14343 results about "Water supply" patented technology

A system for AI-supported leak detection and localization in water distribution infrastructures, consisting of: a large number of distributed sensor nodes mounted along a water pipe, each sensor node comprising the following: a pressure sensor configured to measure local hydraulic pressure fluctuations within the pipeline with a resolution of at least 0.01 bar; a flow sensor configured to measure the volume flow within the pipeline with an accuracy of at least ±0.5% of the measured value; an acoustic sensor configured to detect vibration signatures caused by leaks in a frequency range between 50 Hz and 20 kHz; an embedded microcontroller with integrated analog-to-digital conversion circuitry for digitizing sensor outputs; a wireless communication module configured to transmit time-synchronized sensor data to a cloud-based processing platform; and a local energy subsystem with a rechargeable battery and an optional circuit for generating photovoltaic energy; the cloud-based processing platform includes an artificial intelligence engine that comprises the following: a data acquisition module configured to receive and decode the transmitted sensor data and to perform time alignment; a supervised learning module that is trained on historical data of flagged leaks and non-leaks to classify incoming sensor patterns; an unsupervised learning module configured to detect anomalies by modeling normal operating baselines of the pipeline; and a topology-aware localization module configured to determine leak coordinates using the topology of the pipeline network, modeling the propagation of hydraulic waves, and estimating the arrival time difference from multi-node acoustic detections; and wherein the system is configured to provide real-time leak alerts and georeferenced visualization via a remote monitoring interface.

The invention relates to the technical field of water pump control, and discloses a water pump energy-saving optimization control method and system. The method comprises the steps of obtaining water pump outlet pressure, pipe network tail end flow and water pump motor power monitoring signals; calculating a flow-pressure propagation time compensation coefficient and a pressure fluctuation compensation value to eliminate the influence of signal propagation lag and amplitude fluctuation; extracting an operation condition feature vector by combining the motor power frequency domain energy distribution feature and the pressure fluctuation compensation value, and matching the operation condition feature vector with an energy-saving parameter mapping rule base to generate an energy-saving parameter combination; correcting a water pump efficiency reference curve in real time based on the combination, outputting an efficiency attenuation factor, and generating a control deviation value in combination with a target water supply demand; according to the control deviation value, the water pump rotating speed adjusting quantity and a start-stop priority sequence are calculated, and the priority sequence is optimized in combination with historical operation data to generate a machine number control instruction; and finally synthesizing a control signal to drive an execution mechanism.

A leakage sound signal denoising method based on a combination of optimized VMD and an improved wavelet threshold, for use in solving the problem in existing noise processing methods of low identification accuracy in processing leakage sound signals of water supply pipe networks. The present invention comprises: acquiring leakage sound signals of a real water supply pipe network, and analyzing noise components and ranges of the leakage sound signals; on the basis of a goshawk optimization algorithm, performing parameter optimization on the number K of decomposition modes and a penalty factor α of VMD to obtain optimal parameters, and using the optimal parameters to construct a variational model; using the variational model to decompose the leakage sound signals to obtain a plurality of intrinsic mode components; using a correlation coefficient method to screen the plurality of intrinsic mode components to obtain high-frequency components and low-frequency components; performing wavelet threshold denoising processing on the high-frequency components to obtain denoised high-frequency components; and reconstructing the low-frequency components and the denoised high-frequency components to obtain denoised leakage sound signals. The beneficial effects are that the signal-to-noise ratio of denoising processing is improved, and the identification accuracy is improved.

The present invention discloses a handheld spray fan, the handheld spray fan includes a housing assembly, fan assembly, and spray device. The housing assembly features a handheld part, an air duct at one end, and an air outlet cover at the end of the air duct with a through-hole and multiple air outlet holes. The fan assembly is installed within the air duct, and the spray device comprises a water tank, water pipe, mounting seat, fiber water guide, and atomizer. The water tank is positioned within the housing assembly, the water pipe connects the water tank and the mounting seat, the mounting seat is located at the through-hole, and the atomizer is installed on the mounting seat and exposed through the through-hole. The fiber water guide extends from the water tank to the atomizer, ensuring continuous water supply for stable misting.

