3698 results about "Hydraulic engineering" patented technology

The invention discloses a large-model-driven intelligent calculation method and system for a water conservancy mechanism model. According to the method, natural language input, structured conversion and intelligent optimization calculation of a scheduling target are realized by integrating a field-enhanced large language model and a water conservancy professional mechanism model. The method comprises the steps of receiving a calculation target expressed by a user in a natural language, and analyzing and converting the calculation target into a constraint condition and a target function which can be recognized by a water conservancy mechanism model; a hydrological model, a hydraulic model, a hydrodynamic model and other models are called based on a workflow engine, and reverse calculation is carried out by adopting a hybrid optimization strategy of'coarse adjustment-fine adjustment-verification '; synchronously and visually displaying the parameter change and the result convergence state in the calculation process; and outputting a calculation result including parameter adjustment logic, standard conformity analysis and multi-scheme comparison. The system comprises a natural language interaction module, a target conversion module, an intelligent calculation engine module, a visualization module and a result generation module, and supports multiple application scenes such as multi-target scheduling, emergency decision making and ecological guarantee. Compared with a traditional scheme, the method has the advantages that the model use threshold is lowered, the dispatching efficiency and calculation transparency are improved, and the method is suitable for complex hydraulic engineering calculation tasks such as reservoir dispatching, cross-basin water transfer and flood control emergency.

The invention discloses a reservoir dam safety monitoring method and system based on edge calculation, and relates to the technical field of hydraulic engineering safety monitoring, and the method comprises the steps: obtaining monitoring data by each edge calculation node, carrying out the preprocessing, and generating a standardized data matrix; establishing a reference database, executing anomaly detection, and generating a labeling time sequence data matrix; performing multi-scale decomposition on the labeled time sequence data matrix, and constructing a node response feature matrix; the central processing unit receives data of each edge computing node, analyzes a multi-parameter spatial propagation mode by constructing a parameter-spatial correlation matrix, and obtains a spatial correlation feature matrix; establishing a Bayesian risk assessment model, predicting a dam risk level and outputting a risk evolution trend; and issuing a differentiated early warning instruction according to the risk assessment result. Through a distributed architecture combining edge calculation and central processing, real-time monitoring, intelligent analysis and accurate early warning of the dam safety state are realized, and the monitoring efficiency and the risk identification accuracy are improved.

The invention relates to a reservoir dam operation safety sky-land project intelligent sensing system and an operation method, and relates to the technical field of hydraulic engineering safety monitoring. The system is composed of a sky-land water conservancy project integrated monitoring and sensing system, a self-adaptive sampling module, a layered distributed architecture and a software and hardware integrated module, and multi-source data such as deformation, seepage, stress strain, vibration and environmental quantity are cooperatively collected through five dimensions of sky domain, airspace, territory, water domain and work domain. The monitoring frequency is dynamically adjusted by using an adaptive sampling strategy, and data cleaning, standardization, space-time registration and fusion processing are completed through a distributed architecture to generate a high-quality comprehensive data set. The system can realize total-factor and whole-process refined monitoring, effectively eliminates data islands, improves data quality and monitoring efficiency, has high reliability, real-time performance and expandability, and provides powerful data support and decision basis for dam safety assessment and intelligent early warning.

The invention discloses a reservoir safety intelligent inspection method and system based on YOLO and VLM fusion, and the method comprises the following steps: S1, multi-source data collection and preprocessing: employing an unmanned plane and ground equipment to collect image / video data, and carrying out the noise reduction, enhancement and space-time alignment processing of the data; s2, improving YOLO target detection: optimizing a network structure and a training strategy; s3, link analysis after VLM: target / scene association judgment is realized by adopting the VLM; and S4, report generation. The invention provides a reservoir safety intelligent inspection method and system fusing YOLO and VLM. Cross-modal semantic understanding, zero sample reasoning and video global analysis capabilities of a visual language large model are utilized, the visual language large model is used as a post-processing tool of YOLO and is fused with the post-processing tool to work in parallel, full-process intelligentization of target detection-semantic analysis-report generation can be realized, the existing technical problems are effectively solved, and the visual language large model has a wide application prospect. And the comprehensiveness of the hydraulic engineering safety monitoring system is enhanced.

