6205 results about "Safety risk" patented technology

The invention discloses a construction site safety risk intelligent assessment method and system, and belongs to the technical field of engineering supervision. The method comprises the following steps: S100, collecting multi-source data including an equipment state, an image video, a personnel state, an equipment distance, an environment parameter and an engineering text in real time; s200, performing space-time alignment, preprocessing and cross-modal feature fusion on the multi-source data; s300, outputting a risk score and a risk level label based on the dynamic risk knowledge graph and a multi-model fusion algorithm; s400, generating a hierarchical disposal strategy according to the risk scoring hierarchy, and realizing risk closed-loop management and control through rectification verification and cooperation of multiple parties; and S500, outputting a three-dimensional visual risk distribution map and a compliance report. Through technologies of multi-source data fusion, dynamic risk mapping knowledge, intelligent closed-loop management and the like, comprehensive perception, accurate evaluation, efficient management and control and compliance landing of engineering supervision safety risks are realized, the occurrence rate of safety accidents is remarkably reduced, and reliable technical support is provided for intelligent construction site construction.

The invention discloses an underground engineering geological safety dynamic risk assessment method based on multi-source data fusion, which relates to the technical field of risk assessment, and comprises the following steps: collecting multi-source heterogeneous data related to underground engineering, extracting implicit information, modeling underground engineering geological safety risk factors into a risk network, and establishing a risk network model; calculating the comprehensive importance of the nodes based on a Stacking integration algorithm, and identifying key risk factors; acquiring characteristic parameters of key risk factors by using spatio-temporal characteristics of implicit information, introducing a random walk mechanism to acquire a dynamic accident prediction chain, and performing learning representation by using a graph attention network to acquire probability distribution of an accident evolution path; and assessing the vulnerability of the connection edge in the risk network, establishing a dynamic risk assessment model based on the node importance and the edge vulnerability, and obtaining a dynamic risk value corresponding to the accident according to the accident occurrence probability and the risk mitigation factor. According to the invention, intelligent identification, dynamic evaluation and accurate early warning of risk factors are realized, and the accuracy and real-time performance of risk identification and evaluation are improved.

The invention discloses a communication engineering construction dynamic optimization method based on multi-dimensional perception, and the method comprises the steps: collecting the multi-dimensional perception data of a construction site in real time through a multi-source heterogeneous sensor network, including environment parameters, equipment operation states, construction progress and personnel behavior data; performing space-time alignment processing on the multi-dimensional sensing data, and constructing a space-time associated dynamic construction digital twinborn body; establishing a dynamic optimization model based on a reinforcement learning algorithm, taking construction efficiency maximization, resource loss minimization and safety risk minimization as target optimization functions, and embedding risk constraint conditions; inputting the dynamic construction digital twin into the dynamic optimization model, and outputting a multi-dimensional parameter optimization instruction set comprising equipment scheduling parameters, construction path parameters and resource configuration parameters; and performing real-time performance evaluation on the multi-dimensional parameter optimization instruction set through edge computing nodes, and dynamically adjusting weight parameters of the model to form closed-loop feedback control. The purpose of cooperatively improving the construction efficiency, the safety and the economical efficiency is achieved.

The invention discloses a highway construction safety monitoring system and method, and belongs to the technical field of highway construction. The method comprises the following steps of 1, collecting construction site data in real time, wherein the construction site data comprises environment data, video data and construction management data; step 2, generating global situation awareness data; 3, evaluating the safety risk probability and severity in real time, and predicting a risk propagation path and a cascade effect; step 4, generating a plurality of risk disposal schemes for sorting and screening, and selecting an optimal disposal suggestion; 5, precise early warning and differentiated management are achieved, the false alarm rate is reduced, and high-risk events are found in time; and step 6, displaying the construction site through three-dimensional reconstruction, superposing construction site data and a risk assessment result, and providing remote monitoring and emergency command of a mobile terminal.

A vehicle dash cam may be configured to execute one or more neural networks (and / or other artificial intelligence), such as based on input from one or more of the cameras and / or other sensors associated with the dash cam, to intelligently detect safety events in real-time. Detection of a safety event may trigger an in-cab alert to make the driver aware of the safety risk. The dash cam may include logic for determining which asset data to transmit to a backend server in response to detection of a safety event, as well as which asset data to transmit to the backend server in response to analysis of sensor data that did not trigger a safety event. The asset data transmitted to the backend server may be further analyzed to determine if further alerts should be provided to the driver and / or to a safety manager.

