1156 results about "Data modeling" patented technology

The invention relates to the field of environment monitoring, and discloses an efficient and accurate pollution source monitoring quality control system and method, and the system comprises a multi-modal data collection module, a high-dimensional data modeling module, a time-space correlation analysis module, a time sequence prediction module, a multi-task learning module and an anomaly detection feedback module. The method comprises the following steps: collecting data of pollutants such as air, water quality and noise, performing high-dimensional modeling and optimization on the data, and capturing space-time propagation paths of the pollutants among different sensor nodes; the system predicts the short-term change and the long-term trend of the pollutant concentration in real time; the multi-task learning model is used for predicting concentration changes of multiple pollutants at the same time, multi-scale decomposition is carried out on a time sequence through wavelet transform, abnormal changes in data are detected, and a feedback control strategy is generated. According to the invention, the precision and real-time performance of pollution source monitoring are improved, the dynamic change of multiple pollutants can be effectively handled, and the high efficiency and reliability of environment quality monitoring are ensured.

The invention relates to the technical field of construction data modeling, in particular to a BIM (Building Information Modeling)-based construction progress dynamic optimization system, which comprises an abnormal event identification module used for detecting local abnormity based on multi-source data and marking an abnormal area; the local component reconstruction and dynamic fusion module is used for reconstructing an abnormal region component model and performing space mapping to obtain a global BIM model; the component-semantic double-layer graph modeling module is used for constructing a component topological dependency graph and a component semantic relation graph and jointly reasoning a state transition sequence of the component; the state recognition and self-adaptive classification module is used for fusing the geometric features of the component and the graph structure features and executing state recognition and judgment boundary adjustment; and the graph evolution calculation module is used for performing multi-round state propagation deduction to generate a component state evolution result. According to the method, in a heterogeneous information modeling environment, dynamic processing and state intelligent identification of component-level information are realized, and the data calculation precision and response capability under three-dimensional model driving are improved.

The invention provides a structured data modeling analysis method based on Transform, and relates to the technical field of data analysis, and the method comprises the steps: carrying out the piecewise linear transformation and adaptive quantum coding of numeric features and category features through a double-flow feature encoder, and carrying out the fusion through a gating mechanism to obtain fusion features; inputting the enhanced training sample into a feature extraction network comprising a hierarchical attention module and a self-calibration module, and extracting global feature representation through a feature affinity graph and a causal attention mechanism; and constructing an information bottleneck feature filter to select a key feature subset, performing prediction and reconstruction by adopting a double-branch network, and analyzing the feature contribution degree by using a structured gradient method. According to the method, heterogeneous features can be effectively processed, the model performance is improved, and meanwhile an interpretable feature importance evaluation mechanism is provided.

The invention discloses a children MPP (Mycoplasma Pneumoniae) auxiliary diagnosis system based on multi-modal time series data modeling, and aims to improve the auxiliary diagnosis, clinical typing and severe prediction capabilities of mycoplasma pneumonia and reduce the workload of doctors. The MPP dynamic representation method based on multi-modal time sequence modeling is innovatively provided, multi-modal data such as images, electronic medical records and inspection indexes are fused, the time sequence feature embedding technology is adopted, and the evolution trend of the disease course can be effectively captured. Furthermore, deep fusion of different modal data is realized by using a progressive cross-modal semantic alignment strategy and a modal attention gating mechanism, and the problem of insufficient diagnosis accuracy caused by incompatibility of data modals in a traditional method is solved.

The invention relates to the technical field of building construction, in particular to a building construction safety intelligent early warning system based on multi-sensor fusion and deep learning. Comprising a multi-source sensing unit; an intelligent fusion unit; a depth analysis unit; and a dynamic response unit. According to the method, through a mixed deep learning model, personnel-equipment-environment space association in a 1m * 1m * 0.5 m space grid is extracted through an improved U-Net network, and a space risk association map is output; modeling data of 10 sampling periods by using a bidirectional LSTM network, and outputting a short-term prediction value; and carrying out weighted fusion through an attention mechanism to form a risk feature vector, and removing invalid anomalies in cooperation with parameter anomaly judgment and cross validation. And then a risk grade evaluation module introduces multiple coefficients to calculate a risk grade index, and a grid diffusion range is delimited according to grades, so that real-time identification, quantitative evaluation and range pre-judgment of construction safety risks are realized, and the problem that risk identification evaluation lacks scenarized accuracy and comprehensiveness is solved.