The invention discloses a hydraulic engineering supervision and analysis system and method based on digital twinning, and belongs to the technical field of hydraulic engineering operation monitoring and management, and the method comprises the steps: recognizing data points with periodic fluctuation characteristics and sparse spatial distribution, extracting a weak disturbance data subset, and building a response evolution curve; identifying a potential abnormal region through neighborhood statistics and clustering; for the abnormal region, inverting possibly existing micro-damage types and distribution directions based on historical time sequences and environmental boundary conditions to obtain an inversion defect feature set, and inputting the inversion defect feature set into a twinborn model to execute local heat-seepage-force coupling calculation to obtain a stress response evolution curve in a future preset period; calculating a risk score value according to the growth rate, step characteristics and frequency abrupt change of the curve, and outputting risk level and evolution trend information when the score exceeds a set threshold value; according to the invention, early recognition and dynamic evolution analysis of tiny hidden dangers can be realized, and the intelligent and refined level of hydraulic engineering safety supervision is improved.

The invention discloses a dam safety monitoring agent system based on a multi-dimensional large model and digital twinning, and belongs to the technical field of safety monitoring of water conservancy and hydropower engineering. According to the method, a cognitive decision kernel is used as a core innovation module, multi-source data is collected by relying on a sky-ground hydraulic engineering integrated sensor network, cross-modal data fusion and anomaly recognition are achieved through a multi-dimensional large model cluster, and multi-physics coupling simulation is completed in combination with a digital twinborn body; the cognitive decision kernel generates an optimal combination scheme through case retrieval and measure unit adaptation degree calculation, a control instruction is output after digital twin rehearsal verification, and finally closed-loop control of dam safety real-time early warning, autonomous decision making and efficient disposal is achieved through an autonomous response mechanism. The problems of data island, passive response, expert dependence and the like of a traditional monitoring system are solved, the dam safety management intelligent level and the emergency response capability are remarkably improved, and the method has wide engineering application value.

The invention discloses a hydraulic engineering seepage intelligent monitoring system and method, and belongs to the technical field of hydraulic engineering safety monitoring. The system comprises the following modules: a multi-source data acquisition module used for acquiring multiple types of monitoring data in real time through an intelligent sensor network; the double-window time sequence analysis module is used for constructing a quick response window and a trend analysis window to realize double identification of sudden anomalies and long-term trends; the self-learning threshold optimization module is used for automatically extracting a key quantile threshold based on the distribution characteristics of the monitoring data and continuously optimizing weight configuration and early warning threshold setting of various statistical indexes; the multi-scale fusion early warning module is used for performing multi-source information fusion, generating a comprehensive change index and a multi-stage early warning state, and outputting a seepage abnormity early warning signal and a corresponding confidence coefficient; and the visual decision support module provides visual data display, emergency response guidance and intelligent decision support.

The invention discloses a hydraulic engineering dam body crack detection method and system based on machine vision, and relates to the technical field of computer vision. A high-definition camera is used for shooting a dam body image, obtaining a sample data set, extracting feature parameters of different noise types, and constructing a feature database; filtering the current dam body image to obtain a first dam body image, enhancing the crack gray scale difference of the first dam body image, obtaining a second dam body image, segmenting the second dam body image, screening candidate cracks in the crack image, and identifying real cracks according to the edge features of the candidate cracks. According to the method, a feature database is constructed, a targeted dam body image processing flow is combined, a high-definition camera collection and automatic processing flow is used, filtering parameters are dynamically adjusted by constructing the feature database, gray level enhancement and precise segmentation are combined, real cracks are precisely recognized through edge feature analysis and comparison, and quantitative parameters are output; and a reliable basis is provided for dam body safety assessment.

