269 results about "Drainage network" patented technology

The invention discloses an intelligent monitoring system for a municipal drainage pipe network, and particularly relates to the technical field of drainage pipe network monitoring. The node operation mode identification module carries out real-time classification and confidence evaluation on the operation state of the pipe network, constructs a multi-attribute pipe network weighted graph based on pipe diameter difference, gradient and confluence density, and extracts multi-scale features through graph Fourier transform. A hybrid anomaly detection link is constructed in combination with an LSTM self-encoder, an isolated forest model and chemical oxygen demand and turbidity water quality verification, and the problems that traditional single-index monitoring is prone to false alarm and missing alarm and inaccurate in positioning are solved; sensor data compensation is realized through cooperation with digital twinning, a rapid detection mode is started during rainstorm early warning, key nodes are processed preferentially, and drainage scheduling is controlled in a closed-loop mode; and target nodes which are easy to accumulate grease are screened based on pipe network topology connectivity, accumulation risks are predicted by fusing multi-sensor data, and a preventive clearing instruction is triggered.

This invention disclosed a full-space intelligent detection method and system for underground drainage networks, as well as storage media, including the following steps: image acquisition, intelligent image denoising, internal pipe defect segmentation, concealed defect detection around the pipe, 3D reconstruction with volume quantification, and pipeline life prediction. This invention introduced a bionic four-wheel-drive, all-terrain detection robot that can effectively navigate through mud and flowing water-challenges that hinder traditional detection devices. By leveraging deep learning algorithms as well as various techniques of computing vision, 3D reconstruction, and point cloud processing, the system thoroughly analyzed collected data to determine defect types and precise locations. Utilizing this analysis, precise location of different defect type and quantitative measurement of their dimensions can be realized. Based on the data analysis results, a deep-learning driven model was developed for predicting pipeline longevity to support maintenance staff with timely information on pipe defects and operational lifespan.

The invention discloses an intelligent discharge port dynamic regulation and control system and method based on multi-source time sequence fusion, and relates to the technical field of intelligent water affairs, and the system comprises a data collection module which is used for obtaining the multi-source data of a target area in real time; the space-time fusion module is used for mapping the meteorological radar data to the topological space of the drainage pipe network through a space-time alignment encoder, dynamically fusing the meteorological radar data, the surface runoff sensor data and the historical drainage data by adopting a multi-head attention mechanism, and generating a space-time fusion tensor; the prediction model module is used for constructing a TCN-LSTM hybrid model, taking the space-time fusion tensor as input and taking future drainage flow as output; and the dynamic regulation and control module is used for optimizing the gate opening degree and the pump station power control sequence in a rolling manner based on a model predictive control algorithm, and performing dual-target optimization by combining real-time electricity price data and overflow risks. According to the system, the prediction precision of the drainage flow can be improved, and double-target balance of the overflow risk and the energy consumption cost can be achieved.

The invention discloses an urban life body full-cycle monitoring system based on a digital twinborn technology, relates to the technical field of urban governance, integrates multi-source data such as GIS data, remote sensing information, surface permeability data and an underground drainage pipe network structure, quantifies a flood high-risk area, and provides an urban life body full-cycle monitoring system based on the digital twinborn technology. The AI flood prediction module deduces the ponding range and the drainage capacity under different rainfall situations by adopting extreme weather analogue simulation based on a historical rainfall trend, a current meteorological condition and a drainage network state, and dynamically adjusts a flood diffusion trend prediction model, so that the flood risk identification is more accurate; the intelligent emergency scheduling module automatically matches flood control resources, combines flood high-risk areas, ponding point locations and traffic flow analysis, optimizes a drainage pump station start-stop strategy, remotely regulates and controls an underground drainage gate, dynamically adjusts the overflow adjustment capability of a sewage treatment plant, and optimizes an emergency risk avoiding route in combination with an intelligent traffic management system. And the accuracy and the execution efficiency of the flood control scheduling scheme are improved.

