8558 results about "Drainage tubes" 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 relates to the technical field of underground drainage pipeline monitoring and hidden danger analysis, and provides an underground drainage pipeline hidden danger data analysis method based on big data, which comprises the following steps: acquiring data such as flow, water level, pressure and pipeline structure by using a multi-source sensor to form an original monitoring data set; preprocessing the data, extracting time domain, frequency domain and spatial topological features, and constructing a multi-dimensional feature vector; and inputting the feature vectors into a pre-trained hidden danger prediction model, and outputting hidden danger types and position coordinates. And constructing a spatio-temporal data analysis model based on the hidden danger position, generating a spatio-temporal correlation feature matrix, and performing dynamic risk assessment to obtain hidden danger development trend parameters. And the hidden danger information is mapped to a geographic information system, a three-dimensional visual analysis result is generated, and a graded early warning instruction is transmitted to a drainage pipe network operation and maintenance management system through a communication interface. The operation and maintenance management efficiency of the drainage pipeline can be improved, the hidden danger checking cost is reduced, and the safety and reliability of the drainage system are enhanced.

The invention provides an AI flood real-time simulation method for urban hydrological multi-physics field decoupling supervision, and the method comprises the steps: constructing an intelligent physical prediction model of urban flood, carrying out the layered decoupling of an urban hydrological hydrodynamic system, separating a one-dimensional drainage pipe network submodule from a two-dimensional surface flood module, designing a multi-scale physical constraint loss function for different links, and carrying out the real-time simulation of the AI flood. Microcosmic, mesoscopic and macroscopic multi-scale physical constraint fusion is realized, and residual constraints of the Saint-Venant equation are embedded in a neural network agent of a drainage system in a microcosmic level; in the mid-level, embedding the residual constraint of the shallow water equation in the surface flood routing system; in the macroscopic level, the water balance constraint of the whole system is realized; urban hydrology and hydrodynamic force are decoupled, and multi-scale physical field supervision constraint is achieved.

The invention provides a plant-network integrated intelligent linkage scheduling method and system based on real-time monitoring, and the method comprises the steps: collecting drainage data in a drainage pipe network, obtaining the working condition data of a sewage treatment plant, and obtaining meteorological early warning information; performing comprehensive analysis on the drainage data, the working condition data and the meteorological early warning information based on an intelligent algorithm model, and performing prediction to obtain an analysis result; the intelligent algorithm model is used for simulating the running state of the drainage pipe network and predicting the sewage amount, the water quality change and the mixed sewage overflow risk; and according to the analysis result, generating a dynamic linkage scheduling instruction for the storage tank, the intelligent catch basin and the sewage treatment plant. According to the invention, the running state of the drainage pipe network is accurately simulated through the intelligent algorithm model, the sewage amount, the water quality change and the mixed sewage overflow risk are predicted, then the dynamic linkage scheduling instruction for the regulation and storage tank, the intelligent catch basin and the sewage treatment plant is generated, and the situation that the environment is damaged by overflow is avoided.

The invention provides an urban drainage pipe network monitoring data cleaning and intelligent prediction method, and the method comprises the steps: firstly obtaining pipe network monitoring data, and carrying out the classification tracking and repairing of missing values; adopting a dynamic IQR algorithm based on a sliding window to adaptively identify abnormal candidate points; secondly, introducing a pipe network topological relation, comparing upstream and downstream data change trends, eliminating non-physical anomalies caused by equipment faults, and reserving real hydraulic events; calculating the physical delay time between the nodes by using the cross correlation coefficient; and finally, constructing a random forest model, taking upstream historical data after delay alignment as feature input, and realizing accurate prediction of a future water level and quantification of a feature contribution degree. According to the method, a physical mechanism and machine learning are fused, the problems that data cleaning lacks adaptivity and a deep learning model lacks interpretability are effectively solved, and the accuracy of waterlogging early warning is improved.

An automated urinary output monitoring system includes a urine collection assembly, a scale, and a pump. The urine collection assembly includes a urinary catheter coupled to a first drainage tube. The interim container coupled to the first drainage tube and a second drainage coupled to a final container. The scale measures the weight of urine collected in the final container and a display can depict the volume of urine collected in the final container. A vacuum pump can be coupled with the final container to draw urine from the interim container into the final container, and an air vent can isolate the patient from the vacuum within the final container. The system can be configured to wirelessly transmit urine volume data to an external computing device. A gyroscope can be coupled with the scale to determine the orientation of the scale.

