71 results about "Reservoir management" patented technology

The invention discloses an intelligent water level monitoring system and a data transmission method thereof, and belongs to the technical field of Internet of Things sensing. The system comprises a distributed water level sensing node, an edge computing gateway and a cloud management platform, wherein the sensing node is integrated with a piezoresistive water level transmitter and an adaptive Kalman filtering module; the data transmission method comprises a dynamic threshold triggering mechanism and a compression algorithm based on improved Huffman coding, and low-power-consumption and high-reliability transmission is realized by optimizing transmission frequency and a data packet structure. The edge computing gateway is internally provided with an LSTM water level prediction model, historical data are analyzed through an LSTM neural network, and an early warning signal is generated in real time. And the cloud management platform stores the key data by adopting a block chain technology. Through multi-sensor data fusion and layered encryption transmission, the water level monitoring error rate is reduced to + / -0.5 cm, the data transmission energy consumption is reduced by 40%, and the monitoring efficiency and the data safety are remarkably improved in flood early warning and reservoir management scenes.

The invention discloses a reservoir management optimization method and system based on digital twinning. The method comprises the steps of multi-source data acquisition and virtualization mapping, digital twinning modeling, reservoir risk early warning and reservoir management decision optimization. The invention relates to the technical field of reservoir intelligent management, in particular to a reservoir management optimization method and system based on digital twinning, and the method comprises the steps: constructing a virtualization data field through multi-Bezi wavelets and fractal interpolation, and carrying out the self-learning of dynamic weight fusion through a time sequence convolution network; constructing a macroscopic twinborn body and a microcosmic twinborn body, and coupling through an asynchronous message bus; constructing multi-source risk vectors of water pressure gradient, over-overflow water level, dam body stress and seepage flow, adopting clustering and spatial diffusion, generating a dynamic risk thermodynamic diagram, and performing early warning; by taking flood control safety, water supply guarantee and ecological environment as multiple targets, a multi-target particle swarm optimization model and flow constraints are established, an optimal scheduling scheme is solved and executed in real time, and high-precision simulation, precise early warning and intelligent scheduling are realized.

The invention relates to the field of intelligent management of water conservancy projects, and provides an engineering project management system and method based on big data analysis. According to the method, historical hydrological data including specific indexes such as water level, reservoir inflow and reservoir outflow and historical meteorological data including specific indexes such as rainfall, evaporation capacity and air temperature of a target management reservoir are comprehensively analyzed by adopting a data analysis technology based on deep learning; and a reservoir dispatching operation scheme is generated based on the features obtained through comprehensive analysis. In this way, the problems that a traditional decision-making mode lacks data support and scientific analysis is insufficient can be solved, and the scientificity and rationality of reservoir management decision-making can be improved.

The invention relates to the technical field of water resource management, in particular to a cascade reservoir ecological scheduling system based on artificial intelligence, which comprises a multi-source data integrated processing module, an ecological influence prediction module, a scheduling strategy optimization module, a self-adaptive water quality management module and an intelligent user interaction module. Wherein the multi-source data integrated processing module is used for collecting various data of a meteorological station, a hydrometric station and an ecological monitoring point; the ecological influence prediction module is used for constructing an ecological influence model; the scheduling strategy optimization module is used for formulating a reservoir scheduling strategy; the self-adaptive water quality management module is used for adjusting the reservoir outlet flow and the adjusting strategy of the reservoir; and the intelligent user interaction module provides a human-computer interface. According to the invention, by integrating multi-source real-time data, applying reinforcement learning to optimize the scheduling strategy and providing an efficient user interaction interface, the real-time responsiveness and decision-making efficiency of reservoir management are significantly improved, and optimal utilization of water resources and protection of the ecological environment are ensured at the same time.

The invention discloses an oil reservoir injection-production optimization method based on an improved multi-objective particle swarm algorithm, and belongs to the technical field of oil and gas field development. According to the method, an attention mechanism, a Transform and a long-short-term memory network are introduced based on a relational graph convolutional network, geological features and production system information are combined, inter-well water content prediction is taken as a target, an HSTMF model is constructed, an injection-production well production system does not need to be adjusted, the calculated amount is greatly reduced, and the prediction precision is remarkably superior to that of a traditional method; in the aspect of optimization algorithms, a Cauchy disturbance variation mechanism is introduced into a genetic algorithm and combined with a particle swarm optimization algorithm to form a new optimization strategy, a real-time closed-loop collaborative oil reservoir management framework is constructed in combination with the moisture content prediction capability of an HSTMF model, the net present value and the total oil production are effectively increased, the total moisture content is reduced, and good practical application value is shown.