Disclosed is a multifunctional outdoor fan. The multifunctional outdoor fan includes a fan base, a fan support, and a fan head. The fan head is provided with a rotating shaft, fan blades mounted on the rotating shaft, and a fan grille mounted outside the fan blades. The fan grille is provided with a water tank, the water tank is provided with a water injection port, and the water injection port is connected to a water tank cover. The rotation shaft is provided with an atomization assembly, the atomization assembly is in communication with the water tank through a water tube, the water tank supplies water to the atomization assembly through the water tube, and the atomization assembly is provided with an atomization port.

The invention discloses a reservoir real-time scheduling simulation system based on a deep learning algorithm, and belongs to the technical field of intelligent water conservancy and artificial intelligence. Aiming at the problems of low prediction precision, poor multi-target coordination capability, weak coping uncertainty and the like of a traditional scheduling system, the system is designed to acquire hydrological, meteorological, water quality and engineering safety data through a multi-source data acquisition unit, and a multi-dimensional feature tensor is generated after preprocessing and fusion; the dispatching center server adopts an STGCN-LSTM mixed model to achieve high-precision prediction and uncertainty quantification of the water inflow process in the future 7-30 days, a reservoir hydrodynamic model and an MO-PPO algorithm are combined to complete multi-scene simulation and multi-target optimization decision, and an AF-DT mechanism dynamically adjusts the dispatching rule priority. According to the system, a sensing-decision-execution-feedback closed loop is constructed, the scheduling adaptive capacity and robustness are improved, the synergistic interaction of flood control, water supply, power generation and ecological protection is realized, and the system is suitable for real-time intelligent scheduling of large and medium reservoirs.

A pet water dispenser includes a water tank for storing liquid, an upper cover installed above water tank and having a sink and a water leakage opening communicating with water tank in surface, a water pump extending into or onto upper cover from lower part of water tank and having a water supply opening in surface above upper cover; water pump includes a first accommodating chamber internally having a motor which drives pump blades below first accommodating chamber to rotate through a rotating shaft, second accommodating chambers of which walls define a pump chamber with downwards-extended wall of first accommodating chamber; a centrifugal water pump formed by pump blades in pump chamber and matched motor and including a water inlet communicating with water tank, and a water discharging opening; a water supply channel formed by second accommodating chambers sequentially communicating with water discharging opening and water supply opening.

The invention discloses a reservoir water regimen analysis method and system based on artificial intelligence, and the method comprises the steps: collecting original signals from multi-source monitoring indexes, such as water level, flow, rainfall and water quality, carrying out the standardized conversion through employing distributed calculation nodes, and forming a unified multi-source data set; based on this, using a feature extraction network to fuse upstream rainfall and reservoir flow, extracting space-time correlation features, and determining a short-term water level change trend; historical water quality abnormal data are integrated through a sequence prediction network, a time sequence is modeled, and potential pollution risks are judged; when the risk exceeds a threshold value, dynamically adjusting the weight of the prediction model, and generating an optimized water regimen simulation scene; finally, resource scheduling logic is fused, multi-scene risks are evaluated, and an optimization management strategy is output. Through deep fusion of spatial-temporal feature extraction and dynamic prediction, accurate water regimen prediction and flood control water supply decision support are realized, and the water resource management efficiency and the pollution prevention and control capability are improved.

The invention relates to the technical field of water conservancy management, and discloses a water conservancy dynamic decision-making method and system based on digital twinning and multi-source heterogeneous data, and the method comprises the steps: carrying out the real-time information capturing of a target water conservancy system, and generating a system operation information scheduling matrix based on a grid geographic model and a time dislocation mark; performing hierarchical slicing on the system operation information scheduling matrix according to a multi-scale time window to form a multi-channel flow framework of regional hydrological dynamic behaviors; analyzing the evolution trends of the water level, the flow and the water quality by using a multi-channel flowing framework and a dynamic fusion analysis operator; dividing a river reach, a reservoir and a regulation and control facility corresponding to the potential risk event as candidate scheduling areas; and based on the local risk coupling network, dynamically optimizing a scheduling strategy, a pump gate operation sequence and a data acquisition frequency, and generating a real-time scheduling and emergency response scheme of the candidate region. The method has the advantage of improving flood early warning accuracy and water supply scheduling efficiency.