The invention relates to the technical field of urban drainage network optimization design, and discloses a BIM model-based urban drainage network optimization design method and system, and the method comprises the steps: collecting multi-source hydrological data, and carrying out the preprocessing of the multi-source hydrological data; modeling the drainage system into a space-time diagram structure, and predicting the bearing capacity of the drainage system; an overflow risk assessment model is constructed, key influence factors are identified, node risk degrees are quantified, and high-risk areas are identified; constructing a fault model based on a causal graph, identifying a causal chain of an overflow event, and realizing crossing from correlation analysis to causality understanding; performing drainage system parameter optimization by using the BIM model; by fusing multi-source data analysis, a time-space diagram neural network and a causal inference technology, dynamic bearing capacity prediction, accurate risk assessment and abnormal source overflow diagnosis of the drainage system are realized.

The invention discloses an urban drainage network overflow risk intelligent regulation and control system and method based on the Internet of Things, and relates to the technical field of urban drainage network monitoring, and the system comprises an Internet of Things sensing collection module which is used for fusing sensing data and carrying out the preprocessing, and obtaining an original data flow; the real-time diagnosis and early warning module is used for calculating the original data flow based on a nonlinear dynamic algorithm to obtain a fluid chaos degree index, and performing judgment in combination with a preset early warning threshold to obtain an early warning signal; the risk causal deduction module is used for fusing a graph neural network and a causal discovery algorithm and analyzing a fluid chaos degree index and an early warning signal to obtain a risk propagation space-time atlas; and the cooperative game decision execution module is used for analyzing the risk propagation space-time atlas by using a multi-agent adaptive game algorithm to obtain and execute a cooperative control instruction. According to the method, the Internet of Things, nonlinear dynamics, causal reasoning and the game theory are fused, and the problem of urban drainage pipe network overflow regulation and control is effectively solved.

The invention discloses a regional waterlogging early warning method and device based on a multi-water professional model and a medium, and belongs to the technical field of hydrodynamic models. The method comprises the following steps: selecting a research area, and collecting meteorological data, geographic data and hydrological data of the research area; constructing four basic models of the research area based on the data, and coupling the four basic models to construct a waterlogging forecast simulation overall model; importing an input value of historical measured data of the research area into the waterlogging forecasting simulation overall model to output calculation data, and determining parameters of the waterlogging forecasting simulation overall model when the deviation between the calculation data and the output value of the historical measured data meets the requirement; and based on preset rainfall data, performing regional waterlogging forecasting simulation through the waterlogging forecasting simulation overall model, and outputting a drainage pipe network water flow condition, an open channel water flow condition and a waterlogging ponding submerging condition. According to the method, the multi-water professional model is integrated, and the technical effect of co-simulation of the waterlogging evolution process is achieved.

The invention relates to the technical field of intelligent city management, in particular to an intelligent drainage pipe network management system which comprises a drainage pipe network data monitoring module, a local health assessment module, an intelligent flow adjustment module, an abnormal fault recognition module and an operation state optimization module. According to the method, through comprehensive monitoring and time sequence analysis of the water flow speed, the pipeline pressure, the fluid temperature, the pipe wall vibration frequency and the data transmission state, the accuracy of data integrity evaluation is improved, the stability and reliability of data are enhanced, and the analysis can accurately reflect the pipe network state; the early warning capability of the health state of a local pipeline is improved, the pertinence of flow regulation and control is optimized by calculating the flow distribution ratio and recognizing the flow difference ratio of adjacent pipelines, and meanwhile, the refinement of fault diagnosis is improved by comprehensively analyzing abnormal characteristics and fault data, so that the operation state of a pipe network is optimized, and the fault diagnosis efficiency is improved. And the overall management precision and the operation stability are enhanced.