The invention relates to the field of hydraulic engineering, in particular to a smart reservoir management method based on big data, which comprises the following steps: S1, monitoring the water quality of a reservoir; s2, reservoir water quality analysis; s3, predicting the water level of the reservoir; s4, water scheduling analysis; s5, dam safety monitoring; s6, equipment state monitoring; according to the method, the pollution time periods of all the detection points of the reservoir are screened by analyzing the water quality parameters of the water samples at all the time points and all the detection points of the reservoir, the water delivery amount of all the areas and all the time periods is analyzed by obtaining the water consumption amount of all the areas and all the time periods, and then water delivery is conducted on all the areas in all the time periods; according to the maximum water level prediction value of each future time point of the reservoir, the safety evaluation coefficient of the reservoir dam and the state evaluation coefficient of the reservoir equipment, the safety comprehensive evaluation index of the reservoir is obtained through analysis and fed back, the safety condition of the reservoir can be comprehensively evaluated, and potential risks and problems can be found in time.

The embodiment of the invention provides a reservoir capacity dynamic monitoring method and system based on an unmanned aerial vehicle, and the method comprises the steps: obtaining a reservoir surface feature data set through the unmanned aerial vehicle, determining the boundary of a water area, scanning the bottom of the reservoir, obtaining the underwater terrain elevation and sediment distribution data, and constructing a three-dimensional terrain model; and determining terrain and deposit change data based on the three-dimensional terrain model dynamic change data of the continuous time sequence. Sedimentation space distribution characteristics are obtained through change rate analysis, and the sediment transportation amount and the sedimentation and deposition space rule are determined in combination with the water flow speed. Key driving factors are screened through correlation analysis, a sedimentation high-risk area is determined according to the key driving factors, and the change of sediments in the area is monitored, so that the change of the reservoir capacity is mastered. The reservoir siltation can be accurately and dynamically monitored, the high-risk area and the change mode are defined, the dynamic database is constructed to update the capacity change in real time, and important technical support is provided for reservoir management and flood control and disaster reduction.

A system, for controlling well site operations, comprising a machine learning engine, a predictive engine, a node system stack, and a blockchain. The learning engine includes a machine learning algorithm, an algorithmically generated earth model, and control variables. The learning algorithm generates a trained data model using the algorithmically generated earth model. The predictive engine includes an Artificial Intelligence (AI) algorithm. The AI algorithm generates a trained AI algorithm using the trained data model and earth model variables using the trained AI algorithm. The system stack is communicable coupled to the predictive engine, the learning engine, the blockchain, sensors, and a machine controller. The blockchain having a genesis block and a plurality of subsequent blocks. Each subsequent block comprising a well site entry and a hash of a previous entry. The well site entry comprises transacted operation control variables. The transacted variables are based on the generated earth model variables.

The invention discloses a water affair internet-of-things platform based on credential information, and relates to a water affair internet-of-things cloud platform, the water affair internet-of-things platform comprises an information acquisition unit, an information analysis unit and an early warning and prediction unit, the information acquisition unit is used for acquiring historical data, real-time data, real-time images and historical images, and the information analysis unit comprises a model establishment module; the method is used for establishing a normal reservoir level model, calculating normal standard flow, calculating normal standard water level, further calculating a flow influence coefficient and a water level influence coefficient, obtaining flow prediction data and water level prediction data, calculating comprehensive water volume data based on the flow prediction data and the water level prediction data, presetting a comprehensive water volume interval, and calculating the normal reservoir level model according to the preset comprehensive water volume interval. And judging the comprehensive water volume data according to the comprehensive water volume interval to generate a flood early warning signal and a drought early warning signal. According to the method, flood and drought early warning is carried out by calculating the comprehensive water volume data, the accuracy and effectiveness of early warning are improved, water level change early warning can be carried out, and the safety of reservoir management is improved.