The invention provides a water network monitoring method and system based on intelligent water supply and drainage, and the method comprises the steps: obtaining multi-source heterogeneous monitoring data of a water network in real time, carrying out the unified format conversion and time-space synchronization processing of the multi-source heterogeneous monitoring data based on a protocol adaptive fusion mechanism, and obtaining a standardized multi-source time series data set; performing exception identification processing on the standardized multi-source time sequence data set based on the time-varying statistical characteristics and the dynamic control limit to obtain a candidate exception event set; performing multi-sensor cross validation processing on the candidate abnormal event set based on a pipe network topological space association knowledge graph to obtain a high-confidence abnormal event set; performing reverse scene matching analysis on the high-confidence abnormal event set based on a hydraulic numerical simulation model to obtain a preliminary positioning area; performing positioning optimization processing on the preliminary positioning area based on a graph theory pressure wave propagation algorithm to obtain an abnormal position; and performing multi-dimensional fusion display processing on the abnormal position to generate a monitoring report. According to the method, accurate positioning of the abnormal position is realized.

The invention relates to the technical field of water supply pipeline detection, and discloses an underground water supply pipeline health grade evaluation and risk prediction method, which comprises the following steps: sensor arrangement: arranging a flow sensor, a pressure sensor and a sonic sensor at key positions of an underground water supply pipeline; and data acquisition: acquiring signals of the sensor in real time through a data acquisition module, wherein the signals comprise flow, pressure and sound wave signals. Preprocessing the data: carrying out preprocessing such as denoising and normalization on the collected signals; and multi-source data fusion: inputting flow, pressure and sound wave signals into a deep neural network model, and performing feature extraction and fusion analysis. And health level assessment: assessing the health level of the pipeline based on the output result of the deep neural network model. And risk prediction: predicting abnormal working conditions possibly occurring in the future and risk levels of the abnormal working conditions by analyzing the current pipeline state and historical data. And abnormal positioning: accurately positioning an abnormal position in combination with the propagation time of the sensor signal and a positioning model of the deep neural network.

The invention relates to the technical field of pipeline construction, and discloses a water supply pipeline installation auxiliary method and system based on multi-source positioning and digital twinning, and the method comprises the steps: constructing a digital twinning model based on a three-dimensional point cloud modeling technology, and integrating the geographic information of a construction area, high-voltage line corridor parameters and pipeline installation path planning data; a multi-source positioning sensor group is arranged on hoisting equipment and a pipeline, and spatial position, attitude and environmental electric field data are collected in real time and transmitted to a data fusion processing module; multi-source data are fused through a Kalman filtering algorithm, and high-precision positioning is realized in combination with a digital twinborn model; dynamically adjusting a positioning mode based on an adaptive switching mechanism; construction parameters are generated in real time, dynamic monitoring and early warning are conducted, and when a safety threshold value is exceeded, alarm is triggered and operation is suspended. The system corresponds to the method. By the adoption of the method, the positioning precision and environmental adaptability of water supply pipeline installation are improved, the construction safety risk of complex scenes is reduced, and the construction efficiency is improved.

The invention discloses a reservoir multi-target optimization scheduling decision-making method based on digital twinning and AI simulation, and relates to the technical field of reservoir scheduling, the method effectively solves the problems of data fragmentation and information redundancy in traditional scheduling by constructing a multi-source data fused reservoir digital twinning body, realizes accurate virtual-real mapping of reservoir working conditions, and improves the scheduling efficiency. A high-reliability simulation basis is provided for subsequent optimization scheduling, and decision errors caused by data errors or model deviation are avoided; four core targets of flood control, power generation, ecology and water supply are considered, an engineering safety bottom line and rigidity requirements are defined through a layered constraint system, constraint satisfaction degree screening is enhanced by means of an improved multi-target optimization algorithm, and target weights can be dynamically adjusted according to a real-time scene. And after the non-dominated scheme set is optimized and output, candidate schemes are screened in combination with real-time demand quantitative scores.

The utility model provides hydrogen-rich water production equipment which comprises a purification module, a water storage module, a water supply module and a water supply module, wherein a water inlet of the purification module is used for communicating with a water supply device; a water inlet of the hydrogen-rich module is communicated with a water outlet of the purification module; a water inlet of the hydrogen mixing module is communicated with a water outlet of the purification module, and a gas inlet of the hydrogen mixing module is communicated with a hydrogen outlet of the hydrogen-rich module; the control device is communicated with the purification module, the hydrogen enrichment module and the hydrogen mixing module; the water boiling device is used for being communicated with the water outlet of the hydrogen mixing module under the control of the control device, and the water boiling device is used for heating the hydrogen-rich water output by the hydrogen mixing module; the hydrogen and oxygen absorption device is provided with a hydrogen absorption port and an oxygen absorption port, the hydrogen absorption port is used for being communicated with the hydrogen outlet of the hydrogen enrichment module under the control of the control device, and the oxygen absorption port is used for being communicated with the oxygen outlet of the hydrogen enrichment module under the control of the control device. The hydrogen-rich water supply function, the hot water supply function, the hydrogen absorption function and the oxygen absorption function are integrated.