The invention relates to the technical field of electronics, and discloses a drainage pipe network pollutant tracing method, device and equipment, a medium and a program product. According to the method, the map similarity between a first three-dimensional fluorescence spectrum map corresponding to a first water sample of a target node and a second three-dimensional fluorescence spectrum map corresponding to each upstream pipe network node is calculated; and determining at least one target upstream pipe network node in the plurality of upstream pipe network nodes based on the map similarity, and determining pollution source information of abnormal water in a target node according to first water quality data of the target upstream pipe network node and second water quality data on a sewage flowing path between the target upstream pipe network node and the target node, the advantages of high sensitivity, rapid detection and the like of the three-dimensional fluorescence spectrum technology are utilized in the pollutant traceability process, and efficient and accurate traceability of pipe network pollutants is achieved.

The invention provides a drainage pipe network water quality prediction method based on deep learning, and belongs to the technical field of drainage pipe network water quality monitoring and prediction. The technical problems of poor real-time performance, high labor cost and low efficiency due to the fact that the conventional manual sampling and measuring method is still adopted for daily monitoring and management of the water quality of the urban drainage pipe network at present are solved. According to the technical scheme, the method comprises the steps of S1, determining input data and output data of a drainage pipe network water quality prediction model; s2, data preprocessing; s3, a prediction model is selected, model parameters are set and adjusted, the pump station-sewage treatment plant pipe network water quality process is simulated, and the prediction performance of the model is evaluated; s4, speculating key pump station water quality indexes influencing the biodegradability of the inflow water of the sewage treatment plant. The method has the beneficial effects that the key nodes influencing the inflow water quality of the sewage treatment plant are screened out through deep learning, and suggestions are provided for urban drainage monitoring management subsequently.

The invention relates to the field of urban drainage, and discloses an urban drainage pipe network operation safety assessment method and system, and the method comprises the steps: obtaining pipe network operation state data and pipe network physical structure data, and constructing a drainage pipe network GIS database; acquiring a pipeline defect detection image based on CCTV television detection and QV video detection technologies, and importing the pipeline defect detection image into a GIS system; performing model calibration dynamic simulation on the constructed drainage hydraulic model based on the data in the GIS and the real-time operation monitoring data of the drainage pipe network to obtain drainage pipe hydraulic data; performing defect evaluation on the sewage pipe network, the rainwater pipe network and the confluence pipe network by adopting different safety level models based on the drainage pipe hydraulic data; based on the corresponding defect probability, the risk is divided into a first-level risk, a second-level risk and a third-level risk; analyzing the pairwise variable relationship to find key indexes influencing the risk; and matching a corresponding coping strategy based on the key index influencing the risk, and displaying the coping strategy.

A monitoring point layout method for defect diagnosis of a drainage pipe network comprises the following steps: based on pipe network geographic information system data, constructing a grouping topology table through topological structure extraction and connectivity analysis, layering nodes according to a flow collection path to form a flow-closed upstream and downstream hierarchical relationship, and combining point backtracking, collection and upstream and downstream query in a group to obtain a flow-closed upstream and downstream hierarchical relationship; point locations in each layer of node group are adjusted to ensure the flow closeness; and with the fewest monitoring points and the longest covered pipe diameter as targets, an optimal node group combination is screened through an optimization model, and a final monitoring point position is generated by fusing a pipe diameter threshold value and environmental factors. According to the method, through a flow closing algorithm and a hierarchical optimization mechanism, the problems of low efficiency, insufficient coverage and flow closing deficiency of traditional manual layout are solved, automatic and high-precision point location layout is realized, the timeliness and accuracy of defect diagnosis are remarkably improved, meanwhile, the monitoring cost is reduced, and systematic technical support is provided for operation and maintenance of the urban drainage network.

The invention relates to the technical field of data processing, and discloses a drainage pipe network rain and sewage mixed connection tracing and spatial positioning method for a micro monitoring station network, which comprises the following steps: defining an upstream convergence pipe network range of each micro monitoring station according to a flow direction connection relationship of a drainage pipe network and layout nodes of the micro monitoring stations, establishing a unique mapping relation library between each pipe section and the corresponding multi-stage monitoring topological sequence; obtaining water quality and water level data of a monitoring station, intercepting an analysis time period, and identifying a target monitoring topological sequence which generates a response currently in the mapping relation library based on a time-space propagation association attribute of data between stations; locking an isomorphic mapping candidate pipe section set corresponding to the sequence, and constructing a pollutant source emission inversion model of each candidate pipe section according to a pipe network fluid transport diffusion mechanism; and calculating the fitting deviation degree of the source hypothesis of each candidate pipe section to the monitoring data by using the model, and selecting the pipe section with the minimum deviation degree as a positioning result.