The invention relates to an intelligent management system, in particular to a building construction quality safety risk management system which comprises a data acquisition module, a convolution analysis module, a risk assessment module, an early warning decision module and a feedback optimization module. The data acquisition module generates a sensing signal. And the convolution analysis module receives and adopts a multi-scale feature pyramid and a cross-modal attention mechanism to process the sensing signal to form a four-dimensional fusion signal. And the risk assessment module carries out comparative analysis on the four-dimensional fusion signal under a digital twinborn framework and forms a five-dimensional risk early warning signal. The early warning decision module receives the risk early warning signal and forms a multi-level risk early warning level signal. And the feedback optimization module receives the risk early warning level signal and optimizes the building construction quality safety risk management system through an incremental learning technology. The building construction quality safety risk management system can solve the problems that the dynamic characteristic adaptability is low in the construction process, and misinformation or missing information is likely to be generated.

The invention provides an enterprise employee behavior analysis and safety risk early warning monitoring method and system, and relates to the technical field of enterprise risk management, and the method comprises the steps: collecting employee terminal operation behavior data, building an activity thermal distribution diagram based on office area grid behavior association intensity, and training a behavior evaluation model. And inputting the behavior scoring matrix into a deep neural network to extract target behavior characteristics, calculating an abnormal behavior risk weight coefficient in combination with a department security policy, performing classification and analyzing a risk diffusion probability, and generating a risk situation index to determine an early warning level. Starting a response strategy according to the early warning level, blocking high-risk early warning in real time, tracking associated accounts, establishing a risk association map, identifying potential risk propagation nodes and performing active protection, finally generating an early warning report and feeding back effective protection rules to a behavior baseline model, and realizing continuous optimization of a risk early warning mechanism. And the accuracy and effectiveness of safety risk early warning in the enterprise are improved.

The invention discloses a quality safety optimization method and system based on a multi-modal vertical large model technology, and relates to the technical field of quality safety optimization systems.The method comprises the steps that quality safety related data are collected from a multi-modal data source and preprocessed; fusing data through features and decision levels; a vertical large model of an improved Transform architecture is constructed by using fusion data, a quality feature extraction layer and a safety rule embedding layer are provided, and adaptive learning rate and gradient clipping training are used; inputting the new data into the large model to analyze the quality safety condition, including defect prediction, risk assessment and problem early warning; and according to an analysis result, generating an optimization suggestion through rule reasoning and reinforcement learning. According to the invention, multi-modal quality safety data can be comprehensively collected and preprocessed. The vertical large model is high in pertinence, quality defects, safety risks and potential problems are accurately analyzed, the optimization suggestion module is combined with rules and reinforcement learning, suggestions with expected effects and priorities are generated, and the quality safety level and the resource utilization efficiency are improved.

The invention relates to a high-altitude large steel structure corridor safety risk monitoring method and system and a medium, and belongs to the technical field of civil engineering structure health monitoring, and the monitoring method comprises the steps: collecting original monitoring data through a multi-source sensor group disposed at each node of a steel structure corridor; performing space-time alignment processing on the original monitoring data to generate a sensor data matrix; performing dynamic noise suppression on the sensor data matrix based on an environmental noise transfer function model, and outputting a pure signal matrix and a damage characteristic frequency band identifier; calculating a thermal stress sensitive factor, extracting a multi-physical field coupling feature associated with the damage feature frequency band identifier in the pure signal matrix, fusing to generate a high-dimensional damage feature tensor, inputting the high-dimensional damage feature tensor into a pre-constructed digital twinborn risk assessment model, and outputting an assessment result including a risk level label and a damage position coordinate; and matching the regulation and control strategy according to the risk level label, and generating a corresponding equipment control instruction. According to the invention, the scientificity and accuracy of risk decision can be improved.

A device, system and method comprising computer vision techniques for fire prevention / detection and risk assessment, as well as for determining deviations from an ideal operational state. The present invention includes for example systems and methods which leverage data collected by camera systems composed of infrared and visible light sensors to detect and / or prevent a fire from starting, and additionally, use this data to determine a risk assessment for the building. The present invention also provides for example a system and method for monitoring and controlling safety risks in indoor industrial environments by determining deviations from an ideal operational state using computer vision techniques and game-theoretic competitive ranking frameworks.

