5639 results about "Hydraulic engineering" patented technology

The invention relates to the technical field of data processing, and discloses an AI knowledge base construction method and system for intelligent water conservancy. The method comprises the following steps: forming a water conservancy intelligent perception data set by integrating hydrological monitoring data, engineering operation records and expert literatures; knowledge sprouts are identified, and a water conservancy native knowledge unit is generated; key features are extracted, knowledge element decomposition is carried out, and a knowledge evolution chain network is constructed through correlation analysis; screening in combination with a historical application effect and expert evaluation to obtain a preferred knowledge body; performing intelligent reconstruction on a scheduling rule, risk prevention experience and an operation and maintenance method to form an innovative knowledge base; and finally, intelligently matching with real-time engineering requirements to generate an intelligent water conservancy AI knowledge base. According to the application, by establishing a self-adaptive knowledge acquisition, organization and application mechanism, the knowledge base can autonomously update the knowledge content according to the environment change, the knowledge structure is optimized, and the intelligent operation management level of the water conservancy project is improved.

The invention discloses a freezing and thawing stratum grouting numerical analysis modeling method and system, particularly relates to the technical field of hydraulic engineering numerical simulation, and is used for solving the problem of grouting parameter prediction inaccuracy caused by neglecting the dynamic coupling relationship between the thermodynamic behavior of a phase-change material and a seepage field and a damage field in an existing grouting model. A dynamic correlation system of phase change energy storage performance parameters and seepage field-damage evolution is established, a slurry diffusion path priority channel weight is corrected based on a freeze-thaw interface migration track, a bidirectional iteration equation of a seepage field and a diffusion migration field is coupled, and multi-physical field spatial-temporal scale cooperative solution is realized through a separated iteration algorithm; through multi-dimensional coupling of thermodynamic characteristics of the phase-change material, heterogeneous evolution of seepage paths and damage mechanical response, the prediction precision of the mechanical characteristics of the grouting stone body and the slurry diffusion range under the action of freeze-thaw cycle is remarkably improved, and the leakage and structural instability risks are effectively reduced.

The invention discloses a reservoir scheduling decision support system for multi-objective optimization, and relates to the technical field of hydraulic engineering scheduling. According to the system, comprehensive optimization of flood control, power generation and ecological targets is realized through cooperative work of the multi-source dynamic constraint analysis module, the collaborative optimization module, the scheduling module and the correction module. A dynamic constraint boundary table is generated by collecting meteorological, hydrological and ecological data in real time. And matching a multi-target coordination rule through a physical constraint double-depth network algorithm, generating a Pareto feasible solution set, and mapping the Pareto feasible solution set into a coordination strategy of flood discharge flow and power generation output. In combination with a hydraulic model, flood control and power generation weights are adjusted, an executable dispatching instruction is generated, it is ensured that the ecological flow reaches the standard, and the influence of a downstream submerged area is optimized. And correcting the dynamic constraint boundary table according to actual monitoring data, and continuously optimizing a scheduling decision. Accurate management and multi-objective optimization of reservoir dispatching are realized, and the utilization efficiency of water resources is improved.

The present invention discloses a digital management method and system for a water conservancy project construction process, and belongs to the technical field of water conservancy projects. The method specifically comprises: real-time collection of relevant data from a water conservancy project construction site, and preprocessing, by comparing the actual construction progress with the planned progress, calculating the construction progress deviation, and analyzing the future construction progress trend, analyzing the construction quality data, training a prediction model through historical data, identifying construction quality hazards, combining the construction progress and quality, resource conditions and external environmental factors, intelligently scheduling construction resources, building a risk prediction model, and evaluating the risks in the construction process; in the context of complex water conservancy projects, it is possible to achieve process progress and quality management, reasonable resource scheduling and risk control, and better ensure the smooth progress of the project.

The present invention belongs to the technical field of engineering monitoring, and relates to a method for monitoring construction of water conservancy projects, including: collecting monitoring data from a water conservancy project construction site; inputting the monitoring data into a structural stability prediction model for analysis to generate a safety factor assessment value; inputting the monitoring data into a seepage risk prediction model for analysis to generate a seepage risk probability; combining the safety factor assessment value and the seepage risk probability, presetting a threshold, and triggering a graded warning signal; in response to the graded warning signal, calling a multi-objective optimization algorithm to dynamically adjust the current construction parameters and generate an optimized construction plan; loading the optimized construction plan into a digital twin system for virtual construction simulation to obtain predicted performance parameters; comparing the deviation value of the predicted performance parameters with the measured monitoring data, and iteratively updating the parameters of the prediction model when the deviation exceeds the tolerance range. The present invention solves the problem that traditional methods are difficult to discover potential problems and make optimization adjustments before construction.