The invention discloses a reservoir dam safety risk level assessment method, and particularly relates to the technical field of reservoir dam risk assessment. The method comprises the following steps: acquiring real-time stress data of a reservoir dam, spillway drainage capacity data and reservoir water level change data, and performing abnormal feature extraction and prediction on the data to generate related abnormal values; performing weighted analysis on the plurality of risk factors by adopting a fuzzy comprehensive evaluation method, and calculating a comprehensive risk score of the dam; comparing the comprehensive risk score with the classification threshold of the risk level, and dividing the safety risk level of the reservoir dam; corresponding early warning information and emergency treatment measures are output based on the evaluation result, safe operation of the reservoir dam is ensured, the method can monitor the safety state of the reservoir dam in real time, potential risks can be early warned in time, and scientific decision support is provided for reservoir management personnel.

A system for managing well site operations comprising a well site operations module, a chain of blocks of a distributed network, and a sensor bank and control module. The operations module generates earth model variables using a physics model, well log variables or seismic variables, or both, and a trained AI / ML algorithmic model. The chain of blocks comprises a plurality of subsequent blocks. Each subsequent block comprises a well site entry and a hash value of a previous well site entry. A well site entry comprises transacted operation control variables. The well site operations module generates production operation control variables or development operation control variables from earth model variables. The well site entry can also include transacted earth model variables and sensor variables. The sensor bank and control module provides well log variables and the operations module couples control variables to the control module to control well site equipment.

The invention provides a reservoir water level real-time monitoring and early warning system and method based on the Internet of Things, and the method comprises the steps: extracting credible water level time sequence information from the water level monitoring data of a target reservoir dam body, and determining the potential risk intensity of the target reservoir dam body under the water level change according to a preset sliding time window in combination with the credible water level time sequence information; the potential risk intensity is input to a preset water level early warning unit, the water level early warning unit is synchronously connected into a reservoir capacity-pressure distribution diagram deduced by the hydrological model in real time, and a safe bearing boundary of the target reservoir dam body structure under the current water level is determined according to the reservoir capacity-pressure distribution diagram; determining a water level grading early warning value of the target reservoir dam body under the current water level according to the safety bearing boundary in combination with the potential risk intensity; and sending a water level grading early warning signal of the target reservoir dam body to a reservoir management terminal based on the water level grading early warning value. According to the technical scheme provided by the invention, water level grading early warning can be carried out under the time-varying risk caused by accumulative damage of the dam body structure of the reservoir.

The embodiment of the application provides a reservoir capacity dynamic monitoring method and system based on a UAV, which comprises the following steps: obtaining a reservoir surface layer feature data set through a UAV, determining a water area boundary and scanning a reservoir bottom according to the data set, obtaining underwater topographic elevation and silt distribution data, and constructing a three-dimensional topographic model; determining topographic and silt change data based on dynamic change data of the three-dimensional topographic model of a continuous time sequence. Through change rate analysis, silt space distribution characteristics are obtained, and the amount of silt transport and the silt deposition space rule are determined in combination with water flow velocity. Key driving factors are screened through correlation analysis, a high-risk silt area is determined, silt change in the area is monitored, and thus the reservoir capacity change is mastered. The application can accurately and dynamically monitor reservoir siltation, clearly determine a high-risk area and a change mode, construct a dynamic database, update the capacity change in real time, and provide important technical support for reservoir management and flood control and disaster reduction.

The invention discloses equipment and a method for predicting, controlling and dispatching the scouring and silting amount of a downstream river channel of a dam, which are taken into account the influence of a flood process on the scouring and silting amount of the river channel and comprehensively consider the influence of water and sediment elements and flood characteristics on the scouring and silting amount of the downstream river channel of the dam. Water surface gradient, flow, peak pattern coefficient, jacking coefficient, sediment content and sediment grading elements are incorporated to predict the erosion and deposition amount of the downstream river channel of the dam, a calculation formula structure of the erosion and deposition amount of the downstream river channel of the dam and a matched calculation method are constructed, a corresponding table of the erosion and deposition amount of the downstream river channel of the dam is compiled according to the method, and reverse derivation can be achieved through table query. When the scouring and silting amount of the downstream river channel of the dam is controlled to be a fixed value, key dispatching parameters such as reservoir outlet flow and peak pattern coefficient required to be regulated and controlled by an upstream reservoir; a reservoir management department can formulate a scientific reservoir optimization scheduling scheme through the method, technical guarantee is provided for river regime stability and bank slope protection of a downstream river channel of a dam, flood control standard improvement and shipping safety maintenance are effectively supported, and a decision basis is provided for exerting comprehensive benefits of the reservoir.