The invention relates to the crossing field of hydraulic engineering hydrological monitoring technology and machine learning prediction technology, discloses a sediment concentration prediction method based on deep learning, and aims to solve the problems that in existing sediment concentration prediction, hyper-parameter manual tuning is low in efficiency, key feature attention is insufficient, local and time sequence information is difficult to consider by a single model and the like. Accurate prediction is realized through five core modules: a data preprocessing module performs missing value filling, abnormal value processing and derivative feature generation on hydrological data; the feature selection module screens key features based on mutual information; the time sequence construction module generates time sequence data through a sliding window; the hyper-parameter automatic optimization module adopts Bayesian optimization iteration to obtain an optimal hyper-parameter; the CNN-LSTM-attention prediction module fuses CNN local feature extraction, bidirectional LSTM time sequence dependence capture and multi-head self-attention mechanism key feature focusing capability, is suitable for scenes such as river channels and channels, and provides efficient decision support for hydrological regulation and control.

The invention belongs to the technical field of hydraulic engineering detection, and particularly provides a hydraulic metal structure surface coating defect detection method and a hydraulic metal structure surface coating defect detection device based on image processing. In a wet and dry alternating environment of a hydraulic metal structure, a multispectral imaging device is used for acquiring an image of a metal structure surface coating, and the acquired multispectral image is preprocessed; a complete and clear image to be detected is obtained; extracting features related to coating defects from the preprocessed to-be-detected image, wherein the features comprise spectral features, texture features and color features; and inputting the extracted features related to the coating defects into a pre-trained classification model, and classifying the coating defects through the classification model. According to the method and the device, the state of the coating can be comprehensively described, and the type and the degree of the coating defect can be accurately identified.

The invention relates to the technical field of hydraulic engineering monitoring, and discloses a hydraulic engineering operation risk monitoring management system based on multi-sensor fusion. The system comprises a data acquisition module, a data fusion module, an early warning and instruction generation module, and an instruction distribution and verification and correction module. The data acquisition module acquires structural deformation, water flow pressure and environment temperature and humidity data in real time through multiple types of intelligent sensors; the data fusion module inputs the collected data into a dynamic weighted fusion model, and generates a comprehensive monitoring index in combination with a sensor confidence coefficient weight and a historical data deviation rate; the early warning and instruction generation module triggers multi-stage early warning and generates an equipment regulation and control instruction set according to the comprehensive monitoring index and a preset safety threshold value; the instruction distribution module distributes the instruction to an execution terminal through a distributed message queue; and the verification and correction module checks terminal feedback signals by using a back propagation verification mechanism, and corrects the confidence coefficient weight of the model.

The invention relates to a digital twinborn enabling dam body self-adaptive early warning method and system, and belongs to the technical field of hydraulic engineering safety monitoring, and the method comprises the following steps: constructing a digital twinborn model of a dam body; performing multi-source data fusion; the operation state of the dam body is simulated in real time, and a simulation result and actual monitoring data are compared and analyzed; establishing a dynamic early warning threshold model; when the monitoring data or the simulation analysis result exceeds an early warning threshold value, early warning information is sent out, and the early warning information is fed back to the digital twinborn model to be updated and optimized; the method has the beneficial effects that the early warning threshold value is automatically adjusted according to the historical operation data, the real-time monitoring data and the analysis simulation result of the dam body, the contribution of the historical operation data, the real-time monitoring data and the analysis simulation result is flexibly set by adjusting the weight and the coefficient of each part, and the early warning accuracy is improved. And the accuracy and the reliability of the early warning threshold are further improved.

The invention discloses a river sediment scouring and silting simulation method, and belongs to the technical field of hydraulic engineering and hydrological simulation. In order to solve the problem that it is difficult to accurately simulate the river sediment erosion and deposition evolution trend in the prior art, the method comprises the steps that on the basis of actually measured topographic data and channel chart data of a target river reach, a water-sediment coupling mathematical model is constructed by adopting a non-uniform sediment unbalance sediment transportation theory, and the model interacts with a water power module and a wave module in real time through an MCT coupler; actually measuring a flood and water and sediment series data calibration verification model; and obtaining future water and sediment process data predicted by a plurality of climate modes, inputting the future water and sediment process data into the verified model, dividing river reaches according to the characteristics such as riverbed gradient and river channel curvature, and calculating the erosion and deposition amount of each sub-river reach in the future 50 years. The method can simulate water flow and sediment movement of rivers, reservoirs and lakes, can be applied to the fields of riverway regulation planning, flood control and disaster reduction decision making, reasonable development and utilization of water resources and the like, and provides important data support and scientific basis for construction and management of water conservancy projects.

