the invention discloses a virtual area model processing method and a related device, which can be applied to scenes such as the map field, the cloud technology, artificial intelligence, intelligent traffic, auxiliary driving, digital human, virtual human, games, virtual reality, augmented reality and the like. According to the method, the virtual region is constructed by the region image used for describing the geographic entity region, so that the problem of data misalignment is avoided, the accuracy of subsequently constructing the virtual region model is improved, the complexity of the construction process is reduced, the method is not limited by map vector data, and the application range is expanded. In addition, regional images are identified through multiple image identification models, and semantic features describing all the sub-regions are obtained. And performing three-dimensional reconstruction according to the multiple semantic features to obtain a virtual region model for the geographic entity region. Therefore, three-dimensional reconstruction is carried out based on multiple rich semantic features, and the accuracy of the obtained virtual region model is higher.

The invention relates to the field of artificial intelligence, and provides a transformer state visualization method based on a BIM model, and the method comprises the steps: obtaining a region model of a region where a target transformer is located according to a preset BIM model; determining a first loss parameter of the target transformer according to the regional model; determining a second loss parameter of the target transformer according to the load of the target transformer; and calculating life information of the target transformer according to the first loss parameter and the second loss parameter, and displaying the target transformer in the BIM model according to the life information. The service life information of the transformer is displayed in the BIM model, so that the intelligent degree of applying the BIM model to power equipment management and the intuitive degree of the transformer state in the BIM model are improved.

The application discloses a wind field reconstruction and prediction method and device, electronic equipment and storage medium, and belongs to the technical field of wind field data processing. The method comprises: uniformly encoding historical wind speed data and real-time wind speed data of multiple height layers, so that the model can mine the vertical coupling linkage relationship between wind fields at different height layers and the time evolution law of the wind field; geometric prior information and observation masks are introduced, so that the model can complete wind field flow constraint analysis combined with regional building layout and divide the measured data area and the blank reconstruction area; when the model finally completes global wind field deduction based on multi-dimensional fusion features, not only the accuracy of the wind field reconstruction result is improved, but also multi-step fine wind field prediction is realized, the disadvantages of single wind field deduction dimension, lack of environmental constraints and insufficient prediction accuracy of the traditional technology are solved, and the actual needs of fine wind field reconstruction and short-term accurate wind field prediction in a complex building group scene are met.

The present application relates to the technical field of hub polishing robots, and particularly relates to a hub polishing robot trajectory planning method based on 3D vision curve technology; by comparing an actual 3D curved surface graph with a preset 3D curved surface graph, a to-be-polished region model graph of each to-be-polished region in a target hub is generated; according to the to-be-polished region model graph of each to-be-polished region, optimal polishing parameters for polishing processing of each to-be-polished region in the target hub are obtained by pairing in a database; according to the optimal polishing parameters for polishing processing of each to-be-polished region in the target hub, optimal polishing trajectories of a polishing robot are planned; and the optimal polishing trajectories of the polishing robot and the optimal polishing parameters for polishing processing of each to-be-polished region in the target hub are sent to a control terminal. More accurate, self-adaptive, efficient and consistent polishing effects can be achieved, and production efficiency and product quality are improved.

The application provides a three-dimensional terrain geological modeling method and device based on material point method simulation, and relates to the technical field of geotechnical engineering numerical simulation, and comprises the following steps: S1, extracting elevation data of a three-dimensional terrain geological region model; S2, converting the elevation data into three different type model component data and processing; S3, based on the generated model component data, discretizing the model into material points to generate a material point model; S4, importing the generated material point model into a material point method calculation program to perform material point method simulation, and obtaining large deformation of the geology according to the material point method simulation; the application effectively solves the problem of large deformation simulation modeling caused by complex and diverse terrain and landform, is efficient, stable, simple and feasible, significantly improves the modeling efficiency of the material point method and the quality of the generated material point model, and effectively promotes the application of the material point method simulation in the field of geotechnological engineering numerical simulation.

The application discloses a cross-region crop remote sensing image processing method, device and equipment fused with geographic information, relates to the technical field of crop data processing and remote sensing cross technology, constructs a generalization data set of latitude and terrain joint partitioning through sample geographic information data and sample satellite remote sensing image data, obtains a geographic prototype library corresponding to a geographic unit, then trains a crop classification prediction model based on the geographic prototype library, and performs crop classification prediction by using the crop classification prediction model, which can effectively capture the combined influence of geographic multidimensional factors on crop growth, solve the fundamental problem of poor generalization of the cross-region model, improve the cross-geographic processing capability of the crop classification prediction model, and realize high-precision, high-robustness and low-cost crop remote sensing classification.