The invention relates to the field of heating ventilation air conditioner automatic control, and discloses an efficient central air conditioner cold station optimization control system and method based on physical AI. The system comprises a data acquisition module, an energy efficiency modeling module, a load prediction module, a rolling optimization module, an execution control module and a feedback correction module. The data acquisition module forms a time sequence data set; the energy efficiency modeling module adopts transfer learning to obtain a system energy efficiency model of a target domain cold station; the load prediction module outputs a predicted cold load sequence; the rolling optimization module is used for solving an optimal control sequence under the constraints of cooling capacity balance, an equipment operation boundary and a temperature difference threshold by taking a predicted cooling load sequence, a system energy efficiency model and a current equipment operation state as input under a model prediction control framework; the execution control module issues a first control action to control the water supply temperature, the water pump and fan frequency and unit start and stop; and the feedback correction module calculates a residual error and is used for closed-loop correction of the next period.

A gate linkage control method and device and a parallel water supply and power generation system are provided. The method comprises the following steps of: acquiring current gate opening degree information of the parallel water supply and power generation system and a current water level of a third trunk canal, the current gate opening degree information comprising current first gate opening degree information and current second gate opening degree information; determining a target gate linkage control strategy of the parallel water supply and power generation system according to the current gate opening degree information of the parallel water supply and power generation system and the current water level of the third trunk canal; and controlling a gate opening degree of a target gate according to the target gate linkage control strategy of the parallel water supply and power generation system.

This invention discloses a dust suppression machine for building demolition, belonging to the technical field of building demolition. It includes a water storage base with a water pump inside, and a water supply pipe connected to one end of a dust suppression cylinder, with a motor installed inside the other end of the dust suppression cylinder. It also includes a first annular pipe fixed to the inner wall of the dust suppression cylinder near the water supply pipe, and the first annular pipe communicating with the water supply pipe; a support frame fixed to the upper surface of the water storage base, with the dust suppression cylinder rotatably mounted on the inner side of the support frame, and an expansion mechanism located in the middle of the dust suppression cylinder. This dust suppression machine for building demolition increases the spraying range by adding nozzles to the outside of the dust suppression cylinder. Compared to traditional methods of changing the spray angle and adjusting the horizontal position, it can further expand the spraying range, adjust the spraying distance, and recover liquid dripping near the dust suppression machine, reducing water waste.

The invention relates to the technical field of electric power automation control, and discloses a coordinated control system for a generator set, which adopts an intelligent DCS integrated design architecture and performs real-time data interaction with a set decentralized control system through hard wiring or a communication protocol. According to the system, through deep fusion of a model prediction control algorithm and intelligent DCS integrated design, a boiler-steam turbine dynamic coordination model is constructed, and a control instruction is generated in real time in combination with a rolling optimization strategy. By means of the design, the main steam pressure deviation is controlled within + / -0.2 MPa, and the load response rate reaches 1.5% Pe / s and is improved by more than 40% compared with a traditional system. Particularly, under the working condition of deep peak regulation, by means of decoupling control over a fuel-air supply-water supply loop through a neural network algorithm, main steam temperature fluctuation (within + / -5 DEG C) and reheater spray desuperheating water quantity sudden change (the change rate is smaller than or equal to 10 t / min) are effectively restrained, the control quality of a unit under the conditions of variable loads and variable coal quality is remarkably improved, and the control efficiency of the unit is improved. And the high-standard requirement of the power grid on the AGC performance is met.