The invention discloses irrigation area patrol equipment and method based on an augmented reality technology, and relates to the technical field of agricultural hydraulic engineering and informationization, and the equipment comprises a video collection module, a video and audio interaction module, a communication module, a central processor, a temperature and humidity sensor, a positioning module, and an AI intelligent support module. Through the function of comparing the data with theoretical data, the requirement of patrol on the professionality of patrol personnel is reduced; the invention further provides a working system of AR field perception-AI intelligent support-expert cooperative support-auxiliary operation guidance, basic information is collected and sent based on the head-mounted AR patrol equipment and transmitted to the AI intelligent support module, a theoretical value under normal operation is given through a digital twinborn irrigation area AI calculation function in the AI intelligent support module and compared with the collected information, and the theoretical value is calculated. And in addition, the method can be combined with an expert online study and judgment function, so that the discovery of real-time inspection problems and field study and judgment can be realized.

The invention discloses a hydraulic engineering motor set fault early warning system, and relates to the technical field of hydraulic engineering equipment monitoring, and the hydraulic engineering motor set fault early warning system comprises the following steps: S1, collecting and integrating mechanical state data, including vibration, temperature and oil pollution monitoring; s2, electrical parameter monitoring and complementary analysis are carried out, and mechanical data are associated to identify faults such as short circuit; s3, hydraulic monitoring and cooperative early warning are carried out, pressure flow abnormity is detected, and mechanical and electrical data are linked; s4, multi-source data fusion and fault classification are carried out, and dynamic weight distribution is carried out to improve the diagnosis accuracy; s5, dynamic threshold optimization and cloud decision making are carried out, and the threshold is adjusted according to working conditions to reduce the false alarm rate; s6, closed-loop maintenance and intelligent pushing are carried out, faults are positioned, maintenance suggestions are pushed, and closed-loop verification is carried out; the method has the beneficial effects that accurate fault classification, cross-dimension collaborative early warning and closed-loop maintenance decision making are realized, and the operation reliability and the maintenance efficiency of the unit are remarkably improved.

The invention relates to the technical field of hydraulic engineering monitoring, and discloses a hydraulic engineering intelligent monitoring system and method. The method comprises the following steps: acquiring multi-source monitoring data such as a water level fluctuation sequence, a flow velocity change sequence and a gate opening change sequence of a water conservancy project monitoring point; spatial-temporal feature extraction processing is carried out on the multi-source monitoring data, and a hydraulic engineering operation state feature matrix containing spatial-temporal correlation data of water level fluctuation features, flow velocity change features and gate opening change features is generated; inputting the feature matrix into an anomaly detection model, and generating a water conservancy project abnormal operation state identifier containing anomaly type, anomaly level and anomaly position information; and finally, based on the abnormal operation state identifier, generating a water conservancy project regulation and control instruction set comprising a water level regulation scheme, a flow speed control scheme and a gate opening degree regulation scheme. According to the method, the engineering operation state can be comprehensively monitored, the abnormity can be accurately identified, the regulation and control scheme can be rapidly generated, and the intelligence and efficiency of hydraulic engineering monitoring can be improved.

The invention relates to a BIM-based water conservancy project safety early warning method and device, equipment and a medium. The method comprises the following steps: acquiring multi-source data such as an osmotic pressure gradient field based on a predefined hydraulic engineering monitoring point space coordinate list, and generating a three-dimensional osmotic field matrix after noise reduction, standardization and interpolation processing; physical characteristics of the matrix are extracted by using a pre-trained space-time convolution model, and abnormal parameter labels are determined through threshold comparison and deviation calculation; associating the exception to a BIM component node through a spatial topology matching algorithm; and finally, performing visual early warning based on the BIM abnormal coordinate vector, and generating a structured alarm instruction set by combining risk levels and abnormal type matching treatment measures. According to the method, through multi-source data fusion modeling, feature analysis and extraction, BIM space mapping, three-dimensional visualization decision making and the like, the precision, efficiency and intuition of hydraulic engineering safety early warning are improved, and a reliable technical scheme is provided for guaranteeing safe and stable operation of hydraulic engineering facilities.