The invention provides a drainage well lid bidirectional positioning matching method and system based on target detection and spatial feature index, and aims to solve the problems that unnumbered well lids are difficult in field positioning and low in file association efficiency. The method comprises the following steps: acquiring a manhole cover image and inertial sensor data through a mobile terminal; an improved lightweight target detection model MobileMC-Net is utilized to realize high-precision identification and visual feature extraction of a well lid area; synchronously fusing GPS coordinates and inertial sensor data to construct a dynamic spatial feature index; on the basis, the method is realized through a two-way matching algorithm: forward matching: associating electronic archive information according to visual features and real-time positions of field unnumbered well lids; and reverse positioning: according to the historical coordinates in the electronic file, navigating to the position of the target well lid in combination with the real-time inertial data. The method breaks through a traditional manual verification mode, remarkably improves the operation and maintenance efficiency of the well lid, and is particularly suitable for rapid recognition and file management of hidden well lids of an urban drainage pipe network.

The invention provides a short-distance coal seam group Y-shaped ventilation gas extraction method, which belongs to the technical field of coal mining, and comprises the following steps: firstly, arranging a main drill hole vertical to a coal seam and branch drill holes extending to upstream and downstream at an angle of 45 degrees to form a Y-shaped three-dimensional extraction network; installing a gas concentration monitoring sensor network, and setting monitoring points to collect real-time data; constructing a gas concentration adjusting buffer system and an intelligent mixed gas distribution device, and maintaining the concentration of extracted gas to be 30-45%; establishing a predictive control model based on a long short-term memory neural network, predicting a gas emission rule in advance and dynamically adjusting extraction parameters; a gas concentration grading utilization system is implemented, and gas with different concentrations is guided into the corresponding utilization system; a gas extraction intelligent monitoring platform is deployed to realize remote monitoring and fault early warning; the technical problems that in the coal seam group gas extraction process, the extraction concentration is unstable, and prediction control is difficult are solved.

The invention belongs to the technical field of building design, and particularly discloses a school building design method capable of converting space attributes, which comprises the following steps: designing a structure-equipment integrated core tube and a corresponding modular space, and constructing a space division system; a subsystem design strategy is utilized to construct a modular water treatment unit, an elastic drainage network, a distributed microclimate unit, an energy routing center, structure-equipment integration, an intelligent skin system and a core tube structure monitoring system; an adaptive transformation mechanism is constructed to ensure the flexible conversion capability of the building space in the whole life cycle; an integrated intelligent control center is designed, intelligent management and optimization of the building space are achieved, and finally school building design is completed. The method solves the problems that an existing building design method can only meet planning at the initial stage of design and lacks elasticity of later-stage transformation, so that when new requirements of use functions occur, the building cannot be transformed according to needs or cannot be transformed by considering existing fire protection specifications and the like.

The invention relates to the technical field of soil treatment, in particular to an integrated saline grainfield soil improvement and remediation method. According to the method, precise irrigation, concealed pipe drainage, salt-tolerant crop planting and bioremediation technologies are comprehensively applied, and systematic treatment of the salinized soil is achieved. Precise irrigation dynamically regulates and controls the leaching depth and frequency on the basis of real-time soil salinity and moisture data, the underground water level is intelligently controlled in combination with an underground pipe drainage network, salt accumulation is effectively reduced, and water resource waste is avoided; according to the salt-tolerant crop and green manure rotation mode, salt damage is reduced through plant salt absorption and root system nitrogen fixation, soil organic matter accumulation and microbial community reconstruction are promoted through application of a microbial agent and biochar, and a sustainable soil improvement mechanism is formed. The intelligent monitoring system integrates multiple types of sensors, collects key indexes such as salinity, nutrients and pH values in real time, predicts the salinization trend and regulates and controls irrigation, fertilization and drainage equipment in a linkage mode, full-process automatic management is achieved, and the management precision and efficiency are remarkably improved.