The invention relates to the technical field of safety monitoring and intelligent early warning, discloses a construction safety risk monitoring method and system, and aims to solve the problems that a traditional construction safety monitoring means is isolated, static and lagged. The method comprises the following steps: deploying a multi-modal sensing network to collect environment, personnel, equipment and structural data; constructing a risk factor time sequence matrix through space-time alignment and feature normalization; constructing a dynamic association graph in combination with the risk conduction rule base and historical cases; predicting risk propagation potential energy of each node by using a graph neural network; triggering early warning according to the multi-level threshold value and outputting a conduction path and a blocking suggestion; the field devices are linked to perform hierarchical suppression actions. The system comprises a sensing deployment module, a data fusion module, a map construction module, a potential energy prediction module and an early warning decision and execution linkage module. Through space-time modeling and intelligent deduction, active identification and second-level intervention of a risk link are realized, the transformation of construction safety from passive response to active prevention is promoted, and the accident probability and the loss scale are reduced.

The invention relates to the technical field of building construction, in particular to a building construction safety intelligent early warning system based on multi-sensor fusion and deep learning. Comprising a multi-source sensing unit; an intelligent fusion unit; a depth analysis unit; and a dynamic response unit. According to the method, through a mixed deep learning model, personnel-equipment-environment space association in a 1m * 1m * 0.5 m space grid is extracted through an improved U-Net network, and a space risk association map is output; modeling data of 10 sampling periods by using a bidirectional LSTM network, and outputting a short-term prediction value; and carrying out weighted fusion through an attention mechanism to form a risk feature vector, and removing invalid anomalies in cooperation with parameter anomaly judgment and cross validation. And then a risk grade evaluation module introduces multiple coefficients to calculate a risk grade index, and a grid diffusion range is delimited according to grades, so that real-time identification, quantitative evaluation and range pre-judgment of construction safety risks are realized, and the problem that risk identification evaluation lacks scenarized accuracy and comprehensiveness is solved.

The invention relates to the technical field of building appearance defect detection, in particular to a building appearance defect detection method and system based on an unmanned aerial vehicle, and the method comprises the steps: obtaining the building information of a target building, and generating a hierarchical scanning path and a three-dimensional obstacle avoidance flight path, a visible light image, an infrared thermodynamic diagram and laser radar point cloud information are collected for space-time alignment processing, and an attention mechanism neural network is used for extracting multi-scale features to generate a detection report containing defect three-dimensional coordinates, damage levels and safety risk assessment. The method achieves the purpose of efficiently and accurately detecting the building appearance defects, can adapt to complex building structures and environmental conditions, supports defect trend prediction and maintenance decision, and remarkably improves the building safety management efficiency.

The invention discloses a safety management risk dynamic monitoring system for safety production, and belongs to the technical field of industrial safety management. The system collects monitoring parameter data in an enterprise in real time by integrating toxic gas, temperature, pressure and other sensors and video monitoring equipment, dynamically displays risk distribution of major hazard sources and key areas by combining a three-dimensional map labeling function, identifies high-risk areas and potential hazards through risk grading evaluation and hidden danger correlation analysis, and improves the safety of the enterprise. A response mechanism is dynamically adjusted according to the risk level, an emergency plan and a remote equipment control function are combined, major risks are handled in a rapid linkage mode, the possibility of accident expansion is remarkably reduced, an intelligent inspection module achieves efficient and accurate inspection tasks, inspection data are collected and analyzed in real time, and an inspection report containing the task completion rate and abnormal conditions is generated. Through closed-loop management, hidden danger rectification and continuous optimization are realized, real-time monitoring of safety risks is realized, and the occurrence rate of safety production accidents is effectively reduced.

The invention discloses an electric power Internet of Things industrial control equipment safety assessment method based on a multi-modal large model. The method mainly comprises the steps of data collection and preprocessing, multi-modal large model processing, safety risk assessment and result display and decision support. The method comprises the following steps: firstly, collecting multi-modal data of the power Internet of Things industrial control equipment, and preprocessing to ensure the consistency and accuracy of the data; secondly, performing deep fusion and feature extraction on the multi-source heterogeneous data by adopting a multi-modal large model technology and combining an orthogonal sequential fusion (OSF) strategy; then, establishing and training a security risk assessment model, quantifying the fault probability, the risk level and the potential influence, and providing more accurate security assessment in combination with background information provided by the knowledge graph; and finally, displaying an evaluation result through a visual interface. According to the invention, the automation level and accuracy of safety risk assessment of the power Internet of Things industrial control equipment are effectively improved, and stable operation and safety management of a power system are ensured.