The invention relates to the technical field of water conservancy projects, and discloses a water conservancy project water quality detection method and system based on artificial intelligence, and the method comprises the steps: collecting first multi-source data for a water conservancy project, carrying out the alignment of water quality parameter data, remote sensing image data and meteorological and hydrological data in the first multi-source data based on a timestamp, and obtaining second multi-source data; performing data preprocessing and feature extraction processing on the second multi-source data to obtain target feature data; designing a multi-task space-time diagram convolutional network, performing parameter optimization by adopting a genetic algorithm, and constructing a water quality detection model; inputting the target characteristic data into a water quality detection model, and outputting a water quality detection result through the water quality detection model; when the water quality detection result determines that the water quality is abnormal, generating a pollution diffusion simulation map based on a space-time diagram structure and a water quality diffusion rule, and providing emergency decision suggestions according to a water conservancy project scheduling scheme; the accuracy and functional integrity of water quality detection are improved.

The invention discloses a reservoir dam body deformation monitoring system, and relates to the technical field of hydraulic engineering safety monitoring. The problems that the data acquisition dimension is single, the real state of a dam body under the complex working condition is difficult to comprehensively present, the depth detection capability on the internal structure of the dam body is lacked, and the deformation trend of the dam body is difficult to accurately predict are solved. Through comprehensive consideration of interaction of a displacement field, a stress field, a temperature field and a hydraulic field, the dam body mechanical state and the deformation trend are accurately evaluated, the internal defects of the dam body are rapidly and accurately detected by means of an elastic wave tomography technology, a key basis is provided for dam body maintenance and reinforcement, the defect identification capability of the system is improved, and the system has a wide application prospect. A dynamic risk assessment technology is adopted, historical and real-time data are combined to update the dam body deformation state transition probability, potential safety hazards are found in advance, a complete closed-loop monitoring system is constructed, all-weather intelligent guarantee is achieved, monitoring accuracy, timeliness and reliability are remarkably improved, and safe operation of a reservoir is effectively guaranteed.

The invention relates to the related technical field of engineering pipeline detection, in particular to a pre-embedded hydraulic engineering pipeline pressure resistance detection device which comprises a base and a mounting frame fixedly mounted on the base, a sliding plate is slidably arranged on the base, and clamping pieces are symmetrically arranged on the sliding plate; the pressure testing mechanism is mounted on the mounting frame and comprises a limiting ring and a plurality of elastic detection pieces distributed along the circumference of the limiting ring; the reciprocating pushing piece is mounted on the mounting frame and is connected with the elastic detection piece; the intermittent driving part is mounted on the base, the plurality of elastic detection parts are controlled to rotate relative to the pipeline through temporary detection work intermittence of the elastic detection parts, and the pipeline can move relative to the elastic detection parts, so that the elastic detection parts can capture thin-wall and concave areas caused by non-uniformity of an extrusion die at different angles of the pipeline; and meanwhile, local strength insufficiency caused by defects of the extrusion die on the pipeline is rapidly positioned.

The invention relates to the technical field of fault diagnosis, in particular to a hydraulic engineering equipment fault diagnosis method and system.The hydraulic engineering equipment fault diagnosis method comprises the following steps of collecting water inlet and outlet flow velocity data of a water pump to judge direction reversal, extracting rotating speed and pressure trend to collect linkage characteristics, analyzing starting and stopping behaviors to calibrate abnormal sections, and integrating multi-parameter generation structural block screening combination characteristics; arranging the structure blocks to form a continuous path, and classifying the matching mode to generate a fault trend diagnosis path set. According to the method, the direction change of the adjacent sampling points in the flow velocity data of the water inlet and outlet channel of the water pump is continuously judged, the section with transition instability in the local flow velocity structure is recognized, fault symptoms of multiple dimensions are merged into recognizable structural blocks, the overall integrity of feature recognition is improved, and the accuracy of feature recognition is improved. The fault diagnosis process is improved from single-parameter abnormal identification to composite matching of a multi-dimensional behavior mode, and the method has the capability of tracking and positioning the whole process of the operation trend of hydraulic engineering equipment.