The invention relates to the technical field of hydraulic engineering, and discloses a drainage pipe network data detection method and device based on a graph theory, and the method comprises the steps: constructing a target drainage pipe network directed graph based on the connection information of pipe sections; performing pipe section missing node completion on the known pipe section node data by using the target drainage pipe network directed graph; constructing a current drainage pipe network directed graph by using the known pipe section node data after the completion of the pipe section missing nodes; performing pipe section topology detection on the current drainage pipe network directed graph to obtain pipe section topology detection data; performing reverse slope pipe section classification identification on the current drainage pipe network directed graph to obtain reverse slope pipe section detection data; and determining a drainage pipe network data detection result based on the topology detection data and the reverse slope pipe section detection data. According to the invention, the accuracy and efficiency of target drainage pipe network data quality inspection are greatly improved, and the situation of missing detection or false detection of drainage pipe network data is avoided.

The invention discloses a simulation forecasting method suitable for urban rainstorm waterlogging in areas lacking pipe network data, which is characterized in that for areas without pipe network data, rainwater inspection well information is acquired by calling a Baidu map, pipeline drainage flow is generalized based on a rainwater well equivalent drainage method, and for areas with pipe network data, pipeline drainage flow is generalized by calling a Baidu map. Constructing a drainage pipe network model based on a one-dimensional Saint-View equation by adopting a finite difference method; then, constructing a hydrodynamic model, coupling a rainwater well equivalent drainage method and a drainage pipe network model with an FVCOM model, simulating an earth surface ponding evolution process, and obtaining a rainstorm waterlogging data set; and training and establishing a water depth prediction model based on the obtained data, then predicting the water depth of each prediction point after a specific time in an actual rainfall event, and giving an alarm in advance when a waterlogging early warning range is reached. According to the method, the submerged water depth of a waterlogging-prone point can be predicted in advance, waterlogging forecasting of a city lacking pipe network data can be achieved, and rainstorm disaster loss is greatly reduced.

The present invention belongs to the technical field of urban drainage network generation, and discloses a method and system for generating an urban drainage network based on remote sensing images, including: obtaining multi-source information of a target area; using a drainage network visual model to identify the satellite remote sensing image to obtain a network topology structure diagram of the drainage network; wherein, the vertices in the network topology structure diagram of the drainage network represent well points in the drainage network, and the edges represent pipelines connected between the well points; associating the multi-source information and the network topology structure diagram through geographical coordinates, and storing the associated multi-source information and network topology structure diagram in a GIS database; calculating the pipe crown cover depth, slope and diameter of each pipeline in the network topology structure diagram, and storing the calculation results in the GIS database as the attribute information of the corresponding pipeline, thereby completing the generation of the drainage network. The present invention realizes the rapid, efficient and low-cost acquisition of the layout and attributes of the drainage network.

The invention provides a drainage network monitoring system. The system comprises a sensing module for detecting one or more conditions at a location in the drainage network; a processing module for processing data received from the sensing module; a wireless communications module for communicating the processed data substantially in real-time to one or more wireless devices including a wireless notification device configured to issue notification information to users, wherein the wireless communications module utilizes a narrow bandwidth, low power wireless communications protocol to communicate processed data to the wireless notification device, and wherein the sensing module has a standalone power supply.