The invention discloses a gait rehabilitation body-equipped robot time series data modeling optimization method based on frequency domain learning, and the method comprises the steps: obtaining multi-modal sensor data, carrying out the preprocessing, calculating gait parameters, and generating a patient state vector; enhanced features are obtained by applying multi-scale convolution and frequency domain processing; performing block processing and position coding to obtain a coding enhancement feature, and performing noise suppression processing to obtain a residual fusion feature; a final fusion feature is obtained by applying a self-attention mechanism and frequency domain alignment fusion processing; performing frequency domain decomposition and multi-scale memory filtering to obtain space-time fusion features; evaluating the rehabilitation progress, generating a gait correction vector and a correction time sequence curve, and outputting a smooth safety control sequence. According to the method, the influence of high-frequency noise is reduced, so that the robustness and accuracy of the model are improved; stable characterization of key gait features is realized, and the precision and consistency of the model in processing complex time sequence data are enhanced; and the control performance and the simulation precision of the gait rehabilitation robot are improved.

The invention relates to the field of wastewater treatment, and provides an industrial wastewater membrane process intelligent optimization method and system based on multi-dimensional data modeling. The method comprises the following steps: acquiring wastewater quality parameters of a target scene and a production line equipment state in real time through a multi-dimensional sensor network, and constructing a digital twinborn model of a wastewater treatment system in the target scene; the membrane surface pollution type of each node in the wastewater treatment system is identified through a digital twin model, and the inner membrane pollution evolution condition in the future preset duration is predicted; a multi-strategy evolutionary reinforcement learning algorithm is adopted, the wastewater quality parameters, the production line equipment state and the membrane pollution prediction result are combined, multi-target dynamic optimization is conducted on a wastewater treatment system through a multi-agent collaborative decision-making algorithm, an optimization strategy is generated based on the multi-target dynamic optimization result, and membrane process operation parameters and production control parameters are dynamically adjusted. According to the method, real-time intelligent adjustment of membrane process operation can be realized, the stability of membrane process operation is guaranteed, and the treatment efficiency of membrane process operation is improved.

The invention relates to the technical field of point cloud data modeling, and discloses a point cloud data processing and modeling method, system and device for power distribution equipment and a storage medium, and the method comprises the steps: collecting the point cloud data of the power distribution equipment, and constructing a topological relation graph according to the topological relation of the power distribution equipment; semantic segmentation is performed on the point cloud data based on the topological relation graph, a component association model is constructed, and missing data detection is performed in combination with the topological relation graph; and generating a high-precision three-dimensional model based on the component correlation model. According to the method, semantic segmentation is guided through the topological relation graph, the accuracy of point cloud data classification is improved, and the problem of misclassification in a traditional method is avoided. Meanwhile, a part correlation model is generated based on the segmentation result, the geometric and functional connection relation between the devices is comprehensively described, and deep fusion of the device connection information and the three-dimensional geometric model is achieved.

A system and method measuring data, from user devices, regarding user interactions with a Software-as-a-Service (SaaS) product installed in each of the user devices, using a measurement module in the processor to generate measured data. The measured data is then modeled as a graph, using a graphing application in the processor, wherein the graph includes a plurality of varying metrics, each representing different attributes of a structure of the graph. A viral effectiveness index (VEI) as a single metric summarizing core graph attributes of the graph is determined from the plurality of the varying metrics of the graph using a viral effectiveness index (VEI) module in an analytics application in the processor.

The invention discloses a hardware fault real-time detection method and system based on cooperation of a CPU (Central Processing Unit) and a BMC (Baseboard Management Controller), and the method comprises the following steps: respectively collecting high-frequency state data and tendency indexes by establishing a communication mechanism between the CPU and the BMC; the processor uses a CUSUM algorithm to carry out abrupt change analysis on the periodically collected operation state to generate an abrupt change event; and the management controller uses an EWMA algorithm to model the trend data, and extracts abnormal changes. And the system performs fusion analysis on the mutation event and the trend anomaly to form a fusion anomaly vector. The risk assessment module calculates a risk score based on a preset rule, determines a fault level, positions a target component, and outputs a processing strategy. And triggering a response action according to the strategy and recording an execution state. And the fusion data and the response record are input into the adaptive module together for dynamically adjusting CUSUM and EWMA parameters, so that adaptive updating of the algorithm is realized, and finally a fault detection signal is generated.