The invention belongs to the technical field of hydraulic engineering operation and maintenance, and relates to a prediction and early warning method for operation settlement of a steel dam gate on a complex foundation, in the operation process of the steel dam gate, a multi-parameter sensor network is used for collecting settlement monitoring data of different positions of the steel dam gate, and input characteristics and output characteristics are determined; sorting and screening different input features by using minimum redundancy and maximum correlation, and screening out the input features of which correlation coefficients are lower than a threshold value by using Spearman rank correlation coefficients; a normalization and Kalman filtering iteration prediction model is adopted to preprocess the screened input features, and multivariable time series data are obtained; inputting the multivariable time sequence data into the trained multivariable regression prediction model to obtain a real-time settlement prediction value of foundation settlement; and comparing the real-time predicted value with a set multi-level early warning threshold value, and carrying out corresponding-level early warning on the settlement exceeding the warning limit.

The invention relates to the technical field of sediment section sluicing, in particular to a sediment section sluicing change prediction method under the influence of a water gate. Acquiring a current erosion and deposition topographic sequence corresponding to the target gate at the current moment, and acquiring future rainfall-runoff data, future upstream and downstream water level difference and future tide level change data corresponding to the target gate within a future preset duration; determining future gate scheduling data corresponding to the target gate based on the future rainfall-runoff data, the future upstream and downstream water level difference and the future tide level change data; and inputting the current erosion and deposition topographic sequence, the future rainfall-runoff data, the future upstream and downstream water level difference, the future tide level change data and the future gate scheduling data into a preset erosion and deposition change prediction model, and outputting the silt fracture surface erosion and deposition change corresponding to the target gate in the future preset duration. And timely and reliable technical support is provided for hydraulic scheduling and risk management, and safe and efficient operation of the estuary tide gate is achieved.

The invention discloses a dam seepage intelligent monitoring and risk early warning method, system and device based on an expert large model and a storage medium, and belongs to the field of hydraulic engineering monitoring, and the method comprises the steps: collecting multi-source heterogeneous data from a dam seepage monitoring system, and marking structural domain labels according to data sources and structural attributes; executing corresponding feature fusion processing on different types of data according to the structural domain labels, and constructing a structured feature vector group in a unified format; inputting the feature vector group into a structure-driven expert large model, activating a bound expert path according to a structural domain label, and executing multi-task reasoning; the multi-task reasoning result is input into a dam seepage virtual twin system, a simulation module is driven to conduct seepage diffusion evolution simulation, and a space risk map is generated; and generating an early warning signal and an emergency plan text according to the risk map and the multi-task reasoning result.

The invention relates to a large-model water conservancy business intelligent arrangement calculation method and system. The method comprises the following steps: obtaining hydro meteorological data, hydraulic engineering data, ecological protection data and business management data containing a cross-regional cooperation framework and a scheduling principle, and fusing to generate a hydraulic engineering data set; constructing a water conservancy digital twinborn body based on the water conservancy data set; deducing the water safety situation change through a preset environment evolution model; when the water safety situation change reaches a preset threshold value, analyzing a cooperation framework and a scheduling principle in the service management data through a decision engine based on the situation change and the digital twin, and generating a water conservancy service scheme containing a cross-domain cooperation constraint condition; analyzing the cross-domain collaborative constraint condition by adopting a graph neural network, and generating a multi-service logic topological graph; and generating a water conservancy business execution instruction sequence based on the structural features of the topological graph. According to the invention, water conservancy business full-process intelligent decision-making and cross-domain cooperative scheduling are realized, and the water safety guarantee capability is improved.

The invention relates to the technical field of hydraulic engineering safety monitoring, and discloses a dam safety studying and judging method based on monitoring data multi-physics field simulation, which comprises the following steps: S1, multi-source data acquisition and time-space alignment; s2, dynamically updating the numerical model; s3, safety evaluation and early warning decision making; s4, performing multi-source risk coupling analysis; and S5, issuing the early warning information in a multi-mode manner. According to the method, time-space reference unification of multi-source monitoring data is achieved through feature point matching and sliding window cross-correlation analysis, a self-adaptive Kalman filtering algorithm is adopted to dynamically invert permeability coefficients and elastic modulus parameters, and boundary conditions of a finite element model are adjusted in combination with real-time water level changes; the dynamic simulation precision of a seepage field-displacement field-stress field coupling model is improved, the static evaluation limitation of a fixed threshold value method is broken through through a three-dimensional time-varying safety envelope surface and a Bayesian network grading early warning decision tree, and the risk prediction capability in the flood routing process is enhanced through multi-parameter joint probabilistic reasoning.

