424 results about "Hydraulic structure" patented technology

The invention discloses an acousto-optic multi-view-field detection device and method for underwater defects of a hydraulic structure, and the method comprises the steps: obtaining original data, carrying out the filtering processing of the original data, and obtaining the filtered original data; based on the filtered original data, constructing a preprocessed multi-modal data tensor; performing spatial registration processing on the multi-modal data to obtain multi-modal data under a unified coordinate system; a self-adaptive tensor decomposition method is adopted for processing, and real-time position coordinates and movement track data of the robot are obtained; processing by adopting a multi-scale feature analysis method to obtain a target position coordinate and a target feature description; and processing by adopting a space-time optimization method and an uncertainty analysis method to obtain a final target positioning result and a confidence evaluation result. According to the method, the detection precision and the system robustness are improved, the detection accuracy is improved, the system positioning precision reaches the centimeter level, and the method is particularly suitable for intelligent defect detection in a complex hydraulic environment.

The invention discloses an acoustic-optical imaging semantic fusion detection method for underwater defects of a hydraulic structure, and the method comprises the steps: obtaining underwater acoustic and optical images of the hydraulic structure at the same time through underwater acoustic-optical imaging sensing for the underwater defects of the hydraulic structure; and on the basis, acoustic semantic features in the acoustic image are extracted through a semantic feature extraction module, optical image features in the optical image are processed by taking the acoustic semantic features as guidance, and a defect detection result is obtained when an underwater defect target exists in the image, so that underwater defect sound-light imaging semantic fusion detection is realized. The technology is suitable for health monitoring of various dam system hydraulic structures, and operation safety and water safety of hydraulic engineering are guaranteed.

The invention provides a concentric circle filling device and an application method in mass concrete crack control, and belongs to the technical field of hydraulic structures. The concentric circle filling device comprises a pipe body structure, the pipe body structure comprises an outer pipe body and an inner pipe body, the outer pipe body and the inner pipe body are coaxially arranged in a sleeved mode, and an annular area between the outer wall of the inner pipe body and the inner wall of the outer pipe body serves as a first storage space; an inner cavity of the inner tube body serves as a second storage space; the inner tube body is sleeved with an inflatable annular air bag, and when the annular air bag is not inflated, the annular air bag can slide up and down along the inner tube body; after the annular air bag is inflated, one end of the first storage space can be completely sealed and plugged; an air bag ball is arranged in the inner tube body, and after the air bag ball is inflated, one end of the second storage space can be completely sealed and plugged. The method is implemented based on the concentric circle filling device and is applied to mass concrete crack control. Cracks generated by the gate pier can be effectively controlled, and meanwhile the integrity of the structure is improved.

The invention provides a dam deformation deep learning prediction method fusing hysteretic HST and an interpretable hybrid convolution attention mechanism, and belongs to the field of hydraulic structure health monitoring. According to the HST model, dam deformation influence factors are divided into water level, air temperature and time, and phase difference analysis and related compensation technologies are introduced, so that the parameter quantity of the model is remarkably reduced, traditional 9 cycle terms are reduced to 4 core components, and the modeling efficiency and precision are improved. On the basis, a mixed deep learning model combining 1D convolution, LSTM, 2D convolution, a multi-head self-attention mechanism and an interpretability method SHAP is constructed, and efficient prediction of dam deformation is achieved. The model has both long and short term response modeling capability and long sequence stability, has relatively high interpretability, and is suitable for dam body structure health assessment and early warning in a complex environment.

The invention provides an arch dam shape efficient intelligent optimization method and system based on an agent model. The method comprises the steps that an arch dam physical-numerical model is constructed, a double optimization target with structural safety and economical efficiency as the core is determined, design parameters are selected, a constraint function is set, and an evaluation index system is established; samples are generated through Latin hypercube sampling, a data set is constructed in combination with finite element calculation, and a multi-task learning architecture is adopted to train a high-precision agent model. And then, coupling the proxy model with a multi-objective optimization algorithm, quickly searching a Pareto optimal solution set, and screening out a comprehensive optimal figure by using a multi-attribute decision-making method. And finally, through a finite element simulation verification result, prediction precision and performance improvement are ensured. The method has the advantages of lightweight modeling, efficient prediction and accurate search, and provides an effective tool for intelligent optimization and rapid decision making of hydraulic structures such as arch dams and the like.