The invention discloses an electric power optical communication network alarm analysis method and system based on a time-space diagram neural network. The method comprises the following steps: collecting data of an electric power optical communication network, preprocessing the collected data, and modeling the preprocessed data into a graph structure; extracting and learning the features of the nodes in the graph structure by using a space-time graph neural network, and generating space-time embedded representation of the nodes in combination with space feature extraction and time feature extraction; classifying the nodes based on the spatio-temporal embedding representation of the nodes to obtain prediction labels of devices corresponding to the nodes; and according to the prediction label of the equipment corresponding to the node, carrying out analysis and influence evaluation on the alarm condition in the power optical communication network. According to the method, the electric power optical communication network is represented as the graph structure containing the space-time information, the space-time dependence of the electric power optical communication network can be effectively captured based on the space-time graph neural network, the space relationship in the graph structure and the dynamic change in the time sequence are processed at the same time, and the accuracy and timeliness of alarm analysis are improved.

The invention belongs to the technical field of robot learning, and particularly discloses a robot continuous imitation learning method and system based on a double diffusion model. Comprising the following steps: generating old task data in a submerged space by utilizing a pre-trained latent diffusion model, generating a model through guidance of a task identifier in combination with conditional diffusion, generating an image and state data related to an old task, modeling to construct a diffusion model, realizing multi-modal mapping from a state to an action, and generating a diversified action sequence; high-quality images and state data are generated through a pre-trained variational auto-encoder and a conditional diffusion model, and the consistency of the generated data in time and the accuracy of global semantics are ensured through time sequence continuity constraint and semantic consistency constraint; and performing feedback optimization training on the latent diffusion model. According to the method, the loss weight is dynamically adjusted, and the model is ensured to show good generalization ability and stability in a complex task in combination with collaborative training of an imitation learning strategy and a generator.

The invention discloses a storm surge water increase prediction method and device, electronic equipment and a storage medium, and relates to the technical field of seawater monitoring, and the method comprises the steps: obtaining multi-source observation data of a target sea area, the multi-source observation data comprising satellite remote sensing data, near-shore monitoring data, meteorological mode data and drainage basin data; performing space-time alignment and exception processing on the multi-source observation data to generate standardized space-time grid data; performing hybrid prediction modeling on the standardized space-time grid data through a physical data dual-drive modeling layer to obtain a target probability water increasing field; and dynamically correcting the target probability water increasing field by using real-time observation data to obtain a storm surge water increasing predicted value. A physical mechanism model and a probability generation model are combined, limitation of a single model is overcome, and the nonlinear evolution process of the storm surge is effectively captured; the time-space dual-drive architecture is adapted to complex coast terrains and changeable meteorological conditions, and the risk of missing report or false report is reduced.

The invention discloses a plastic effect simulation display method and system based on a 3D simulation technology, and relates to the technical field of medical information, and the method comprises the steps: collecting the facial data of a patient, and constructing a multi-level facial structure initial model; constructing a hybrid deformation model; individual characteristic parameters of a patient are extracted, and an individual biomechanical parameter set is generated through the biomechanical parameter prediction network; applying the personalized biomechanical parameter set to the mixed deformation model; operation input parameters of plastic surgery are received, and the deformation result of each layer of structure is calculated and displayed in real time based on a GPU parallel computing framework; constructing a tissue healing and recovery kinetic model, and generating appearance prediction data at different postoperative time points in combination with the deformation result; and performing three-dimensional visualization processing on the appearance prediction data. According to the method, CT / MRI, 3D structured light scanning and expression dynamic video sequences are creatively integrated, the limitation of traditional single-mode data modeling is broken through, and the soft tissue boundary recognition precision is remarkably improved.

The embodiment of the invention relates to the technical field of data modeling analysis, and discloses a tunnel rock stratum large deformation analysis method and system based on three-dimensional modeling, and the method comprises the steps: collecting initial three-dimensional point cloud data through three-dimensional laser scanning; constructing an initial three-dimensional geologic model containing spatial morphological characteristics and a rock stratum interface geometric topological relation through space coordinate matching and curved surface reconstruction; finite element unstructured mesh generation is carried out on the model, and a rock stratum mechanical analysis model is established in combination with physical and mechanical parameters; inputting construction parameters and process information, activating unit nodes in sequence, and solving a force balance equation to simulate stress distribution; identifying a potential deformation area through iterative calculation, and adjusting boundary conditions to predict a deformation trend; the three-dimensional space distribution cloud atlas, the time evolution curve and the position marks are fused to generate multi-view deformation early warning information, the accuracy and the visualization degree of tunnel rock stratum deformation analysis are improved, and support is provided for construction safety.