The invention discloses a digital intelligent management method and system for hydraulic engineering construction, particularly relates to the technical field of hydraulic engineering construction management, and is used for solving the problems of regulation lag and insufficient environmental disturbance adaptability of an existing method under the action of multi-physics field coupling. According to the method, a concrete three-dimensional temperature field model is established, initial regulation and control parameters are generated, and a heat conductivity coefficient under the seepage influence is corrected based on space-time relevance of pore water pressure and temperature data; the constraint boundary is dynamically adjusted in combination with the construction progress and the material strength curve, a cooling water pipe coordinated regulation and control matrix is constructed, and target flow instructions of space differentiation are generated through the included angle between the distance and the water flow direction; setting a lag time threshold and optimizing an execution time sequence according to the thermal inertia parameter, splitting the threshold and allocating a weight coefficient when the environmental wind speed suddenly changes, and finally updating the model parameter and the regulation and control instruction; accurate temperature control response and abnormal heat conduction suppression under multi-field coupling are achieved, and the structural safety and construction efficiency under complex working conditions are improved.

The invention provides a hydraulic engineering three-dimensional design method, system and equipment and a storage medium, and relates to the field of three-dimensional design. The method comprises the steps of obtaining topographic data, hydrological data and engineering design requirements of an area where a project is located; performing feature extraction on the topographic data and the hydrological data to obtain key feature parameters; determining design constraint conditions according to engineering design requirements, and establishing a three-dimensional design optimization model based on the key feature parameters as design variables and the design constraint conditions; solving the three-dimensional design optimization model through a genetic algorithm to obtain a group of non-inferior solutions as a first candidate design scheme; optimizing the first candidate design scheme through a preset reinforcement learning model to obtain a second candidate design scheme; and performing simulation analysis based on the second candidate design scheme to obtain feedback information, and optimizing the second candidate design scheme based on the feedback information to obtain a target design scheme. Through the scheme, the hydraulic engineering design efficiency and the optimization degree of the design scheme are improved.

The invention relates to the technical field of computers, and discloses a digital twin hydraulic engineering operation and maintenance monitoring system which comprises a data acquisition module, a twin modeling module, a principal stress extraction module, a stress evolution prediction module, a safety domain judgment module, a regulation and control decision module, a control execution module and a state feedback module. The invention also discloses a digital twin hydraulic engineering operation and maintenance monitoring method, which comprises the following steps: data acquisition: real-time sensing data of a hydraulic engineering structure area is acquired through the data acquisition module, and the sensing data comprises strain, water pressure, temperature and displacement information and is used for representing the current state of the structure; and carrying out twin modeling, and receiving the real-time sensing data transmitted by the data acquisition module. According to the method, intelligent sensing and dynamic regulation and control of the structure state are realized by constructing a closed-loop system of principal stress prediction, regulation and control decision, control execution and state feedback.

The invention discloses a dam seepage stability analysis method based on flood forecasting, and relates to the technical field of hydraulic engineering safety monitoring, comprising the following steps: S1, integrating a flood forecasting system and a seepage monitoring system into a data acquisition layer, and acquiring flood forecasting data and seepage monitoring data by a sensor of the data acquisition layer; s2, performing space-time alignment on the flood forecast data and the seepage monitoring data through a space-time coupling interface of the dynamic fusion layer, and generating a dynamic driving variable of a seepage field boundary condition based on a flood routing model and a geographic information system spatial interpolation technology; through the space-time coupling interface, space-time alignment and dynamic driving variable generation of flood forecast data and seepage monitoring data are achieved, the problem that the real-time flood routing process cannot be reflected due to the fact that the boundary condition is static or lagged in traditional analysis is solved, the seepage field calculation boundary condition can be dynamically updated along with flood forecast, and the real-time flood routing process cannot be reflected. And the timeliness and accuracy of model calculation are improved.