The invention belongs to the technical field of hydraulic structure engineering, and relates to a dam foundation curtain anti-seepage performance evaluation method and system under the action of seepage corrosion, and the method comprises the steps: drawing a hydropower station dam engineering riverbed cross section geometric model, and carrying out the mesh generation of the geometric model to obtain a calculation model; constructing a dam foundation anti-seepage curtain corrosion degradation analysis initial model; coupling a seepage field, a calcium ion transport field and a corrosion field in the dam foundation anti-seepage curtain corrosion degradation analysis initial model by utilizing the calculation model to obtain a permeability coefficient, porosity and a diffusion coefficient; updating parameters to obtain a dam foundation anti-seepage curtain corrosion degradation analysis updating model; whether the permeability coefficient, the porosity and the diffusion coefficient converge or not is judged, and if yes, the anti-seepage curtain structure calcium ion concentration, the solid-phase calcium concentration and the water head corresponding to the parameters are output; evaluating the anti-seepage performance of the dam foundation curtain; and otherwise, carrying out coupling solution on the analysis updating model by utilizing a calculation model. The technical problem of inaccurate calculation results in the prior art is solved.

The invention discloses an intelligent pumped storage hydropower station reconstruction design simulation verification method and system. The method comprises the following steps: establishing a digital twin model of the pumped storage hydropower station, wherein the digital twin model comprises a hydraulic structure model, an electromechanical equipment model and a control system model; importing a transformation design scheme into the digital twin model, and optimizing model parameters based on a deep learning algorithm and historical operation data; multi-working-condition simulation verification is carried out, dynamic response characteristics of all systems are analyzed, and the influence of the transformation design scheme on the overall performance of the power station is evaluated; and performing intelligent analysis on a simulation result based on machine learning, identifying potential risks and proposing optimization suggestions. The invention also discloses a system for realizing the method. According to the method, the deep learning algorithm is innovatively introduced to realize intelligent optimization of the model parameters, the workload of manual parameter adjustment is reduced, and the scheme optimization efficiency is improved.

The invention discloses a high arch dam deformation state fractional order numerical analysis method and module, and relates to the technical field of hydraulic structure service state analysis and computational mechanics. The method comprises the following steps: establishing a high arch dam body and dam foundation fractional order rheological mechanical element model by constructing a fractional order characterization method of attenuation rheological, steady-state rheological and accelerated rheological in a three-dimensional stress state; calculating an internal stress increment, a viscoelastic strain increment, a viscoplastic strain increment and a deformation increment in a delta tn time period by adopting a front difference formula according to the stress, the viscoelastic strain, the viscoplastic strain and the deformation at the tn moment by virtue of an increment finite element method, so as to obtain the stress, the strain and the deformation at the tn + 1 moment; and repeatedly calculating to obtain the stress, strain and deformation of the high arch dam in the analysis time period. According to the method, the nonlinear rheological effect of the dam body and the dam foundation of the high arch dam is objectively described, scientific deformation property numerical value quantitative analysis is achieved, technical support is provided for safety monitoring and dangerous case early warning of the in-service high arch dam, and the method has wide application prospects and market prospects.

The invention discloses a concrete dam damage identification method based on multi-modal parameter deep learning, and the method comprises the steps: extracting the actual measurement modal parameters of a concrete dam through distributed optical fiber vibration sensing data, building a high-precision dam body dynamic model based on finite element simulation, randomly generating different material parameter combinations, obtaining the corresponding modal parameters, and carrying out the recognition of the damage of the concrete dam. And the simulation model is optimized and corrected, so that the simulation model better conforms to the actual dynamic characteristics of the dam body. In the corrected simulation model, randomly generating different damage positions, ranges and degrees, and constructing a training sample set covering multiple damage modes; and establishing a one-dimensional convolutional neural network, performing deep learning model training by using the training sample, and substituting the actually measured distributed vibration data to realize intelligent identification of the damage position, range and degree of the concrete dam. According to the method, the accuracy and adaptability of concrete dam damage identification are improved, the method can be widely applied to long-term health monitoring of hydraulic structures, and efficient and accurate technical support is provided for dam safety assessment.