The invention relates to the technical field of electric energy meter metering, and discloses an electric energy meter metering anomaly analysis method and system, and the method comprises the steps: collecting data, such as voltage waveforms, to generate a metering feature vector set, and constructing an anomaly detection rule base; setting a scene parameter type set, and establishing an abnormal association judgment model; dynamically correcting the threshold value by combining the model, and generating an optimized error threshold value set and an abnormal triggering condition set; and updating a metering analysis strategy to generate an abnormal judgment scheme, and correcting data verification sequential logic. The system comprises a data acquisition module, an abnormal rule base construction module, a scene parameter configuration module, a correlation model training module, a dynamic threshold optimization module, a strategy updating module and a time sequence correction module. Through multi-dimensional data modeling, scene-based threshold configuration and dynamic time sequence calibration, the accuracy and adaptability of electric energy meter measurement anomaly detection are improved, and the method is suitable for measurement anomaly analysis of diversified power consumption scenes in a smart power grid.

The invention provides a pulse output sensor control system for an electromagnetic flowmeter, which relates to the technical field of electromagnetic flowmeters and comprises an acquisition module, a modeling module, a generation module, a judgment module and an output control module. The modeling module performs state vector construction and drift identification; the generation module generates control parameters based on state vectors and synthesizes pulse signals; the judgment module evaluates signal deviation and generates scores; the output control module dynamically updates the control parameters according to the scores and drift information; and real-time adaptive adjustment of pulse output is realized. The system can effectively improve the stability and accuracy of output signals of the electromagnetic flowmeter under complex working conditions.

The invention discloses a forward collision early warning device and method based on risk assessment, electronic equipment and a storage medium, and relates to the field of driving prediction, and the device comprises a sensing unit which is used for obtaining the relative position data of a detected object, and carrying out the modeling according to the relative position data of the recognized detected object; detecting a driving state of a driver; detecting the motion state of the vehicle in real time; the processing unit is used for processing the data of the sensing unit in real time, performing target classification, running a dynamic risk assessment algorithm and outputting an early warning level; the prompt unit is used for displaying prompt information on the instrument panel display module and the vehicle-mounted terminal; by means of the scheme, the problems that when a static vehicle or a low-speed obstacle is detected by an existing system, due to the conservative property of TTC calculation, the time window for triggering early warning is later, collision is difficult to avoid when the vehicle is close to the static vehicle, and potential safety hazards exist are solved.

The invention relates to the technical field of project management, in particular to a project cost data calculation management system and method.The project cost data calculation management system comprises a data acquisition module, a data modeling module, a data fusion and optimization module, a dynamic analysis and early warning module, a cost analysis module and a data storage module; a global cost prediction model is established through standardized processing of acquired multi-dimensional data, and real-time data is dynamically analyzed in a project implementation process to generate a cost control report and an adjustment suggestion. And after the project is completed, the system comprehensively analyzes all cost data and generates a multi-dimensional cost analysis report. The security and expandability of data are guaranteed by adopting a distributed storage technology, and the confidentiality and integrity of the data are ensured through a multi-user authority management function. According to the invention, the accuracy, real-time performance and safety of engineering cost management can be effectively improved, and comprehensive support is provided for cost control and optimization of an engineering project.

The embodiment of the invention relates to the technical field of ground surveying and mapping data modeling, in particular to a ground surveying and mapping driven space-time evolution modeling method and system.The method comprises the steps that firstly, multi-dimensional correlation features of elevations, coverage types and deformation data in at least three continuous periods are integrated; the limitation of single time point or single parameter analysis is broken through, and the nonlinear space-time coupling rule in earth surface evolution can be accurately captured; secondly, a spatial-temporal model construction method based on dynamic parameter optimization effectively fuses geographic spatial correlation features and coverage evolution trends, so that the model has adaptive characterization capability for a complex geographic process; in addition, by establishing a closed-loop feedback mechanism of prediction results and model parameters, real-time iterative optimization of the model parameters is realized, and long-term prediction stability in different geographical scenes is remarkably improved.