The invention discloses an urban underground pipe network multi-dimensional state perception and risk assessment method and system, and relates to the field of urban pipe network monitoring. The method comprises the following steps: S1, multi-source data acquisition: acquiring physical, chemical and environmental perception differentiation data of a gas pipe network and a drainage pipe network through a multi-source heterogeneous sensor network; s2, edge side data processing: preprocessing the data and dynamically weighting the data, and executing local anomaly recognition; s3, performing multi-parameter coupling analysis to obtain a coupling risk index; s4, carrying out three-dimensional dynamic evaluation, and carrying out differentiated evaluation from dimensions of probability, consequence and vulnerability; and S5, intelligent decision making is carried out, and grading early warning and linkage control are triggered. The system comprises a sensor layer, an edge calculation layer, a cloud analysis layer and an early warning platform. The problems of single dimension, response lag and the like of traditional monitoring are solved, full-life-cycle intelligent management of a pipe network is realized, and safe operation of a city is guaranteed.

The invention discloses an urban inland inundation optimization transformation method and system based on a pipe network earth surface coupling hydrodynamic model, and the method comprises the steps: firstly generalizing a pipeline and a catch basin, constructing a pipe network confluence model according to a pipe network hydrodynamic method, then dividing a sub-catchment area according to a river network flood routing method, building an urban comprehensive drainage model through the sub-catchment area, and carrying out the optimization transformation of the urban inland inundation. Then establishing a surface runoff model by using a surface flood routing method and a Horton penetration method; according to the system, the function of accurately and efficiently simulating the urban inland inundation process is achieved, urban rainwater circulation is completely simulated, an urban drainage pipe network and a river channel model are integrated, and the interaction between an urban drainage system and a ground surface storage water body is truly simulated; and necessary support is provided for disaster situation rehearsal and rainwater engineering planning and layout, so that urban inland inundation points can be removed in advance, influences on production and life of people are avoided, and the method is suitable for being widely popularized and used.

The invention discloses an intelligent method and system for multi-source heterogeneous data fusion management, and belongs to the field of data processing, and the intelligent method for multi-source heterogeneous data fusion management comprises the following steps: deploying a sensor network in a historical ponding area based on historical ponding point data and urban hydrological geographic information, acquiring real-time monitoring data, wherein the real-time monitoring data comprises rainfall intensity, road waterlogging depth, community waterlogging condition and drainage pipe network water level / flow; compared with the prior art, the method has the beneficial effects that sensors are arranged in the historical ponding area to obtain real-time monitoring data, and weather forecast, a terrain model and a pipe network model are combined to construct a waterlogging prediction model to predict whether a ponding risk exists in the historical ponding area in future set time (such as 0.5-3 hours in the future); the ponding risk is treated in advance, so that the problems of traffic interruption caused by ponding, citizen travel obstruction and the like are solved.

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.

The invention relates to the technical field of image processing, in particular to a drainage pipeline defect three-dimensional quantification method and device based on vision, and the method comprises the steps: carrying out the at least one processing of sparse point cloud reconstruction, dense point cloud reconstruction, grid reconstruction, texture mapping and scale calibration of a collected visual image, obtaining a real three-dimensional model of the drainage pipeline; calculating a central point of the drainage pipeline based on the real three-dimensional model, and calculating a central line of the drainage pipeline according to the central point; and based on the center line, identifying at least one actual defect of a staggered joint defect, a disjunction defect, a fluctuation defect and a deformation defect of the drainage pipeline, so as to output actual defect information of the drainage pipeline according to the at least one actual defect. Therefore, the problems of incomplete identification or misjudgment of information such as cracks, leakage and sediments and the like caused by low efficiency and detail missing due to relatively great influence of manual subjective judgment or sparse laser point cloud in related technologies are solved.

The invention discloses a drainage pipeline defect detection system and method based on multi-scale feature fusion and occlusion perception, and the system comprises a data set construction module which is used for constructing a drainage pipeline data set covering multi-defect, multi-scale and multi-occlusion scenes; the model training module is based on establishment of a defect collaborative detection model, and a backbone network of the model training module adopts a feature pyramid sharing convolution module to reinforce the multi-scale detail extraction capability; the neck network introduces an advanced screening path aggregation network and a selective feature fusion module to realize dynamic feature screening and fusion; the detection head is integrated with an MCFEM module, and shielding perception and scale adaptability are enhanced. According to the method, the problems of high omission ratio and poor robustness caused by large defect scale change, serious shielding and complex background in drainage pipeline detection are effectively solved, the small target defect identification precision and the shielding scene detection accuracy are remarkably improved, and the method is suitable for high-precision and light-weight detection of multi-scale defects in a complex shielding environment.