The invention discloses a regional high-resolution atmosphere reanalysis data construction method based on dynamic downscaling, and aims to improve the depiction capability of medium and small-scale weather processes. According to the method, global reanalysis data is used as a background field, multi-source observation data and high-resolution land utilization information are fused, multi-time and short-period simulation is executed by constructing a multi-layer nested WRF region mode and combining an assimilation strategy combining analysis Nudging and observation Nudging, and a reanalysis data set with hour-level and kilometer-level resolution is constructed step by step. Through comparison and verification of multi-source observation data, the constructed data set is obviously superior to ERA5 in the aspects of ground meteorological elements and vertical profile structure reduction, and has higher regional adaptability and simulation precision. The method is suitable for a plurality of high-resolution meteorological application scenes such as regional artificial intelligence meteorological large models, urban fine meteorological services, high-influence weather disaster early warning, regional climate change evaluation, energy meteorological resource evaluation and the like.

The application provides a meteorological disaster high-resolution regional mode prediction system and method, relates to the technical field of meteorological disaster prediction, and comprises the following steps: performing scale reduction processing on target data of a region to be processed to obtain scale-reduced target data, and performing fusion on multi-source observation data to obtain grid fusion data; according to a preset period, different target observation data are subjected to physical quantity correction, error estimation, quality control and format conversion processing by using specific technical methods, and the processed target observation data are subjected to hourly rapid cycle assimilation processing to obtain target assimilation analysis data; and the scale-reduced target data, the target assimilation analysis data and localized data are used to simulate the operation of a localized numerical prediction mode, meteorological disaster numerical prediction results of different scale requirements are generated, and the meteorological disaster numerical prediction results are corrected by using corresponding scale grid fusion data, so that the technical problem of low prediction accuracy of an existing meteorological disaster system is solved.

The invention relates to the technical field of karst geological exploration, in particular to an intelligent detection method and system for karst action of a pumped storage power station in a karst area, and the method comprises the steps: firstly constructing a large-range sample database covering multiple types of karst areas; training a Transform basic model based on the database, and performing adaptive fine tuning through a target area lightweight sample to obtain an area model capable of automatically outputting a DBSCAN parameter and a transient electromagnetic low resistance threshold adaptive to the area; meanwhile, mining an earth surface-underground feature association rule based on a sample database to construct an association library, training a GCN model according to the association library, and performing fine tuning through a target region fine tuning data set to obtain a target GCN model; and finally, identifying earth surface and underground karst nodes of the target area by using parameters output by the area model, and inputting the earth surface and underground karst nodes into the target GCN model to output a communication state between the nodes. According to the method, the cross-regional parameter adaptive precision and efficiency of karst detection are improved, and accurate identification of the earth surface-underground karst communication system is realized.

The invention provides a steel wire rope monitoring analysis method and system, a storage medium and electronic equipment, and the method comprises the steps: continuously obtaining total working condition data and total steel wire rope data, so as to recognize a steel wire rope defect region, and extracting region data of previous and later time nodes of the steel wire rope defect region; establishing a first regional model and a second regional model based on the regional data, simulating and producing a third regional model in combination with the total working condition and the first regional data, comparing the third regional model with the second regional model to calculate a difference evaluation value and a difference parameter, and if the difference evaluation value exceeds a threshold value, adjusting corresponding working condition data according to the difference parameter, and re-simulating to generate a fourth area model until the judgment value of the difference value between the fourth area model and the second area model meets the requirement, and determining the maintenance strategy and the residual life according to the optimized working condition data and the total steel wire rope data. The problems that in the prior art, a steel wire rope detection technology cannot establish quantitative correlation between defect characteristics and dynamic working condition parameters, so that prediction accuracy is not high, and steel wire rope use and maintenance are not facilitated are solved.