The invention discloses a wind turbine generator running state analysis method and system based on rotating speed monitoring, and relates to the technical field of wind power generation, and the method comprises the steps: collecting wind turbine generator data and historical data of the wind turbine generator data, and carrying out the preprocessing of the collected wind turbine generator data; performing rotation speed monitoring verification based on fault tree analysis, training a machine learning model in combination with historical data, and performing fault diagnosis by using a rotation speed monitoring verification result; and automatically generating a repair scheme based on the fault diagnosis result, executing the repair scheme, and recovering the normal working state of the wind turbine generator. According to the wind turbine generator running state analysis method based on rotating speed monitoring, data modeling is carried out by using the optimal characteristics of the wind turbine generator, the correlation among the characteristics is captured through the multi-dimensional Gaussian model, the fault probability of each component is calculated, the fault diagnosis precision is improved, and the fault diagnosis efficiency is improved. And the limitation of only depending on a single data source in the prior art is overcome.

The invention discloses a throat lozenge production auxiliary material adding control system, and belongs to the technical field of data modeling and analysis, and the throat lozenge production auxiliary material adding control system comprises a threshold matrix establishment module which is used for constructing a dynamic threshold matrix; the data acquisition module is used for acquiring current production data; the data analysis module is used for generating a mode selection signal according to a comparison result of the current production data and the dynamic threshold matrix; the mode selection module is used for activating a corresponding control mode based on the mode selection signal, and the control mode comprises a conventional mode, an adjustment mode and an exception handling mode; and the control execution module is used for executing a control instruction corresponding to the control mode and sending the verified control instruction to the auxiliary material adding execution mechanism. According to the method, intelligent association and control strategy optimization of multi-source heterogeneous data are realized through data modeling and iterative optimization of an algorithm level.

The invention relates to the field of post data modeling, in particular to an enterprise cross-domain data modeling system and method based on data twinning, and the method comprises the following steps: obtaining an enterprise cross-modal real-time data flow, carrying out key service node identification and service load processing peak evaluation, and constructing a post workload peak datum line; the method comprises the following steps: collecting enterprise multi-post performance information, performing growth trend evolution analysis, performing dynamic post skill perception, and generating a skill assessment map of multiple posts; project processing logs are collected, organization communication association mining is carried out, organization topology association evolution is carried out, and an organization topology interaction visual network is constructed; carrying out multi-attribute evolution evaluation based on a post workload peak reference line and the skill evaluation map, carrying out accurate digital modeling on an organization topology interactive visual network, and constructing an adaptive synchronous optimization twin model; the enterprise operation efficiency is improved, and the post requirement is accurately recognized.

The invention discloses a wind field data modeling method and system for a small and medium-sized unmanned aerial vehicle wind resistance test, and the method comprises the steps: collecting the topographic data, wind speed data and fan data of a test wind field, and determining a preset region and an unmeasured region according to the preset trajectory of an unmanned aerial vehicle based on the test wind field; the method comprises the following steps: acquiring boundary conditions of an unmeasured area and disturbance grid wake flow through fluid simulation based on topographic data and a preset area, performing wind speed vector decomposition on a grid according to a preset wind field boundary condition, and acquiring a preset wind field model according to a disturbance time sequence, an unmanned aerial vehicle maneuvering weight and the disturbance grid wake flow, and obtaining a predicted wind field of the unmeasured area through Gaussian process regression according to the boundary condition of the unmeasured area based on a preset wind field model, and coupling the predicted wind field through a grid time sequence by using different unmanned aerial vehicle preset trajectories to obtain a target wind field model. According to the method, through space-time collaborative interpolation and fluid simulation of Gaussian process regression, the inlet boundary condition better conforms to the fluid mechanics law, and the physical consistency and reliability of the target wind field are improved.

The invention relates to the technical field of geographic space intelligence, and discloses a territorial space purpose control intelligent analysis system, which comprises the following modules: a multi-source data acquisition module, which is based on satellite remote sensing and an IoT sensor, plans vector data, adopts a spatio-temporal data fusion algorithm, and integrates territorial, ecological and economic field heterogeneous data through a distributed crawler technology; generating a territorial space total element data set; the multi-source data acquisition module comprises a remote sensing acquisition sub-module, an Internet of Things access sub-module and a planning data analysis sub-module. Through a distributed data crawling and real-time stream fusion technology and spatio-temporal data modeling, rapid integration of multi-source heterogeneous information is realized, low-efficiency delay of traditional manual acquisition is eliminated, high-precision deformation monitoring and land use change identification are synchronously completed, the violation behavior discovery timeliness is remarkably improved, and the method is suitable for large-scale popularization and application. The three-dimensional space analysis algorithm accurately quantifies the above-ground and underground space element interaction relation, and the engineering conflict risk is effectively avoided.