The invention belongs to the field of desilting robots, and particularly discloses a suction type underground drainage pipeline intelligent desilting robot and a desilting method.The desilting robot is composed of an in-pipe integrated robot and an inspection well box; the in-pipe integrated robot comprises a hardware structure and a software module, the hardware structure comprises a mechanical assembly, a circuit assembly, a sediment breaking assembly, a sediment conveying assembly and a machine body, and the software module comprises a pipeline sediment intelligent diagnosis module. A drainage pipeline siltation diagnosis module based on a deep learning network is arranged in a deep learning development board to achieve siltation diagnosis, and a central instruction receiving and sending module is used for sending desilting operation instructions to a nail surface type electric hammer, a high-pressure water spray head, a siltation crushing cutter, a silt pumping pipe, a silt discharging pipe and a vacuum sewage suction pump. According to the invention, automatic and intelligent operation of siltation diagnosis and desilting of the underground drainage pipe network in a certain maintenance area can be realized, the efficiency is greatly improved, personnel do not need to enter the pipeline, and the danger is greatly reduced.

The invention provides a method for constructing an urban drainage unit twin system based on an AI data analysis technology. The drainage unit twin system is composed of an online monitoring device, a network communication system and a remote monitoring platform. The NBIOT or OneNET technology is adopted to carry out networking on the online monitoring device, so that online accurate data acquisition and real-time transmission of a drainage unit pipe network catchment point, a drainage port and a rainfall station intelligent terminal carrying lightweight model are realized; and the remote monitoring platform is combined with an unreal engine technology to truly reflect a 3D real scene of the drainage unit. The intelligent monitoring system has a plurality of data monitoring functions such as rainfall, underground liquid level, flow velocity and flow, water quality, pavement ponding and well lid abnormity alarm monitoring, and provides a comprehensive data source for a drainage unit twin system. The AI technology is used for comprehensively mastering the condition of a drainage system, and visual monitoring of pavement ponding and waterlogging in the urban drainage unit and intelligent monitoring of a drainage pipe network are achieved.

The invention discloses an operation and maintenance method, system and device of a drainage pipe network and a medium. The method comprises the steps of obtaining historical monitoring data of the drainage pipe network in a target area; inputting the historical monitoring data into a preset pipe network risk identification model for training to obtain a trained pipe network risk identification model; acquiring real-time monitoring data of the drainage pipe network in the target area, and inputting the real-time monitoring data into the trained pipe network risk identification model to obtain a pipe network risk identification result of the target area; generating a health index of the drainage pipe network in the target area according to the real-time monitoring data; and generating an operation and maintenance scheme of the drainage pipe network according to the health index and the pipe network risk identification result. According to the method, the risk state in the pipe network is automatically recognized, multiple factors such as the pipe network topological relation and the flow load are combined and considered, the multi-dimensional operation and maintenance scheme is generated, secondary disasters such as urban waterlogging can be effectively avoided, and a scientific basis is provided for pipe network planning and investment decision making.

The invention relates to the technical field of intelligent detection, and further relates to a non-excavation underground water supply and drainage pipe network leakage detection method based on multi-data fusion. The method comprises the steps that 1, acoustic sensors are arranged along a pipe network at equal intervals, each acoustic sensor collects an acoustic time domain signal generated by pipeline leakage, and then the frequency spectrum intensity of the acoustic time domain signal at the pipeline characteristic frequency is calculated; step 2, calculating a cross-correlation function between two different acoustic sensors according to the frequency spectrum intensity, and determining acoustic delay of the acoustic time domain signal between the different acoustic sensors by searching a peak value position of the cross-correlation function; step 3, according to the acoustic delay, calculating the position of a leakage point by using a sound wave propagation principle; according to the position of the leakage point, the water leakage amount is evaluated in combination with pipeline use data. According to the method, through multi-data fusion and intelligent analysis, effective combination of trenchless, high-sensitivity, accurate positioning and scientific quantitative evaluation is realized.