The invention discloses an optimal scheduling method for a ground source heat pump system, and relates to the technical field of new energy utilization and intelligent control of a heat supply system.The method comprises the steps that the ground source heat pump system is divided into a tail end heat exchange layer, a water pump transmission and distribution layer and a source side heat pump coupling layer on the basis of a heat energy conversion and transmission path; constructing a response coupling relation and a mapping model; operating data of three layers of equipment are collected, a time sequence of each layer is established, a dynamic delay structure matching coefficient is calculated, and a multi-layer linkage response lag risk is identified based on a first threshold value; further monitoring a second derivative of the source side temperature change, calculating a thermal inertia interference index, and judging that the thermal response of the source side is abnormal in combination with a second threshold value; and the water supply flow of each branch is extracted, the target water supply value and the total flow of the system are combined, a flow deviation factor is calculated, flow distribution deviation is recognized according to a third threshold value, accurate strategy generation and intelligent scheduling control based on multi-parameter dynamic characteristics are achieved, and the response coordination and operation stability of the system are improved.

The invention relates to the technical field of building engineering management, in particular to a building water supply and drainage system based on a building BIM (Building Information Modeling), which comprises a pipeline drawing generation module, an interface arrangement verification module, an arrangement space detection module, an operation data monitoring module and a fluid risk early warning module. According to the method, standardized drawing mapping of the pipeline trend, size and node layout is achieved through structured extraction of the BIM model, it is ensured that drawing output is complete and consistent, interface offset is automatically compared, abnormal arrangement is accurately recognized, the installation accuracy is improved, component conflicts can be pre-judged through space node clear distance analysis, field coordination is assisted, and the construction efficiency is improved. The method comprises the following steps: monitoring flow velocity pressure of a key drainage section, screening abnormal nodes, filing a hydraulic state, analyzing high-frequency risk points in a time sequence, marking continuous abnormity, providing operation early warning support, constructing drawing generation, arrangement verification, space coordination and a data feedback closed-loop process, and improving construction design accuracy and system operation stability.

The invention discloses a water supply pipeline leakage detection method and system, and relates to the field of artificial intelligence, and the method comprises the steps: collecting a multi-source signal through a plurality of sensor nodes disposed on a pipeline; preprocessing and space-time alignment are carried out on the collected multi-source signal at an edge computing node to obtain a standardized signal; analyzing the standardized signal by using a preset lightweight one-dimensional convolutional neural network model at the edge computing node to obtain a preliminary leakage detection result and a corresponding confidence coefficient; executing a hierarchical response strategy according to the confidence coefficient; and in the cloud analysis module, multi-model fusion analysis is performed in combination with the feature vectors, historical time sequence data and a hydraulic model simulation result, so that leakage points are accurately positioned and verified, and a leakage result is determined. According to the invention, a real-time, accurate and reliable complete solution can be provided for pipeline leakage monitoring.

The invention relates to the technical field of water plant inspection, and discloses a water plant inspection method and system. A water quality monitoring module of the method comprises a plurality of water quality parameter acquisition units and a water quality abnormal fluctuation judgment unit, the plurality of acquisition units are respectively arranged on a plurality of water supply pipelines of a water plant, and the abnormal fluctuation judgment unit determines a water quality fluctuation entropy value based on the output of the acquisition units. And the equipment state monitoring module is used for judging whether to trigger equipment monitoring based on the water quality fluctuation entropy of each acquisition unit, and acquiring the pressure fluctuation spectrum characteristics of the corresponding pipeline during triggering. And the inspection decision module generates a pipe network risk level based on the pressure fluctuation spectrum characteristics and outputs an optimal inspection route. According to the system, abnormal perception is realized through water quality parameter acquisition and fluctuation entropy calculation, the equipment state is analyzed in combination with the pressure fluctuation spectrum, then the risk level and the inspection route are scientifically generated, the accuracy and efficiency of water plant inspection are improved, and the system is suitable for the inspection requirements of a complex water supply pipe network.

The utility model discloses a dragging-type multichannel direct current resistivity acquisition system. The dragging-type multichannel direct current resistivity acquisition system comprises a mobile platform and a plurality of dragging-type electrodes connected with the mobile platform, the dragging type electrodes comprise two power supply electrodes and at least one group of exploration electrodes, the exploration electrodes and the power supply electrodes are arranged in a dipole-dipole device, all the dragging type electrodes are in dragging connection with the mobile platform according to a fixed arrangement interval, and all the dragging type electrodes are driven by the mobile platform to integrally move and drag on the exploration ground; all the dragging type electrodes are electrically connected with a multi-channel direct-current resistivity acquisition host arranged on the mobile platform through a multi-channel large cable; and the active water supply device is used for improving the electric conductivity between the dragging type electrode and the exploration ground. According to the utility model, the design of the active water supply device and the dragging type electrode is adopted, so that non-driven type electrode power supply and potential measurement on flat grounds such as land dams and roads are realized, and the problem of low data acquisition efficiency of a traditional high-density resistivity method is effectively improved.