The invention relates to the technical field of artificial intelligence, in particular to an industrial equipment fault diagnosis method and system based on multilevel federated learning, and the method comprises the steps: collecting operation time sequence data of industrial equipment, training a fault diagnosis model deployed at a current equipment end, and obtaining trained local model parameters; based on a dynamic weight distribution strategy, performing weighted aggregation on the received local model parameters of the plurality of equipment ends to generate region-level model parameters; integrating all region-level model parameters, and injecting differential privacy noise to generate a global fault diagnosis model; carrying out collaborative optimization compression on the global fault diagnosis model, and distributing the compressed model to each equipment end to update the fault diagnosis model currently deployed by the equipment end; and based on the contribution data of each equipment end in the current round and historical federated learning, executing a contribution evaluation and incentive distribution process, and updating the dynamic weight distribution strategy with an evaluation result. And the quick response requirement of an industrial scene is met.

This invention discloses a multi-level dynamic monitoring method and system for slope deformation and instability, relating to the field of slope monitoring technology. It includes defining the degree of continuity of the medium under the slope according to the geological information of the medium, marking the degree of continuity at corresponding locations under the slope, and dividing the slope into different regional types. This standardizes the slope geological conditions and provides a reliable foundation for the subsequent construction of regional models. Setting target scales for different regional types at different locations under the slope improves the adaptability of different regional models and balances timeliness and computational accuracy. Analyzing the acoustic emission characteristic rules corresponding to each part of the slope, a FLAC-PFC model is established to monitor slope deformation and instability. Targeted acoustic emission characteristic rules are set, establishing a slope model that takes into account both geological description and acoustic emission characteristics, improving the reliability and adaptability of slope deformation and instability monitoring, and ensuring the stability of slope deformation monitoring.

The invention provides an adaptive model precision control method and system oriented to city-level multi-physics field simulation. The method comprises the following steps: aiming at urban simulation tasks such as a wind environment, a thermal environment, sunlight, a pedestrian flow, energy consumption and the like, reading regional models with different precision levels from an urban model library with multi-level detail levels as candidate models; based on the simulation target quantity corresponding to the simulation task, carrying out regional division on the city space, and calculating a regional sensitivity index according to the influence of the precision change of each regional model on the simulation target quantity; and determining a model precision grade adopted by each region according to a sensitivity priority principle by combining an error estimator introduced by model precision change and computing resource constraints. Through the above mode, on the premise of ensuring the reliability of a simulation result, the calculation scale of a low-sensitivity area is reduced, so that the overall simulation calculation amount and the calculation duration are reduced, and reasonable distribution of limited calculation power resources in urban simulation is realized.

The invention discloses an intelligent furniture adaptive control system based on external environment conditions, which relates to the field of intelligent furniture control and comprises a data acquisition module, a regional model construction module and an adaptive control module. The data acquisition module comprises a parameter acquisition unit and an environment sensing unit which are respectively used for acquiring operation parameters of the air conditioner and external environment data; the regional model building module is used for building a regional simulation model for the region where the air conditioner is located and importing the obtained external environment data and the operation parameters of the air conditioner into the regional simulation model; the self-adaptive control module is used for adaptively adjusting the operation parameters of the air conditioner according to the real-time response threshold value; therefore, the running parameters of the air conditioner can be dynamically and adaptively adjusted according to the change of the overall environmental parameters in the service area, and the temperature in the service area better meets the user requirement.

The present application relates to the technical field of field layout modeling processing, and particularly relates to a prefabricated field layout planning modeling method and system based on digital auxiliary design. The present application screens multiple construction feature points of each three-dimensional point cloud model; then obtains multiple model regions at a real-time time; according to the number of construction feature points, the position distribution of different points and the initial voxel size in each model region at the real-time time, obtains the adjusted voxel size of each model region at the real-time time, and obtains the three-dimensional adjusted point cloud model at the real-time time; obtains the construction offset direction vector and the construction offset amount of each unregistered point in the initial three-dimensional point cloud model, and obtains the unregistered point region model of the initial three-dimensional point cloud model; splices the three-dimensional adjusted point cloud model at the real-time time and the unregistered point region model, and obtains the prefabricated field layout model at the next time. The present application obtains the accurate offset trend in the construction process, and improves the accuracy of planning modeling.

The invention provides a model processing method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a first face model and a to-be-processed second face model, and performing global rigid alignment on the first face model and the second face model to obtain an initial alignment result; for each first area model, performing first rigid alignment on the first area model to obtain an area alignment result corresponding to the first area model; based on a region weight corresponding to the first region model, performing first smoothing processing on the region alignment result to obtain a smoothed region alignment result; performing second rigid alignment on the accessory component corresponding to each first area model to obtain a target alignment result corresponding to the first area model; and performing second smoothing processing on the target alignment result corresponding to each first area model to obtain a target model corresponding to the second face model. According to the invention, the accuracy and fineness of face model processing can be improved.