The invention provides a water conservancy construction progress intelligent management method and system. Wherein hydraulic engineering project plans are collected, dynamic changes in a life cycle are obtained through an integrated building information model technology, and a hydraulic engineering construction progress management framework is generated; extracting multi-source heterogeneous data, performing cross-modal information integration by applying a multi-modal fusion network algorithm, evaluating specific influence of external environment factors by adopting a causal forest technology, and generating a water conservancy construction progress and risk early warning report; establishing a kinetic equation by applying a graph neural differential equation algorithm, performing dynamic system simulation, and constructing a virtual water conservancy construction environment by adopting an enhanced digital twinborn simulation technology to capture construction efficiency and resource utilization conditions and generate a water conservancy construction resource allocation optimization report; and a lean six-sigma methodology is introduced to set a lean improvement plan, project feedback is collected, and a water conservancy construction progress intelligent management scheme is generated. According to the technical scheme provided by the invention, the intelligent level of water conservancy construction progress management is remarkably improved.

The invention provides a water conservancy grid parameter inversion method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-source monitoring data of a target water conservancy project region, combining the multi-source monitoring data with a preset water conservancy grid physical mechanism model, and building an artificial intelligence probability inference frame for heterogeneous medium parameter inversion; and iteratively optimizing an implicit association relationship between permeability coefficient spatial distribution and hydraulic conductivity dynamic change in the water conservancy grid physical mechanism model, generating a parameter probability distribution set, and according to the parameter probability distribution set, combining with a historical seepage field evolution mode of the target water conservancy project area to obtain a historical seepage field evolution model of the target water conservancy project area. And the equivalent parameter value of each grid unit in the water conservancy grid physical mechanism model is dynamically corrected, a water conservancy grid parameter inversion result is generated, the precision and stability of water conservancy grid parameter inversion under complex working conditions are improved, and reliable support is provided for engineering safety and risk early warning of reservoirs and dams.

The invention provides a small and medium-sized reservoir operation safety evaluation system based on sky-ground work, and belongs to the technical field of hydrological monitoring. The device comprises a space-time alignment layer, a feature weighting layer and a dynamic coupling layer which are connected in sequence, the space-time alignment layer is used for performing time alignment and space alignment on the input data of the sky-land water conservancy project; the feature weighting layer is used for quantifying contribution degrees of different monitoring features to safety assessment according to the data after time-space alignment; and the dynamic coupling layer adopts a dynamic weight distribution strategy for different modules through a multi-module coordination engine, fuses feature weighting to obtain a fusion result, and outputs a small and medium-sized reservoir operation safety index according to the fusion result. The evaluation result is more accurate, and the timeliness is better.

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 gravity dam safety risk management and control method and system based on a knowledge graph and a large model. The method comprises the following steps: collecting accident case data of a water conservancy project; performing entity and relationship extraction on all case data in the case library by using the fine-tuned model with domain knowledge to obtain a case data knowledge graph; the obtained case data knowledge graph is converted into natural language description, a text is used for fine tuning of a large language model, and finally a large language model with domain knowledge is obtained; performing information extraction on the engineering general situation information by using an engineering model, forming a natural language description problem by using an extraction result, and inputting the natural language description problem into a large language model with domain knowledge; the big language model answers the questions, and the expert judgment module provides expert opinions according to replies of the big language model. The intelligent system integrating artificial intelligence, reasoning and decision making is formed by utilizing the powerful language and reasoning ability of a large model.

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 digital design method based on digital twinning, and relates to the technical field of hydraulic engineering design. According to the digital twin-based hydraulic engineering digital design method, design demand information of a to-be-designed diversion tunnel is obtained and input into a pre-trained building recognition model for comprehensive analysis, a spatial layout map of the to-be-designed diversion tunnel is obtained, and a tunnel design digital twin model is constructed; simulation data of the tunnel design digital twinborn model are obtained, simulation analysis is carried out respectively, a tunnel construction risk index and a hydraulic action risk comprehensive index of the tunnel design digital twinborn model are obtained, comprehensive analysis is carried out, and a tunnel feasibility index of the tunnel design digital twinborn model is obtained; according to the method, the tunnel design digital twin model is subjected to design optimization based on the feasibility index, so that a lagging mode of re-evaluation after building is effectively avoided, later frequent reinforcement and reworking are prevented, and the engineering safety coefficient is improved.