An irrigation system includes at least one sensor configured to acquire data regarding a water saturation level of an area of soil, a sprinkler configured to provide water to the area of soil, and a control system. The control system is configured to acquire the data from the at least one sensor and adjust a watering scheme of the sprinkler based on the data to provide a desired water saturation level to the area of soil.

The invention provides a water-cooling heat dissipation type motor end cover and shell structure which comprises a motor outer shell, a motor inner shell, a first water cooling mechanism, a second water cooling mechanism, a water supply mechanism and a control console. Therefore, automatic switching between a basic heat dissipation mode in which only the first water cooling mechanism is started, an intelligent cooperation mode in which the two water cooling mechanisms are started cooperatively, and a full-power heat dissipation mode in which the two water cooling mechanisms are fully opened is intelligently realized. Through cooperation of the double water cooling channels and an intelligent control strategy, the problems of fixed heat dissipation capability, slow response and low energy efficiency in the prior art are solved, accurate matching of the heat dissipation capability and the actual working condition of the motor is realized, and the heat dissipation efficiency, the energy-saving effect and the system reliability are remarkably improved.

The utility model discloses a high-pressure protection structure for pipeline safety management, which is arranged at the butt joint position of a conveying main pipe and comprises a housing component sleeved on the conveying main pipe and an active pressure relief component capable of switching the communication state of the housing component and the conveying main pipe according to the pressure in the conveying main pipe, the active pressure relief assembly comprises a pressure relief pipe communicated with the main conveying pipe, the pressure relief pipe is sequentially divided into a liquid inlet section, a transition section and a liquid outlet section in the length direction, the liquid inlet section is located in the main conveying pipe, a plugging cock is arranged in the liquid inlet section and the transition section in a penetrating mode, and the communication state between the liquid inlet section and the liquid outlet section is switched through the plugging cock; when the pressure in the conveying main pipe fluctuates greatly, liquid in the conveying main pipe is discharged through the pressure relief pipe, so that pressure relief is achieved, normal operation of the water supply and drainage system is guaranteed, safety and stability caused by pressure over-limit leakage are avoided, and safety management of the water supply and drainage system is facilitated.

When the intelligent fire-fighting unmanned aerial vehicle cluster system works, the method comprises the following steps that 1, an intelligent fire-fighting unmanned aerial vehicle cluster detects a fire scene and searches a water source near the fire scene, and an optimal route is searched between the water source and the fire scene to lay a pressurizing water pump and a fire hose; 2, the intelligent fire-fighting unmanned aerial vehicle cluster controls a fire-fighting nozzle to conduct fire extinguishing operation, and the positions of a pressurizing water pump and a fire hose are adjusted according to fire behavior changes of a fire scene; and 3, the intelligent fire-fighting unmanned aerial vehicle cluster performs handover control on a fire-fighting nozzle, a pressurizing water pump and a fire-fighting hose in turn, and fuel oil is supplemented to the pressurizing water pump until fire extinguishing operation is finished. A fire-fighting water supply channel can be laid between a water source and a fire scene at an ultra-fast speed, the fire extinguishing operation speed is high, manual intervention is not needed, and nearly infinite fire-fighting water can be continuously provided.

The invention discloses a water supply and drainage pipeline anomaly detection method based on image recognition. Initial video image data are acquired through an image sensor; establishing a three-channel feature extraction network model, wherein the three-channel feature extraction network model at least comprises a visible light channel, a geometrical shape channel for extracting pipeline structure deformation features through 3D convolution and a dynamic optical flow channel for capturing liquid flow abnormal features based on an RAFT algorithm; inputting an initial video image into the three-channel feature extraction network model to perform feature extraction, establishing an analysis model based on a bidirectional LSTM neural network, inputting feature image data into the analysis model to establish time sequence association, performing abnormal evolution through a GNN graph neural network, performing abnormal type classification according to a pipeline state evaluation index, and obtaining a pipeline state evaluation result. And performing early warning based on a classification result. The detection efficiency is improved, the labor cost and the safety risk are reduced, and operation and maintenance personnel can master the state of the pipeline in time.