The invention relates to the technical field of intelligent water conservancy, and discloses and provides a hydraulic structure large language model fine tuning and graph retrieval enhanced generation method based on knowledge distillation, and the method specifically comprises the steps: obtaining a hydraulic structure instruction fine tuning data set in combination with a knowledge distillation method; performing supervised training on the large language model by using a low-rank decomposition technology to obtain a hydraulic structure large language model; identifying a question intention of a user by using a hydraulic structure large language model, and converting a question statement into a knowledge graph query language; and enhancing the professional and credibility of water conservancy generated by the answer of the large model of the hydraulic structure by using a knowledge graph retrieval result. According to the method, the hydraulic structure knowledge is embedded into the large language model, the large language model customized for the hydraulic structure field is obtained, and the explicit knowledge stored in the water conservancy knowledge graph and the implicit knowledge of the large language model are effectively combined, so that the answer is more accurate and traceable, and an effective technical means is provided for intelligent water conservancy construction.

The invention discloses a method for efficiently and finely evaluating nitrogen and phosphorus consumption of a polder small micro water body, and relates to the technical field of water environment health evaluation. The method comprises the following steps: extracting pools, ditches, river network systems and hydraulic structures in a polder; based on a digital elevation model, rainfall and water level data of a polder, a geographic information system is utilized to analyze natural confluence, extreme conditions and a ground surface confluence process under strong manual regulation and control, a source-flow-confluence path of a small and micro water body in the polder is defined, and a small and micro water body topology network structure of the polder is formed; constructing a coupling source land nitrogen and phosphorus load accounting algorithm, an aquatic vegetation interception algorithm and a biogeochemical cycle process accounting algorithm, and depicting the nitrogen and phosphorus load current situation and the absorption potential of a single small and micro water body; and constructing a polder small and micro water body topology network structure-based nitrogen and phosphorus consumption network flow algorithm to realize small and micro water body nitrogen and phosphorus consumption load amount and retention amount accounting so as to complete polder small and micro water body nitrogen and phosphorus consumption evaluation.

The present invention relates to the technical field of water conservancy projects, and discloses an efficient silt removal device for water conservancy projects, including: a hand cylinder and a parts protective shell. Both sides of the parts protective shell are fixedly connected with side plates. One side of the side plate away from the parts protective shell is fixedly connected with a diversion shell. The outer wall of the parts protective shell is fixedly connected with a mud scraping shovel. One side of the parts protective shell away from the mud scraping shovel is fixedly connected with a confluence bin. This efficient silt removal device for water conservancy projects can provide power through the first motor, drive the centrifugal fan to rotate inside the diversion shell by the transmission of the driving bevel gear and the driven bevel gear, suck the external silt into the diversion shell and push it to the confluence bin by the rotation of the main centrifugal fan blades, and push out the silt by the spiral blades, which is convenient for silt removal in relatively remote locations or hydraulic structures with relatively narrow construction environments. It has a relatively small volume, is convenient for transportation, has relatively high convenience, and improves the silt removal efficiency.

The invention discloses an underwater target volume measurement method, device and equipment based on three-dimensional reconstruction and a medium, and belongs to the technical field of photogrammetry, underwater image processing and point cloud volume calculation cross, and the method comprises the steps: carrying out the video data collection of an underwater target through video collection equipment, and obtaining an original image with image format data; the underwater image enhancement method model based on depth estimation performs image enhancement on the original image to obtain an underwater enhanced image; reconstructing a real scene containing a target based on the underwater enhanced image; and based on the three-dimensional reconstruction point cloud, segmenting to obtain the to-be-measured target, and obtaining the volume parameter of the underwater target. According to the method, a three-dimensional reconstruction technology is utilized, a real target structure is restored, the measurement accuracy of the underwater target defect volume is improved, a reference standard is provided for later repair, the maintenance cost is reduced, and the operation safety of a hydraulic structure is guaranteed.