The invention discloses a drainage pipeline multi-parameter sensing trenchless repair dynamic regulation and control method and system. Through a fusion technology path of multi-source comprehensive data acquisition, edge calculation real-time cross validation of abnormity, pipeline state simulation analysis of defect levels, intelligent algorithm dynamic adjustment of repair parameters and geographic information visualization platform full-process monitoring, closed-loop management from abnormity identification to repair regulation and control is realized. A comprehensive data set is formed through multi-source data acquisition, an edge computing technology is utilized to quickly identify abnormities, a simulation technology is combined to evaluate the severity of defects, then process parameters are optimized and repaired based on defect levels, and a regulation and control instruction is generated through linkage of a visual platform and a pump station dispatching system. And finally, a pipeline repair and operation regulation and control scheme is formed through integration, and the repair effect and the system stability are ensured. According to the method, the pipeline abnormity processing accuracy and the repairing efficiency are remarkably improved, and a technical guarantee is provided for safe and stable operation of an urban drainage system.

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 a drainage tube fixing device which comprises a fixing body, a clamping portion and an adsorption portion, the clamping portion and the adsorption portion are arranged on the fixing body, a cavity is formed in the middle of the fixing body, a through hole allowing a drainage tube to penetrate through is formed in the bottom wall of the cavity, and the clamping portion is arranged on the fixing body and located on the outer side of the bottom wall of the cavity. The clamping part can be driven to rotate so as to adjust the diameter of the through hole, a locking part used for limiting rotation of the clamping part is arranged on the fixing body, an adsorption part is arranged on the other side, opposite to the clamping part, of the fixing body, and the adsorption part comprises a negative pressure adsorption assembly used for fixing the fixing body. According to the drainage tube fixing device, the clamping part can rotationally adjust and control the size of the through hole, the drainage tube fixing device can adapt to drainage tubes with different diameters, it is ensured that the drainage tubes are firmly clamped, the adsorption part enables the fixing body to be firmly adsorbed to the skin, and therefore stable support is provided for the drainage tubes, and the overall fixing reliability is enhanced.

The invention relates to the technical field of sponge city sewage control, and discloses a sewage treatment control system of a sponge city. A rainwater collection module of the system obtains rainfall data and initial water quality parameters of rainwater collection points in real time; the pipe network monitoring module monitors water level and flow velocity data of key nodes of the drainage pipe network in real time; the reservoir state module monitors current water level and water quality change parameters of each reservoir in real time; the environment evaluation module receives the data, judges the environment state and generates an environment abnormal signal; the pipe network analysis module analyzes the pipe network sedimentation risk according to the signal and generates a sedimentation evaluation value; the reservoir analysis module analyzes the reservoir load according to the signal and generates a load evaluation value; and the decision control module performs sewage treatment strategy analysis and controls opening or closing of a tail end gate of the drainage pipe network in the target area according to an analysis result. The system can improve scientificity and efficiency of sewage treatment and rainwater regulation and storage of the sponge city.

The invention discloses a sampling mechanism for water conservancy sand content detection, and belongs to the technical field of sampling mechanisms, the sampling mechanism comprises a supporting platform and a servo motor mounted at the upper end of the supporting platform, the output end of the servo motor is connected with a sampling barrel, an interception hole is formed in the side edge of the upper end of the sampling barrel, and a water leakage cover is fixed in the sampling barrel; a protective bottom cover is arranged at the bottom of the sampling barrel, a drainage pipe is installed on the protective bottom cover, a first air cylinder is installed at the upper end of the supporting platform, the telescopic end of the first air cylinder is connected with a sealing ring through a guide connecting rod, the sealing ring is used for controlling opening and closing of an interception hole, and sand blocking filter cloth is arranged in a water leakage cover. According to the sampling mechanism for water conservancy sand content detection, when a water body sample is sampled, a surrounding water area to be sampled is conveniently stirred, so that the sand content of the water area is equal, and the accuracy of a subsequent detection result is improved.