The invention relates to the technical field of urban sewage discharge management, and discloses a time-of-flight mass spectrometry-based rain and sewage mixing detection and traceability method, system and equipment for a drainage pipe network, a storage medium and a program product, which are used for solving the technical problems of high detection and traceability difficulty and low speed in the prior art. Comprising the following steps: acquiring a first detection result of volatile organic compounds of an in-situ time-of-flight mass spectrum at a first detection position along a drainage pipe network, inputting the first detection result into a trained mixed connection judgment model, and determining whether a mixed connection condition exists or not; if yes, according to the path trend information of the drainage pipe network, each upstream drainage pipe network detection well is searched, and at least one second detection position is obtained; acquiring a second detection result of the volatile organic compound of the in-situ time-of-flight mass spectrum of each second detection position; and inputting the second detection results into the trained mixed connection judgment model according to the sequence from the downstream to the upstream to perform mixed connection judgment, and searching the detection well with the mixed connection condition at the most upstream to obtain a mixed connection point.

The application discloses a kind of municipal engineering technical field's multi-source data fusion's urban drainage pipe network evaluation method, for evaluating the drainage quality of drainage pipe network in target area, the method includes obtaining the water quantity data of each water treatment system in the first period of target area, water quality data, climate data, tidal data, water quantity corresponding permanent population data, pollution emission background value and low concentration external water three-dimensional fluorescence spectrum.The application is fused to multi-source data, and the water quality and quantity data of influent of sewage treatment plant or pump station are cleaned, the type of influent and seepage external water in sewage treatment plant is distinguished, the problem that current sewage treatment system quality improvement and efficiency evaluation method only focuses on external water quantity evaluation but external water type is not clear is solved, and then the improvement direction of drainage pipe network quality improvement and efficiency is clear, it is helpful to clear the priority of mixed error connection, damage and external water inflow problem in the quality improvement and efficiency range of drainage pipe network.

The invention belongs to the technical field of urban drainage pipe network optimization, and discloses an optimal arrangement method and system for urban drainage pipe network sensors, and the method comprises the steps: collecting the data of urban waterlogging-prone points, and obtaining a drainage pipe network GIS model through combining the node and pipeline data of an urban drainage pipe network; in the drainage pipe network GIS model, a buffer area is established by taking each waterlogging-prone point as a center, and nodes in the buffer area are drainage pipe network nodes having an influence relationship with the waterlogging-prone points; calculating waterlogging-prone point detection information content, waterlogging-prone event detection reliability, monitoring coverage, information entropy between sensors, joint entropy and mutual information of each node of the drainage pipe network; and with the detection reliability, the monitoring coverage, the maximum information entropy and joint entropy between the sensors and the minimum mutual information as optimization objectives, a multi-objective optimization algorithm is adopted to obtain optimized arrangement points of the sensors. According to the method, the accuracy, reliability and economical efficiency of urban drainage pipe network sensor arrangement can be improved, and the method can adapt to the complexity of an actual drainage pipe network.

The invention provides an intelligent grading identification method for a potential infiltration risk area of a drainage pipe network, and relates to the technical field of risk identification of the drainage pipe network. Establishing spatial relation mapping between triangular grid units of the surface water-underground water coupling model and pipe sections of the drainage pipe network model, calculating hourly liquid level difference time sequence data by utilizing underground water level time sequence data and corresponding pipe section liquid level time sequence data which are obtained through simulation calculation, and constructing risk indexes according to the hourly liquid level difference time sequence data; and then constructing an uncertainty scene sample set and respectively inputting the two models to obtain corresponding simulation calculation results, calculating to obtain a risk index corresponding to each pipe section, and judging a grading result of a potential infiltration risk area according to the risk indexes. Simulation calculation and intelligent diagnosis analysis are fused, and efficient identification of the potential infiltration risk area of the drainage pipe network is achieved on the premise that large-scale sensor deployment is not needed.

The invention relates to the technical field of drainage networks, in particular to a drainage network siltation risk assessment method based on a random forest algorithm, and the method comprises the steps: obtaining pipeline basic data, siltation detection data, land utilization data and social economic data of a drainage network; constructing a pipeline siltation regression model based on a random forest algorithm and obtaining a pipeline siltation degree; calculating the importance degree of input factors by using an OOB sample during training of the pipeline siltation regression model; calculating a pipeline risk factor according to the importance degree of the input factor, the value of the input factor and the pipeline siltation degree; fitting is conducted on the pipeline siltation degree and the pipeline danger factors through a logistic curve, and a curve fitting result is obtained; and calculating a pipeline risk factor corresponding to a preset pipeline siltation degree based on a curve fitting result, and obtaining a pipeline siltation risk assessment result according to the pipeline risk factor and a preset siltation risk grade. According to the method, multi-dimensional comprehensive assessment of the sedimentation risk of the drainage pipeline is realized, and the efficiency and accuracy of risk assessment are greatly improved.