The invention provides a combined type layered water taking method and device, and the method comprises the steps: combining the layered water taking operation mode of each project and the characteristics of a hydraulic structure according to the environmental protection requirements, the water level amplitude variation condition of a reservoir area and the adaptive conditions of different layered water taking facilities; two or more layered water taking technologies are selected to be combined, and the running water level range of each layered water taking technology is delimited, so that each layered water taking technology runs in a corresponding proper water level interval, and the purpose of controlling a discharged water body by adopting a more reasonable layered water taking facility in different water level intervals is achieved. While the engineering ecological environment-friendly water taking requirement is met, the operation is simple and convenient, and the investment is relatively low.

The invention relates to the technical field of robot navigation, in particular to a hydraulic structure inspection robot self-adaptive navigation method based on multi-source data fusion. Comprising the following steps: acquiring visual image data, three-dimensional point cloud data, I MU data, ultrasonic data, historical hydrological data and current hydrological data; performing fusion processing on the visual image data, the three-dimensional point cloud data, the I MU data, the ultrasonic data and the historical hydrological data to obtain fusion state data and an environment map; determining an initial positioning result based on the fusion state data, the environmental map, the current hydrological data and a preset obstacle avoidance model; based on the initial positioning result and a preset verification model, determining a navigation positioning result; and obtaining a path navigation result based on the navigation positioning result, the environment map and the current hydrological data. The invention aims to solve the problem that the existing inspection robot is easy to collide in a sheltered environment or a dynamic scene, so that the inspection task is interrupted or the inspection efficiency is low.

The invention discloses an arch dam aging deformation mechanism analysis method, and relates to the field of hydraulic structure safety. The method comprises the steps that based on three-dimensional terrain and geological data, a high-precision arch dam-foundation overall finite element mesh model is established, concrete materials and permeation partitions are divided carefully, and an anti-seepage and drainage system structure is embedded; by means of multi-source monitoring data, real boundary conditions such as water temperature and air temperature and thermodynamic parameters are dynamically recognized and optimized through the intelligent inversion technology; respectively analyzing the aging influence of single factors such as a temperature field and valley amplitude deformation on arch dam deformation by adopting finite element simulation; on the basis of the normalized deformation response data, a Bayesian optimized long-short-term memory neural network and a hierarchical correlation propagation algorithm are adopted, contribution weights of all factors to deformation are calculated, and weight rationality is verified through global sensitivity analysis. According to the method, quantitative separation and evaluation of the main deformation influence effect of the arch dam are realized, and a theoretical basis can be provided for structural health monitoring and safety management.

The invention discloses a hydropower station unit tail water gate groove water surface floating object salvaging device which comprises a rail fixed to a hydraulic structure, a remote control driving device capable of moving in the extending direction of the rail and a salvaging basket hung on a lifting appliance. The remote control driving device is connected with a lifting appliance and can drive the fishing basket to move in the up-down direction and the extending direction of the rail, so that water surface floating objects in the tail water gate groove are fished through the fishing basket. A remote control driving device and a movable fishing basket are adopted, so that the working efficiency of fishing the floating objects on the water surface is improved; and in addition, the fishing device efficiently and cleanly fires water surface floating objects in the tail water gate groove before the tail water gate is ready to fall during unit maintenance, it is guaranteed that the tail water gate of the unit falls in place at a time, the safety risk that personnel dive is avoided, and the maintenance work period is shortened.