The invention relates to the technical field of reservoir detection, in particular to a method and system for detecting a pore structure of a shale gas reservoir, and the method comprises the following steps: based on pore structure data of the shale gas reservoir, extracting and analyzing pore volume parameters, correcting initial parameters of pore permeability, extracting local flow characteristics, and generating a permeability distribution model. According to the method, through extraction of the pore volume parameter and the distribution density value, enhancement of correction of the reservoir permeability parameter, improvement of the accuracy of pore structure analysis, detailed analysis of regional characteristics by using the distribution gradient value of the pore morphology, and optimization of the application of pore connectivity and fluid response characteristics, the understanding of the dynamic relationship of the reservoir is improved; fluid dynamic data calculation is optimized through multi-point operation and dynamic relation correction, analysis of dynamic characteristics of a communication path is enhanced, decision-making efficiency of reservoir management is improved, fluid path analysis is optimized, high-permeability path recognition is enhanced, and economic benefits and development efficiency of a detection technology are remarkably improved.

This invention relates to the field of reservoir engineering management technology, specifically a reservoir lifecycle management system based on spatiotemporal matrix and digital twin. The system includes a digital twin model construction module, a spatiotemporal matrix module, a data fusion module, a lifecycle simulation module, a real-time data acquisition and update module, a model dynamic adjustment module, and a management decision support module. The system uses a spatiotemporal matrix to uniformly organize and manage multi-source data throughout the reservoir's lifecycle, utilizes a digital twin model to construct a virtual representation of the reservoir, achieves bidirectional synchronization through data fusion, and dynamically adjusts model parameters based on real-time data, ultimately generating lifecycle management decision outputs. This invention solves the problems of scattered data and static models in existing technologies, improving the accuracy and reliability of reservoir management.

In order to meet the objectives described above, the present invention provides a robot for detecting breakdown of NORM (Naturally Occurring Radioactive Materials) in a production system, in order to plan operations to remove scale containing radioactive material, and for decommissioning subsea system operations. The fields of application are the area of flow assurance and reservoir management, using operations to chemically remove scale containing NORM in the production system, and to improve the safety of the decommissioning processes of production systems. The invention allows the position of the scale inside the production system to be safely detected, such as, for example, the beginning of the deposit inside a production line, the extension of the deposit, and the end of the deposit, as well as whether the deposit occurred at different points along the production line.

The invention provides a method, system and device for depicting a strike-slip fault zone transverse structure and a medium, and the method comprises the following steps: obtaining fault zone data, and building a strike-slip fault zone transverse structure geologic model; depicting the strike-slip fault zone in the strike-slip fault zone transverse structure geologic model by applying geophysical attributes to obtain a depicting result; three-dimensional space modeling is carried out on the strike-slip fault zone based on the depiction result, and transverse structure depiction of the strike-slip fault zone is completed; according to the method, accurate depiction of transverse different zone spaces can be achieved, when the method is used for fault zone well pattern deployment, the drilling encounter rate, the drilling success rate and the efficient well proportion of a reservoir can be increased, and then the carbonate oil and gas reservoir management and development level is improved.

The embodiment of the invention provides a thin interlayer and phase control physical property cause sand body configuration model construction method and system, and belongs to the technical field of reservoir modeling. The method comprises the following steps: identifying fluvial facies reservoir distribution characteristics based on exploration data, and constructing a corresponding cause sand body reservoir configuration model; obtaining a corresponding small-layer sand body deterministic model based on the cause sand body reservoir configuration model; on the basis of the deterministic model of the small-layer sand body, performing representation of an internal configuration interface of the sand body based on a lateral lamination model and an adaptive triangular mesh generation algorithm; and generating a distribution model of each lithofacies unit based on representation of a configuration interface in the sand body, and constructing a physical property model for fine modeling and heterogeneity description of high and low permeability sections in the configuration based on a phase control stochastic simulation method. According to the scheme, the limitations of insufficient precision, unreasonable transition and low calculation efficiency in traditional modeling are effectively overcome, and high-precision geologic model support is provided for reservoir management and oil reservoir development.