The invention discloses a multi-source data fusion urban drainage pipe network evaluation method in the technical field of municipal engineering, which is used for evaluating the drainage quality of a drainage pipe network in a target area. The method comprises the following steps: acquiring inflow water quantity data, inflow water quality data, climate data, tide data, permanent population data corresponding to inflow water quantity, a pollution emission background value and a low-concentration external water three-dimensional fluorogram of each water treatment system in a target area in a first period; according to the invention, through multi-source data fusion, inlet water quality and quantity data of the sewage treatment plant or the pump station are cleaned, the types of the inlet water and the seepage water in the sewage treatment plant are distinguished, and the problem that the current quality and efficiency improvement evaluation method of the sewage treatment system only focuses on external water quantity evaluation, but the types of the external water are not clear is solved; therefore, the quality-improving and efficiency-improving transformation direction of the drainage pipe network is determined, and the priority of the problems of misconnection, damage and external water inflow in the quality-improving and efficiency-improving range of the drainage pipe network can be determined.

The present invention discloses a digital management system for drainage pipe network operation and maintenance data, which relates to the field of digital management and includes an operation and maintenance management center, in which a pipe network management module, a data acquisition module, a data analysis module, a data collaboration module and an operation and maintenance management module are provided; the pipe network management module is used to construct a three-dimensional spatial model of the pipe network and set an operation and maintenance management node; the data acquisition module is used to collect pipeline drainage data; the data analysis module is used to obtain pipeline load data at various positions and set drainage pipe links; the data collaboration module is used to set collaborative drainage information at the operation and maintenance management node according to the pipeline load data and the drainage pipe link; the operation and maintenance management module is used to optimize the collaborative drainage information, obtain operation and maintenance decision information, and adjust the pipeline flow according to the operation and maintenance decision information; the present invention improves the collaboration and rationality in the operation and maintenance process of the drainage pipe network.

The invention relates to the technical field of geotechnical engineering detection, in particular to an intelligent tunnel geotechnical stability real-time prediction device which comprises a support arranged on the periphery of a tunnel, a drainage network arranged on the periphery of the tunnel and a processing terminal used for inputting and storing geological information on the periphery of the tunnel. The processing terminal is electrically connected with a plurality of optical fiber detection modules, a vibration detection module and an underground water level detection module; the support is fixedly connected with a plurality of hydraulic rods used for installing the vibration detection module, the processing terminal is used for inputting and storing position data of the hydraulic rods, the first detection module obtains a suspected deformation area based on the current time, and then a corresponding comparison deformation area is obtained based on terrain deformation data collected by the annular detection module and the linear detection module. Displaying a corresponding early warning prompt; the method is used for reducing the interference influence on the sensors arranged around the tunnel and guaranteeing the accuracy of data in the real-time monitoring process so as to improve the accuracy of rock and soil stability evaluation.

This invention relates to the diagnosis of sedimentation in drainage networks, specifically providing an unmanned underwater vehicle (UUV) based maintenance decision-making method for drainage pipeline sedimentation diagnosis and waterlogging warning. The UUV of this invention consists of an integrated hull inside the pipeline and a manhole box. The integrated hull inside the pipeline includes a Doppler ultrasound module, a three-axis gyroscope, a high-definition infrared camera, a main controller, a deep learning development board, baffles, a cabin cover, a cabin, fins, towing rings, and a navigation interface. The Doppler ultrasound module, three-axis gyroscope, high-definition infrared camera, navigation interface, and deep learning development board are each connected to the main controller. The fins, towing rings, cabin cover, and baffles are connected to the cabin.