The invention provides a waterproof design method and a long-term performance maintaining method for a reinforced concrete structure. Reinforcing steel bars are arranged in concrete, and the top face of the concrete is covered with a waterproof layer. The method comprises the following steps: determining an essential mechanism of waterproof layer cracking caused by concrete cracking from a microscopic scale; based on the essential mechanism, the width value of the concrete top surface crack is applied to the bottom surface of the waterproof layer as forced displacement in the opposite direction; iteratively calculating the cracking height of the waterproof layer; it is ensured that the remaining uncracked solid thickness of the waterproof layer is still larger than the minimum solid thickness meeting the waterproof requirement under the condition that concrete cracking is not considered. And at the moment of initial cracking of the concrete, the longitudinal tensile thickness of the waterproof layer has obvious influence on the final cracking height of the waterproof layer, but the calculation result of the cracking height tends to be stable when the thickness is close to the nanoscale. According to the invention, the long-life design of the waterproof layer is facilitated, and the method is applicable to bridges, roads, tunnels, buildings and hydraulic structures.

The invention relates to the technical field of structural design of hydraulic structures, and particularly discloses a method, a system and a terminal for determining a combined lining of a high-pressure water delivery tunnel. In the establishment process of a numerical calculation model of the combined lining, a composite drainage plate is equivalent to a uniform isotropic entity medium with the thickness of d; the mechanical property of the composite drainage plate is represented by adopting a comprehensive elastic modulus E, the structural stress of the composite drainage plate and the water pressure bearing ratio in the steel pipe are restrained through a steel pipe allowable stress control threshold value, a pipe piece crack resistance allowable stress control threshold value and a steel pipe bearing ratio control threshold value, and finally the d / E value range of the composite drainage plate is determined. Through the mode, a scientific and clear basis is provided for the value of the composite drainage d / E in the combined lining, the problem that the value is obtained depending on experience at the present stage is solved, and the subjectivity of manual selection is avoided.

The invention relates to the technical field of hydraulic structure monitoring, and particularly discloses a drainage structure overall safety assessment method, which comprises the steps of collecting working effect data, characterization state data and working environment data of a drainage structure; processing the working effect data to obtain a stability evaluation index of the drainage structure, processing the characterization state data to obtain a damage evaluation index of the drainage structure, and performing comprehensive analysis to obtain an overall safety evaluation index of the drainage structure; and evaluating the overall drainage structure according to the overall safety evaluation index of the drainage structure. According to the overall safety assessment method for the drainage structure, the durability condition of the structure can be known more accurately, targeted maintenance measures can be made, safety accidents caused by aging, damage and the like of the structure are reduced, the problems existing in the structure can be found and solved in time, and the service life of the structure is prolonged. And the overall safety and stability of the project are ensured, so that measures are taken in advance for early warning and intervention.

The invention discloses an underwater detection system and method for a hydraulic structure, belongs to the field of underwater detection systems for hydraulic structures, and adopts fractal tree-shaped path planning to realize double-optimal solutions of detection efficiency and precision, namely a fractal algorithm based on a self-similar growth rule of fern leaves. A tree-shaped path is recursively generated by taking an initial detection point as a root node, a main branch can quickly cover a detection surface area to form a macroscopic skeleton path, a sub-branch dynamically adjusts the extension direction according to coordinates of key parts, the coverage rate of a curvature mutation area is improved, a detection blind area of a traditional fixed route is effectively eliminated, and the detection efficiency is improved. Through innovative combination of a bionic fractal algorithm, multi-source data intelligent fusion and dynamic feedback control, the problem of a one-cut detection strategy in traditional hydraulic underwater detection is systematically solved, and an efficient, accurate and intelligent technical scheme is provided for structural safety monitoring in the fields of hydraulic engineering, ocean engineering and the like. And the method has remarkable engineering application value and economic benefit.