The invention discloses a power distribution network operation safety risk assessment method considering typhoon and rainstorm composite disasters, and the method comprises the following steps: generating different typhoon paths as main scenes based on the typhoon and rainstorm disaster forecast data, and endowing each main scene with a weight according to the occurrence probability; the influence of microtopography and rainfall intensity on the wind speed V (r) in the main scene is considered, the corrected wind speed is calculated, the total failure probability of the distribution line between the node i and the node j is calculated according to the fault probability of the distribution line between the node i and the node j and the failure probability caused by foreign matter hanging, and the power distribution equipment failure probability is calculated according to the water accumulation amount of the power distribution network area; simulating and generating N fault scenes s through a Monte Carlo method to form a fault scene set S, and endowing each fault scene with a weight; and taking the minimum expected total load loss amount in all fault scenes as an objective function, constructing a risk assessment model, and solving the risk assessment model in combination with constraint conditions.

The invention relates to a coal mine safety risk intelligent management and control method, device and equipment and a medium. The method comprises the following steps: constructing a multi-source heterogeneous coal mine safety data system according to received data of a target coal mine park; the multi-source heterogeneous coal mine safety data system comprises static structure data, dynamic environment data, personnel behavior data and management data; constructing a coal mine three-dimensional space model according to the static structure data and the dynamic environment data; risk indexes in the dynamic environment data are extracted based on multi-algorithm fusion for evaluation, and a risk level is obtained; and if the risk level reaches a preset threshold value, triggering a corresponding linkage response mechanism, and forming a visual result in the coal mine three-dimensional space model. By the adoption of the method, closed-loop logic from sensing, evaluation to linkage treatment can be achieved, and the real-time performance, predictability and controllability of coal mine safety management are effectively improved through algorithm support of each stage and fine design of implementation details.

The invention relates to the technical field of data processing, and discloses a multi-data fusion method and system in airport station safety supervision and a medium. The method comprises the following steps: based on a target feature matrix, judging potential conflicts through a set safety distance threshold and a speed threshold, and constructing a risk assessment index in combination with historical risk event data to obtain a safety risk score; according to the security risk score, establishing a spatial association relationship and a risk propagation relationship between the apron targets, calculating regional risk density through a risk accumulation effect, and generating real-time security situation data; and for the real-time security situation data, calculating a target function value of security control, and setting a security constraint condition for optimization solution to form a control strategy parameter. According to the invention, effective fusion of multi-source heterogeneous data is realized, accurate awareness of the security situation of the station level is constructed, and an optimized management and control strategy is generated based on situation information, so that the accuracy and real-time performance of security supervision of the station level are improved.

The invention relates to the technical field of constructional engineering, in particular to a steel structure construction intelligent monitoring method based on unmanned aerial vehicle inspection and point cloud processing, which comprises the following steps: S1, collecting point cloud data of geometric shape, size and position information of each part of a steel structure; s2, registering and fusing the point cloud data from different visual angles to form a three-dimensional point cloud model; s3, geometric features of the steel structure are extracted from the three-dimensional point cloud model, and a steel structure three-dimensional model is established; s4, comparing the steel structure three-dimensional model established in the S3 with a construction drawing, and identifying geometric differences; and S5, analyzing the construction deviation report, and identifying potential quality problems in construction. According to the invention, by combining the unmanned aerial vehicle inspection, the point cloud processing and the graph neural network algorithm, the quality problem in steel structure construction can be efficiently and accurately identified, the safety risk can be evaluated, and the automation and intelligence level of construction quality monitoring can be significantly improved.

The invention provides a stock yard data management method and system based on fusion point clouds, and relates to the technical field of fusion point clouds, and the method comprises the steps: arranging high-precision three-dimensional scanning equipment in a target monitoring area, constructing a complete three-dimensional point cloud model, carrying out the recognition and segmentation of the preprocessed point cloud data, constructing a three-dimensional model for a segmented material pile, and carrying out the recognition and segmentation of the three-dimensional model. The method comprises the following steps: identifying the shape and position change of a material pile, updating stock data information of the material pile in a stock yard in real time, acquiring stock data and stock yard geographic information in real time based on the Internet of Things technology and a geographic information system GIS, integrating the stock data and the stock yard geographic information into a genetic algorithm, and planning an optimal material taking and stacking path of stock yard equipment in real time. The technical problems that in the prior art, due to manual management, the data precision is not high, time and labor are consumed in data recording and updating, real-time data updating is difficult to achieve, and even potential safety hazards exist can be solved, the purpose of improving the data precision is achieved, and the technical effects of rapidly updating stock ground data and reducing safety risks are achieved.