A reservoir management method includes: in response to receiving input data to which label information is mapped, determining whether to add the input data to a reservoir based on a sampling probability; in response to determining to add the input data to the reservoir when the reservoir is filled, selecting candidate data to be removed from among sets of sample data included in the reservoir based on a target label distribution and a current label distribution of the reservoir, and removing the selected candidate data from the reservoir; and training a neural network model using sample data of the reservoir from which the selected candidate data is removed.

The invention discloses a risk and benefit balanced reservoir seasonal flood season water level multi-objective decision control method, and particularly relates to the technical field of data management. A genetic algorithm and a support vector machine are introduced to carry out multi-generation optimization and nonlinear regression model training, a water level change trend and an optimal water discharge amount are respectively predicted to serve as input variables of a fuzzy control system, and a final continuous control amount is calculated by adopting a weighted fusion algorithm. The matching degree between the control command and the equipment is evaluated according to the equipment response delay and the discretization precision; and if the matching degree is low, the system dynamically adjusts the discretization rule and the drainage strategy, and performs accurate butt joint with the equipment through the optimized control command, so that the accuracy and efficiency of reservoir management are improved, the dam break risk is reduced, and the drainage operation of the reservoir is optimized.

Implementations described and claimed herein provide systems and methods for an integrated development optimization platform for well sequencing and unconventional reservoir management. The platform integrates key elements of unconventional development planning, such as production forecast, lease obligations, surface facilities, and economics and provides analysis and data associated with past and future field development and production. In addition, development optimization platform includes the parent-child relationship as one of the determining factors of production performance, which can provide valuable insights into the frac-hit impact and infill performance. The defensive re-fracs may also be incorporated to provide a more holistic view on project investment and field development. The development optimization platform is not only an optimization platform for well sequence and development planning, but also a reservoir management tool.

The invention relates to the technical field of reservoir water level monitoring, and discloses a multi-sensor integrated reservoir water level monitoring method, system and device and a medium, and the method comprises the steps: obtaining the measurement values of all water level monitoring sensors in a target reservoir range, and carrying out the first elimination operation of the measurement values of all water level monitoring sensors; establishing a measurement input vector according to the measurement values of the plurality of water level monitoring sensors after the first elimination operation is completed; and establishing a filter set, and inputting the measurement input vector into the filter set to obtain a target state value. The precision of water level monitoring is improved, and the stability and reliability of the system are remarkably enhanced. In practical application, the system can accurately reflect the change condition of the water level of the reservoir area in real time, and provides an important decision basis for reservoir management, flood control scheduling and the like.

The application provides a reservoir water level real-time monitoring and early warning system and method based on Internet of Things, which extracts credible water level time sequence information from water level monitoring data of a target reservoir dam body, determines potential risk intensity of the target reservoir dam body under water level change according to a preset sliding time window and the credible water level time sequence information, inputs the potential risk intensity into a preset water level early warning unit, synchronously accesses a reservoir capacity-pressure distribution map derived in real time by a hydrological model, and determines a safe bearing boundary of the target reservoir dam body structure under the current water level according to the reservoir capacity-pressure distribution map, determines a water level grading early warning value of the target reservoir dam body under the current water level according to the safe bearing boundary and the potential risk intensity, and sends a water level grading early warning signal of the target reservoir dam body to a reservoir management terminal based on the water level grading early warning value. The technical scheme provided by the application can perform water level grading early warning under time-varying risk caused by cumulative damage of a reservoir dam body structure.

The utility model relates to the technical field of reservoir management, in particular to a reservoir ice melting device which is characterized in that a movable frame is connected in an open frame in a sliding manner, connecting plates are fixedly connected to two sides of the upper end of the movable frame, each connecting plate penetrates through the movable frame, a plurality of limiting holes are formed in each connecting plate at equal intervals in the length direction, and the limiting holes are communicated with the movable frame. Fixing bolts are connected to the two sides of the open frame, each fixing bolt penetrates through the corresponding limiting hole, an icebreaking mechanism is fixedly connected into the movable frame, and a driving machine is fixedly connected to the movable frame. The connecting plate drives the movable frame to move, the movable frame drives the icebreaking mechanism to move in the vertical direction, the height of the icebreaking mechanism is adjusted, after the height of the icebreaking mechanism is determined, the fixing bolts are connected to the open frame, the fixing bolts penetrate through the limiting holes to fix the connecting plate, and therefore the movable frame is fixed, and the icebreaking mechanism is fixed. The height of the icebreaking mechanism can be adjusted according to the thickness of the ice surface, and the ice chiseling effect on the ice layer is improved.