The invention relates to the field of underwater pipeline defect detection, in particular to a lightweight drainage pipeline defect detection method based on improved YOLOv8. Comprising the following steps: S1, collecting a drainage pipeline defect data image, and preprocessing and enhancing the image; s2, inputting the underwater pipeline defect image obtained in the step S1 into an improved YOLOv8 lightweight network model, synchronously generating detection results of three scales, and finally outputting a three-dimensional tensor containing target frame coordinates, confidence and classification probability; and S3, carrying out training and hyper-parameter adjustment on the improved YOLOv8 lightweight network model, and evaluating the detection performance of the model. The provided model realizes the real-time reasoning speed of 336 FPS under the 4.7 MB ultra-small volume, the detection accuracy of a small target is improved, and pipeline defect detection in a complex underwater environment can meet the requirements of detection precision, detection speed and detection cost at the same time.

The invention relates to the technical field of data processing, and discloses a drainage liquid monitoring method and device based on a drainage liquid real-time monitoring device. The light signal to be analyzed comprises a near-infrared light signal emitted to drainage liquid in the drainage tube by a light source emitting module in the drainage liquid real-time monitoring device and a receiving signal detected by a photoelectric detection module in the drainage liquid real-time monitoring device after passing through the drainage liquid; based on the to-be-analyzed optical signal, determining target substances in the drainage liquid as well as substance amount concentrations and substance amounts corresponding to the target substances one by one; judging whether the drainage liquid is abnormal or not based on the substance amount concentration and the substance amount; under the condition that the drainage liquid is abnormal, an alarm signal is generated, and complication prediction is carried out. The system can assist medical staff in discovering potential complications as soon as possible and preventing disease deterioration, and does not need to depend on regular observation and professional judgment of the medical staff, so that the workload of the medical staff is effectively relieved, and the working efficiency is improved.

The invention discloses a blocked pipe section identification method of a drainage pipe network system based on dynamic characteristics, and relates to the technical field of drainage pipe network monitoring. Real-time hydraulic association between pipe network nodes is quantified through a dynamic adjacency matrix to generate a spatial topology matrix, and a time sequence is divided based on a sliding window; a space-time fusion model based on GCN and Transform is constructed to carry out multi-scale dynamic coding, and then a decoder is utilized to reconstruct normal working condition data of a pipe network. By calculating the deviation degree of the pipe section level reconstruction error and the threshold value, the blocked pipe section is accurately recognized, end-to-end modeling from'pipe network topology-drainage time sequence data-external rainfall 'multi-source data to blocked pipe section recognition is achieved, and the technical difficulties of a traditional method in the aspects of dynamic topology modeling, long time sequence dependence capture and multi-modal feature fusion are solved.

The invention relates to the technical field of pipeline water leakage monitoring, and discloses an artificial intelligence leakage distinguishing and positioning method for a municipal water supply and drainage pipe network, and the method comprises the following steps: 1, data collection; 2, analyzing a water leakage detection data model; 3, establishing a leakage point positioning model; step 4, alarm and work order generation; and 5, on-site confirmation and maintenance are carried out. By introducing artificial intelligence and big data technologies, all-day real-time monitoring of a pipe network is realized, the efficiency and accuracy of leakage detection are improved, a leakage point positioning algorithm model is established, accurate positioning of a leakage point is realized, the maintenance time and cost are reduced, and meanwhile, the leakage rate of the pipe network is reduced through real-time monitoring and timely maintenance, so that the maintenance efficiency of the pipe network is improved. The production-marketing ratio and the water-saving rate of a water company are improved, an algorithm model is continuously optimized, and the leakage identification and positioning precision is improved, so that the pipe network monitoring requirements in different scenes and complex geological environments are met.

The invention discloses a filtering anti-blocking structure of a drainage pipe for road maintenance, and relates to the technical field of sewage discharge. The filtering anti-blocking structure of the drainage pipe for road maintenance comprises a first pipeline, and the top of the first pipeline is fixedly connected with a first filtering mechanism. According to the filtering anti-blocking structure of the drainage pipe for road maintenance, sewage is preliminarily filtered by arranging the conical filtering cylinder, and the sewage is secondarily filtered by arranging the first filtering disc and the second filtering disc in a matched manner, so that the situation that a single filtering net is easily and instantly blocked by large-particle impurities is avoided, the pressure of post-stage filtering is reduced, and the filtering efficiency is improved. A limiting rotating ring block is arranged, a first driving motor is started, the limiting rotating ring block is finally driven to rotate through gear meshing, at the moment, a rotating ball continuously slides on the surface of the limiting rotating ring block, and a first spring is matched, so that a second fixing block periodically reciprocates; finally, the banister brush is promoted to clean the through holes of the conical filter cartridge, and the through holes are prevented from being blocked.

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.