The invention relates to a long water tank mobile surface flow field measurement method, which comprises the following steps: utilizing a river channel local scouring experiment model, the model is a water tank, and the middle part of the water tank is provided with a vertical support rod for simulating a building component; a constant-speed motion traction camera device is built above a water tank, so that a mobile camera moves at a constant speed at a certain inclination angle, meanwhile, water flow in the water tank is shot, and a water flow surface flow field is obtained through calculation according to a shooting result. The method comprises the following steps: performing perspective transformation processing on an obtained image to obtain a continuous front view image, identifying checkerboard control points to position each frame of corrected image, and cutting a tracer particle image by using coordinates of intelligent identification side wall control points. After surface tracer particles are extracted through a morphological method, flow field splicing is carried out on instantaneous flow fields at different moments to obtain a surface flow field in a large range, the problems that traditional fixed flow field measurement is low in precision and small in range are effectively solved, and an effective technical means is provided for hydraulic structure research and water body monitoring.

The invention belongs to the field of hydraulic structure safety monitoring and predictive analysis, and particularly relates to a method for estimating abnormal seepage flow of a concrete faced rockfill dam based on parameter transplantation and overflow weir monitoring flow. The scientificity and the precision of seepage flow estimation are improved by combining an upstream watershed hydrological model and dam rear micro-region runoff and confluence simulation; the overflow weir is adopted to monitor flow data, and is combined with the actual reservoir water level change to construct a more reliable water level-seepage flow relationship. The method is suitable for dam seepage flow estimation under the normal working condition, abnormal seepage can be recognized and quantified, and the early warning capacity for the hidden danger of seepage is improved. Dependence of a traditional method on a large number of monitoring devices is avoided, engineering operation and maintenance cost is reduced, and stability and reliability of data are improved.

The invention relates to the field of hydraulic structures, in particular to a three-dimensional damage rapid positioning method for a complex aqueduct structure. Acoustic emission or microseismic signals in the aqueduct structure are collected, and the first arrival time of the signals is picked up through an STA / LTA long and short time window energy ratio method; constructing a curved surface structure model composed of a groove surface and a side wall; parameterizing the cross section, and constructing a groove surface propagation path set according to the position relationship between the damage point and the sensor in a parameter space; for each pair of damage points and sensors, selecting a path with the minimum groove surface path length as a shortest propagation path, and calculating theoretical arrival time according to the shortest propagation path; establishing a residual function between the theoretical arrival time and the actually measured arrival time, and constructing a jacobian matrix of arrival time change; and solving the coordinates and occurrence time of the damage source. According to the method, the parameterized model of the aqueduct section is established, traditional ray tracing is replaced with the curved surface shortest path thought, and the numerical difference and rapid iteration methods are combined, so that efficient and robust damage source positioning is achieved.

The invention discloses an open-web gravity dam deformation prediction and mechanism interpretation method, system, equipment and medium, and belongs to the technical field of dam and hydraulic structure health monitoring and state evaluation. Constructing the preprocessed monitoring data into corresponding feature vectors, dividing a time sequence data set, and performing XGBoost model training and hyper-parameter optimization to obtain a prediction model; calculating a corresponding SHAP value, and screening a key factor as an independent variable for model analysis; and selecting a control index to evaluate the performance of the model, and deploying the model to a dam safety monitoring system after verification so as to carry out real-time prediction and early warning. According to the method, while high-precision deformation prediction of the open-web gravity dam is realized, quantifiable mechanism explanation of the prediction result is provided, so that transparent and reliable decision support is provided for safety monitoring and early warning of the dam.

The invention discloses a dam seepage flow calculation method based on a neural network model, and belongs to the field of hydraulic structure safety monitoring and predictive analysis. The method comprises the following steps: collecting and processing characteristic data related to dam seepage by taking historical monitoring flow data behind a dam as basic data; correlation and causal relationship analysis is carried out on the related data, and key features having significant influence on the seepage flow are screened; constructing an improved Elman neural network prediction model, taking the screened historical data as an input variable, and respectively testing the influence of different key feature combinations on the seepage flow prediction effect; optimizing neural network model parameters; and evaluating the generalization ability of the neural network model to obtain a dam seepage calculation model suitable for various working conditions. The dam body normal seepage flow calculation method is suitable for dam body normal seepage flow calculation under the conditions that seepage conditions are complex and sufficient conditions are lacked to support accurate physical modeling, and important technical support can be provided for recognizing hidden seepage, evaluating dam body stability and preventing potential safety risks.