The invention provides a mine abnormal event real-time identification method and system based on time sequence characteristics, and relates to the technical field of mode identification, and the method comprises the steps: carrying out the time-space alignment and semantic annotation of multi-modal monitoring data, and constructing a time sequence knowledge graph; calculating a dynamic association weight between entities, and analyzing a risk propagation path; predicting a risk situation based on a historical evolution rule; and dynamically generating a differential early warning strategy and establishing a closed-loop tracking system. According to the invention, early identification, accurate prediction and efficient disposal of mine safety risks can be realized, and the mine safety management level is improved.

The invention discloses a highway situation awareness and accident risk early warning method fusing multi-source data. The method comprises the following steps: S1, constructing multi-source data fusion and multi-scale traffic flow data; s2, studying and judging an accident influence factor mechanism, and excavating factors influencing the occurrence of a traffic accident; s3, early warning of accident risks is carried out, and the safety risk level of the road section is evaluated; and S4, researching the beforehand management and control system, and constructing a beforehand management and control strategy system based on deep mining analysis of traffic accidents and actual operation experience. According to the invention, on the basis of the high-speed portal data and the charging flow data, the detection data of the thunder-vision integrated roadside equipment are fused for fusion processing, so that fine traffic operation parameters such as traffic flow and vehicle speed before and after a historical accident are obtained; traffic flow characteristics causing accidents are mined, and a traffic accident risk dynamic early warning management and control model and a post-accident evaluation mechanism are established based on the traffic flow characteristics, so that a management department is helped to recognize potential risks in advance.

The invention relates to the technical field of water conservancy project detection, and discloses a water conservancy project safety detection early warning method based on artificial intelligence. The method comprises the following steps: acquiring multi-modal monitoring data of a key part through a distributed sensor network, and extracting a dynamic feature sequence in a preset time period through space-time alignment and noise filtering; inputting the image into a deep neural network fused with an attention mechanism, constructing a multi-scale space-time correlation map through hierarchical feature learning, and generating a high-dimensional representation of an engineering structure state; historical accident case data is used as a supervision signal, a hybrid expert model is used for performing multi-task training on high-dimensional representation, and the contribution weight of each monitoring index to the safety risk is obtained; combining real-time environment parameters and structural response characteristics to construct a dynamic threshold adjustment model, adaptively updating an early warning threshold according to a risk probability, and screening out key risk factors of which the contribution weights are greater than the updated threshold; and on the basis of spatial and temporal distribution characteristics, through graph neural network node association reasoning, multi-source early warning information is fused to generate a graded early warning result.

The invention relates to the technical field of safety monitoring, in particular to a smart park safety management method and system based on AI visual identification, and the method comprises the following steps: obtaining a video stream captured by a monitoring camera of a smart park, extracting the movement track data of a person through AI, and carrying out the statistics of the stay duration and access times of a target object in each grid; and constructing retention heat distribution of the grid region to obtain a personnel trajectory retention heat value. According to the invention, through the data processing of the monitoring video stream and the combination of the gridding analysis of the personnel motion trail, the accurate capture of the abnormal cruise behavior is enhanced, and through the calculation of the change rate of the access times between the adjacent grids, the statistics of the round-trip condition of the target among the plurality of areas and the combination of the passing authority data, the abnormal screening is carried out. The method improves the recognition capability of abnormal movement modes, achieves the dynamic adjustment of regional safety risk levels, improves the response capability of park security, enables the recognition capability of abnormal behaviors to be enhanced, and reduces the false alarm rate.

The invention, which relates to the technical field of logistics transportation and intelligent transportation, discloses an intelligent sensing and active early warning system for security risks of multimodal transport of long and large goods based on multi-source data fusion, comprising: an original data input module; a service scene intelligent sensing module; an acquired data analysis module; a safety risk early warning module; an operation guidance module; a networking information receiving module; the invention further discloses a long and large cargo multimodal transport safety risk intelligent sensing and active early warning method based on multi-source data fusion. According to the invention, the complex safety challenge faced by long and large goods in the multimodal transportation process is solved, and the safety and efficiency of the transportation process are improved.