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 dike safety monitoring and maintenance system and method based on multi-sensor fusion, and belongs to the technical field of hydraulic engineering safety. Comprising a pore water pressure sensor, a horizontal displacement sensor, a vertical displacement sensor, a seepage monitoring sensor, a data acquisition and transmission module and a data acquisition center. And the precision of the sensor is high, for example, the precision of a pore water pressure sensor is within + / -0.1 kPa. And the transmission module adopts a hybrid communication network which is mainly composed of an optical fiber and is assisted by ZigBee and LoRa wireless. The data acquisition center is provided with a high-performance processor and the like. The method comprises the steps of data acquisition, preprocessing, anomaly detection and early warning, risk assessment, maintenance strategy formulation and the like. Data are processed through joint filtering, dynamic normalization and other algorithms, the safety state is judged through clustering analysis and a composite early warning model, and risks are evaluated by integrating multiple factors. According to the method, the accuracy, scientificity and effectiveness of dike safety monitoring and maintenance are remarkably improved, and a powerful guarantee is provided for hydraulic engineering safety management.

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 crossing field of water conservancy project scheduling and risk management, and particularly discloses a drainage basin water engineering system scheduling risk reverse traceability diagnosis and regulation method and system, and the method comprises the steps: obtaining drainage basin water engineering system data; constructing a multi-target collaborative scheduling risk field of the watershed water engineering system based on the watershed water engineering system data; based on the multi-target collaborative scheduling risk field of the watershed water engineering system, performing reverse multi-dimensional probability flux tracking on the scheduling of the watershed water engineering system to obtain a risk contribution degree entropy weight; according to the risk contribution degree entropy weight, performing multi-level risk diagnosis on drainage basin water engineering system scheduling to obtain a diagnosis result; according to a diagnosis result, carrying out asymmetric game dynamic compensation decision making on drainage basin water engineering system scheduling to obtain an optimal decision making scheme; and regulating and controlling the drainage basin water engineering system scheduling by using the optimal decision scheme. According to the method, the cascade instability risk caused by reservoir group scheduling can be dynamically identified.

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 discloses a water conservancy project construction management system and method based on three-dimensional visualization, and belongs to the technical field of water conservancy projects, and the method specifically comprises the steps: collecting related data of a water conservancy project construction region, carrying out the preprocessing, obtaining first monitoring data, building a three-dimensional digital model of a water conservancy project based on the first monitoring data, and carrying out the construction of the water conservancy project. On the basis of the hydraulic engineering three-dimensional digital model, hydraulic engineering construction risks are evaluated in combination with hydraulic engineering construction historical data, and risk grades are divided and hydraulic engineering construction is dynamically managed according to risk evaluation; on the basis of a three-dimensional visual model, all-around and real-time risk monitoring and evaluation can be carried out on a construction site, through real-time data collection and analysis, potential safety hazards such as geological changes, equipment faults and earthwork collapse can be found in time, early warning and emergency treatment are carried out in advance, and safety accidents are effectively avoided.

The invention relates to the technical field of project cost management, in particular to a hydraulic engineering construction cost control system and method, and the system comprises a material consumption analysis module, an inventory early warning calculation module, a dynamic inventory adjustment module, a replenishment trigger optimization module, and a consumption stability evaluation module. According to the method, the material consumption rate is dynamically monitored, the construction progress and engineering quantity are combined to measure and calculate data, a consumption trend curve is established, the actual use condition is compared, the consumption rate deviation is accurately quantified, real-time evaluation of material use is achieved, the number of supply days is calculated in combination with inventory data, the replenishment period is matched, and the replenishment time is dynamically adjusted; the inventory level is optimized, fund occupation is reduced, the stability is evaluated according to the consumption fluctuation range, the maximum consumption fluctuation value is calculated, the demand is adjusted in combination with replenishment, the inventory early warning threshold value is dynamically corrected, the supply chain breakage risk is reduced, the adaptability of construction material management is improved, and the cost control accuracy is improved.

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 the technical field of hydraulic engineering and geotechnical engineering, in particular to a parameter updating method and device for a rock-fill dam deformation analysis model.The method comprises the steps that a plurality of Duncan-Zhang E-B model parameter combinations of an indoor triaxial experiment of a target rock-fill dam project are obtained to construct a copula multivariate joint distribution model; sampling a parameter combination sample of the target rock-fill dam project, and inputting the parameter combination sample into the finite element deformation analysis model to generate a finite element calculation sample; training a pre-constructed deep neural network by using the parameter combination sample and the finite element calculation sample to generate a deep learning agent model; and fusing the copula multivariate joint distribution model, the deep learning agent model and an NSGA-III multi-objective optimization algorithm to search for final model parameters for updating the rock-fill dam deformation analysis model generation by generation. Therefore, the problem that an existing dam project parameter updating method neglects complex correlation existing between different parameters is solved.