The invention discloses an underwater stabilization and posture regulation and control system and method for underwater maintenance equipment, and belongs to the field of underwater concrete damage repair of hydraulic structures. The invention aims to solve the problem that the attitude is unstable due to the influence of water flow and waves when equipment moves in an underwater environment. According to the technical scheme, the device comprises a lower supporting module, a hydraulic rod, an oil pressure system, an annular mass block, a suspension steel cable and a posture regulation and control system composed of various sensors, namely an accelerometer, a gyroscope, an inclination angle observation instrument, a distance measuring sensor and the like; the core lies in that a PID controller receives sensor data and controls an oil pressure system to drive a hydraulic rod to adjust the position of an annular mass block so as to absorb and reduce adverse effects of an underwater dynamic environment, underwater stabilization and posture regulation and control are achieved, the posture of equipment is monitored and adjusted in real time, and the operation precision is ensured; stability and safety of the underwater maintenance equipment are improved, the underwater maintenance equipment is suitable for maintenance tasks in various complex underwater environments, applicability is wide, practicability is high, and maintenance and upgrading are convenient due to the modular design.

The invention relates to a hydraulic structure safety monitoring data abnormity self-diagnosis method and system. The method comprises the following steps: arranging a plurality of variables of monitoring data to form a multivariable time sequence; a dynamic time warping algorithm is adopted to analyze the correlation between the effect variables in the multivariable time sequence and the respective variables; on the basis of correlation analysis and time lag effect analysis results, constructing a multiple regression model between the effect quantity and the independent variable; and performing anomaly identification on the effect quantity and the independent variable measurement value by using a multivariable local anomaly factor, comparing the distribution characteristics and the occurrence time of the multivariable local anomaly factor from different time scales based on a local anomaly factor threshold, judging whether the effect quantity and the independent variable local anomaly factor exceed the threshold at the same time, and if so, executing the next step. And if yes, determining security state abnormity caused by multiple factors, and identifying to obtain a structural state conversion mechanism. According to the method, the accuracy and reliability of abnormality diagnosis are systematically guaranteed.

The invention discloses a dam underwater concrete spalling detection method based on weak supervision small sample learning, and relates to the technical field of dam underwater target detection. According to the method, a deep convolutional generative adversarial network is used as a generative model, details in an image can be effectively processed, a high-quality composite image can be generated, and the problem that underwater concrete spalling images are rare is solved; a Faster R-CNN model with ResNet50 as a main network is used as a student model and a teacher model, and a self-training strategy of weak supervised learning is comprehensively used to reduce the adverse effect of the difference between synthetic data and real data distribution on the model, so that a high-precision dam underwater concrete spalling detection model is constructed. According to the method, high-precision underwater concrete peeling (new class) detection can be realized under the condition that only part of underwater concrete peeling (new class) images are marked, and the method can be further popularized and applied to structural defect detection of various wading hydraulic structures.

The invention discloses a landslide surge physical model test drag force prediction method based on AM-LSTM, and the method comprises the steps: constructing a three-dimensional hydrodynamic coupling physical model of landslide surge for a target landslide region and a hydraulic structure structure based on Froude similarity criterion, and carrying out the prediction of the drag force of the landslide surge. The method comprises the following steps: acquiring dynamic data of dam surface drag force by setting test groups with different landslide mass volumes and water depths, constructing a data set containing time sequence features, performing normalization preprocessing, and dividing the data set into a training set, a test set, a feature data set and a label data set; a long short-term memory neural network LSTM layer and an attention mechanism AM layer are introduced, data are input for training, a long short-term dependency relationship between the data is captured, dynamic prediction of drag force is realized, and result visualization is performed. According to the method, the towing force sequential evolution rule caused by landslide surge is captured through the LSTM model with the attention mechanism, dynamic prediction of the towing force is realized, and the prediction accuracy is improved.