The application discloses a kind of reservoir water resource optimal allocation method based on multi-stage multi-objective belonging to water resource management technical field, comprising the following steps: A, predict the water resource demand, water resource available quantity, social economy and other basic data in basin;B, with the maximum unit water resource utilization benefit as target, with reservoir water balance, irrigation and power generation water demand, salinity leaching and so on as constraint condition, construct water resource optimal allocation model;C, coupling fixed combination stochastic programming and fractional programming method is solved to model, according to the water resource shortage obtained under different probability level arrangement production to reach maximum unit allocation water benefit.The application fully considers the necessity of joint management of upstream hydropower generation and downstream agricultural irrigation in reservoir management system, reasonably allocates water resources, land resources and energy in the basin, obtains the maximum unit allocation water benefit, so as to relieve water contradiction and improve the utilization efficiency of resources.

This invention relates to the field of water resource management technology, specifically to an artificial intelligence-based cascade reservoir ecological scheduling system, comprising a multi-source data integration and processing module, an ecological impact prediction module, a scheduling strategy optimization module, an adaptive water quality management module, and an intelligent user interaction module. Specifically: the multi-source data integration and processing module collects various data from meteorological stations, hydrological stations, and ecological monitoring points; the ecological impact prediction module constructs an ecological impact model; the scheduling strategy optimization module formulates reservoir scheduling strategies; the adaptive water quality management module adjusts the reservoir's outflow and regulation strategies; and the intelligent user interaction module provides a human-machine interface. This invention, by integrating multi-source real-time data, applying reinforcement learning to optimize scheduling strategies, and providing an efficient user interface, significantly improves the real-time responsiveness and decision-making efficiency of reservoir management, while ensuring optimal utilization of water resources and protection of the ecological environment.

A method and system for real-time monitoring of the dynamic water level and storage capacity of a reservoir based on GNSS-RTK belongs to the fields of environmental monitoring, environmental governance and computer control technology. The system includes a power supply system, a water level monitoring system, a control system and a data storage and display system. The steps are: select several fixed measuring stations around the reservoir, install high-precision GNSS receivers, and conduct real-time continuous observation of mobile stations in the reservoir area; transmit the real-time collected data to the data processing center via wireless communication or the Internet; calculate the real-time water level of the reservoir based on the buoy position information and the functional relationship between the water level and the position; substitute the real-time measured water level data into the volume calculation formula to calculate the real-time storage capacity of the reservoir; store the calculated water level and storage capacity data in a database, and display them in real time using visualization software. The present invention has the advantages of high real-time performance, high precision and strong comprehensiveness, and is suitable for the field of reservoir management and operation.

The application discloses a reservoir data matrix management platform and method based on artificial intelligence, relates to the technical field of reservoir data management, and comprises a reservoir three-dimensional simulation module, a reservoir data analysis module, a water quality factor index prediction module, a warning module, a water supply and diversion scheduling plan generation module and a reservoir management module; the reservoir three-dimensional simulation module is used for constructing a real-time three-dimensional simulation model of a target reservoir; the reservoir data analysis module is used for generating real-time water supply and diversion scheduling characteristics and basin flow characteristics of the target reservoir in each basin; the water quality factor index prediction module is used for predicting real-time water quality factor indexes of each basin of the target reservoir; and the warning module selectively sends a warning signal according to real-time water supply and diversion scheduling warning values of each basin of the target reservoir. Through mutual support among the modules, the application realizes matrix management of the target reservoir, and further guarantees safe operation of the target reservoir.

This invention discloses a method for assessing the safety risk level of a reservoir dam, specifically relating to the field of reservoir dam risk assessment technology. It involves acquiring real-time stress data of the reservoir dam, spillway discharge capacity data, and reservoir water level change data, extracting and predicting abnormal features from these data, and generating relevant outliers. A fuzzy comprehensive evaluation method is used to perform weighted analysis on multiple risk factors to calculate the dam's comprehensive risk score. The comprehensive risk score is compared with a risk level classification threshold to determine the reservoir dam's safety risk level. Based on the assessment results, corresponding early warning information and emergency response measures are output to ensure the safe operation of the reservoir dam. This method can monitor the safety status of the reservoir dam in real time, provide timely early warning of potential risks, and offer scientific decision support for reservoir management personnel.