The invention discloses a hydraulic transition process optimization method based on constraint sensitive variable dominant two stages. According to the method, a hydraulic transition process calculation model is constructed, a constraint sensitive variable dominant two-stage cooperative multi-objective evolutionary algorithm is accessed, and dominant constraint variables are determined by utilizing Monte Carlo sampling. After the algorithm parameters and the archive set are initialized, the iterative optimization strategies of the first stage and the second stage are executed in sequence; in the first stage, global search is emphasized, and a population is guided to approach a constrained Pareto frontier; in the second stage, oriented optimization based on constraint sensitive variable recognition results is focused, a cooperative individual updating strategy guided by variable types is adopted, and the sensitive variable optimization precision is improved. According to the method, influence differences of different decision variables are fully mined, resource processing key constraints are concentrated, the search direction is dynamically adjusted, accurate and efficient optimization is achieved, a powerful guarantee is provided for safe and stable operation of hydraulic engineering facilities, the service life of the hydraulic engineering facilities is effectively prolonged, and the maintenance cost is reduced.

The utility model relates to the technical field of hydraulic engineering monitoring, in particular to a water level monitoring device for water resource management, which comprises a stand column, a signal box and a solar cell panel are fixedly connected to the surface and the top end of the stand column, and an adjusting mechanism comprises a first supporting rod hinged to the surface of the stand column. An electric push rod is fixedly connected to the inner wall of the bottom end of the stand column, a sliding groove is formed in the surface of the stand column, and a connecting block is slidably connected into the sliding groove; when the ultrasonic sensor needs to be maintained, the first supporting rod can be directly pulled to rotate through the electric push rod, so that the ultrasonic sensor moves towards the shore while descending, and a worker can conveniently maintain the ultrasonic sensor; the height and the distance between the ultrasonic sensor and the water area to be monitored can be randomly adjusted according to the environment, so that the monitoring data is more practical and more stable.

The invention discloses an intelligent water depth measurement method for waterway maintenance of a water transportation project, and particularly relates to the technical field of water conservancy projects. Historical sounding data, meteorological and hydrological parameters, real-time sonar echo data and unmanned surveying ship path information of the target channel area are acquired; a multi-source sounding data fusion model is constructed, and heterogeneous data fusion is realized by adopting wavelet domain feature extraction, remote sensing delay compensation and a graph attention mechanism; constructing a water depth evolution characteristic spectrum based on multi-scale spatial domain characteristic extraction; modeling a water depth propagation relationship between the sounding units by using a graph neural network; projecting the evolution graph to a section grid, and outputting a continuous water depth prediction value in combination with residual error correction and a rolling prediction strategy; identifying a water depth deviation by comparing with a designed navigation depth, and constructing a risk map layer; the method has the advantages of high prediction precision, strong adaptability, good response real-time performance and the like, and can be widely applied to scenes of channel siltation monitoring, scouring early warning, shipping maintenance management and the like.

The utility model discloses an efficient drainage ditch excavation device based on an adjustable shaping excavator bucket, and relates to the technical field of municipal engineering and hydraulic engineering construction. Different design requirements are met by hydraulically adjusting the size of the bucket, the bucket body is formed at a time, manual finishing and template use are reduced, the construction cost is reduced by more than 20%, the excavation precision is controlled by combining GPS positioning and real-time measurement, the groove longitudinal slope and elevation precision is ensured, and the construction efficiency is improved. One-time forming of the drainage ditch and synchronous outward transportation of spoil are achieved, and site accumulation is avoided. According to the scheme, the construction efficiency and quality are remarkably improved, and the method is suitable for municipal drainage engineering under soil texture and strong weathered rock geological conditions.