The invention discloses an industrial communication protocol conversion system and conversion method. The method comprises the following steps: acquiring data of an industrial communication source protocol; protocol conversion: converting the source protocol data into target protocol data according to a sequence of protocol analysis, standardized modeling and protocol repackaging; according to protocol analysis, original data transmitted from different hardware interfaces are analyzed according to corresponding industrial protocols; the standardized modeling parses the data into a data model in a unified format; protocol repackaging: recoding the data model into a data frame conforming to a communication specification according to a target protocol rule; and data output: pushing the obtained target protocol data to a third-party server through an MQTT protocol or directly storing the target protocol data in a database for a third-party system to access. Through three core technologies of protocol analysis, unified data modeling and protocol packaging, lossless conversion among different industrial protocols is realized, and an efficient and reliable data communication infrastructure is provided for an industrial automation system.

The invention relates to the technical field of atomic clock control, and discloses a punctuality type atomic clock control method based on a microwave frequency band. The method comprises the following steps: firstly, acquiring microwave frequency band signal data and atomic state data, and constructing a time calculation model in combination with historical operation data to output a theoretical time value; performing multi-dimensional difference analysis on the theoretical time value and the actual measurement time value to generate a time difference coefficient matrix; inputting the matrix into a spatial propagation analysis network, and combining atomic clock component parameters and environmental parameters to generate a time anomaly probability distribution diagram; and finally, configuring microwave control parameters according to the distribution diagram, including starting a high-frequency monitoring mode for a high-probability abnormal region and applying a disturbance test to adjacent frequency bands. According to the method, through multi-dimensional data modeling, difference analysis and dynamic parameter configuration, the accuracy and adaptability of atomic clock control are improved, and the operation stability of the atomic clock in a complex environment can be enhanced.

A computer-implemented method may comprise receiving a first user input from a computing device and identifying a plurality of data assets from a catalog of data assets based on the first user input, where each data asset in the catalog of data assets comprises a corresponding entity that comprises data. The method may further comprise causing the identified plurality of data assets to be displayed on the computing device, receiving a first user selection of one or more of the identified plurality of data assets from the computing device, obtaining a plurality of data models based on the one or more of the identified plurality of data assets using a large language model, and causing the plurality of data models to be displayed on the computing device.

The invention discloses an intelligent monitoring and early warning method for gas concentration of an ultra-thin coal seam, and belongs to the technical field of gas monitoring. The method comprises the following steps: transmitting gas data to a ground platform in real time by using a 5G technology through monitoring devices and worker portable equipment which are arranged in each underground area; constructing an underground three-dimensional model by combining infrared remote sensing and a visualization technology, dividing a working area and generating a gas concentration stereogram; a width learning neural network optimized by an SCSO algorithm is adopted to predict concentration change, and a dynamic early warning system is established in combination with weighted optimization analysis of regional ventilation capacity; and finally, a regional standard exceeding alarm is sent to a monitoring device and personnel equipment through 5G communication, so that accurate positioning early warning is realized. According to the method, multi-dimensional data modeling and an intelligent algorithm are fused, and the real-time performance and accuracy of gas monitoring of the ultra-thin coal seam are improved.

The invention relates to the technical field of urban drainage management, and discloses a municipal sewage pipe network dynamic drainage system and a control method, and the system comprises a sewage pipe network, a central controller, a pump station, a valve and an edge control terminal; the control method comprises the following steps that real-time data are obtained from a plurality of sensors arranged in a sewage pipe network, the plurality of sensors comprise a flow sensor, a water level sensor, a pressure sensor and a rainfall sensor, and the obtained real-time data comprise the sewage flow, the water level, the pipeline pressure and the external rainfall inflow in the pipe network; based on the collected real-time data, dynamic hydraulic modeling is carried out on the sewage pipe network through a central controller, a mathematical model for describing the hydraulic state change of the pipe network is established, and the mathematical model comprises spatio-temporal evolution of flow, water level and pressure. According to the invention, by constructing a drainage scheduling mechanism based on real-time data modeling and edge control linkage, dynamic adjustment and stable control of the running state of the sewage pipe network are realized.