The invention relates to the technical field of inspection monitoring, and discloses an unmanned aerial vehicle intelligent inspection monitoring method and system based on a sensor, and the method comprises the steps: obtaining an initial inspection data set; obtaining a feature data set; generating a unified target feature data set; performing anomaly detection on the target feature data set to obtain an anomaly inspection area data set; performing security level division on the abnormal inspection area to obtain a division result; performing risk degree screening on the safety risk area in the division result to obtain a plurality of high-risk point position types, and when the change threshold value of one high-risk point position reaches a preset threshold value, preliminarily determining a high-risk occurrence zone; the unmanned aerial vehicle is controlled to carry out spot hovering to carry out key monitoring and refined inspection on the high-risk occurrence zone, and a secondary inspection data set is obtained; obtaining an analysis result; and secondarily confirming that the current inspection area is in the high-risk zone, and generating a corresponding emergency response early warning strategy and a corresponding regulation and control strategy, thereby accurately monitoring the inspection area in real time.

The invention relates to a rail transit full-scene intelligent construction cooperative control method and system, and the method comprises the steps: constructing a three-layer closed-loop architecture of a unified data base, a dynamic digital twin engine and a cooperative control platform, carrying out the real-time collection and fusion of multi-source heterogeneous data through the unified data base, and carrying out the synchronous processing based on a space-time alignment protocol; constructing a real-time digital twinning model by utilizing a dynamic digital twinning engine, and performing dynamic evolution prediction on a construction scene by adopting a mode of fusing a graph neural network and a physical constraint model; based on a real-time digital twinborn model, autonomous path planning, task allocation and fault prediction are performed on a plurality of construction devices through a cooperative control platform by adopting a multi-agent reinforcement learning algorithm, and cooperative operation and risk pre-control between the devices are realized. According to the invention, a data barrier can be broken, high-fidelity real-time twinning is realized, an autonomous cooperative control capability is provided, the construction efficiency is obviously improved, the safety risk is reduced, and manual intervention is reduced.

The invention discloses a building construction safety risk assessment method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing a neural network model, and recognizing an unsafe behavior mode of a worker; receiving a data stream from a construction site in real time, dynamically adjusting risk assessment parameters according to a current construction state and environment change, and predicting potential safety risks; according to the prediction result, generating corresponding prevention measure suggestions; key indexes and abnormal conditions are displayed through a monitoring interface, and a detailed analysis report is generated; according to the invention, through dynamic assessment and timely response to the safety risk of the construction site, the risk assessment parameters can be rapidly adjusted according to the environmental change, the potential risk can be predicted, and the prevention measures and suggestions can be provided, so that the occurrence probability of safety accidents is effectively reduced, and the construction efficiency is improved.

The invention discloses a safety risk assessment system based on slope construction, relates to the technical field of slope construction, constructs a complete system composed of a data acquisition module, a dynamic disturbance identification module, a risk assessment module and an intelligent early warning and decision-making module, and acquires multi-source data such as displacement, cracks, pore water pressure, seismic disturbance and blasting vibration at high frequency. The time sequence analysis and disturbance feature extraction algorithm is combined to realize the rapid identification of the sudden disturbance event; risk score prediction is further carried out based on a polynomial regression model, and multi-level early warning is generated through comparison with a set threshold value; and finally, by combining the construction state, the support parameters and the disturbance intensity, a safety control suggestion is dynamically generated, so that the response speed and prediction precision of the system to the sudden slope instability risk are effectively improved, and real-time perception and intelligent management and control of the risk in the slope construction process are realized.

The invention relates to the technical field of water gate maintenance, and discloses a water gate engineering safety risk assessment and early warning system which integrates five modules, namely a data analysis module, a real-time monitoring module, a state comparison module, a risk assessment module and an early warning notification module. The future safety state of the sluice project is predicted through a machine learning model, key safety parameters are monitored in real time, real-time data and predicted data are compared and analyzed, and a curve graph is drawn and visually displayed. And the risk assessment module calculates the difference degree of the two groups of curves and identifies risk points. And the early warning notification module comprehensively evaluates the safety state of the sluice project according to the risk point and the safety evaluation rule, and timely sends an evaluation result and early warning information to a manager so as to quickly take countermeasures. According to the system, the intelligent level of safety management of the water gate engineering is improved, early discovery and early warning of safety risks are realized, and safe operation of the water gate engineering is effectively guaranteed.