The application relates to the technical field of production monitoring, and particularly discloses an Internet of Things centralized monitoring method and device for industrial automation equipment, which comprises the following steps: acquiring image information containing corresponding monitoring terminal parameters; receiving image information containing monitoring terminal parameters sent by a monitoring terminal, and performing regional identification on the image information; the monitoring terminal parameters comprise a monitoring terminal number; sending a monitoring instruction to the monitoring terminal according to the regional identification result, acquiring a detail image obtained by the monitoring terminal based on the monitoring instruction, and generating a monitoring report according to the detail image. The technical scheme of the application can acquire images of the whole region through multiple image acquisition devices with high degrees of freedom, update a regional model in real time, perform segmentation on the regional model into a device area and a personnel area, further acquire a detail image based on the segmentation result, and can play a good reference role and reduce the monitoring fatigue of monitoring personnel.

The present application relates to the technical field of environmental monitoring, and particularly relates to an air-ground multi-source spatio-temporal information fusion perception method and system based on artificial intelligence, comprising: constructing a three-source data acquisition architecture, using an intelligent calibration model to dynamically calibrate the three-source data in space and time, and obtaining calibrated data; correlating and verifying the calibrated data and quantifying the weight of interference factors through a causal reasoning model, dynamically adjusting the field constraint parameters through a time sequence feature coding model, completing deep fusion through an intelligent fusion model, and outputting high-precision fusion results; constructing a field knowledge graph, mining potential business rules and identifying unknown abnormal patterns through rule mining and abnormality identification, and obtaining an iteratively updated knowledge graph dynamic; based on a collaborative learning framework and combined with privacy protection technology, realizing cross-region model collaborative evolution, constructing a scenario-based value conversion engine to generate a business structured report. The present application can improve the data fusion accuracy and the full-link traceability accuracy.

This application provides a method and apparatus for training a diversity data aggregation model based on industrial network partitioning, relating to the field of computer technology. It enables model training by adjusting client partitions according to data diversity, thereby improving data security and robustness during model training. The method includes: partitioning multiple clients into industrial network regions based on client characteristics and robustness parameters of each client, obtaining client regions, each client region including at least one client; acquiring client data from each client region; training the model based on client data to obtain regional model parameters corresponding to each client region; aggregating the regional model parameters from each client region to obtain global model parameters; and updating the model parameters based on the global model parameters.

The invention discloses a slope deformation instability multi-level dynamic monitoring method and system, and relates to the technical field of slope monitoring, and the method comprises the steps: defining a medium continuity degree under a slope according to medium geological information, marking the medium continuity degree at a corresponding position under the slope, and dividing region types of different positions under the slope, the slope geological condition is standardized, and a reliable basis is provided for construction of a subsequent region model. And the target scales of different area types at different positions of the slope are set, so that the adaptability of different area models is improved, and the timeliness and the calculation precision are balanced. According to the method, the acoustic emission characteristic rule corresponding to each slope partial region is analyzed, the FLAC-PFC model is established to monitor the slope deformation instability condition, the acoustic emission characteristic rule is set in a targeted manner, the slope model capable of giving consideration to geological description and acoustic emission characteristics is established, the reliability and adaptability of slope deformation instability monitoring are improved, and the slope deformation instability monitoring accuracy is improved. And the stability of slope deformation monitoring is ensured.

The invention relates to the technical field of hydro-fluctuation belts and water and soil conservation, in particular to a hydro-fluctuation belt mulberry bean interaction system construction method based on hydrological rhythms. According to the method, a water level change rate gradient parameter set is constructed, and area mode division, step sequence cooperative execution and self-adaptive optimization feedback are sequentially driven; gradient parameters are generated based on measured data of mechanical water level gauges arranged along the elevation, regional threshold values are dynamically set in combination with soil type labels, mode switching reliability is ensured through upstream and downstream physical cooperative verification, a cross-regional operation time sequence is woven according to the gradient direction and numerical values, and finally an execution effect is fed back to gradient parameter reconstruction. And closed-loop regulation is formed. The invention provides a hydro-fluctuation belt mulberry-bean interaction system construction method based on hydrological rhythm, and solves or at least alleviates the problems of regional response mismatch, high system collapse rate and step blockage caused by global synchronous operation triggered by a single-point water level in the prior art.